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Jeff Spence
@jeffspence.github.io

Signal in association studies depends on effect size, γ, and variant frequency, p: signal ~ p(1-p) γ² But natural selection throws a wrench in this – variants with bigger effects will be under stronger selection, making them rarer. 2/6

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Jeff Spence
@jeffspence.github.io

If a variant experiences strong selection, with selection coefficient s then p(1-p) ~ μ/s , where μ is the mutation rate (thanks JBS Haldane!!!). We see this in the UKB! This means: signal ~ μγ²/s 3/6

Jeff Spence
@jeffspence.github.io

But this means that what matters for association study ranking is how specific a variant’s effects are to the trait you’re studying! signal ~ μ γ² / Σₜ γₜ² The most important variants don't end up with the largest z-scores! 5/6

Jeff Spence
@jeffspence.github.io

See our main thread about the paper 👇 for what this means about how we should interpret GWAS and rare variant burden tests! 6/6 bsky.app/profile/jeff...

Jeff Spence@jeffspence.github.io

What do GWAS and rare variant burden tests discover, and why? Do these studies find the most IMPORTANT genes? If not, how DO they rank genes? Here we present a surprising result: these studies actually test for SPECIFICITY! A 🧵on what this means... (🧪🧬) www.biorxiv.org/content/10.1...

biorxiv.orgSpecificity, length, and luck: How genes are prioritized by rare and common variant association studiesStandard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by anal...