diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 0c28c751..c7c24931 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -1,6 +1,26 @@ Changelog ********* +0.10.0 (2021-12-19) +------------------- + +* :issue:`32`: Update :command:`import-variants` command to accept phased VCF as input. It will output VcfFrame[Consolidated] if the input VCF is fully phased or otherwise VcfFrame[Imported] as usual. +* Add new property ``sdk.utils.Archive.type`` to quickly access the archive's semantic type. +* Update :meth:`sdk.utils.Archive.check_type` method to be able to test more than one semantic type at once. +* Update :meth:`api.plot.plot_vcf_allele_fraction` method to accept both VcfFrame[Imported] and VcfFrame[Consolidated]. +* :issue:`32`: Update :command:`run-ngs-pipeline` command to accept phased VCF as input. In this case, the command will skip statistical haplotype phasing. +* :issue:`34`: Update commands :command:`run-ngs-pipeline` and :command:`run-chip-pipeline` to load large VCF files significantly faster by allowing random access. This also means, from now on, input VCF files must be BGZF compressed (.gz) and indexed (.tbi). +* :issue:`36`: Update phenotype data for CACNA1S, CFTR, IFNL3, RYR1 (thanks `@NTNguyen13 `__). +* :pr:`39`: Add new gene F5 (thanks `@NTNguyen13 `__). +* Update :command:`import-variants` command to be able to subset/exclude specified samples. +* Update :command:`import-read-depth` command to be able to subset/exclude specified samples. +* Rename ``--samples`` argument from :command:`compute-copy-number` command to ``--samples-without-sv``. +* Rename ``--samples`` argument from :command:`run-ngs-pipeline` command to ``--samples-without-sv``. +* Update :command:`run-ngs-pipeline` and :command:`run-chip-pipeline` commands to be able to subset/exclude specified samples. +* Remove ``--fn`` argument from :command:`filter-samples` command. +* Update CNV caller for CYP2D6, GSTM1, and UGT1A4. +* Update :meth:`api.plot.plot_cn_af` method to accept both VcfFrame[Imported] and VcfFrame[Consolidated]. + 0.9.0 (2021-12-05) ------------------ @@ -19,7 +39,7 @@ Changelog * Add new method :meth:`api.core.get_strand`. * Add new method :meth:`api.core.get_exon_starts`. * Add new method :meth:`api.core.get_exon_ends`. -* :pr:`31`: Fix minor bug in commands :command:`run-ngs-pipeline` and :command:`import-read-depth` (thanks to `@NTNguyen13 `__). +* :pr:`31`: Fix minor bug in commands :command:`run-ngs-pipeline` and :command:`import-read-depth` (thanks `@NTNguyen13 `__). * Fix minor bug in :meth:`api.core.predict_score` method. * Update variant information for following alleles: CYP2D6\*27, CYP2D6\*32, CYP2D6\*131, CYP2D6\*141. diff --git a/README.rst b/README.rst index 569d8119..9f73278d 100644 --- a/README.rst +++ b/README.rst @@ -36,7 +36,7 @@ available at the `Read the Docs `_. PyPGx is compatible with both of the Genome Reference Consortium Human (GRCh) builds, GRCh37 (hg19) and GRCh38 (hg38). -There are currently 57 pharmacogenes in PyPGx: +There are currently 58 pharmacogenes in PyPGx: .. list-table:: @@ -71,35 +71,35 @@ There are currently 57 pharmacogenes in PyPGx: - CYP19A1 - CYP26A1 * - DPYD + - F5 - G6PD - GSTM1 - GSTP1 - - GSTT1 - * - IFNL3 + * - GSTT1 + - IFNL3 - NAT1 - NAT2 - NUDT15 - - POR - * - PTGIS + * - POR + - PTGIS - RYR1 - SLC15A2 - SLC22A2 - - SLCO1B1 - * - SLCO1B3 + * - SLCO1B1 + - SLCO1B3 - SLCO2B1 - SULT1A1 - TBXAS1 - - TPMT - * - UGT1A1 + * - TPMT + - UGT1A1 - UGT1A4 - UGT2B7 - UGT2B15 - - UGT2B17 - * - VKORC1 + * - UGT2B17 + - VKORC1 - XPC - - - - Your contributions (e.g. feature ideas, pull requests) are most welcome. @@ -179,7 +179,7 @@ the presence of ALT contigs reduces the sensitivity of variant calling and many other analyses including SV detection. Therefore, if you have sequencing data in GRCh38, make sure it's aligned to the main contigs only. -The only exception to above rule is the *GSTT1* gene, which is located on +The only exception to above rule is the GSTT1 gene, which is located on ``chr22`` for GRCh37 but on ``chr22_KI270879v1_alt`` for GRCh38. This gene is known to have an extremely high rate of gene deletion polymorphism in the population and thus requires SV analysis. Therefore, if you are interested in @@ -288,28 +288,30 @@ currently defined semantic types: Phenotype prediction ==================== -Many of the genes in PyPGx have a diplotype-phenotype table available from -the Clinical Pharmacogenetics Implementation Consortium (CPIC). PyPGx will -use this information to perform phenotype prediction. Note that there two -types of phenotype prediction: - -- Method 1. Diplotype-phenotype mapping: This method directly uses the - diplotype-phenotype mapping as defined by CPIC. Using the CYP2B6 gene as an - example, the diplotypes \*6/\*6, \*1/\*29, \*1/\*2, \*1/\*4, and \*4/\*4 - correspond to Poor Metabolizer, Intermediate Metabolizer, Normal - Metabolizer, Rapid Metabolizer, and Ultrarapid Metabolizer. -- Method 2. Activity score: This method uses a standard unit of enzyme - activity known as an activity score. Using the CYP2D6 gene as an example, - the fully functional reference \*1 allele is assigned a value of 1, - decreased-function alleles such as \*9 and \*17 receive a value of - 0.5, and nonfunctional alleles including \*4 and \*5 have a value of - 0. The sum of values assigned to both alleles constitutes the activity - score of a diplotype. Consequently, subjects with \*1/\*1, \*1/\*4, - and \*4/\*5 diplotypes have an activity score of 2 (Normal Metabolizer), - 1 (Intermediate Metabolizer), and 0 (Poor Metabolizer), respectively. +Many genes in PyPGx have a genotype-phenotype table available from the +Clinical Pharmacogenetics Implementation Consortium (CPIC) or +the Pharmacogenomics Knowledge Base (PharmGKB). PyPGx uses these tables to +perform phenotype prediction with one of the two methods: + +- Method 1. Simple diplotype-phenotype mapping: This method directly uses the + diplotype-phenotype mapping as defined by CPIC or PharmGKB. Using the + CYP2B6 gene as an example, the diplotypes \*6/\*6, \*1/\*29, \*1/\*2, + \*1/\*4, and \*4/\*4 correspond to Poor Metabolizer, Intermediate + Metabolizer, Normal Metabolizer, Rapid Metabolizer, and Ultrarapid + Metabolizer. +- Method 2. Summation of haplotype activity scores: This method uses a + standard unit of enzyme activity known as an activity score. Using the + CYP2D6 gene as an example, the fully functional reference \*1 allele is + assigned a value of 1, decreased-function alleles such as \*9 and \*17 + receive a value of 0.5, and nonfunctional alleles including \*4 and \*5 + have a value of 0. The sum of values assigned to both alleles constitutes + the activity score of a diplotype. Consequently, subjects with \*1/\*1, + \*1/\*4, and \*4/\*5 diplotypes have an activity score of 2 (Normal + Metabolizer), 1 (Intermediate Metabolizer), and 0 (Poor Metabolizer), + respectively. Please visit the `Genes `__ page to see the list of genes with a CPIC diplotype-phenotype +genes.html>`__ page to see the list of genes with a genotype-phenotype table and each of their prediction method. Getting help @@ -345,7 +347,7 @@ For getting help on the CLI: Estimate haplotype phase of observed variants with the Beagle program. filter-samples Filter Archive file for specified samples. import-read-depth Import read depth data for the target gene. - import-variants Import variant data for the target gene. + import-variants Import variant (SNV/indel) data for the target gene plot-bam-copy-number Plot copy number profile from CovFrame[CopyNumber]. plot-bam-read-depth @@ -383,7 +385,6 @@ Below is the list of submodules available in the API: - **plot** : The plot submodule is used to plot various kinds of profiles such as read depth, copy number, and allele fraction. - **utils** : The utils submodule contains main actions of PyPGx. - For getting help on a specific submodule (e.g. utils): .. code:: python3 diff --git a/docs/cli.rst b/docs/cli.rst index b139b898..cffbedd1 100644 --- a/docs/cli.rst +++ b/docs/cli.rst @@ -37,7 +37,7 @@ For getting help on the CLI: Estimate haplotype phase of observed variants with the Beagle program. filter-samples Filter Archive file for specified samples. import-read-depth Import read depth data for the target gene. - import-variants Import variant data for the target gene. + import-variants Import variant (SNV/indel) data for the target gene plot-bam-copy-number Plot copy number profile from CovFrame[CopyNumber]. plot-bam-read-depth @@ -204,7 +204,7 @@ compute-copy-number .. code-block:: text $ pypgx compute-copy-number -h - usage: pypgx compute-copy-number [-h] [--samples TEXT [TEXT ...]] + usage: pypgx compute-copy-number [-h] [--samples-without-sv TEXT [TEXT ...]] read-depth control-statistcs output Compute copy number from read depth for the target gene. @@ -215,7 +215,7 @@ compute-copy-number During copy number analysis, if the input data is targeted sequencing, the command will apply inter-sample normalization using summary statistics across all samples. For best results, it is recommended to specify known samples - without SV using --samples. + without SV using --samples-without-sv. Positional arguments: read-depth Archive file with the semantic type @@ -227,7 +227,7 @@ compute-copy-number Optional arguments: -h, --help Show this help message and exit. - --samples TEXT [TEXT ...] + --samples-without-sv TEXT [TEXT ...] List of known samples with no SV. compute-target-depth @@ -334,8 +334,8 @@ estimate-phase-beagle Optional arguments: -h, --help Show this help message and exit. --panel PATH VCF file corresponding to a reference haplotype panel - (zipped or unzipped). By default, the 1KGP panel is - used. + (compressed or uncompressed). By default, the 1KGP panel + is used. --impute Perform imputation of missing genotypes. filter-samples @@ -344,24 +344,22 @@ filter-samples .. code-block:: text $ pypgx filter-samples -h - usage: pypgx filter-samples [-h] [--samples TEXT [TEXT ...]] [--fn PATH] - [--exclude] - input output + usage: pypgx filter-samples [-h] [--exclude] + input output samples [samples ...] Filter Archive file for specified samples. Positional arguments: - input Input archive file. - output Output archive file. + input Input archive file. + output Output archive file. + samples Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. Optional arguments: - -h, --help Show this help message and exit. - --samples TEXT [TEXT ...] - List of samples names (the order matters). Cannot be - used with --fn. - --fn PATH File containing one sample name per line. Cannot be - used with --samples. - --exclude Exclude specified samples. + -h, --help Show this help message and exit. + --exclude Exclude specified samples. import-read-depth ================= @@ -369,18 +367,25 @@ import-read-depth .. code-block:: text $ pypgx import-read-depth -h - usage: pypgx import-read-depth [-h] gene depth-of-coverage read-depth + usage: pypgx import-read-depth [-h] [--samples PATH [PATH ...]] [--exclude] + gene depth-of-coverage read-depth Import read depth data for the target gene. Positional arguments: - gene Target gene. - depth-of-coverage Archive file with the semantic type - CovFrame[DepthOfCoverage]. - read-depth Archive file with the semantic type CovFrame[ReadDepth]. + gene Target gene. + depth-of-coverage Archive file with the semantic type + CovFrame[DepthOfCoverage]. + read-depth Archive file with the semantic type CovFrame[ReadDepth]. Optional arguments: - -h, --help Show this help message and exit. + -h, --help Show this help message and exit. + --samples PATH [PATH ...] + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. + --exclude Exclude specified samples. import-variants =============== @@ -389,21 +394,35 @@ import-variants $ pypgx import-variants -h usage: pypgx import-variants [-h] [--assembly TEXT] [--platform TEXT] + [--samples PATH [PATH ...]] [--exclude] gene vcf imported-variants - Import variant data for the target gene. + Import variant (SNV/indel) data for the target gene. + + The command will first slice input VCF for the target gene and then assess + whether every genotype call in the sliced VCF is haplotype phased. It will + return an archive file with the semantic type VcfFrame[Consolidated] if the + VCF is fully phased or otherwise VcfFrame[Imported]. Positional arguments: - gene Target gene. - vcf VCF file (zipped or unzipped). - imported-variants Archive file with the semantic type VcfFrame[Imported]. + gene Target gene. + vcf Input VCF file must be already BGZF compressed (.gz) and + indexed (.tbi) to allow random access. + imported-variants Archive file with the semantic type VcfFrame[Imported] + or VcfFrame[Consolidated]. Optional arguments: - -h, --help Show this help message and exit. - --assembly TEXT Reference genome assembly (default: 'GRCh37') (choices: - 'GRCh37', 'GRCh38'). - --platform TEXT NGS platform (default: 'WGS') (choices: 'WGS', - 'Targeted', 'Chip'). + -h, --help Show this help message and exit. + --assembly TEXT Reference genome assembly (default: 'GRCh37') (choices: + 'GRCh37', 'GRCh38'). + --platform TEXT Genotyping platform (default: 'WGS') (choices: 'WGS', + 'Targeted', 'Chip'). + --samples PATH [PATH ...] + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. + --exclude Exclude specified samples. plot-bam-copy-number ==================== @@ -427,7 +446,10 @@ plot-bam-copy-number --fitted Show the fitted line as well. --path PATH Create plots in this directory. --samples TEXT [TEXT ...] - Create plots only for these samples. + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. --ymin FLOAT Y-axis bottom (default: -0.3). --ymax FLOAT Y-axis top (default: 6.3). --fontsize FLOAT Text fontsize (default: 25). @@ -453,7 +475,10 @@ plot-bam-read-depth -h, --help Show this help message and exit. --path PATH Create plots in this directory. --samples TEXT [TEXT ...] - Create plots only for these samples. + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. --ymin FLOAT Y-axis bottom. --ymax FLOAT Y-axis top. @@ -479,7 +504,10 @@ plot-cn-af -h, --help Show this help message and exit. --path PATH Create plots in this directory. --samples TEXT [TEXT ...] - Create plots only for these samples. + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. --ymin FLOAT Y-axis bottom (default: -0.3). --ymax FLOAT Y-axis top (default: 6.3). --fontsize FLOAT Text fontsize (default: 25). @@ -505,7 +533,10 @@ plot-vcf-allele-fraction -h, --help Show this help message and exit. --path PATH Create plots in this directory. --samples TEXT [TEXT ...] - Create plots only for these samples. + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. --fontsize FLOAT Text fontsize (default: 25). plot-vcf-read-depth @@ -531,7 +562,10 @@ plot-vcf-read-depth (choices: 'GRCh37', 'GRCh38'). --path PATH Create plots in this directory. --samples TEXT [TEXT ...] - Create plots only for these samples. + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. --ymin FLOAT Y-axis bottom. --ymax FLOAT Y-axis top. @@ -641,27 +675,37 @@ run-chip-pipeline $ pypgx run-chip-pipeline -h usage: pypgx run-chip-pipeline [-h] [--assembly TEXT] [--impute] [--force] + [--samples TEXT [TEXT ...]] [--exclude] gene output variants Run PyPGx's genotyping pipeline for chip data. Positional arguments: - gene Target gene. - output Output directory. - variants VCF file (zipped or unzipped). + gene Target gene. + output Output directory. + variants Input VCF file must be already BGZF compressed (.gz) + and indexed (.tbi) to allow random access. Statistical + haplotype phasing will be skipped if input VCF is + already fully phased. Optional arguments: - -h, --help Show this help message and exit. - --assembly TEXT Reference genome assembly (default: 'GRCh37') (choices: - 'GRCh37', 'GRCh38'). - --impute Perform imputation of missing genotypes. - --force Overwrite output directory if it already exists. + -h, --help Show this help message and exit. + --assembly TEXT Reference genome assembly (default: 'GRCh37') (choices: + 'GRCh37', 'GRCh38'). + --impute Perform imputation of missing genotypes. + --force Overwrite output directory if it already exists. + --samples TEXT [TEXT ...] + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. + --exclude Exclude specified samples. [Example] To genotype the CYP3A5 gene from chip data: $ pypgx run-chip-pipeline \ CYP3A5 \ CYP3A5-pipeline \ - variants.vcf + variants.vcf.gz run-ngs-pipeline ================ @@ -673,7 +717,8 @@ run-ngs-pipeline [--depth-of-coverage PATH] [--control-statistics PATH] [--platform TEXT] [--assembly TEXT] [--panel PATH] [--force] - [--samples TEXT [TEXT ...]] + [--samples TEXT [TEXT ...]] [--exclude] + [--samples-without-sv TEXT [TEXT ...]] [--do-not-plot-copy-number] [--do-not-plot-allele-fraction] gene output @@ -683,7 +728,7 @@ run-ngs-pipeline During copy number analysis, if the input data is targeted sequencing, the command will apply inter-sample normalization using summary statistics across all samples. For best results, it is recommended to specify known samples - without SV using --samples. + without SV using --samples-without-sv. Positional arguments: gene Target gene. @@ -691,21 +736,30 @@ run-ngs-pipeline Optional arguments: -h, --help Show this help message and exit. - --variants PATH VCF file (zipped or unzipped). + --variants PATH Input VCF file must be already BGZF compressed (.gz) + and indexed (.tbi) to allow random access. Statistical + haplotype phasing will be skipped if input VCF is + already fully phased. --depth-of-coverage PATH - Depth of coverage file (zipped or unzipped). + Depth of coverage file (compressed or uncompressed). --control-statistics PATH Archive file with the semandtic type SampleTable[Statistcs]. --platform TEXT Genotyping platform (default: 'WGS') (choices: 'WGS', 'Targeted') - --assembly TEXT Reference genome assembly (default: 'GRCh37') (choices: - 'GRCh37', 'GRCh38'). + --assembly TEXT Reference genome assembly (default: 'GRCh37') + (choices: 'GRCh37', 'GRCh38'). --panel PATH VCF file corresponding to a reference haplotype panel - (zipped or unzipped). By default, the 1KGP panel is - used. + (compressed or uncompressed). By default, the 1KGP + panel is used. --force Overwrite output directory if it already exists. --samples TEXT [TEXT ...] + Specify which samples should be included for analysis + by providing a text file (.txt, .tsv, .csv, or .list) + containing one sample per line. Alternatively, you can + provide a list of samples. + --exclude Exclude specified samples. + --samples-without-sv TEXT [TEXT ...] List of known samples without SV. --do-not-plot-copy-number Do not plot copy number profile. @@ -716,13 +770,13 @@ run-ngs-pipeline $ pypgx run-ngs-pipeline \ CYP3A5 \ CYP3A5-pipeline \ - --variants variants.vcf + --variants variants.vcf.gz [Example] To genotype the CYP2D6 gene, which does have SV, from WGS data: $ pypgx run-ngs-pipeline \ CYP2D6 \ CYP2D6-pipeline \ - --variants variants.vcf \ + --variants variants.vcf.gz \ --depth-of-coverage depth-of-coverage.tsv \ --control-statistcs control-statistics-VDR.zip @@ -730,7 +784,7 @@ run-ngs-pipeline $ pypgx run-ngs-pipeline \ CYP2D6 \ CYP2D6-pipeline \ - --variants variants.vcf \ + --variants variants.vcf.gz \ --depth-of-coverage depth-of-coverage.tsv \ --control-statistcs control-statistics-VDR.zip \ --platform Targeted diff --git a/docs/create.py b/docs/create.py index eee7c95d..d04ebd74 100644 --- a/docs/create.py +++ b/docs/create.py @@ -63,7 +63,7 @@ PyPGx is compatible with both of the Genome Reference Consortium Human (GRCh) builds, GRCh37 (hg19) and GRCh38 (hg38). -There are currently 57 pharmacogenes in PyPGx: +There are currently 58 pharmacogenes in PyPGx: .. list-table:: @@ -98,35 +98,35 @@ - CYP19A1 - CYP26A1 * - DPYD + - F5 - G6PD - GSTM1 - GSTP1 - - GSTT1 - * - IFNL3 + * - GSTT1 + - IFNL3 - NAT1 - NAT2 - NUDT15 - - POR - * - PTGIS + * - POR + - PTGIS - RYR1 - SLC15A2 - SLC22A2 - - SLCO1B1 - * - SLCO1B3 + * - SLCO1B1 + - SLCO1B3 - SLCO2B1 - SULT1A1 - TBXAS1 - - TPMT - * - UGT1A1 + * - TPMT + - UGT1A1 - UGT1A4 - UGT2B7 - UGT2B15 - - UGT2B17 - * - VKORC1 + * - UGT2B17 + - VKORC1 - XPC - - - - Your contributions (e.g. feature ideas, pull requests) are most welcome. @@ -206,7 +206,7 @@ and many other analyses including SV detection. Therefore, if you have sequencing data in GRCh38, make sure it's aligned to the main contigs only. -The only exception to above rule is the *GSTT1* gene, which is located on +The only exception to above rule is the GSTT1 gene, which is located on ``chr22`` for GRCh37 but on ``chr22_KI270879v1_alt`` for GRCh38. This gene is known to have an extremely high rate of gene deletion polymorphism in the population and thus requires SV analysis. Therefore, if you are interested in @@ -315,28 +315,30 @@ Phenotype prediction ==================== -Many of the genes in PyPGx have a diplotype-phenotype table available from -the Clinical Pharmacogenetics Implementation Consortium (CPIC). PyPGx will -use this information to perform phenotype prediction. Note that there two -types of phenotype prediction: - -- Method 1. Diplotype-phenotype mapping: This method directly uses the - diplotype-phenotype mapping as defined by CPIC. Using the CYP2B6 gene as an - example, the diplotypes \*6/\*6, \*1/\*29, \*1/\*2, \*1/\*4, and \*4/\*4 - correspond to Poor Metabolizer, Intermediate Metabolizer, Normal - Metabolizer, Rapid Metabolizer, and Ultrarapid Metabolizer. -- Method 2. Activity score: This method uses a standard unit of enzyme - activity known as an activity score. Using the CYP2D6 gene as an example, - the fully functional reference \*1 allele is assigned a value of 1, - decreased-function alleles such as \*9 and \*17 receive a value of - 0.5, and nonfunctional alleles including \*4 and \*5 have a value of - 0. The sum of values assigned to both alleles constitutes the activity - score of a diplotype. Consequently, subjects with \*1/\*1, \*1/\*4, - and \*4/\*5 diplotypes have an activity score of 2 (Normal Metabolizer), - 1 (Intermediate Metabolizer), and 0 (Poor Metabolizer), respectively. +Many genes in PyPGx have a genotype-phenotype table available from the +Clinical Pharmacogenetics Implementation Consortium (CPIC) or +the Pharmacogenomics Knowledge Base (PharmGKB). PyPGx uses these tables to +perform phenotype prediction with one of the two methods: + +- Method 1. Simple diplotype-phenotype mapping: This method directly uses the + diplotype-phenotype mapping as defined by CPIC or PharmGKB. Using the + CYP2B6 gene as an example, the diplotypes \*6/\*6, \*1/\*29, \*1/\*2, + \*1/\*4, and \*4/\*4 correspond to Poor Metabolizer, Intermediate + Metabolizer, Normal Metabolizer, Rapid Metabolizer, and Ultrarapid + Metabolizer. +- Method 2. Summation of haplotype activity scores: This method uses a + standard unit of enzyme activity known as an activity score. Using the + CYP2D6 gene as an example, the fully functional reference \*1 allele is + assigned a value of 1, decreased-function alleles such as \*9 and \*17 + receive a value of 0.5, and nonfunctional alleles including \*4 and \*5 + have a value of 0. The sum of values assigned to both alleles constitutes + the activity score of a diplotype. Consequently, subjects with \*1/\*1, + \*1/\*4, and \*4/\*5 diplotypes have an activity score of 2 (Normal + Metabolizer), 1 (Intermediate Metabolizer), and 0 (Poor Metabolizer), + respectively. Please visit the `Genes `__ page to see the list of genes with a CPIC diplotype-phenotype +genes.html>`__ page to see the list of genes with a genotype-phenotype table and each of their prediction method. Getting help @@ -362,7 +364,6 @@ Below is the list of submodules available in the API: {submodule_help} - For getting help on a specific submodule (e.g. utils): .. code:: python3 diff --git a/docs/genes.rst b/docs/genes.rst index e52dc1d1..b611de5d 100644 --- a/docs/genes.rst +++ b/docs/genes.rst @@ -5,168 +5,248 @@ Introduction ============ This page describes gene-specific information. PyPGx currently supports a -total of 57 pharmacogenes. +total of 58 pharmacogenes. -Many of the genes are known to have structural variation (SV) including -gene deletions, duplications, and hybrids. +Many of the genes are known to have :ref:`structural variation (SV) +` including gene deletions, duplications, +and hybrids. -Some genes have a diplotype-phenotype table available from the Clinical -Pharmacogenetics Implementation Consortium (CPIC). PyPGx will use this -information to perform phenotype prediction with one of the two methods: -diplotype-phenotype mapping or activity score. Please read the -:ref:`readme:Phenotype prediction` page for more details. +Some genes have a genotype-phenotype table available from the +:ref:`glossary:Clinical Pharmacogenetics Implementation Consortium (CPIC)` or +the :ref:`glossary:Pharmacogenomics Knowledge Base (PharmGKB)`. Please read +the :ref:`readme:Phenotype prediction` page for more details. Below is a summary table: .. list-table:: :header-rows: 1 - :widths: 15 10 15 60 + :widths: 15 10 15 15 15 60 * - Gene - SV - Phenotype + - PharmVar + - CPIC - Note * - ABCB1 - - - - * - CACNA1S - - + * - :ref:`genes:CACNA1S` - - * - CFTR + - ✅ + - + - ✅ + - + * - :ref:`genes:CFTR` + - + - ✅ + - + - ✅ + - + * - :ref:`genes:CYP1A1` - - + - ✅ - - * - CYP1A1 - + * - :ref:`genes:CYP1A2` - - - * - CYP1A2 + - ✅ - - + * - :ref:`genes:CYP1B1` - - * - CYP1B1 - + - ✅ - - * - :ref:`genes:CYP2A6` + - ✅ + - - ✅ - - Has pseudogene (CYP2A7). - * - CYP2A13 + * - :ref:`genes:CYP2A13` + - - + - ✅ - - * - :ref:`genes:CYP2B6` + - ✅ + - ✅ - ✅ - ✅ - Has pseudogene (CYP2B7). - * - CYP2C8 + * - :ref:`genes:CYP2C8` - - + - ✅ + - - * - :ref:`genes:CYP2C9` - - ✅ + - ✅ + - ✅ - * - :ref:`genes:CYP2C19` - - ✅ + - ✅ + - ✅ - * - :ref:`genes:CYP2D6` + - ✅ + - ✅ - ✅ - ✅ - Has pseudogene (CYP2D7). * - :ref:`genes:CYP2E1` - ✅ - + - ✅ + - + - + * - :ref:`genes:CYP2F1` + - + - + - ✅ + - + - + * - :ref:`genes:CYP2J2` - - * - CYP2F1 + - + - ✅ - - + * - :ref:`genes:CYP2R1` - - * - CYP2J2 - + - ✅ - - - * - CYP2R1 + * - :ref:`genes:CYP2S1` - - + - ✅ - - * - CYP2S1 - + * - :ref:`genes:CYP2W1` - - - * - CYP2W1 + - ✅ - - + * - :ref:`genes:CYP3A4` - - * - CYP3A4 - + - ✅ - - * - :ref:`genes:CYP3A5` + - + - ✅ + - ✅ + - ✅ + - + * - :ref:`genes:CYP3A7` + - + - + - ✅ + - + - + * - :ref:`genes:CYP3A43` + - - - ✅ - - * - CYP3A7 - + * - :ref:`genes:CYP4A11` - - - * - CYP3A43 + - ✅ - - + * - :ref:`genes:CYP4A22` - - * - CYP4A11 - + - ✅ - - - * - CYP4A22 + * - :ref:`genes:CYP4B1` - - + - ✅ - - * - CYP4B1 - + * - :ref:`genes:CYP4F2` - - - * - CYP4F2 + - ✅ - - + * - :ref:`genes:CYP17A1` - - * - CYP17A1 - + - ✅ - - - * - CYP19A1 + * - :ref:`genes:CYP19A1` - - + - ✅ - - * - CYP26A1 - + * - :ref:`genes:CYP26A1` + - + - + - ✅ - - * - :ref:`genes:DPYD` - - ✅ + - ✅ + - ✅ + - + * - :ref:`genes:F5` + - + - ✅ + - + - - * - G6PD - - - + - + - * - :ref:`genes:GSTM1` - ✅ - - + - + - * - GSTP1 - - - + - + - * - :ref:`genes:GSTT1` - ✅ - + - + - - Contig differs between GRCh37 and GRCh38. - * - IFNL3 + * - :ref:`genes:IFNL3` + - + - ✅ - - - @@ -174,86 +254,202 @@ Below is a summary table: - - - + - + - * - NAT2 - - - + - + - * - :ref:`genes:NUDT15` - - ✅ + - ✅ + - ✅ - - * - POR + * - :ref:`genes:POR` - - + - ✅ - - * - PTGIS - + * - :ref:`genes:PTGIS` - - - * - RYR1 + - ✅ - - + * - :ref:`genes:RYR1` + - + - ✅ + - ✅ + - - * - SLC15A2 - - - + - + - * - :ref:`genes:SLC22A2` - ✅ - - + - + - * - :ref:`genes:SLCO1B1` - - ✅ + - ✅ + - ✅ - * - SLCO1B3 - - - + - + - * - SLCO2B1 - - - + - + - * - SULT1A1 - - - - * - TBXAS1 - - + * - :ref:`genes:TBXAS1` + - + - + - ✅ + - - * - :ref:`genes:TPMT` - - ✅ - + - ✅ + - * - :ref:`genes:UGT1A1` - - ✅ - + - ✅ + - * - :ref:`genes:UGT1A4` - ✅ - - + - + - * - UGT2B7 - - - + - + - * - :ref:`genes:UGT2B15` - ✅ - - + - + - * - :ref:`genes:UGT2B17` - ✅ - - + - + - * - VKORC1 - - - + - ✅ + - * - XPC - - - + - + - + +CACNA1S +======= + +Phenotype summary for CACNA1S +----------------------------- + +Diplotype-phenotype mapping is used for phenotype prediction. + + .. list-table:: + :header-rows: 1 + + * - Phenotype + - Example + * - Uncertain Susceptibility + - Reference/Reference + * - Malignant Hyperthermia Susceptibility + - Reference/c.520C>T + +Resources for CACNA1S +--------------------- + +- `Annotation of CPIC Guideline for desflurane and CACNA1S, RYR1 `__ +- `CPIC® Guideline for Potent Volatile Anesthetic Agents and Succinylcholine and RYR1 and CACNA1S `__ + +CFTR +==== + +Phenotype summary for CFTR +-------------------------- + +Diplotype-phenotype mapping is used for phenotype prediction. + + .. list-table:: + :header-rows: 1 + + * - Phenotype + - Example + * - Favorable Response + - Reference/G551D + * - Unfavorable Response + - F508del/F508del + * - Indeterminate + - Reference/F508del + +Resources for CFTR +------------------ + +- `Annotation of CPIC Guideline for ivacaftor and CFTR `__ +- `CPIC® Guideline for Ivacaftor and CFTR `__ + +CYP1A1 +====== + +Resources for CYP1A1 +-------------------- + +- `PharmVar CYP1A1 page `__ + +CYP1A2 +====== + +Resources for CYP1A2 +-------------------- + +- `PharmVar CYP1A2 page `__ + +CYP1B1 +====== + +Resources for CYP1B1 +-------------------- + +- `PharmVar CYP1B1 page `__ CYP2A6 ====== @@ -330,6 +526,14 @@ Some alleles in PharmVar will not be called by PyPGx because one or more of thei - `22-42523514-C-T `__ - `22-42127512-C-T `__ +CYP2A13 +======= + +Resources for CYP2A13 +--------------------- + +- `PharmVar CYP2A13 page `__ + CYP2B6 ====== @@ -383,6 +587,19 @@ Diplotype-phenotype mapping is used for phenotype prediction. * - Indeterminate - \*1/\*3 +Resources for CYP2B6 +-------------------- + +- `CPIC® Guideline for Efavirenz based on CYP2B6 genotype `__ + +CYP2C8 +====== + +Resources for CYP2C8 +-------------------- + +- `PharmVar CYP2C8 page `__ + CYP2C9 ====== @@ -407,6 +624,11 @@ Activity score is used for phenotype prediction. - 0 <= score < 1 - \*2/\*3 +Resources for CYP2C9 +-------------------- + +- `CPIC® Guideline for NSAIDs based on CYP2C9 genotype `__ + CYP2C19 ======= @@ -437,6 +659,11 @@ Diplotype-phenotype mapping is used for phenotype prediction. * - Indeterminate - \*1/\*12 +Resources for CYP2C19 +--------------------- + +- `CPIC® Guideline for Voriconazole and CYP2C19 `__ + CYP2D6 ====== @@ -458,7 +685,7 @@ Below is comprehensive summary of SV described from real NGS studies: - Source - Coriell ID * - \*5 - - Deletion + - DeletionHet - \*5/\*29 - - :download:`Profile ` @@ -466,6 +693,15 @@ Below is comprehensive summary of SV described from real NGS studies: - WGS - `GeT-RM `__ - NA18861 + * - \*5 + - DeletionHom + - \*5/\*5 + - + - :download:`Profile ` + - :download:`Profile ` + - WGS + - + - * - \*4x2 - Duplication - \*2/\*4x2 @@ -502,6 +738,15 @@ Below is comprehensive summary of SV described from real NGS studies: - WGS - `GeT-RM `__ - NA18524 + * - \*36x3+\*10 + - Tandem2C + - \*1/\*36x3+\*10 + - + - :download:`Profile ` + - :download:`Profile ` + - WGS + - + - Phenotype summary for CYP2D6 ---------------------------- @@ -561,6 +806,11 @@ Some alleles in PharmVar will not be called by PyPGx because one or more of thei - `22-42524327-A-G `__ - `22-42128325-A-G `__ +Resources for CYP2D6 +-------------------- + +- `CPIC® Guideline for Tamoxifen based on CYP2D6 genotype `__ + CYP2E1 ====== @@ -590,6 +840,15 @@ Below is comprehensive summary of SV described from real NGS studies: - WGS - `GeT-RM `__ - NA19920 + * - \*1x2 + - Duplication + - \*1/\*1x2 + - + - :download:`Profile ` + - :download:`Profile ` + - WGS + - + - * - \*7x2 - Duplication - \*1/\*7x2 @@ -609,6 +868,54 @@ Below is comprehensive summary of SV described from real NGS studies: - `GeT-RM `__ - NA19908 +CYP2F1 +====== + +Resources for CYP2F1 +-------------------- + +- `PharmVar CYP2F1 page `__ + +CYP2J2 +====== + +Resources for CYP2J2 +-------------------- + +- `PharmVar CYP2J2 page `__ + +CYP2R1 +====== + +Resources for CYP2R1 +-------------------- + +- `PharmVar CYP2R1 page `__ + +CYP2S1 +====== + +Resources for CYP2S1 +-------------------- + +- `PharmVar CYP2S1 page `__ + +CYP2W1 +====== + +Resources for CYP2W1 +-------------------- + +- `PharmVar CYP2W1 page `__ + +CYP3A4 +====== + +Resources for CYP3A4 +-------------------- + +- `PharmVar CYP3A4 page `__ + CYP3A5 ====== @@ -633,6 +940,83 @@ Diplotype-phenotype mapping is used for phenotype prediction. * - Indeterminate - \*2/\*2 +Resources for CYP3A5 +-------------------- + +- `CPIC® Guideline for Tacrolimus and CYP3A5 `__ + +CYP3A7 +====== + +Resources for CYP3A7 +-------------------- + +- `PharmVar CYP3A7 page `__ + +CYP3A43 +======= + +Resources for CYP3A43 +--------------------- + +- `PharmVar CYP3A43 page `__ + +CYP4A11 +======= + +Resources for CYP4A11 +--------------------- + +- `PharmVar CYP4A11 page `__ + +CYP4A22 +======= + +Resources for CYP4A22 +--------------------- + +- `PharmVar CYP4A22 page `__ + +CYP4B1 +====== + +Resources for CYP4B1 +-------------------- + +- `PharmVar CYP4B1 page `__ + +CYP4F2 +====== + +Resources for CYP4F2 +-------------------- + +- `PharmVar CYP4F2 page `__ + +CYP17A1 +======= + +Resources for CYP17A1 +--------------------- + +- `PharmVar CYP17A1 page `__ + +CYP19A1 +======= + +Resources for CYP19A1 +--------------------- + +- `PharmVar CYP19A1 page `__ + +CYP26A1 +======= + +Resources for CYP26A1 +--------------------- + +- `PharmVar CYP26A1 page `__ + DPYD ==== @@ -657,6 +1041,34 @@ Activity score is used for phenotype prediction. - 0 <= score < 1 - c.295_298delTCAT (\*7)/c.703C>T (\*8) +Resources for DPYD +------------------ + +- `CPIC® Guideline for Fluoropyrimidines and DPYD `__ + +F5 +== + +Phenotype summary for F5 +------------------------ + +Diplotype-phenotype mapping is used for phenotype prediction. + + .. list-table:: + :header-rows: 1 + + * - Phenotype + - Example + * - Favorable Response + - Reference/Reference + * - Unfavorable Response + - Reference/Leiden + +Resources for F5 +---------------- + +- `Annotation of DPWG Guideline for hormonal contraceptives for systemic use and F5 `__ + GSTM1 ===== @@ -707,6 +1119,15 @@ Below is comprehensive summary of SV described from real NGS studies: - WGS - `GeT-RM `__ - NA19908 + * - \*Bx2 + - Duplication + - \*A/\*Bx2 + - + - :download:`Profile ` + - :download:`Profile ` + - WGS + - + - GSTT1 ===== @@ -758,6 +1179,31 @@ Below is comprehensive summary of SV described from real NGS studies: - `GeT-RM `__ - NA11832 +IFNL3 +===== + +Phenotype summary for IFNL3 +--------------------------- + +Diplotype-phenotype mapping is used for phenotype prediction. + +.. list-table:: + :header-rows: 1 + + * - Phenotype + - Example + * - Favorable Response + - Reference/Reference + * - Unfavorable Response + - Reference/rs12979860 + * - Indeterminate + - Reference/rs8099917 + +Resources for IFNL3 +------------------- + +- `Annotation of CPIC Guideline for peginterferon alfa-2a,peginterferon alfa-2b,ribavirin and IFNL3 `__ + NUDT15 ====== @@ -782,6 +1228,51 @@ Diplotype-phenotype mapping is used for phenotype prediction. * - Indeterminate - \*1/\*4 +Resources for NUDT15 +-------------------- + +- `CPIC® Guideline for Thiopurines and TPMT and NUDT15 `__ + +POR +=== + +Resources for POR +----------------- + +- `PharmVar POR page `__ + +PTGIS +===== + +Resources for PTGIS +------------------- + +- `PharmVar PTGIS page `__ + +RYR1 +==== + +Phenotype summary for RYR1 +-------------------------- + +Diplotype-phenotype mapping is used for phenotype prediction. + +.. list-table:: + :header-rows: 1 + + * - Phenotype + - Example + * - Uncertain Susceptibility + - Reference/Reference + * - Malignant Hyperthermia Susceptibility + - Reference/c.103T>C + +Resources for RYR1 +------------------ + +- `Annotation of CPIC Guideline for desflurane and CACNA1S, RYR1 `__ +- `CPIC® Guideline for Potent Volatile Anesthetic Agents and Succinylcholine and RYR1 and CACNA1S `__ + SLC22A2 ======= @@ -849,6 +1340,19 @@ Diplotype-phenotype mapping is used for phenotype prediction. * - Indeterminate - \*1A/\*7 +Resources for SLCO1B1 +--------------------- + +- `CPIC® Guideline for Simvastatin and SLCO1B1 `__ + +TBXAS1 +====== + +Resources for TBXAS1 +-------------------- + +- `PharmVar TBXAS1 page `__ + TPMT ==== @@ -873,6 +1377,11 @@ Diplotype-phenotype mapping is used for phenotype prediction. * - Indeterminate - \*1/\*18 +Resources for TPMT +------------------ + +- `CPIC® Guideline for Thiopurines and TPMT and NUDT15 `__ + UGT1A1 ====== @@ -895,6 +1404,11 @@ Diplotype-phenotype mapping is used for phenotype prediction. * - Indeterminate - \*28/\*80 +Resources for UGT1A1 +-------------------- + +- `CPIC® Guideline for Atazanavir and UGT1A1 `__ + UGT1A4 ====== @@ -916,7 +1430,7 @@ Below is comprehensive summary of SV described from real NGS studies: - Source - Coriell ID * - \*S1 - - Intron1Deletion + - Intron1DeletionA - \*1/\*S1 - - :download:`Profile ` @@ -924,6 +1438,15 @@ Below is comprehensive summary of SV described from real NGS studies: - WGS - `GeT-RM `__ - NA19908 + * - \*S2 + - Intron1DeletionB + - \*1/\*S2 + - + - :download:`Profile ` + - :download:`Profile ` + - WGS + - + - UGT2B15 ======= diff --git a/docs/glossary.rst b/docs/glossary.rst new file mode 100644 index 00000000..d2ea1189 --- /dev/null +++ b/docs/glossary.rst @@ -0,0 +1,67 @@ +Glossary +******** + +Clinical Pharmacogenetics Implementation Consortium (CPIC) +========================================================== + +The `Clinical Pharmacogenetics Implementation Consortium (CPIC) +`__ is an international consortium whose primary goal +is to facilitate use of PGx tests for patient care by creating, curating, and +posting freely available, peer-reviewed, evidence-based, updatable, and +detailed gene/drug clinical practice guidelines. + +Canadian Pharmacogenomics Network for Drug Safety (CPNDS) +========================================================= + +The `Canadian Pharmacogenomics Network for Drug Safety (CPNDS) +`__ is a multicenter active surveillance and +pharmacogenomics consortium. It was founded in 2004 with the goal to uncover +the genetic and mechanistic basis of drug response phenotypes and develop +clinical pharmacogenetic implementation tools to improve the safety and +efficacy of medications used in children and adults. It is a +multidisciplinary team of scientists and practicing physicians from across +Canada and around the world. + +Dutch Pharmacogenetics Working Group (DPWG) +=========================================== + +The `Dutch Pharmacogenetics Working Group (DPWG) `__ was established in +2005 by the Royal Dutch Pharmacist's Association. The DPWG is +multidisciplinary and includes clinical pharmacists, physicians, clinical +pharmacologists, clinical chemists, epidemiologists, and toxicologists. + +Pharmacogenomics (PGx) +====================== + +According to this PharmGKB `post `__: + + In general pharmacogenetics usually refers to how variation in one single + gene influences the response to a single drug. Pharmacogenomics is a + broader term, which studies how all of the genes (the genome) can + influence responses to drugs. However, these terms are often used + interchangeably. + +Pharmacogenomics Knowledge Base (PharmGKB) +========================================== + +The `Pharmacogenomics Knowledge Base (PharmGKB) +`__ is a publicly available, online knowledge base +responsible for the aggregation, curation, integration and dissemination of +knowledge regarding the impact of human genetic variation on drug response. +PharmGKB annotates PGx-based drug dosing guidelines published by CPIC, DPWG, +CPNDS, and other professional societies. PharmGKB annotations present a brief +summary of the genotype-based dosing recommendations and links to the source +publications/documents. + +Structural variation (SV) +========================= + +Structural variation (SV) is generally defined as a region of DNA +approximately 1 kb and larger in size and can include inversions and balanced +translocations or genomic imbalances (duplications and deletions), commonly +referred to as copy number variants (CNVs). Additionally, for pharmacogenes +that are known to have one or more pseudogenes (e.g. CYP2D6), SV can be often +found in the form of hybrid genes (e.g. CYP2D6/CYP2D7). diff --git a/docs/index.rst b/docs/index.rst index 30daf6c5..5b7d22f7 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,6 +7,7 @@ Welcome to PyPGx's documentation! readme genes + glossary tutorials cli api diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 96184d84..d79829e8 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -37,9 +37,11 @@ those from: .. code-block:: text $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch37-variants.vcf.gz + $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch37-variants.vcf.gz.tbi $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch37-depth-of-coverage.zip $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch37-control-statistics-VDR.zip $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch38-variants.vcf.gz + $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch38-variants.vcf.gz.tbi $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch38-depth-of-coverage.zip $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch38-control-statistics-VDR.zip @@ -60,7 +62,7 @@ Let's look at the metadata for some of these files: Genotyping genes with SV ------------------------ -The first gene we are going to genotype is *CYP2D6*, which has almost 150 +The first gene we are going to genotype is CYP2D6, which has almost 150 star alleles including those with SV (e.g. gene deletions, duplications, and hybrids). To this end, we will run PyPGx's next-generation sequencing (NGS) pipeline: @@ -92,12 +94,22 @@ Above will create a number of archive files: In addition to these files, PyPGx will have also created two directories called ``copy-number-profile`` and ``allele-fraction-profile``. -Now that's it, you have successfully genotyped *CYP2D6* with WGS data! +Now let's make sure the genotype results are correct by comparing them with the validation data: + +.. code-block:: text + + $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/getrm-wgs-tutorial/grch37-CYP2D6-results.zip + $ pypgx compare-genotypes grch37-CYP2D6-pipeline/results.zip grch37-CYP2D6-results.zip + Total: 70 + Compared: 70 + Concordance: 1.000 (70/70) + +That's it, you have successfully genotyped CYP2D6 with WGS data! Genotyping genes without SV --------------------------- -The next gene we're going to genotype is *CYP3A5*. Unlike *CYP2D6*, this gene +The next gene we're going to genotype is CYP3A5. Unlike CYP2D6, this gene does not have any star alleles with SV. Therefore, we only need to provide ``grch37-variants.vcf.gz`` to the NGS pipeline: @@ -122,7 +134,7 @@ Above will create a number of archive files: Plus the ``allele-fraction-profile`` directory. -Now you have successfully genotyped *CYP3A5* as well! +Now you have successfully genotyped CYP3A5 as well! .. note:: Note that if you provide ``grch37-depth-of-coverage.zip`` and @@ -136,7 +148,7 @@ Genotyping with GRCh38 data --------------------------- Thus far, we have only considered GRCh37 data. But we can also run the -genotyping pipeline for GRCh38 data by changing the ``--assembly`` option: +pipeline for GRCh38 data by changing the ``--assembly`` option: .. code-block:: text @@ -163,7 +175,7 @@ Congratulations, you have completed this tutorial! Coriell Affy tutorial ===================== -In this tutorial I will show you how to genotype the *CYP3A5* gene with chip data. +In this tutorial I will show you how to genotype the CYP3A5 gene with chip data. Coriell Institute has carried out Affy 6.0 genotyping on many of the 1000 Genomes Project (1KGP) samples whose data are available on 1KGP's `FTP site `__. For this tutorial we will be using the file ``ALL.wgs.nhgri_coriell_affy_6.20140825.genotypes_no_ped.vcf.gz`` which contains variant data for 355 samples. @@ -174,6 +186,7 @@ For convenience, I prepared input files: $ mkdir coriell-affy-tutorial $ cd coriell-affy-tutorial $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/coriell-affy-tutorial/variants.vcf.gz + $ wget https://raw.githubusercontent.com/sbslee/pypgx-data/main/coriell-affy-tutorial/variants.vcf.gz.tbi Next, run the chip pipeline: @@ -196,4 +209,4 @@ Above will create a number of archive files: Saved SampleTable[Phenotypes] to: CYP3A5-pipeline/phenotypes.zip Saved SampleTable[Results] to: CYP3A5-pipeline/results.zip -Now that’s it! You have successfully genotyped *CYP3A5* with chip data. +Now that’s it! You have successfully genotyped CYP3A5 with chip data. diff --git a/pypgx/api/1kgp/GRCh37/F5.vcf.gz b/pypgx/api/1kgp/GRCh37/F5.vcf.gz new file mode 100644 index 00000000..7393a792 Binary files /dev/null and b/pypgx/api/1kgp/GRCh37/F5.vcf.gz differ diff --git a/pypgx/api/1kgp/GRCh38/F5.vcf.gz b/pypgx/api/1kgp/GRCh38/F5.vcf.gz new file mode 100644 index 00000000..7db42329 Binary files /dev/null and b/pypgx/api/1kgp/GRCh38/F5.vcf.gz differ diff --git a/pypgx/api/cnv/GRCh37/CYP2D6.zip b/pypgx/api/cnv/GRCh37/CYP2D6.zip index 32fd2dfa..6050b6ea 100644 Binary files a/pypgx/api/cnv/GRCh37/CYP2D6.zip and b/pypgx/api/cnv/GRCh37/CYP2D6.zip differ diff --git a/pypgx/api/cnv/GRCh37/UGT1A4.zip b/pypgx/api/cnv/GRCh37/UGT1A4.zip index 7e2abc2d..8325aefd 100644 Binary files a/pypgx/api/cnv/GRCh37/UGT1A4.zip and b/pypgx/api/cnv/GRCh37/UGT1A4.zip differ diff --git a/pypgx/api/cnv/GRCh38/CYP2D6.zip b/pypgx/api/cnv/GRCh38/CYP2D6.zip index eef5b3e4..906c9574 100644 Binary files a/pypgx/api/cnv/GRCh38/CYP2D6.zip and b/pypgx/api/cnv/GRCh38/CYP2D6.zip differ diff --git a/pypgx/api/cnv/GRCh38/UGT1A4.zip b/pypgx/api/cnv/GRCh38/UGT1A4.zip index 838944d3..1242688e 100644 Binary files a/pypgx/api/cnv/GRCh38/UGT1A4.zip and b/pypgx/api/cnv/GRCh38/UGT1A4.zip differ diff --git a/pypgx/api/core.py b/pypgx/api/core.py index 1f4ebc6e..40632e51 100644 --- a/pypgx/api/core.py +++ b/pypgx/api/core.py @@ -22,9 +22,12 @@ 'Class I (Deficient with CNSHA)', 'Class II (Deficient)', 'Class III (Deficient)', + 'Unfavorable Response', + 'Malignant Hyperthermia Associated', 'Uncertain Function', 'Unknown Function', 'Normal Function', + 'Favorable Response', 'Class IV (Normal)', ] @@ -60,7 +63,7 @@ def build_definition_table(gene, assembly='GRCh37'): Returns ------- - fuc.pyvcf.VcfFrame + fuc.api.pyvcf.VcfFrame Definition table. Examples diff --git a/pypgx/api/data/allele-table.csv b/pypgx/api/data/allele-table.csv index 75afb5f6..5353017e 100644 --- a/pypgx/api/data/allele-table.csv +++ b/pypgx/api/data/allele-table.csv @@ -2,8 +2,8 @@ Gene,StarAllele,ActivityScore,Function,GRCh37Core,GRCh37Tag,GRCh38Core,GRCh38Tag ABCB1,*1,N/A,Normal Function,"7-87138645-A-G,7-87160618-A-C,7-87179601-A-G",N/A,"7-87509329-A-G,7-87531302-A-C,7-87550285-A-G",N/A,FALSE ABCB1,*2,N/A,Increased Function,N/A,N/A,N/A,N/A,FALSE CACNA1S,Reference,N/A,Normal Function,N/A,N/A,N/A,N/A,FALSE -CACNA1S,c.520C>T,N/A,Unknown Function,1-201061121-G-A,N/A,1-201091993-G-A,N/A,FALSE -CACNA1S,c.3257G>A,N/A,Unknown Function,1-201029943-C-T,N/A,1-201060815-C-T,N/A,FALSE +CACNA1S,c.520C>T,N/A,Malignant Hyperthermia Associated,1-201061121-G-A,N/A,1-201091993-G-A,N/A,FALSE +CACNA1S,c.3257G>A,N/A,Malignant Hyperthermia Associated,1-201029943-C-T,N/A,1-201060815-C-T,N/A,FALSE CFTR,Reference,N/A,Normal Function,N/A,N/A,N/A,N/A,FALSE CFTR,F508del,N/A,Unknown Function,7-117199644-ATCT-A,N/A,7-117559590-ATCT-A,N/A,FALSE CFTR,2789+5G->A,N/A,Unknown Function,7-117242922-G-A,N/A,7-117602868-G-A,N/A,FALSE @@ -700,6 +700,8 @@ DPYD,c.3061G>C,1,Normal Function,1-97544549-C-G,N/A,1-97078993-C-G,N/A,FALSE DPYD,c.3067C>A,1,Normal Function,1-97544543-G-T,N/A,1-97078987-G-T,N/A,FALSE DPYD,c.525G>A,1,Normal Function,1-98165062-C-T,N/A,1-97699506-C-T,N/A,FALSE DPYD,c.1371C>T,1,Normal Function,1-98015269-G-A,N/A,1-97549713-G-A,N/A,FALSE +F5,Reference,N/A,Normal Function,1-169519049-T-C,N/A,N/A,N/A,FALSE +F5,Leiden,N/A,Unknown Function,N/A,N/A,1-169549811-C-T,N/A,FALSE G6PD,B,N/A,Class IV (Normal),N/A,N/A,N/A,N/A,FALSE G6PD,A,N/A,Class IV (Normal),X-153763492-T-C,N/A,X-154535277-T-C,N/A,FALSE G6PD,A-.202A.376G,N/A,Class III (Deficient),"X-153763492-T-C,X-153764217-C-T",N/A,"X-154535277-T-C,X-154536002-C-T",N/A,FALSE @@ -895,8 +897,8 @@ GSTP1,*C,N/A,Unknown Function,"11-67352689-A-G,11-67353579-C-T",N/A,"11-67585218 GSTP1,*D,N/A,Unknown Function,11-67353579-C-T,N/A,11-67586108-C-T,N/A,FALSE GSTT1,*A,N/A,Normal Function,N/A,N/A,N/A,N/A,FALSE GSTT1,*0,N/A,No Function,N/A,N/A,N/A,N/A,TRUE -IFNL3,Reference,N/A,Normal Function,N/A,N/A,N/A,N/A,FALSE -IFNL3,rs12979860,N/A,Unknown Function,19-39738787-C-T,N/A,19-39248147-C-T,N/A,FALSE +IFNL3,Reference,N/A,Favorable Response,N/A,N/A,N/A,N/A,FALSE +IFNL3,rs12979860,N/A,Unfavorable Response,19-39738787-C-T,N/A,19-39248147-C-T,N/A,FALSE IFNL3,rs8099917,N/A,Unknown Function,19-39743165-T-G,N/A,19-39252525-T-G,N/A,FALSE IFNL3,rs12980275,N/A,Unknown Function,19-39731783-A-G,N/A,19-39241143-A-G,N/A,FALSE NAT1,*4,N/A,Normal Function,N/A,N/A,N/A,N/A,FALSE @@ -1006,54 +1008,54 @@ PTGIS,*4,N/A,Unknown Function,20-48129688-G-T,N/A,20-49513151-G-T,N/A,FALSE PTGIS,*5,N/A,Unknown Function,20-48127584-C-T,N/A,20-49511047-C-T,N/A,FALSE PTGIS,*6,N/A,Unknown Function,20-48130848-C-A,N/A,20-49514311-C-A,N/A,FALSE RYR1,Reference,N/A,Normal Function,N/A,N/A,N/A,N/A,FALSE -RYR1,c.103T>C,N/A,Increased Function,19-38931442-T-C,N/A,19-38440802-T-C,N/A,FALSE -RYR1,c.130C>T,N/A,Increased Function,19-38931469-C-T,N/A,19-38440829-C-T,N/A,FALSE -RYR1,c.487C>T,N/A,Increased Function,19-38934851-C-T,N/A,19-38444211-C-T,N/A,FALSE -RYR1,c.488G>T,N/A,Increased Function,19-38934852-G-T,N/A,19-38444212-G-T,N/A,FALSE -RYR1,c.742G>A,N/A,Increased Function,19-38937350-G-A,N/A,19-38446710-G-A,N/A,FALSE -RYR1,c.742G>C,N/A,Increased Function,19-38937350-G-C,N/A,19-38446710-G-C,N/A,FALSE -RYR1,c.982C>T,N/A,Increased Function,19-38939313-C-T,N/A,19-38448673-C-T,N/A,FALSE -RYR1,c.1021G>A,N/A,Increased Function,19-38939352-G-A,N/A,19-38448712-G-A,N/A,FALSE -RYR1,c.1021G>C,N/A,Increased Function,19-38939352-G-C,N/A,19-38448712-G-C,N/A,FALSE -RYR1,c.1201C>T,N/A,Increased Function,19-38942482-C-T,N/A,19-38451842-C-T,N/A,FALSE -RYR1,c.1209C>G,N/A,Increased Function,19-38942490-C-G,N/A,19-38451850-C-G,N/A,FALSE -RYR1,c.1565A>C,N/A,Increased Function,19-38945999-A-C,N/A,19-38455359-A-C,N/A,FALSE -RYR1,c.1589G>A,N/A,Increased Function,19-38946103-G-A,N/A,19-38455463-G-A,N/A,FALSE -RYR1,c.1597C>T,N/A,Increased Function,19-38946111-C-T,N/A,19-38455471-C-T,N/A,FALSE -RYR1,c.1598G>A,N/A,Increased Function,19-38946112-G-A,N/A,19-38455472-G-A,N/A,FALSE -RYR1,c.1654C>T,N/A,Increased Function,19-38946168-C-T,N/A,19-38455528-C-T,N/A,FALSE -RYR1,c.1840C>T,N/A,Increased Function,19-38948185-C-T,N/A,19-38457545-C-T,N/A,FALSE -RYR1,c.1841G>T,N/A,Increased Function,19-38948186-G-T,N/A,19-38457546-G-T,N/A,FALSE -RYR1,c.6487C>T,N/A,Increased Function,19-38985204-C-T,N/A,19-38494564-C-T,N/A,FALSE -RYR1,c.6488G>A,N/A,Increased Function,19-38985205-G-A,N/A,19-38494565-G-A,N/A,FALSE -RYR1,c.6502G>A,N/A,Increased Function,19-38985219-G-A,N/A,19-38494579-G-A,N/A,FALSE -RYR1,c.6617C>G,N/A,Increased Function,19-38986923-C-G,N/A,19-38496283-C-G,N/A,FALSE -RYR1,c.6617C>T,N/A,Increased Function,19-38986923-C-T,N/A,19-38496283-C-T,N/A,FALSE -RYR1,c.7007G>A,N/A,Increased Function,19-38989863-G-A,N/A,19-38499223-G-A,N/A,FALSE -RYR1,c.7039_7041delGAG,N/A,Increased Function,19-38990284-TGGA-T,N/A,19-38499644-TGGA-T,N/A,FALSE -RYR1,c.7048G>A,N/A,Increased Function,19-38990295-G-A,N/A,19-38499655-G-A,N/A,FALSE -RYR1,c.7063C>T,N/A,Increased Function,19-38990310-C-T,N/A,19-38499670-C-T,N/A,FALSE -RYR1,c.7124G>C,N/A,Increased Function,19-38990371-G-C,N/A,19-38499731-G-C,N/A,FALSE -RYR1,c.7282G>A,N/A,Increased Function,19-38990615-G-A,N/A,19-38499975-G-A,N/A,FALSE -RYR1,c.7300G>A,N/A,Increased Function,19-38990633-G-A,N/A,19-38499993-G-A,N/A,FALSE -RYR1,c.7304G>A,N/A,Increased Function,19-38990637-G-A,N/A,19-38499997-G-A,N/A,FALSE -RYR1,c.7354C>T,N/A,Increased Function,19-38991276-C-T,N/A,19-38500636-C-T,N/A,FALSE -RYR1,c.7360C>T,N/A,Increased Function,19-38991282-C-T,N/A,19-38500642-C-T,N/A,FALSE -RYR1,c.7361G>A,N/A,Increased Function,19-38991283-G-A,N/A,19-38500643-G-A,N/A,FALSE -RYR1,c.7372C>T,N/A,Increased Function,19-38991294-C-T,N/A,19-38500654-C-T,N/A,FALSE -RYR1,c.7373G>A,N/A,Increased Function,19-38991295-G-A,N/A,19-38500655-G-A,N/A,FALSE -RYR1,c.7522C>G,N/A,Increased Function,19-38991538-C-G,N/A,19-38500898-C-G,N/A,FALSE -RYR1,c.7522C>T,N/A,Increased Function,19-38991538-C-T,N/A,19-38500898-C-T,N/A,FALSE -RYR1,c.7523G>A,N/A,Increased Function,19-38991539-G-A,N/A,19-38500899-G-A,N/A,FALSE -RYR1,c.9310G>A,N/A,Increased Function,19-39002961-G-A,N/A,19-38512321-G-A,N/A,FALSE -RYR1,c.11969G>T,N/A,Increased Function,19-39034472-G-T,N/A,19-38543832-G-T,N/A,FALSE -RYR1,c.14387A>G,N/A,Increased Function,19-39070644-A-G,N/A,19-38580004-A-G,N/A,FALSE -RYR1,c.14477C>T,N/A,Increased Function,19-39070734-C-T,N/A,19-38580094-C-T,N/A,FALSE -RYR1,c.14497C>T,N/A,Increased Function,19-39070754-C-T,N/A,19-38580114-C-T,N/A,FALSE -RYR1,c.14512C>G,N/A,Increased Function,19-39071010-C-G,N/A,19-38580370-C-G,N/A,FALSE -RYR1,c.14545G>A,N/A,Increased Function,19-39071043-G-A,N/A,19-38580403-G-A,N/A,FALSE -RYR1,c.14582G>A,N/A,Uncertain Function,19-39071080-G-A,N/A,19-38580440-G-A,N/A,FALSE -RYR1,c.14693T>C,N/A,Decreased Function,19-39075629-T-C,N/A,19-38584989-T-C,N/A,FALSE +RYR1,c.103T>C,N/A,Malignant Hyperthermia Associated,19-38931442-T-C,N/A,19-38440802-T-C,N/A,FALSE +RYR1,c.130C>T,N/A,Malignant Hyperthermia Associated,19-38931469-C-T,N/A,19-38440829-C-T,N/A,FALSE +RYR1,c.487C>T,N/A,Malignant Hyperthermia Associated,19-38934851-C-T,N/A,19-38444211-C-T,N/A,FALSE +RYR1,c.488G>T,N/A,Malignant Hyperthermia Associated,19-38934852-G-T,N/A,19-38444212-G-T,N/A,FALSE +RYR1,c.742G>A,N/A,Malignant Hyperthermia Associated,19-38937350-G-A,N/A,19-38446710-G-A,N/A,FALSE +RYR1,c.742G>C,N/A,Malignant Hyperthermia Associated,19-38937350-G-C,N/A,19-38446710-G-C,N/A,FALSE +RYR1,c.982C>T,N/A,Malignant Hyperthermia Associated,19-38939313-C-T,N/A,19-38448673-C-T,N/A,FALSE +RYR1,c.1021G>A,N/A,Malignant Hyperthermia Associated,19-38939352-G-A,N/A,19-38448712-G-A,N/A,FALSE +RYR1,c.1021G>C,N/A,Malignant Hyperthermia Associated,19-38939352-G-C,N/A,19-38448712-G-C,N/A,FALSE +RYR1,c.1201C>T,N/A,Malignant Hyperthermia Associated,19-38942482-C-T,N/A,19-38451842-C-T,N/A,FALSE +RYR1,c.1209C>G,N/A,Malignant Hyperthermia Associated,19-38942490-C-G,N/A,19-38451850-C-G,N/A,FALSE +RYR1,c.1565A>C,N/A,Malignant Hyperthermia Associated,19-38945999-A-C,N/A,19-38455359-A-C,N/A,FALSE +RYR1,c.1589G>A,N/A,Uncertain Function,19-38946103-G-A,N/A,19-38455463-G-A,N/A,FALSE +RYR1,c.1597C>T,N/A,Malignant Hyperthermia Associated,19-38946111-C-T,N/A,19-38455471-C-T,N/A,FALSE +RYR1,c.1598G>A,N/A,Uncertain Function,19-38946112-G-A,N/A,19-38455472-G-A,N/A,FALSE +RYR1,c.1654C>T,N/A,Malignant Hyperthermia Associated,19-38946168-C-T,N/A,19-38455528-C-T,N/A,FALSE +RYR1,c.1840C>T,N/A,Malignant Hyperthermia Associated,19-38948185-C-T,N/A,19-38457545-C-T,N/A,FALSE +RYR1,c.1841G>T,N/A,Malignant Hyperthermia Associated,19-38948186-G-T,N/A,19-38457546-G-T,N/A,FALSE +RYR1,c.6487C>T,N/A,Malignant Hyperthermia Associated,19-38985204-C-T,N/A,19-38494564-C-T,N/A,FALSE +RYR1,c.6488G>A,N/A,Malignant Hyperthermia Associated,19-38985205-G-A,N/A,19-38494565-G-A,N/A,FALSE +RYR1,c.6502G>A,N/A,Malignant Hyperthermia Associated,19-38985219-G-A,N/A,19-38494579-G-A,N/A,FALSE +RYR1,c.6617C>G,N/A,Malignant Hyperthermia Associated,19-38986923-C-G,N/A,19-38496283-C-G,N/A,FALSE +RYR1,c.6617C>T,N/A,Malignant Hyperthermia Associated,19-38986923-C-T,N/A,19-38496283-C-T,N/A,FALSE +RYR1,c.7007G>A,N/A,Malignant Hyperthermia Associated,19-38989863-G-A,N/A,19-38499223-G-A,N/A,FALSE +RYR1,c.7039_7041delGAG,N/A,Malignant Hyperthermia Associated,19-38990284-TGGA-T,N/A,19-38499644-TGGA-T,N/A,FALSE +RYR1,c.7048G>A,N/A,Malignant Hyperthermia Associated,19-38990295-G-A,N/A,19-38499655-G-A,N/A,FALSE +RYR1,c.7063C>T,N/A,Malignant Hyperthermia Associated,19-38990310-C-T,N/A,19-38499670-C-T,N/A,FALSE +RYR1,c.7124G>C,N/A,Malignant Hyperthermia Associated,19-38990371-G-C,N/A,19-38499731-G-C,N/A,FALSE +RYR1,c.7282G>A,N/A,Malignant Hyperthermia Associated,19-38990615-G-A,N/A,19-38499975-G-A,N/A,FALSE +RYR1,c.7300G>A,N/A,Malignant Hyperthermia Associated,19-38990633-G-A,N/A,19-38499993-G-A,N/A,FALSE +RYR1,c.7304G>A,N/A,Malignant Hyperthermia Associated,19-38990637-G-A,N/A,19-38499997-G-A,N/A,FALSE +RYR1,c.7354C>T,N/A,Malignant Hyperthermia Associated,19-38991276-C-T,N/A,19-38500636-C-T,N/A,FALSE +RYR1,c.7360C>T,N/A,Malignant Hyperthermia Associated,19-38991282-C-T,N/A,19-38500642-C-T,N/A,FALSE +RYR1,c.7361G>A,N/A,Malignant Hyperthermia Associated,19-38991283-G-A,N/A,19-38500643-G-A,N/A,FALSE +RYR1,c.7372C>T,N/A,Malignant Hyperthermia Associated,19-38991294-C-T,N/A,19-38500654-C-T,N/A,FALSE +RYR1,c.7373G>A,N/A,Malignant Hyperthermia Associated,19-38991295-G-A,N/A,19-38500655-G-A,N/A,FALSE +RYR1,c.7522C>G,N/A,Malignant Hyperthermia Associated,19-38991538-C-G,N/A,19-38500898-C-G,N/A,FALSE +RYR1,c.7522C>T,N/A,Malignant Hyperthermia Associated,19-38991538-C-T,N/A,19-38500898-C-T,N/A,FALSE +RYR1,c.7523G>A,N/A,Malignant Hyperthermia Associated,19-38991539-G-A,N/A,19-38500899-G-A,N/A,FALSE +RYR1,c.9310G>A,N/A,Malignant Hyperthermia Associated,19-39002961-G-A,N/A,19-38512321-G-A,N/A,FALSE +RYR1,c.11969G>T,N/A,Malignant Hyperthermia Associated,19-39034472-G-T,N/A,19-38543832-G-T,N/A,FALSE +RYR1,c.14387A>G,N/A,Malignant Hyperthermia Associated,19-39070644-A-G,N/A,19-38580004-A-G,N/A,FALSE +RYR1,c.14477C>T,N/A,Malignant Hyperthermia Associated,19-39070734-C-T,N/A,19-38580094-C-T,N/A,FALSE +RYR1,c.14497C>T,N/A,Malignant Hyperthermia Associated,19-39070754-C-T,N/A,19-38580114-C-T,N/A,FALSE +RYR1,c.14512C>G,N/A,Malignant Hyperthermia Associated,19-39071010-C-G,N/A,19-38580370-C-G,N/A,FALSE +RYR1,c.14545G>A,N/A,Malignant Hyperthermia Associated,19-39071043-G-A,N/A,19-38580403-G-A,N/A,FALSE +RYR1,c.14582G>A,N/A,Malignant Hyperthermia Associated,19-39071080-G-A,N/A,19-38580440-G-A,N/A,FALSE +RYR1,c.14693T>C,N/A,Malignant Hyperthermia Associated,19-39075629-T-C,N/A,19-38584989-T-C,N/A,FALSE SLC15A2,*1,N/A,Normal Function,N/A,N/A,N/A,N/A,FALSE SLC15A2,*2,N/A,Unknown Function,"3-121643804-C-T,3-121647286-C-T,3-121648168-G-A",N/A,"3-121924957-C-T,3-121928439-C-T,3-121929321-G-A",N/A,FALSE SLC22A2,*1,N/A,Normal Function,"6-160645832-C-T,6-160670282-A-C",N/A,"6-160224800-C-T,6-160249250-A-C",N/A,FALSE diff --git a/pypgx/api/data/cnv-table.csv b/pypgx/api/data/cnv-table.csv index e076fa63..c74b997d 100644 --- a/pypgx/api/data/cnv-table.csv +++ b/pypgx/api/data/cnv-table.csv @@ -14,6 +14,8 @@ CYP2D6,Tandem2A,4 CYP2D6,Tandem2B,5 CYP2D6,"DeletionHet,Tandem1",6 CYP2D6,"Duplication,Tandem1",7 +CYP2D6,DeletionHom,8 +CYP2D6,Tandem2C,9 CYP2E1,Normal,0 CYP2E1,Duplication,1 CYP2E1,PartialDuplication,2 @@ -29,7 +31,8 @@ SLC22A2,Normal,0 SLC22A2,Exon11Deletion,1 SLC22A2,Intron9Deletion,2 UGT1A4,Normal,0 -UGT1A4,Intron1Deletion,1 +UGT1A4,Intron1DeletionA,1 +UGT1A4,Intron1DeletionB,2 UGT2B15,Normal,0 UGT2B15,PartialDeletion,1 UGT2B17,Normal,0 diff --git a/pypgx/api/data/diplotype-table.csv b/pypgx/api/data/diplotype-table.csv index cb34f343..a989d4f7 100755 --- a/pypgx/api/data/diplotype-table.csv +++ b/pypgx/api/data/diplotype-table.csv @@ -1,4 +1,871 @@ Gene,Diplotype,Phenotype +CACNA1S,Reference/Reference,Uncertain Susceptibility +CACNA1S,Reference/c.520C>T,Malignant Hyperthermia Susceptibility +CACNA1S,Reference/c.3257G>A,Malignant Hyperthermia Susceptibility +CACNA1S,c.520C>T/c.520C>T,Malignant Hyperthermia Susceptibility +CACNA1S,c.520C>T/c.3257G>A,Malignant Hyperthermia Susceptibility +CACNA1S,c.3257G>A/c.3257G>A,Malignant Hyperthermia Susceptibility +CFTR,Reference/F508del,Indeterminate +CFTR,G1069R/G1244E,Favorable Response +CFTR,G1069R/G1349D,Favorable Response +CFTR,G1069R/G178R,Favorable Response +CFTR,G1069R/G551D,Favorable Response +CFTR,G1069R/G551S,Favorable Response +CFTR,G1069R/K1060T,Favorable Response +CFTR,G1069R/L206W,Favorable Response +CFTR,G1069R/P67L,Favorable Response +CFTR,G1069R/R1070Q,Favorable Response +CFTR,G1069R/R1070W,Favorable Response +CFTR,F1074L/S977F,Favorable Response +CFTR,G1069R/R117C,Favorable Response +CFTR,G1069R/R347H,Favorable Response +CFTR,G1069R/R352Q,Favorable Response +CFTR,G1069R/R74W,Favorable Response +CFTR,G1069R/S1251N,Favorable Response +CFTR,G1069R/S1255P,Favorable Response +CFTR,G1069R/S549N,Favorable Response +CFTR,G1069R/S549R(A>C),Favorable Response +CFTR,G1069R/S549R(T>G),Favorable Response +CFTR,G1069R/S945L,Favorable Response +CFTR,G1069R/S977F,Favorable Response +CFTR,G1069R/R117H,Favorable Response +CFTR,F1074L/S945L,Favorable Response +CFTR,F1074L/S549R(T>G),Favorable Response +CFTR,F1074L/S549R(A>C),Favorable Response +CFTR,F1052V/S549R(A>C),Favorable Response +CFTR,F1052V/S549R(T>G),Favorable Response +CFTR,F1052V/S945L,Favorable Response +CFTR,F1052V/S977F,Favorable Response +CFTR,F1074L/G1069R,Favorable Response +CFTR,F1074L/G1244E,Favorable Response +CFTR,F1074L/G1349D,Favorable Response +CFTR,F1074L/G178R,Favorable Response +CFTR,F1074L/G551D,Favorable Response +CFTR,F1074L/G551S,Favorable Response +CFTR,F1074L/K1060T,Favorable Response +CFTR,F1074L/L206W,Favorable Response +CFTR,F1074L/P67L,Favorable Response +CFTR,F1074L/R1070Q,Favorable Response +CFTR,F1074L/R1070W,Favorable Response +CFTR,F1074L/R117C,Favorable Response +CFTR,F1074L/R117H,Favorable Response +CFTR,F1074L/R347H,Favorable Response +CFTR,F1074L/R352Q,Favorable Response +CFTR,F1074L/R74W,Favorable Response +CFTR,F1074L/S1251N,Favorable Response +CFTR,F1074L/S1255P,Favorable Response +CFTR,F1074L/S549N,Favorable Response +CFTR,G1244E/G1349D,Favorable Response +CFTR,G1244E/G178R,Favorable Response +CFTR,G1244E/G551D,Favorable Response +CFTR,G1244E/G551S,Favorable Response +CFTR,G1349D/R347H,Favorable Response +CFTR,G1349D/R352Q,Favorable Response +CFTR,G1349D/R74W,Favorable Response +CFTR,G1349D/S1251N,Favorable Response +CFTR,G1349D/S1255P,Favorable Response +CFTR,G1349D/S549N,Favorable Response +CFTR,G1349D/S549R(A>C),Favorable Response +CFTR,G1349D/S549R(T>G),Favorable Response +CFTR,G1349D/S945L,Favorable Response +CFTR,G1349D/S977F,Favorable Response +CFTR,G178R/G551D,Favorable Response +CFTR,G178R/G551S,Favorable Response +CFTR,G178R/K1060T,Favorable Response +CFTR,G178R/L206W,Favorable Response +CFTR,G178R/P67L,Favorable Response +CFTR,G178R/R1070Q,Favorable Response +CFTR,G178R/R1070W,Favorable Response +CFTR,G178R/R117C,Favorable Response +CFTR,G178R/R117H,Favorable Response +CFTR,G178R/R347H,Favorable Response +CFTR,G178R/R352Q,Favorable Response +CFTR,G178R/R74W,Favorable Response +CFTR,G178R/S1251N,Favorable Response +CFTR,G1349D/R117H,Favorable Response +CFTR,F1052V/S549N,Favorable Response +CFTR,G1349D/R117C,Favorable Response +CFTR,G1349D/R1070Q,Favorable Response +CFTR,G1244E/K1060T,Favorable Response +CFTR,G1244E/L206W,Favorable Response +CFTR,G1244E/P67L,Favorable Response +CFTR,G1244E/R1070Q,Favorable Response +CFTR,G1244E/R1070W,Favorable Response +CFTR,G1244E/R117C,Favorable Response +CFTR,G1244E/R117H,Favorable Response +CFTR,G1244E/R347H,Favorable Response +CFTR,G1244E/R352Q,Favorable Response +CFTR,G1244E/R74W,Favorable Response +CFTR,G1244E/S1251N,Favorable Response +CFTR,G1244E/S1255P,Favorable Response +CFTR,G1244E/S549N,Favorable Response +CFTR,G1244E/S549R(A>C),Favorable Response +CFTR,G1244E/S549R(T>G),Favorable Response +CFTR,G1244E/S945L,Favorable Response +CFTR,G1244E/S977F,Favorable Response +CFTR,G1349D/G178R,Favorable Response +CFTR,G1349D/G551D,Favorable Response +CFTR,G1349D/G551S,Favorable Response +CFTR,G1349D/K1060T,Favorable Response +CFTR,G1349D/L206W,Favorable Response +CFTR,G1349D/P67L,Favorable Response +CFTR,G1349D/R1070W,Favorable Response +CFTR,F1052V/S1255P,Favorable Response +CFTR,F1052V/S1251N,Favorable Response +CFTR,F1052V/R74W,Favorable Response +CFTR,E193K/R347H,Favorable Response +CFTR,E193K/R352Q,Favorable Response +CFTR,E193K/R74W,Favorable Response +CFTR,E193K/S1251N,Favorable Response +CFTR,E193K/S1255P,Favorable Response +CFTR,E193K/S549N,Favorable Response +CFTR,E193K/S549R(A>C),Favorable Response +CFTR,E193K/S549R(T>G),Favorable Response +CFTR,E193K/S945L,Favorable Response +CFTR,E193K/S977F,Favorable Response +CFTR,E56K/E831X,Favorable Response +CFTR,E56K/F1052V,Favorable Response +CFTR,E56K/F1074L,Favorable Response +CFTR,E56K/G1069R,Favorable Response +CFTR,E56K/G1244E,Favorable Response +CFTR,E56K/G1349D,Favorable Response +CFTR,E56K/G178R,Favorable Response +CFTR,E56K/G551D,Favorable Response +CFTR,E56K/G551S,Favorable Response +CFTR,E56K/K1060T,Favorable Response +CFTR,E56K/L206W,Favorable Response +CFTR,E56K/P67L,Favorable Response +CFTR,E56K/R1070Q,Favorable Response +CFTR,E193K/R117H,Favorable Response +CFTR,E56K/R1070W,Favorable Response +CFTR,E193K/R117C,Favorable Response +CFTR,E193K/R1070Q,Favorable Response +CFTR,D579G/R347H,Favorable Response +CFTR,D579G/R352Q,Favorable Response +CFTR,D579G/R74W,Favorable Response +CFTR,D579G/S1251N,Favorable Response +CFTR,D579G/S1255P,Favorable Response +CFTR,D579G/S549N,Favorable Response +CFTR,D579G/S549R(A>C),Favorable Response +CFTR,D579G/S549R(T>G),Favorable Response +CFTR,D579G/S945L,Favorable Response +CFTR,D579G/S977F,Favorable Response +CFTR,E193K/E56K,Favorable Response +CFTR,E193K/E831X,Favorable Response +CFTR,E193K/F1052V,Favorable Response +CFTR,E193K/F1074L,Favorable Response +CFTR,E193K/G1069R,Favorable Response +CFTR,E193K/G1244E,Favorable Response +CFTR,E193K/G1349D,Favorable Response +CFTR,E193K/G178R,Favorable Response +CFTR,E193K/G551D,Favorable Response +CFTR,E193K/G551S,Favorable Response +CFTR,E193K/K1060T,Favorable Response +CFTR,E193K/L206W,Favorable Response +CFTR,E193K/P67L,Favorable Response +CFTR,E193K/R1070W,Favorable Response +CFTR,G178R/S1255P,Favorable Response +CFTR,E56K/R117C,Favorable Response +CFTR,E56K/R347H,Favorable Response +CFTR,E831X/S1251N,Favorable Response +CFTR,E831X/S1255P,Favorable Response +CFTR,E831X/S549N,Favorable Response +CFTR,E831X/S549R(A>C),Favorable Response +CFTR,E831X/S549R(T>G),Favorable Response +CFTR,E831X/S945L,Favorable Response +CFTR,E831X/S977F,Favorable Response +CFTR,F1052V/F1074L,Favorable Response +CFTR,F1052V/G1069R,Favorable Response +CFTR,F1052V/G1244E,Favorable Response +CFTR,F1052V/G1349D,Favorable Response +CFTR,F1052V/G178R,Favorable Response +CFTR,F1052V/G551D,Favorable Response +CFTR,F1052V/G551S,Favorable Response +CFTR,F1052V/K1060T,Favorable Response +CFTR,F1052V/L206W,Favorable Response +CFTR,F1052V/P67L,Favorable Response +CFTR,F1052V/R1070Q,Favorable Response +CFTR,F1052V/R1070W,Favorable Response +CFTR,F1052V/R117C,Favorable Response +CFTR,F1052V/R117H,Favorable Response +CFTR,F1052V/R347H,Favorable Response +CFTR,F1052V/R352Q,Favorable Response +CFTR,E831X/R74W,Favorable Response +CFTR,E56K/R117H,Favorable Response +CFTR,E831X/R352Q,Favorable Response +CFTR,E831X/R117H,Favorable Response +CFTR,E56K/R352Q,Favorable Response +CFTR,E56K/R74W,Favorable Response +CFTR,E56K/S1251N,Favorable Response +CFTR,E56K/S1255P,Favorable Response +CFTR,E56K/S549N,Favorable Response +CFTR,E56K/S549R(A>C),Favorable Response +CFTR,E56K/S549R(T>G),Favorable Response +CFTR,E56K/S945L,Favorable Response +CFTR,E56K/S977F,Favorable Response +CFTR,E831X/F1052V,Favorable Response +CFTR,E831X/F1074L,Favorable Response +CFTR,E831X/G1069R,Favorable Response +CFTR,E831X/G1244E,Favorable Response +CFTR,E831X/G1349D,Favorable Response +CFTR,E831X/G178R,Favorable Response +CFTR,E831X/G551D,Favorable Response +CFTR,E831X/G551S,Favorable Response +CFTR,E831X/K1060T,Favorable Response +CFTR,E831X/L206W,Favorable Response +CFTR,E831X/P67L,Favorable Response +CFTR,E831X/R1070Q,Favorable Response +CFTR,E831X/R1070W,Favorable Response +CFTR,E831X/R117C,Favorable Response +CFTR,E831X/R347H,Favorable Response +CFTR,G178R/S549N,Favorable Response +CFTR,G178R/S549R(A>C),Favorable Response +CFTR,G178R/S549R(T>G),Favorable Response +CFTR,R347H/S945L,Favorable Response +CFTR,R347H/S977F,Favorable Response +CFTR,R352Q/R74W,Favorable Response +CFTR,R352Q/S1251N,Favorable Response +CFTR,R352Q/S1255P,Favorable Response +CFTR,R352Q/S549N,Favorable Response +CFTR,R352Q/S549R(A>C),Favorable Response +CFTR,R352Q/S549R(T>G),Favorable Response +CFTR,R352Q/S945L,Favorable Response +CFTR,R352Q/S977F,Favorable Response +CFTR,R74W/S1251N,Favorable Response +CFTR,R74W/S1255P,Favorable Response +CFTR,R74W/S549N,Favorable Response +CFTR,R74W/S549R(A>C),Favorable Response +CFTR,R74W/S549R(T>G),Favorable Response +CFTR,R74W/S945L,Favorable Response +CFTR,R74W/S977F,Favorable Response +CFTR,S1251N/S1255P,Favorable Response +CFTR,S1251N/S549N,Favorable Response +CFTR,S1251N/S549R(A>C),Favorable Response +CFTR,S1251N/S549R(T>G),Favorable Response +CFTR,S1251N/S945L,Favorable Response +CFTR,S1251N/S977F,Favorable Response +CFTR,R347H/S549R(T>G),Favorable Response +CFTR,S1255P/S549N,Favorable Response +CFTR,R347H/S549R(A>C),Favorable Response +CFTR,R347H/S1255P,Favorable Response +CFTR,R117C/R347H,Favorable Response +CFTR,R117C/R352Q,Favorable Response +CFTR,R117C/R74W,Favorable Response +CFTR,R117C/S1251N,Favorable Response +CFTR,R117C/S1255P,Favorable Response +CFTR,R117C/S549N,Favorable Response +CFTR,R117C/S549R(A>C),Favorable Response +CFTR,R117C/S549R(T>G),Favorable Response +CFTR,R117C/S945L,Favorable Response +CFTR,R117C/S977F,Favorable Response +CFTR,R117H/R347H,Favorable Response +CFTR,R117H/R352Q,Favorable Response +CFTR,R117H/R74W,Favorable Response +CFTR,R117H/S1251N,Favorable Response +CFTR,R117H/S1255P,Favorable Response +CFTR,R117H/S549N,Favorable Response +CFTR,R117H/S549R(A>C),Favorable Response +CFTR,R117H/S549R(T>G),Favorable Response +CFTR,R117H/S945L,Favorable Response +CFTR,R117H/S977F,Favorable Response +CFTR,R347H/R352Q,Favorable Response +CFTR,R347H/R74W,Favorable Response +CFTR,R347H/S1251N,Favorable Response +CFTR,R347H/S549N,Favorable Response +CFTR,R117C/R117H,Favorable Response +CFTR,S1255P/S549R(A>C),Favorable Response +CFTR,S1255P/S945L,Favorable Response +CFTR,F1052V/F1052V,Favorable Response +CFTR,F1074L/F1074L,Favorable Response +CFTR,G1069R/G1069R,Favorable Response +CFTR,G1244E/G1244E,Favorable Response +CFTR,G1349D/G1349D,Favorable Response +CFTR,G178R/G178R,Favorable Response +CFTR,G551D/G551D,Favorable Response +CFTR,G551S/G551S,Favorable Response +CFTR,K1060T/K1060T,Favorable Response +CFTR,L206W/L206W,Favorable Response +CFTR,P67L/P67L,Favorable Response +CFTR,R1070Q/R1070Q,Favorable Response +CFTR,R1070W/R1070W,Favorable Response +CFTR,R117C/R117C,Favorable Response +CFTR,R117H/R117H,Favorable Response +CFTR,R347H/R347H,Favorable Response +CFTR,R352Q/R352Q,Favorable Response +CFTR,R74W/R74W,Favorable Response +CFTR,S1251N/S1251N,Favorable Response +CFTR,S1255P/S1255P,Favorable Response +CFTR,S549N/S549N,Favorable Response +CFTR,S549R(A>C)/S549R(A>C),Favorable Response +CFTR,S549R(T>G)/S549R(T>G),Favorable Response +CFTR,E831X/E831X,Favorable Response +CFTR,S1255P/S549R(T>G),Favorable Response +CFTR,E56K/E56K,Favorable Response +CFTR,D579G/D579G,Favorable Response +CFTR,S1255P/S977F,Favorable Response +CFTR,S549N/S549R(A>C),Favorable Response +CFTR,S549N/S549R(T>G),Favorable Response +CFTR,S549N/S945L,Favorable Response +CFTR,S549N/S977F,Favorable Response +CFTR,S549R(A>C)/S549R(T>G),Favorable Response +CFTR,S549R(A>C)/S945L,Favorable Response +CFTR,S549R(A>C)/S977F,Favorable Response +CFTR,S549R(T>G)/S945L,Favorable Response +CFTR,S549R(T>G)/S977F,Favorable Response +CFTR,S945L/S977F,Favorable Response +CFTR,Reference/Reference,Indeterminate +CFTR,F508del/F508del,Unfavorable Response +CFTR,2789+5G->A/2789+5G->A,Favorable Response +CFTR,3272-26A->G/3272-26A->G,Favorable Response +CFTR,3849+10kbC->T/3849+10kbC->T,Favorable Response +CFTR,711+3A->G/711+3A->G,Favorable Response +CFTR,A1067T/A1067T,Favorable Response +CFTR,A455E/A455E,Favorable Response +CFTR,D110E/D110E,Favorable Response +CFTR,D110H/D110H,Favorable Response +CFTR,D1152H/D1152H,Favorable Response +CFTR,D1270N/D1270N,Favorable Response +CFTR,E193K/E193K,Favorable Response 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Response +CFTR,D110E/G1244E,Favorable Response +CFTR,D110E/G1349D,Favorable Response +CFTR,D110E/G178R,Favorable Response +CFTR,D110E/G551D,Favorable Response +CFTR,D110E/G551S,Favorable Response +CFTR,D110E/K1060T,Favorable Response +CFTR,D110E/L206W,Favorable Response +CFTR,D110E/P67L,Favorable Response +CFTR,D110E/R1070Q,Favorable Response +CFTR,D110E/R1070W,Favorable Response +CFTR,D110E/R117C,Favorable Response +CFTR,D110E/R117H,Favorable Response +CFTR,D110E/R347H,Favorable Response +CFTR,D110E/R352Q,Favorable Response +CFTR,D110E/R74W,Favorable Response +CFTR,D110E/S1251N,Favorable Response +CFTR,D110E/S1255P,Favorable Response +CFTR,D110E/S549N,Favorable Response +CFTR,D110E/E193K,Favorable Response +CFTR,A455E/F1074L,Favorable Response +CFTR,D110E/D579G,Favorable Response +CFTR,D110E/D1152H,Favorable Response +CFTR,A455E/G1244E,Favorable Response +CFTR,A455E/G1349D,Favorable Response +CFTR,A455E/G178R,Favorable Response +CFTR,A455E/G551D,Favorable Response +CFTR,A455E/G551S,Favorable Response +CFTR,A455E/K1060T,Favorable Response +CFTR,A455E/L206W,Favorable Response +CFTR,A455E/P67L,Favorable Response +CFTR,A455E/R1070Q,Favorable Response +CFTR,A455E/R1070W,Favorable Response +CFTR,A455E/R117C,Favorable Response +CFTR,A455E/R117H,Favorable Response +CFTR,A455E/R347H,Favorable Response +CFTR,A455E/R352Q,Favorable Response +CFTR,A455E/R74W,Favorable Response +CFTR,A455E/S1251N,Favorable Response +CFTR,A455E/S1255P,Favorable Response +CFTR,A455E/S549N,Favorable Response +CFTR,A455E/S549R(A>C),Favorable Response +CFTR,A455E/S549R(T>G),Favorable Response +CFTR,A455E/S945L,Favorable Response +CFTR,A455E/S977F,Favorable Response +CFTR,D110E/D110H,Favorable Response +CFTR,D110E/D1270N,Favorable Response +CFTR,S977F/S977F,Favorable Response CYP2B6,*1/*1,Normal Metabolizer CYP2B6,*1/*2,Normal Metabolizer CYP2B6,*1/*3,Indeterminate @@ -1377,6 +2244,19 @@ CYP3A5,*7/*9,Indeterminate CYP3A5,*8/*8,Indeterminate CYP3A5,*8/*9,Indeterminate CYP3A5,*9/*9,Indeterminate +F5,Reference/Reference,Favorable Response +F5,Reference/Leiden,Unfavorable Response +F5,Leiden/Leiden,Unfavorable Response +IFNL3,Reference/Reference,Favorable Response +IFNL3,Reference/rs12979860,Unfavorable Response +IFNL3,Reference/rs8099917,Indeterminate +IFNL3,Reference/rs12980275,Indeterminate +IFNL3,rs12979860/rs12979860,Unfavorable Response +IFNL3,rs12979860/rs8099917,Unfavorable Response +IFNL3,rs12979860/rs12980275,Unfavorable Response +IFNL3,rs8099917/rs8099917,Indeterminate +IFNL3,rs8099917/rs12980275,Indeterminate +IFNL3,rs12980275/rs12980275,Indeterminate NUDT15,*1/*4,Indeterminate NUDT15,*1/*5,Indeterminate NUDT15,*1/*6,Indeterminate @@ -1422,6 +2302,1231 @@ NUDT15,*5/*9,Possible Intermediate Metabolizer NUDT15,*6/*9,Possible Intermediate Metabolizer NUDT15,*7/*9,Possible Intermediate Metabolizer NUDT15,*8/*9,Possible Intermediate Metabolizer +RYR1,Reference/c.103T>C,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.130C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.487C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.488G>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.742G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.742G>C,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1589G>A,Uncertain Susceptibility +RYR1,Reference/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1598G>A,Uncertain Susceptibility +RYR1,Reference/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,Reference/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.130C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.488G>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.742G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.742G>C,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.103T>C/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.488G>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.742G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.742G>C,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.488G>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.742G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.742G>C,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.742G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.742G>C,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.742G>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1589G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1598G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.7372C>T,Malignant Hyperthermia Susceptibility 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+RYR1,c.6617C>G/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.14497C>T,Malignant 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+RYR1,c.14387A>G/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.14477C>T/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.14477C>T/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.14477C>T/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14477C>T/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14477C>T/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.14497C>T/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.14497C>T/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14497C>T/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14497C>T/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.14512C>G/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14512C>G/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14512C>G/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.14545G>A/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14545G>A/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,c.14582G>A/c.14693T>C,Malignant Hyperthermia Susceptibility +RYR1,Reference/Reference,Uncertain Susceptibility +RYR1,c.103T>C/c.103T>C,Malignant Hyperthermia Susceptibility +RYR1,c.130C>T/c.130C>T,Malignant Hyperthermia Susceptibility +RYR1,c.487C>T/c.487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.488G>T/c.488G>T,Malignant Hyperthermia Susceptibility +RYR1,c.742G>A/c.742G>A,Malignant Hyperthermia Susceptibility +RYR1,c.742G>C/c.742G>C,Malignant Hyperthermia Susceptibility +RYR1,c.982C>T/c.982C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>A/c.1021G>A,Malignant Hyperthermia Susceptibility +RYR1,c.1021G>C/c.1021G>C,Malignant Hyperthermia Susceptibility +RYR1,c.1201C>T/c.1201C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1209C>G/c.1209C>G,Malignant Hyperthermia Susceptibility +RYR1,c.1565A>C/c.1565A>C,Malignant Hyperthermia Susceptibility +RYR1,c.1589G>A/c.1589G>A,Uncertain Susceptibility +RYR1,c.1597C>T/c.1597C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1598G>A/c.1598G>A,Uncertain Susceptibility +RYR1,c.1654C>T/c.1654C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1840C>T/c.1840C>T,Malignant Hyperthermia Susceptibility +RYR1,c.1841G>T/c.1841G>T,Malignant Hyperthermia Susceptibility +RYR1,c.6487C>T/c.6487C>T,Malignant Hyperthermia Susceptibility +RYR1,c.6488G>A/c.6488G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6502G>A/c.6502G>A,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>G/c.6617C>G,Malignant Hyperthermia Susceptibility +RYR1,c.6617C>T/c.6617C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7007G>A/c.7007G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7039_7041delGAG/c.7039_7041delGAG,Malignant Hyperthermia Susceptibility +RYR1,c.7048G>A/c.7048G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7063C>T/c.7063C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7124G>C/c.7124G>C,Malignant Hyperthermia Susceptibility +RYR1,c.7282G>A/c.7282G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7300G>A/c.7300G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7304G>A/c.7304G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7354C>T/c.7354C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7360C>T/c.7360C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7361G>A/c.7361G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7372C>T/c.7372C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7373G>A/c.7373G>A,Malignant Hyperthermia Susceptibility +RYR1,c.7522C>G/c.7522C>G,Malignant Hyperthermia Susceptibility +RYR1,c.7522C>T/c.7522C>T,Malignant Hyperthermia Susceptibility +RYR1,c.7523G>A/c.7523G>A,Malignant Hyperthermia Susceptibility +RYR1,c.9310G>A/c.9310G>A,Malignant Hyperthermia Susceptibility +RYR1,c.11969G>T/c.11969G>T,Malignant Hyperthermia Susceptibility +RYR1,c.14387A>G/c.14387A>G,Malignant Hyperthermia Susceptibility +RYR1,c.14477C>T/c.14477C>T,Malignant Hyperthermia Susceptibility +RYR1,c.14497C>T/c.14497C>T,Malignant Hyperthermia Susceptibility +RYR1,c.14512C>G/c.14512C>G,Malignant Hyperthermia Susceptibility +RYR1,c.14545G>A/c.14545G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14582G>A/c.14582G>A,Malignant Hyperthermia Susceptibility +RYR1,c.14693T>C/c.14693T>C,Malignant Hyperthermia Susceptibility SLCO1B1,*1A/*1A,Normal Function SLCO1B1,*1A/*1B,Normal Function SLCO1B1,*1A/*2,Possible Decreased Function @@ -3115,4 +5220,4 @@ UGT1A1,*28/*80+*37,Poor Metabolizer UGT1A1,*36/*80+*37,Intermediate Metabolizer UGT1A1,*37/*80+*37,Poor Metabolizer UGT1A1,*80/*80+*37,Indeterminate -UGT1A1,*80+*37/*80+*37,Poor Metabolizer +UGT1A1,*80+*37/*80+*37,Poor Metabolizer \ No newline at end of file diff --git a/pypgx/api/data/gene-table.csv b/pypgx/api/data/gene-table.csv index 16afcab0..40ffeb9d 100644 --- a/pypgx/api/data/gene-table.csv +++ b/pypgx/api/data/gene-table.csv @@ -1,63 +1,64 @@ -Gene,Target,Control,Paralog,Variants,SV,PhenotypeMethod,CPIC,PharmVar,RefAllele,GRCh37Default,GRCh38Default,Strand,GRCh37Region,GRCh38Region,GRCh37ExonStarts,GRCh37ExonEnds,GRCh38ExonStarts,GRCh38ExonEnds 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a/pypgx/api/data/phenotype-table.csv b/pypgx/api/data/phenotype-table.csv index e2b79203..2eaf902e 100644 --- a/pypgx/api/data/phenotype-table.csv +++ b/pypgx/api/data/phenotype-table.csv @@ -1,4 +1,9 @@ Gene,Phenotype,Priority +CACNA1S,Uncertain Susceptibility,Normal Risk +CACNA1S,Malignant Hyperthermia Susceptibility,Abnormal/Priority/High Risk +CFTR,Favorable Response,None +CFTR,Unfavorable Response,None +CFTR,Indeterminate,None CYP2B6,Intermediate Metabolizer,Abnormal/Priority/High Risk CYP2B6,Normal Metabolizer,Normal/Routine/Low Risk CYP2B6,Poor Metabolizer,Abnormal/Priority/High Risk @@ -31,11 +36,18 @@ DPYD,Normal Metabolizer,Normal/Routine/Low Risk DPYD,Intermediate Metabolizer,Abnormal/Priority/High Risk DPYD,Poor Metabolizer,Abnormal/Priority/High Risk DPYD,Poor Metabolizer,Abnormal/Priority/High Risk +F5,Favorable Response,None +F5,Unfavorable Response,None +IFNL3,Favorable Response,None +IFNL3,Unfavorable Response,None +IFNL3,Indeterminate,None NUDT15,Possible Intermediate Metabolizer,Abnormal/Priority/High Risk NUDT15,Poor Metabolizer,Abnormal/Priority/High Risk NUDT15,Normal Metabolizer,Normal/Routine/Low risk NUDT15,Intermediate Metabolizer,Abnormal/Priority/High Risk NUDT15,Indeterminate,Abnormal/Priority/High Risk +RYR1,Uncertain Susceptibility,Normal Risk +RYR1,Malignant Hyperthermia Susceptibility,Abnormal/Priority/High Risk SLCO1B1,Normal Function,Normal/Routine/Low Risk SLCO1B1,Decreased Function,Abnormal/Priority/High Risk SLCO1B1,Poor Function,Abnormal/Priority/High Risk diff --git a/pypgx/api/data/variant-table.csv b/pypgx/api/data/variant-table.csv index a3fa0b21..7177b318 100644 --- a/pypgx/api/data/variant-table.csv +++ b/pypgx/api/data/variant-table.csv @@ -671,6 +671,7 @@ DPYD,1-98060705-T-C,1-97595149-T-C,rs146356975,1,98060705,97595149,T,T,C,K290E,N DPYD,1-98060644-A-G,1-97595088-A-G,rs150437414,1,98060644,97595088,A,A,G,L310S,N/A,N/A DPYD,1-98060639-G-A,1-97595083-G-A,rs145112791,1,98060639,97595083,G,G,A,L312F,N/A,N/A DPYD,1-98058935-C-T,1-97593379-C-T,rs201018345,1,98058935,97593379,C,C,T,A323T,N/A,N/A +F5,1-169519049-T-C,1-169549811-C-T,rs6025,1,169519049,169549811,T,C,T,R534Q,N/A,N/A G6PD,X-153760261-A-C,X-154532046-A-C,N/A,X,153760261,154532046,A,A,C,F501C,N/A,N/A G6PD,X-153760272-CTTC-C,X-154532057-CTTC-C,N/A,X,153760272,154532057,CTTC,CTTC,C,K497del,N/A,N/A G6PD,X-153760297-G-A,X-154532082-G-A,N/A,X,153760297,154532082,G,G,A,P489L,N/A,N/A diff --git a/pypgx/api/genotype.py b/pypgx/api/genotype.py index f55d9920..84275d95 100644 --- a/pypgx/api/genotype.py +++ b/pypgx/api/genotype.py @@ -182,6 +182,17 @@ def one_row(self, r): result = [a1, '*36x2+*10'] else: result = ['Indeterminate'] + elif r.CNV == 'Tandem2C': + h1 = '*10' in r.Haplotype1 + h2 = '*10' in r.Haplotype2 + if h1 and h2: + result = ['*36+*10', '*36x2+*10'] + elif h1 and not h2: + result = [a2, '*36x3+*10'] + elif not h1 and h2: + result = [a1, '*36x3+*10'] + else: + result = ['Indeterminate'] elif 'DeletionHet' in r.CNV and 'Tandem1' in r.CNV: if '*4' in a1 or '*4' in a2: result = ['*5', '*68+*4'] @@ -256,10 +267,7 @@ def one_row(self, r): elif r.CNV == 'DeletionHom': result = ['*0', '*0'] elif r.CNV == 'Duplication': - if a1 == a2: - result = [a1, a2 + 'x2'] - else: - result = ['Indeterminate'] + result = _call_duplication(r) elif r.CNV in ['Normal', 'AssumeNormal']: result = [a1, a2] else: @@ -341,11 +349,22 @@ def one_row(self, r): a1, a2 = r.Haplotype1[0], r.Haplotype2[0] if r.CNV in ['Normal', 'AssumeNormal']: result = [a1, a2] - elif r.CNV == 'Intron1Deletion': + elif r.CNV == 'Intron1DeletionA': if a1 == a2: result = [a1, '*S1'] else: result = ['Indeterminate'] + elif r.CNV == 'Intron1DeletionB': + h1 = '*1'in r.Haplotype1 + h2 = '*1'in r.Haplotype2 + if h1 and h2: + result = [a1, '*S2'] + elif h1 and not h2: + result = [a2, '*S2'] + elif not h1 and h2: + result = [a1, '*S2'] + else: + result = ['Indeterminate'] else: result = ['Indeterminate'] return '/'.join(core.sort_alleles(result, by='name')) diff --git a/pypgx/api/pipeline.py b/pypgx/api/pipeline.py index 84c005f5..c7b2aaf6 100644 --- a/pypgx/api/pipeline.py +++ b/pypgx/api/pipeline.py @@ -13,7 +13,7 @@ def run_chip_pipeline( gene, output, variants, panel=None, assembly='GRCh37', impute=False, - force=False + force=False, samples=None, exclude=False ): """ Run PyPGx's genotyping pipeline for chip data. @@ -24,14 +24,22 @@ def run_chip_pipeline( Target gene. output : str Output directory. - variants : str - VCF file (zipped or unzipped). + variants : str, optional + Input VCF file must be already BGZF compressed (.gz) and indexed + (.tbi) to allow random access. Statistical haplotype phasing will be + skipped if input VCF is already fully phased. assembly : {'GRCh37', 'GRCh38'}, default: 'GRCh37' Reference genome assembly. impute : bool, default: False If True, perform imputation of missing genotypes. force : bool, default : False Overwrite output directory if it already exists. + samples : str or list, optional + Subset the VCF for specified samples. This can be a text file (.txt, + .tsv, .csv, or .list) containing one sample per line. Alternatively, + you can provide a list of samples. + exclude : bool, default: False + If True, exclude specified samples. """ if not core.is_target_gene(gene): raise core.NotTargetGeneError(gene) @@ -42,14 +50,21 @@ def run_chip_pipeline( os.mkdir(output) imported_variants = utils.import_variants(gene, variants, - assembly=assembly, platform='Chip') + assembly=assembly, platform='Chip', samples=samples, exclude=exclude) imported_variants.to_file(f'{output}/imported-variants.zip') - phased_variants = utils.estimate_phase_beagle(imported_variants, - panel=panel, impute=impute) - phased_variants.to_file(f'{output}/phased-variants.zip') - consolidated_variants = utils.create_consolidated_vcf( - imported_variants, phased_variants) - consolidated_variants.to_file(f'{output}/consolidated-variants.zip') + + # Skip statistical phasing if input VCF is already fully phased. + if imported_variants.type == 'VcfFrame[Consolidated]': + consolidated_variants = imported_variants + else: + phased_variants = utils.estimate_phase_beagle( + imported_variants, panel=panel) + phased_variants.to_file(f'{output}/phased-variants.zip') + consolidated_variants = utils.create_consolidated_vcf( + imported_variants, phased_variants) + consolidated_variants.to_file( + f'{output}/consolidated-variants.zip') + alleles = utils.predict_alleles(consolidated_variants) alleles.to_file(f'{output}/alleles.zip') genotypes = genotype.call_genotypes(alleles=alleles) @@ -64,8 +79,8 @@ def run_chip_pipeline( def run_ngs_pipeline( gene, output, variants=None, depth_of_coverage=None, control_statistics=None, platform='WGS', assembly='GRCh37', panel=None, - force=False, samples=None, do_not_plot_copy_number=False, - do_not_plot_allele_fraction=False + force=False, samples=None, exclude=False, samples_without_sv=None, + do_not_plot_copy_number=False, do_not_plot_allele_fraction=False ): """ Run PyPGx's genotyping pipeline for NGS data. @@ -73,7 +88,7 @@ def run_ngs_pipeline( During copy number analysis, if the input data is targeted sequencing, the method will apply inter-sample normalization using summary statistics across all samples. For best results, it is recommended to specify known - samples without SV using ``samples``. + samples without SV using ``samples_without_sv``. Parameters ---------- @@ -82,7 +97,9 @@ def run_ngs_pipeline( output : str Output directory. variants : str, optional - VCF file (zipped or unzipped). + Input VCF file must be already BGZF compressed (.gz) and indexed + (.tbi) to allow random access. Statistical haplotype phasing will be + skipped if input VCF is already fully phased. depth_of_coverage : str, optional Archive file or object with the semantic type CovFrame[DepthOfCoverage]. @@ -97,7 +114,13 @@ def run_ngs_pipeline( unzipped). By default, the 1KGP panel is used. force : bool, default : False Overwrite output directory if it already exists. - samples : list, optional + samples : str or list, optional + Subset the VCF for specified samples. This can be a text file (.txt, + .tsv, .csv, or .list) containing one sample per line. Alternatively, + you can provide a list of samples. + exclude : bool, default: False + If True, exclude specified samples. + samples_without_sv : list, optional List of known samples without SV. do_not_plot_copy_number : bool, default: False Do not plot copy number profile. @@ -145,16 +168,25 @@ def run_ngs_pipeline( if small_var and variants is not None: imported_variants = utils.import_variants(gene, variants, - assembly=assembly, platform=platform) + assembly=assembly, platform=platform, samples=samples, + exclude=exclude) imported_variants.to_file(f'{output}/imported-variants.zip') - phased_variants = utils.estimate_phase_beagle( - imported_variants, panel=panel) - phased_variants.to_file(f'{output}/phased-variants.zip') - consolidated_variants = utils.create_consolidated_vcf( - imported_variants, phased_variants) - consolidated_variants.to_file(f'{output}/consolidated-variants.zip') + + # Skip statistical phasing if input VCF is already fully phased. + if imported_variants.type == 'VcfFrame[Consolidated]': + consolidated_variants = imported_variants + else: + phased_variants = utils.estimate_phase_beagle( + imported_variants, panel=panel) + phased_variants.to_file(f'{output}/phased-variants.zip') + consolidated_variants = utils.create_consolidated_vcf( + imported_variants, phased_variants) + consolidated_variants.to_file( + f'{output}/consolidated-variants.zip') + alleles = utils.predict_alleles(consolidated_variants) alleles.to_file(f'{output}/alleles.zip') + if not do_not_plot_allele_fraction: os.mkdir(f'{output}/allele-fraction-profile') plot.plot_vcf_allele_fraction( @@ -175,13 +207,19 @@ def run_ngs_pipeline( if isinstance(control_statistics, str): control_statistics = sdk.Archive.from_file(control_statistics) + if samples is not None: + control_statistics = utils.filter_samples(control_statistics, + samples=samples, exclude=exclude) + control_statistics.check_type('SampleTable[Statistics]') control_statistics.check_metadata('Platform', platform) control_statistics.check_metadata('Assembly', assembly) - read_depth = utils.import_read_depth(gene, depth_of_coverage) + read_depth = utils.import_read_depth(gene, depth_of_coverage, + samples=samples, exclude=exclude) read_depth.to_file(f'{output}/read-depth.zip') - copy_number = utils.compute_copy_number(read_depth, control_statistics, samples=samples) + copy_number = utils.compute_copy_number(read_depth, + control_statistics, samples_without_sv=samples_without_sv) copy_number.to_file(f'{output}/copy-number.zip') cnv_calls = utils.predict_cnv(copy_number) cnv_calls.to_file(f'{output}/cnv-calls.zip') diff --git a/pypgx/api/plot.py b/pypgx/api/plot.py index 4f052155..ca156c7a 100644 --- a/pypgx/api/plot.py +++ b/pypgx/api/plot.py @@ -107,8 +107,10 @@ def plot_bam_copy_number( If True, show the fitted line as well. path : str, optional Create plots in this directory. - samples : list, optional - Create plots only for these samples. + samples : str or list, optional + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. ymin : float, default: -0.3 Y-axis bottom. ymax : float, default: 6.3 @@ -166,8 +168,10 @@ def plot_bam_read_depth( Archive file or object with the semantic type CovFrame[ReadDepth]. path : str, optional Create plots in this directory. - samples : list, optional - Create plots only for these samples. + samples : str or list, optional + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. ymin : float, optional Y-axis bottom. ymax : float, optional @@ -220,11 +224,14 @@ def plot_cn_af( copy_number : str or pypgx.Archive Archive file or object with the semantic type CovFrame[CopyNumber]. imported_variants : str or pypgx.Archive - Archive file or object with the semantic type VcfFrame[Imported]. + Archive file or object with the semantic type VcfFrame[Imported] or + VcfFrame[Consolidated]. path : str, optional Create plots in this directory. - samples : list, optional - Create plots only for these samples. + samples : str or list, optional + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. ymin : float, default: -0.3 Y-axis bottom. ymax : float, default: 6.3 @@ -240,11 +247,13 @@ def plot_cn_af( if isinstance(imported_variants, str): imported_variants = sdk.Archive.from_file(imported_variants) - imported_variants.check_type('VcfFrame[Imported]') + imported_variants.check_type( + ['VcfFrame[Imported]', 'VcfFrame[Consolidated]']) if samples is None: samples = copy_number.data.samples else: + samples = common.parse_list_or_file(samples) copy_number = utils.filter_samples(copy_number, samples=samples) processed_copy_number = utils._process_copy_number(copy_number) @@ -280,23 +289,27 @@ def plot_vcf_allele_fraction( imported_variants, path=None, samples=None, fontsize=25 ): """ - Plot allele fraction profile from VcfFrame[Imported]. + Plot allele fraction profile with VCF data. Parameters ---------- imported_variants : str or pypgx.Archive - Archive file or object with the semantic type VcfFrame[Imported]. + Archive file or object with the semantic type VcfFrame[Imported] or + VcfFrame[Consolidated]. path : str, optional Create plots in this directory. - samples : list, optional - Create plots only for these samples. + samples : str or list, optional + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. fontsize : float, default: 25 Text fontsize. """ if isinstance(imported_variants, str): imported_variants = sdk.Archive.from_file(imported_variants) - imported_variants.check_type('VcfFrame[Imported]') + imported_variants.check_type( + ['VcfFrame[Imported]', 'VcfFrame[Consolidated]']) gene = imported_variants.metadata['Gene'] assembly = imported_variants.metadata['Assembly'] @@ -339,8 +352,10 @@ def plot_vcf_read_depth( Reference genome assembly. path : str, optional Create plots in this directory. - samples : list, optional - Create plots only for these samples. + samples : str or list, optional + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. ymin : float, optional Y-axis bottom. ymax : float, optional diff --git a/pypgx/api/utils.py b/pypgx/api/utils.py index b64442ed..3b312f6c 100644 --- a/pypgx/api/utils.py +++ b/pypgx/api/utils.py @@ -284,7 +284,9 @@ def compute_control_statistics( return result -def compute_copy_number(read_depth, control_statistics, samples=None): +def compute_copy_number( + read_depth, control_statistics, samples_without_sv=None +): """ Compute copy number from read depth for the target gene. @@ -302,8 +304,9 @@ def compute_copy_number(read_depth, control_statistics, samples=None): read_depth : str or pypgx.Archive Archive file or object with the semantic type CovFrame[ReadDepth]. control_statistcs : str or pypgx.Archive - Archive file or object with the semandtic type SampleTable[Statistics]. - samples : list, optional + Archive file or object with the semandtic type + SampleTable[Statistics]. + samples_without_sv : list, optional List of known samples without SV. Returns @@ -331,20 +334,20 @@ def compute_copy_number(read_depth, control_statistics, samples=None): # Apply inter-sample normalization. if read_depth.metadata['Platform'] == 'Targeted': - if samples is None: + if samples_without_sv is None: medians = df.iloc[:, 2:].median(axis=1).replace(0, np.nan) else: - medians = df[samples].median(axis=1).replace(0, np.nan) + medians = df[samples_without_sv].median(axis=1).replace(0, np.nan) df.iloc[:, 2:] = df.iloc[:, 2:].div(medians, axis=0) * 2 cf = pycov.CovFrame(df) metadata = read_depth.copy_metadata() metadata['SemanticType'] = 'CovFrame[CopyNumber]' metadata['Control'] = control_statistics.metadata['Control'] - if samples is None: + if samples_without_sv is None: metadata['Samples'] = 'None' else: - metadata['Samples'] = ','.join(samples) + metadata['Samples'] = ','.join(samples_without_sv) return sdk.Archive(metadata, cf) @@ -627,7 +630,7 @@ def create_regions_bed( Returns ------- - fuc.pybed.BedFrame + fuc.api.pybed.BedFrame BED file. """ df = core.load_gene_table() @@ -701,7 +704,7 @@ def estimate_phase_beagle( data = pyvcf.VcfFrame.from_file(f'{t}/output.vcf.gz') return sdk.Archive(metadata, data) -def filter_samples(archive, samples=None, fn=None, exclude=False): +def filter_samples(archive, samples, exclude=False): """ Filter Archive for specified samples. @@ -710,11 +713,9 @@ def filter_samples(archive, samples=None, fn=None, exclude=False): archive : str or pypgx.archive Archive file or object. samples : str or list - Sample name or list of names (the order matters). Cannot be used with - ``fn``. - fn : str - File containing one filename per line. Cannot be used with - ``samples``. + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. exclude : bool, default: False If True, exclude specified samples. @@ -726,19 +727,10 @@ def filter_samples(archive, samples=None, fn=None, exclude=False): if isinstance(archive, str): archive = sdk.Archive.from_file(archive) - if isinstance(samples, str): - samples = [samples] - - if samples is not None and fn is None: - pass - elif samples is None and fn is not None: - samples = common.convert_file2list(fn) - elif samples is not None and fn is not None: - raise ValueError('Found two sets of samples') - else: - raise ValueError('Samples not found') + samples = common.parse_list_or_file(samples) - if 'CovFrame' in archive.metadata['SemanticType']: + if ('VcfFrame' in archive.metadata['SemanticType'] or + 'CovFrame' in archive.metadata['SemanticType']): data = archive.data.subset(samples, exclude=exclude) elif 'SampleTable' in archive.metadata['SemanticType']: data = archive.data.loc[samples] @@ -748,7 +740,7 @@ def filter_samples(archive, samples=None, fn=None, exclude=False): return sdk.Archive(archive.copy_metadata(), data) def import_read_depth( - gene, depth_of_coverage + gene, depth_of_coverage, samples=None, exclude=False ): """ Import read depth data for target gene. @@ -760,6 +752,12 @@ def import_read_depth( depth_of_coverage : str or pypgx.Archive Archive file or object with the semantic type CovFrame[DepthOfCoverage]. + samples : str or list, optional + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. + exclude : bool, default: False + If True, exclude specified samples. Returns ------- @@ -772,51 +770,86 @@ def import_read_depth( depth_of_coverage.check_type('CovFrame[DepthOfCoverage]') metadata = depth_of_coverage.copy_metadata() - region = core.get_region(gene, assembly=metadata['Assembly']) - data = depth_of_coverage.data.update_chr_prefix(mode='remove').slice(region) metadata['Gene'] = gene metadata['SemanticType'] = 'CovFrame[ReadDepth]' - return sdk.Archive(metadata, data) + region = core.get_region(gene, assembly=metadata['Assembly']) -def import_variants(gene, vcf, assembly='GRCh37', platform='WGS'): + cf = depth_of_coverage.data.update_chr_prefix(mode='remove') + cf = cf.slice(region) + + if samples is not None: + samples = common.parse_list_or_file(samples) + cf = cf.subset(samples, exclude=exclude) + + return sdk.Archive(metadata, cf) + +def import_variants( + gene, vcf, assembly='GRCh37', platform='WGS', samples=None, exclude=False +): """ - Import variant data for the target gene. + Import variant (SNV/indel) data for the target gene. + + The method will first slice input VCF for the target gene and then assess + whether every genotype call in the sliced VCF is haplotype phased. It + will return an archive object with the semantic type + VcfFrame[Consolidated] if the VCF is fully phased or otherwise + VcfFrame[Imported]. Parameters ---------- gene : str Target gene. - vcf : fuc.pyvcf.VcfFrame or str - VCF file (zipped or unzipped). + vcf : str or fuc.api.pyvcf.VcfFrame + Input VCF file must be already BGZF compressed (.gz) and indexed + (.tbi) to allow random access. Alternatively, you can provide a + VcfFrame object. assembly : {'GRCh37', 'GRCh38'}, default: 'GRCh37' Reference genome assembly. platform : {'WGS', 'Targeted', 'Chrip'}, default: 'WGS' Genotyping platform. + samples : str or list, optional + Specify which samples should be included for analysis by providing a + text file (.txt, .tsv, .csv, or .list) containing one sample per + line. Alternatively, you can provide a list of samples. + exclude : bool, default: False + If True, exclude specified samples. Returns ------- pypgx.Archive - Archive object with the semantic type VcfFrame[Imported]. + Archive object with the semantic type VcfFrame[Imported] or + VcfFrame[Consolidated]. """ + region = core.get_region(gene, assembly=assembly) + if isinstance(vcf, str): - vf = pyvcf.VcfFrame.from_file(vcf) + vf = pyvcf.VcfFrame.from_file(vcf, regions=region) else: - vf = vcf + vf = vcf.slice(region) - region = core.get_region(gene, assembly=assembly) + vf = vf.update_chr_prefix(mode='remove') + vf = vf.strip('GT:AD:DP') + vf = vf.add_af() - data = vf.update_chr_prefix(mode='remove').slice(region).strip('GT:AD:DP') - data = data.add_af().unphase() + if samples is not None: + samples = common.parse_list_or_file(samples) + vf = vf.subset(samples, exclude=exclude) + + if vf.phased: + semantic_type = 'VcfFrame[Consolidated]' + else: + vf = vf.unphase() + semantic_type = 'VcfFrame[Imported]' metadata = { 'Platform': platform, 'Gene': gene, 'Assembly': assembly, - 'SemanticType': 'VcfFrame[Imported]', + 'SemanticType': semantic_type, } - return sdk.Archive(metadata, data) + return sdk.Archive(metadata, vf) def predict_alleles(consolidated_variants): """ @@ -830,7 +863,7 @@ def predict_alleles(consolidated_variants): Returns ------- pypgx.Archive - Archive object with the semantic type VcfFrame SampleTable[Alleles]. + Archive object with the semantic type SampleTable[Alleles]. """ if isinstance(consolidated_variants, str): consolidated_variants = sdk.Archive.from_file(consolidated_variants) diff --git a/pypgx/cli/compute_copy_number.py b/pypgx/cli/compute_copy_number.py index bec6e97b..5d6d8e00 100644 --- a/pypgx/cli/compute_copy_number.py +++ b/pypgx/cli/compute_copy_number.py @@ -14,7 +14,7 @@ During copy number analysis, if the input data is targeted sequencing, the command will apply inter-sample normalization using summary statistics across all samples. For best results, it is recommended to specify known samples -without SV using --samples. +without SV using --samples-without-sv. """ def create_parser(subparsers): @@ -42,7 +42,7 @@ def create_parser(subparsers): 'CovFrame[CopyNumber].' ) parser.add_argument( - '--samples', + '--samples-without-sv', metavar='TEXT', nargs='+', help='List of known samples with no SV.' @@ -50,6 +50,7 @@ def create_parser(subparsers): def main(args): result = utils.compute_copy_number( - args.read_depth, args.control_statistcs, samples=args.samples + args.read_depth, args.control_statistcs, + samples=args.samples_without_sv ) result.to_file(args.output) diff --git a/pypgx/cli/estimate_phase_beagle.py b/pypgx/cli/estimate_phase_beagle.py index 9030dce3..c2a07874 100644 --- a/pypgx/cli/estimate_phase_beagle.py +++ b/pypgx/cli/estimate_phase_beagle.py @@ -31,8 +31,8 @@ def create_parser(subparsers): '--panel', metavar='PATH', help='VCF file corresponding to a reference haplotype panel \n' - '(zipped or unzipped). By default, the 1KGP panel is \n' - 'used.' + '(compressed or uncompressed). By default, the 1KGP panel \n' + 'is used.' ) parser.add_argument( '--impute', diff --git a/pypgx/cli/filter_samples.py b/pypgx/cli/filter_samples.py index 0693a6e9..19e70c46 100644 --- a/pypgx/cli/filter_samples.py +++ b/pypgx/cli/filter_samples.py @@ -25,17 +25,12 @@ def create_parser(subparsers): help='Output archive file.' ) parser.add_argument( - '--samples', - metavar='TEXT', + 'samples', nargs='+', - help='List of samples names (the order matters). Cannot be \n' - 'used with --fn.' - ) - parser.add_argument( - '--fn', - metavar='PATH', - help='File containing one sample name per line. Cannot be \n' - 'used with --samples.' + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' ) parser.add_argument( '--exclude', @@ -45,6 +40,6 @@ def create_parser(subparsers): def main(args): archive = utils.filter_samples( - args.input, samples=args.samples, fn=args.fn, exclude=args.exclude + args.input, args.samples, exclude=args.exclude ) archive.to_file(args.output) diff --git a/pypgx/cli/import_read_depth.py b/pypgx/cli/import_read_depth.py index 9bded81f..ce383fec 100644 --- a/pypgx/cli/import_read_depth.py +++ b/pypgx/cli/import_read_depth.py @@ -31,9 +31,24 @@ def create_parser(subparsers): metavar='read-depth', help='Archive file with the semantic type CovFrame[ReadDepth].' ) + parser.add_argument( + '--samples', + metavar='PATH', + nargs='+', + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' + ) + parser.add_argument( + '--exclude', + action='store_true', + help='Exclude specified samples.' + ) def main(args): archive = utils.import_read_depth( - args.gene, args.depth_of_coverage + args.gene, args.depth_of_coverage, samples=args.samples, + exclude=args.exclude ) archive.to_file(args.read_depth) diff --git a/pypgx/cli/import_variants.py b/pypgx/cli/import_variants.py index 5a51f77d..9b4ac7fc 100644 --- a/pypgx/cli/import_variants.py +++ b/pypgx/cli/import_variants.py @@ -6,7 +6,12 @@ import pysam description = f""" -Import variant data for the target gene. +Import variant (SNV/indel) data for the target gene. + +The command will first slice input VCF for the target gene and then assess +whether every genotype call in the sliced VCF is haplotype phased. It will +return an archive file with the semantic type VcfFrame[Consolidated] if the +VCF is fully phased or otherwise VcfFrame[Imported]. """ def create_parser(subparsers): @@ -14,7 +19,7 @@ def create_parser(subparsers): subparsers, fuc.api.common._script_name(), description=description, - help='Import variant data for the target gene.', + help='Import variant (SNV/indel) data for the target gene', ) parser.add_argument( 'gene', @@ -22,12 +27,14 @@ def create_parser(subparsers): ) parser.add_argument( 'vcf', - help='VCF file (zipped or unzipped).' + help='Input VCF file must be already BGZF compressed (.gz) and \n' + 'indexed (.tbi) to allow random access.' ) parser.add_argument( 'imported_variants', metavar='imported-variants', - help='Archive file with the semantic type VcfFrame[Imported].' + help='Archive file with the semantic type VcfFrame[Imported] \n' + 'or VcfFrame[Consolidated].' ) parser.add_argument( '--assembly', @@ -41,12 +48,27 @@ def create_parser(subparsers): metavar='TEXT', default='WGS', choices=['WGS', 'Targeted', 'Chip'], - help="NGS platform (default: 'WGS') (choices: 'WGS', \n" + help="Genotyping platform (default: 'WGS') (choices: 'WGS', \n" "'Targeted', 'Chip')." ) + parser.add_argument( + '--samples', + metavar='PATH', + nargs='+', + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' + ) + parser.add_argument( + '--exclude', + action='store_true', + help='Exclude specified samples.' + ) def main(args): archive = utils.import_variants( - args.gene, args.vcf, assembly=args.assembly, platform=args.platform + args.gene, args.vcf, assembly=args.assembly, platform=args.platform, + samples=args.samples, exclude=args.exclude ) archive.to_file(args.imported_variants) diff --git a/pypgx/cli/plot_bam_copy_number.py b/pypgx/cli/plot_bam_copy_number.py index 49637a71..f70f0554 100644 --- a/pypgx/cli/plot_bam_copy_number.py +++ b/pypgx/cli/plot_bam_copy_number.py @@ -36,7 +36,10 @@ def create_parser(subparsers): '--samples', metavar='TEXT', nargs='+', - help='Create plots only for these samples.' + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' ) parser.add_argument( '--ymin', diff --git a/pypgx/cli/plot_bam_read_depth.py b/pypgx/cli/plot_bam_read_depth.py index 4c22faef..a2260cd5 100644 --- a/pypgx/cli/plot_bam_read_depth.py +++ b/pypgx/cli/plot_bam_read_depth.py @@ -30,7 +30,10 @@ def create_parser(subparsers): '--samples', metavar='TEXT', nargs='+', - help='Create plots only for these samples.' + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' ) parser.add_argument( '--ymin', diff --git a/pypgx/cli/plot_cn_af.py b/pypgx/cli/plot_cn_af.py index cf81f83f..266efb4b 100644 --- a/pypgx/cli/plot_cn_af.py +++ b/pypgx/cli/plot_cn_af.py @@ -38,7 +38,10 @@ def create_parser(subparsers): '--samples', metavar='TEXT', nargs='+', - help='Create plots only for these samples.' + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' ) parser.add_argument( '--ymin', diff --git a/pypgx/cli/plot_vcf_allele_fraction.py b/pypgx/cli/plot_vcf_allele_fraction.py index 466637a7..ecb49f9e 100644 --- a/pypgx/cli/plot_vcf_allele_fraction.py +++ b/pypgx/cli/plot_vcf_allele_fraction.py @@ -31,7 +31,10 @@ def create_parser(subparsers): '--samples', metavar='TEXT', nargs='+', - help='Create plots only for these samples.' + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' ) parser.add_argument( '--fontsize', diff --git a/pypgx/cli/plot_vcf_read_depth.py b/pypgx/cli/plot_vcf_read_depth.py index d05e8656..88efb03e 100644 --- a/pypgx/cli/plot_vcf_read_depth.py +++ b/pypgx/cli/plot_vcf_read_depth.py @@ -40,7 +40,10 @@ def create_parser(subparsers): '--samples', metavar='TEXT', nargs='+', - help='Create plots only for these samples.' + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' ) parser.add_argument( '--ymin', diff --git a/pypgx/cli/run_chip_pipeline.py b/pypgx/cli/run_chip_pipeline.py index bef1bb61..65057c27 100644 --- a/pypgx/cli/run_chip_pipeline.py +++ b/pypgx/cli/run_chip_pipeline.py @@ -13,7 +13,7 @@ $ pypgx {fuc.api.common._script_name()} \\ CYP3A5 \\ CYP3A5-pipeline \\ - variants.vcf + variants.vcf.gz """ def create_parser(subparsers): @@ -34,7 +34,10 @@ def create_parser(subparsers): ) parser.add_argument( 'variants', - help='VCF file (zipped or unzipped).' + help='Input VCF file must be already BGZF compressed (.gz) \n' + 'and indexed (.tbi) to allow random access. Statistical \n' + 'haplotype phasing will be skipped if input VCF is \n' + 'already fully phased.' ) parser.add_argument( '--assembly', @@ -53,9 +56,24 @@ def create_parser(subparsers): action='store_true', help='Overwrite output directory if it already exists.' ) + parser.add_argument( + '--samples', + metavar='TEXT', + nargs='+', + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' + ) + parser.add_argument( + '--exclude', + action='store_true', + help='Exclude specified samples.' + ) def main(args): pipeline.run_chip_pipeline( args.gene, args.output, args.variants, assembly=args.assembly, - impute=args.impute, force=args.force + impute=args.impute, force=args.force, samples=args.samples, + exclude=args.exclude ) diff --git a/pypgx/cli/run_ngs_pipeline.py b/pypgx/cli/run_ngs_pipeline.py index d9a213ff..a274f5b8 100644 --- a/pypgx/cli/run_ngs_pipeline.py +++ b/pypgx/cli/run_ngs_pipeline.py @@ -10,7 +10,7 @@ During copy number analysis, if the input data is targeted sequencing, the command will apply inter-sample normalization using summary statistics across all samples. For best results, it is recommended to specify known samples -without SV using --samples. +without SV using --samples-without-sv. """ epilog = f""" @@ -18,13 +18,13 @@ $ pypgx {fuc.api.common._script_name()} \\ CYP3A5 \\ CYP3A5-pipeline \\ - --variants variants.vcf + --variants variants.vcf.gz [Example] To genotype the CYP2D6 gene, which does have SV, from WGS data: $ pypgx {fuc.api.common._script_name()} \\ CYP2D6 \\ CYP2D6-pipeline \\ - --variants variants.vcf \\ + --variants variants.vcf.gz \\ --depth-of-coverage depth-of-coverage.tsv \\ --control-statistcs control-statistics-VDR.zip @@ -32,7 +32,7 @@ $ pypgx {fuc.api.common._script_name()} \\ CYP2D6 \\ CYP2D6-pipeline \\ - --variants variants.vcf \\ + --variants variants.vcf.gz \\ --depth-of-coverage depth-of-coverage.tsv \\ --control-statistcs control-statistics-VDR.zip \\ --platform Targeted @@ -57,12 +57,15 @@ def create_parser(subparsers): parser.add_argument( '--variants', metavar='PATH', - help='VCF file (zipped or unzipped).' + help='Input VCF file must be already BGZF compressed (.gz) \n' + 'and indexed (.tbi) to allow random access. Statistical \n' + 'haplotype phasing will be skipped if input VCF is \n' + 'already fully phased.' ) parser.add_argument( '--depth-of-coverage', metavar='PATH', - help='Depth of coverage file (zipped or unzipped).' + help='Depth of coverage file (compressed or uncompressed).' ) parser.add_argument( '--control-statistics', @@ -82,15 +85,15 @@ def create_parser(subparsers): '--assembly', metavar='TEXT', default='GRCh37', - help="Reference genome assembly (default: 'GRCh37') (choices: \n" - "'GRCh37', 'GRCh38')." + help="Reference genome assembly (default: 'GRCh37') \n" + "(choices: 'GRCh37', 'GRCh38')." ) parser.add_argument( '--panel', metavar='PATH', help='VCF file corresponding to a reference haplotype panel \n' - '(zipped or unzipped). By default, the 1KGP panel is \n' - 'used.' + '(compressed or uncompressed). By default, the 1KGP \n' + 'panel is used.' ) parser.add_argument( '--force', @@ -101,6 +104,20 @@ def create_parser(subparsers): '--samples', metavar='TEXT', nargs='+', + help='Specify which samples should be included for analysis \n' + 'by providing a text file (.txt, .tsv, .csv, or .list) \n' + 'containing one sample per line. Alternatively, you can \n' + 'provide a list of samples.' + ) + parser.add_argument( + '--exclude', + action='store_true', + help='Exclude specified samples.' + ) + parser.add_argument( + '--samples-without-sv', + metavar='TEXT', + nargs='+', help="List of known samples without SV." ) parser.add_argument( @@ -118,8 +135,9 @@ def main(args): pipeline.run_ngs_pipeline( args.gene, args.output, variants=args.variants, depth_of_coverage=args.depth_of_coverage, - control_statistics=args.control_statistics, assembly=args.assembly, + control_statistics=args.control_statistics, assembly=args.assembly, panel=args.panel, force=args.force, samples=args.samples, + exclude=args.exclude, samples_without_sv=args.samples_without_sv, do_not_plot_copy_number=args.do_not_plot_copy_number, do_not_plot_allele_fraction=args.do_not_plot_allele_fraction, platform=args.platform diff --git a/pypgx/sdk/__init__.py b/pypgx/sdk/__init__.py index 14270c99..467a99cc 100644 --- a/pypgx/sdk/__init__.py +++ b/pypgx/sdk/__init__.py @@ -1,3 +1,3 @@ -from .utils import (Archive, parse_input_bams, compare_metadata) +from .utils import (Archive, parse_input_bams, compare_metadata, simulate_copy_number) -__all__ = ['Archive', 'parse_input_bams', 'compare_metadata'] +__all__ = ['Archive', 'parse_input_bams', 'compare_metadata', 'simulate_copy_number'] diff --git a/pypgx/sdk/utils.py b/pypgx/sdk/utils.py index d47fbc13..4329e9aa 100644 --- a/pypgx/sdk/utils.py +++ b/pypgx/sdk/utils.py @@ -6,6 +6,7 @@ import pickle import pandas as pd +import numpy as np from fuc import pyvcf, pycov, common, pybam class IncorrectMetadataError(Exception): @@ -33,6 +34,11 @@ def __init__(self, metadata, data): self.metadata = metadata self.data = data + @property + def type(self): + """str : Semantic type.""" + return self.metadata['SemanticType'] + def copy_metadata(self): """dict : Copy of the metadata.""" return copy.deepcopy(self.metadata) @@ -107,15 +113,29 @@ def from_file(cls, fn): raise SemanticTypeNotFoundError(metadata['SemanticType']) return cls(metadata, data) - def check_type(self, semantic_type): + def check_type(self, semantic_types): """ Raise IncorrectSemanticTypeError if the archive does not have - specified semantic type. + specified semantic type(s). + + Parameters + ---------- + semantic_types : str or list + One or more semantic types. """ - actual_type = self.metadata['SemanticType'] - if actual_type != semantic_type: - raise IncorrectSemanticTypeError( - f"Expected '{semantic_type}' but found '{actual_type}'") + if isinstance(semantic_types, str): + semantic_types = [semantic_types] + elif isinstance(semantic_types, list): + pass + else: + message = ('Semantic types must be str or list, ' + f'not {type(semantic_types).__name__}') + raise TypeError(message) + + if self.type not in semantic_types: + message = (f"Expected {'/'.join(semantic_types)}, but" + f" instead found {self.type}") + raise IncorrectSemanticTypeError(message) def check_metadata(self, key, value): """ @@ -278,3 +298,51 @@ def parse_input_bams(bam=None, fn=None): chr_prefix = '' return bam_files, chr_prefix + +def simulate_copy_number( + target, source, sample, sv, n=3, mu=0, sigma=0.05 +): + """ + Simuluate copy number by adding noise to the data of an existing sample. + + The method will generate simulated samples by introducing noise to an + existing sample in the source archive and then append those to the target + archive. + + Parameters + ---------- + target : str + Target archive file with the semantic type CovFrame[CopyNumber]. + source : str + Source archive file with the semantic type CovFrame[CopyNumber]. + sample : str + Name of the sample. + sv : str + Name of the SV. + n : int, default: 1 + Number of samples to simulate. Must be non-negative. + mu : float, default: 0 + Mean ("centre") of the distribution. + sigma : float, default: 0.05 + Standard deviation (spread or "width") of the distribution. + Must be non-negative. + + Returns + ------- + pypgx.Archive + Target archive with simultated samples appended. + """ + target = Archive.from_file(target) + source = Archive.from_file(source) + data = source.data.df[sample] + + target.data.df[sample] = data + + for i in range(n): + noise = np.random.normal(mu, sigma, len(data)) + s = data - noise + s[data == 0] = 0 + s[s < 0] = 0 + target.data.df[f'{sv}_{i+1}'] = s + + return target diff --git a/pypgx/version.py b/pypgx/version.py index e4e49b3b..9d1bb721 100644 --- a/pypgx/version.py +++ b/pypgx/version.py @@ -1 +1 @@ -__version__ = '0.9.0' +__version__ = '0.10.0' diff --git a/setup.py b/setup.py index 8d64ac48..1cf03a25 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ version=__version__, author='Seung-been "Steven" Lee', author_email='sbstevenlee@gmail.com', - description='A Python package for pharmacogenomics research', + description='A Python package for pharmacogenomics (PGx) research', long_description=open('README.rst').read(), long_description_content_type='text/x-rst', url='https://github.com/sbslee/pypgx',