FarmCPU - how to explain the reported 'effect'?
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5.7 years ago

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 • 4.1k views
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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.

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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!

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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

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Hi Phillip,

Did you ever figure out the answer to this? I also have rMVP results with a column 'effect', values ranging from ~-1.4 to +1.3. Thanks!

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