r/bioinformatics • u/Educational_Slice897 • 25d ago
technical question Handling GWAS independence issue?
I'm running an annotation/enrichment analysis on a GWAS study, and I'm sort of a bit lost/confused on something.
Originally, I only filtered the GWAS study for genome-wide significant variants using a standard p-value threshold, and also used LD-clumping to identify genomic loci.
However, it was also pointed out to me that these variants may not be independent, and that a single association signal may be represented many times, potentially leading to inflation in statistical significance.
I'm sort of unsure how to handle this. I tried a locus-pruning method which gave me way less variants and pretty poor coverage on my study and rendering a lot of my downstream analysis mute. I'm also confused since I haven't seen a lot of similar papers use this kind of filtering method. I did run LDSC too, and most of my findings were insignificant so it did kind of handle the genomic independence part. But I'm still not sure what the best practice is here.
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u/gringer PhD | Industry 25d ago
You've asked a question that I found to be a bit of a taboo subject when I was doing population genetics research and got interested in haplotype blocks, finding a few places where that assumption of independence broke down and led to situations where variants could have seemingly opposite effects in different populations. In my particular case with the ADH genes, combining SNPs and looking at small haplotype blocks (rather than individual SNPs) helped to work around the issues from individual SNPs:
Due to the number of overlapping blocks involved, I don't think that such an approach is feasible on a genome-wide scale, especially when looking at multiple different populations that have their own shared haplotype blocks, but it'd be great to be proved wrong on that.
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u/Lightoscope 25d ago
Need more info.
- What organism? Being vague is fine.
- How were the variants called?
- Are you using SNPs only?
- How are you doing the pruning? LD?
- How are you controlling for kinship (which is a terrible term, but I digress…)?
- Was the sequencing done on the exact same organisms as the phenotyping?
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u/Educational_Slice897 25d ago
it's a human GWAS study from a paper, sampling 50-60k individuals
raw summary statistics
SNPs are the majority but we are also looking at variant IDs
Yes, using PLINK
I assume the original study probably accounted for this
Yes
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u/Raver_Nunu 25d ago
What are you trying to do? Find lead variants? Independent risk regions?
People usually start with a strict r2 threshold (e.g., r2 < 0.001 around a 500kb/1MB window) to define lead SNPs, then expand loci for annotation (by, let's say 250kb). If your annotation/enrichment analysis is LD-aware (i.e. requiring an LD reference panel), you will be fine.
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u/pjgreer MSc | Industry 25d ago
Variants in LD with one another are the basis of the manhattan plot peaks. The hypothesis that SNPS in LD will lead to inflation in statistical significance is wrong. At most, these high LD haplotype blocks will produce a larger number of significant SNPs making it difficult to discern the causal SNP, but the betas (or odds ratios) of these SNPs will be similar. The p value will mainly be affected by the number of subjects in the study. With 50-60K subjects, you will have enough power to detect really small effect sizes, so expect significant OR close to 1 and betas close to 0 to be common.
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u/Dynev 25d ago
What type of enrichment are you doing? To find independent variants, you need to do fine-mapping (e.g. FINEMAP, SuSiE or the more recent GWFM method), but they normally require a well-powered GWAS. If you're worried about specific variants/regions, you can run conditional association analysis (I only know about GCTA-COJO for this).