Question: FarmCPU - how to explain the reported 'effect'?
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gravatar for Philipp Bayer
5 months ago by
Philipp Bayer6.0k
Australia/Perth/UWA
Philipp Bayer6.0k wrote:

When you run a GWAS using FarmCPU via GAPIT or MVP, you get an 'effect' reported per SNP (a number somewhere around -1 to +1, with a bunch of outliers).

I've looked at the papers and the GAPIT forum but I'm not understanding what the effect itself reports. Is it the effect of the minor allele? The major allele? The entire SNP in the model? And is that effect measured in the same unit as the associated phenotype, i.e., if I have a plant height phenotype around 50 cm, does a SNP with an effect of +5 lead to a 5cm higher plant in the model?

gwas • 428 views
ADD COMMENTlink written 5 months ago by Philipp Bayer6.0k
1

Hey Philipp, just on the sign of the effect (+ / -), it seems that you can fix the major allele to have 0 by specifying Major.allele.zero = TRUE. Thus, the given effect is always relating to the minor allele.

By default, the HapMap numericalization is performed so that the sign of the allelic effect estimate (in the GAPIT output) is with respect to the nucleotide that is second in alphabetical order. For example, if the nucleotides at a SNP are “A” and “T”, then a positive allelic effect indicates that “T” is favorable. Selecting “Major.allele.zero = TRUE” in the GAPIT() function will result in the sign of the allelic effect estimate being with respect to the minor allele. In this scenario, a positive allelic effect estimate will indicate that the minor allele is favorable. [from: http://www.zzlab.net/GAPIT/gapit_help_document.pdf ]

Regarding the interpretation of the number, I'd have thought that it was merely the estimate / coefficient from the fitted model? Thus, the exponent of the effect should be the odds ratio? May need to confirm with the authors.

ADD REPLYlink written 5 months ago by Kevin Blighe41k
1

Thank you Kevin! Yes, the coefficient makes much more sense - I'm using MVP currently which doesn't let me set Major.allele.zero, I will have to ask the authors!

ADD REPLYlink written 5 months ago by Philipp Bayer6.0k

Hello Phillip,

A better way to think about this would be if aa=0 aA=1 and AA = 2 which is typical numerical encodings used in GWAS. Then for the simplest case where you have 1 snp your model would be Pheno = b0 + b1*snp where b0 is the mean of the trait and b1 is the "effect"

So for your example with plant Height (PH) if the effect of the allele is 5 and the mean of the trait is 50 I can show how the model would look.

aa: PH = 50 + 5x0 = 50 aA: PH = 50 + 5x1 = 55 AA: PH = 50 + 5x2 = 60

I think the effect is the estimated effect of a gene in the Final fixed effect model of farmCPU. You have to be careful with effects, if you have very small MAF then the effect was calculated with a small fraction of individuals as 0. Also I would not read into the effect too much in my opinion. farmCPU is a GWAS model, not genomic selection model so the point of it is not necessarily to tell you how much of an effect a particular gene has but rather give you a good idea of what genes control a trait. It would guess if you do farmCPU for two populations with the same snp chip that the effect of a particular gene will not be the same (likely you wont even get the same hits either).

If you are concerned alot with the qtn effect try fitting either a ridge regression (rrBlup) or lasso regression (glmnet) model to your data and then pull out the effect for the qtn you found in farmCPU.

ADD REPLYlink modified 16 days ago • written 16 days ago by samuel.revolinski0
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