Simulation major gene

[hosseinmehrban]

Dear GAYNOR
Hello
First of all, thanks so much for developing AlphaSimR software.
Would you mind guiding me on how to simulate a major gene using AlphasimR for a quantitative trait?
Warm regards, Hossein

[gregorgorjanc]

@hosseinmehrban do you need just one major QTL? Then you can only specify 1 QTL when you define your trait. For example:

founderGenomes = runMacs(nInd = 50, nChr = 2, segSites = 10, species = "CATTLE")
SP = SimParam$new(founderGenomes)
SP$addTraitA(nQtlPerChr = 1)

Another option is to simulate QTL effects from a gamma distribution - that will give you some major QTL and you can control the shape of that distribution with the shape argument. For example:

SP$addTraitA(fill-other-arguments, gamma = TRUE)

[hosseinmehrban]

Hi Gaynorr First of all thanks so much Imagine, a trait is controlled by 100 QTL which 1 QTL capture 20% additive genetic variance, another QTL capture 10% additive genetic variance and remains QTLs (98 QTL) capture 70% genetic variance (follow Gamma distribution only for 98 QTL). I don't know how to simulate these QTL in AlphaSimR? Thanks so so much Warm regards, Hossein

…

On Tue, Jun 14, 2022 at 3:14 PM Gregor Gorjanc ***@***.***> wrote: @hosseinmehrban <https://github.com/hosseinmehrban> do you need just one major QTL? Then you can only specify 1 QTL when you define your trait. For example: founderGenomes = runMacs(nInd = 50, nChr = 2, segSites = 10, species = "CATTLE") SP = SimParam$new(founderGenomes) SP$addTraitA(nQtlPerChr = 1) Another option is to simulate QTL effects from a gamma distribution - that will give you some major QTL and you can control the shape of that distribution with the shape argument. For example: SP$addTraitA(fill-other-arguments, gamma = TRUE) — Reply to this email directly, view it on GitHub <#61 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AKSARJKQH4D3LZGPOKPC24TVPBPAPANCNFSM5YKRKUSQ> . You are receiving this because you were mentioned.Message ID: ***@***.***>

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[gregorgorjanc]

@hosseinmehrban I suggest you start with the gamma distribution first and manipulate the shape parameter - check how much variance is captured by each QTL.

I believe we could tweak QTL effects after they are simulated too, but haven't done it myself yet. @gaynorr any comments on this idea?

[hosseinmehrban]

Dear Gaynorr Thank you so much for your help. warm regards, Hossein

…

On Tue, Jun 14, 2022 at 4:08 PM Gregor Gorjanc ***@***.***> wrote: @hosseinmehrban <https://github.com/hosseinmehrban> I suggest you start with the gamma distribution first and manipulate the shape parameter - check how much variance is captured by each QTL. I believe we could tweak QTL effects after they are simulated too, but haven't done it myself yet. @gaynorr <https://github.com/gaynorr> any comments on this idea? — Reply to this email directly, view it on GitHub <#61 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AKSARJNW4JCW34RTE4P7GBTVPBVKHANCNFSM5YKRKUSQ> . You are receiving this because you were mentioned.Message ID: ***@***.***>

-- Animal Breeding and Genetics (Ph.D) Department of Animal Science Shahrekord University Shahrekord, Iran,Postal code:88186-34141, P.O.Box: 115 Mobile: +98-915-320 6686 Mobile: +98-933-056 4001 Fax: +98-381-4424428

[poca87]

Hi,
you could also try playing with the modification of addEff slot with whatever values you want, and then apply the changes and potentially rescale the values. In the attached example I am simulating a trait controlled by 5 QTLs, and for each, I will set the mean to 0 and values of the effects to 100, 20, 20, 20, 20 (numbers mean nothing), and then I will rescale for a trait to have mean 0 and variance of 1.

@gaynorr can comment if the logic behind is correct...

# Naive example for changing QTL Effects

rm(list = ls())
library(AlphaSimR)

founderPop = quickHaplo(nInd = 6, nChr = 1, segSites = 50)

SP = SimParam$new(founderPop)

SP$addTraitA(nQtlPerChr = 5)

# QTL effects before manual change
SP$traits[[1]]@addEff
SP$traits[[1]]@intercept

# How AlphaSimR creates GVs
((pullQtlGeno(founderPop) - 1) %*% (SP$traits[[1]]@addEff)) + SP$traits[[1]]@intercept

# Manually change QTL effects: 
trait1 = SP$traits[[1]] 

# I'm using some arbitrary values here. Usually, you would sample effects from Normal or Gamma distribution 
trait1@intercept = 0
trait1@addEff = c(100, 20, 20, 20, 20)

# Apply the changes 
SP$switchTrait(lociMap = trait1, trait = 1)

# Check if effects changed 
SP$traits[[1]]@addEff
SP$traits[[1]]@intercept 

# Rescale magnitude of QTL effects and intercept for mean 0 and variance 1 in initial population
# Chris once told me this step is optional

SP$rescaleTraits(mean = 0, var = 1)

# Check if effects rescaled
SP$traits[[1]]@addEff
SP$traits[[1]]@intercept 

# You continue with population creation and breeding...

# Set heritabilty
# This step after all (above) manual changes have been applied
SP$setVarE(h2 = 0.25)

pop = newPop(founderPop, simParam = SP)

gv(pop)
((pullQtlGeno(pop) - 1) %*% (SP$traits[[1]]@addEff)) + SP$traits[[1]]@intercept

# etc.

[hosseinmehrban]

Thank you so much.

On Tue, Oct 11, 2022 at 15:55 Ivan Pocrnic ***@***.***> wrote: Hi, you could also try playing with the modification of addEff slot with whatever values you want, and then apply the changes and potentially rescale the values. In the attached example I am simulating a trait controlled by 5 QTLs, and for each, I will set the mean to 0 and values of the effects to 100, 20, 20, 20, 20 (numbers mean nothing), and then I will rescale for a trait to have mean 0 and variance of 1. @gaynorr <https://github.com/gaynorr> can comment if the logic behind is correct... # Naive example for changing QTL Effects rm(list = ls()) library(AlphaSimR) founderPop = quickHaplo(nInd = 6, nChr = 1, segSites = 50) SP = SimParam$new(founderPop) SP$addTraitA(nQtlPerChr = 5) # QTL effects before manual change ***@***.*** ***@***.*** # How AlphaSimR creates GVs ((pullQtlGeno(founderPop) - 1) %*% ***@***.***)) + ***@***.*** # Manually change QTL effects: trait1 = SP$traits[[1]] # I'm using some arbitrary values here. Usually, you would sample effects from Normal or Gamma distribution ***@***.*** = 0 ***@***.*** = c(100, 20, 20, 20, 20) # Apply the changes SP$switchTrait(lociMap = trait1, trait = 1) # Check if effects changed ***@***.*** ***@***.*** # Rescale magnitude of QTL effects and intercept for mean 0 and variance 1 in initial population # Chris once told me this step is optional SP$rescaleTraits(mean = 0, var = 1) # Check if effects rescaled ***@***.*** ***@***.*** # You continue with population creation and breeding... # Set heritabilty # This step after all (above) manual changes have been applied SP$setVarE(h2 = 0.25) pop = newPop(founderPop, simParam = SP) gv(pop) ((pullQtlGeno(pop) - 1) %*% ***@***.***)) + ***@***.*** # etc. — Reply to this email directly, view it on GitHub <#61 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AKSARJMNAAK6ZOAKG4A6R3DWCVMCRANCNFSM5YKRKUSQ> . You are receiving this because you were mentioned.Message ID: ***@***.***>

-- Animal Breeding and Genetics (Ph.D) Department of Animal Science Shahrekord University Shahrekord, Iran,Postal code:88186-34141, P.O.Box: 115 Mobile: +98-915-320 6686 Mobile: +98-933-056 4001 Fax: +98-381-4424428