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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...

GWAS and burden tests are conceptually similar, so we might expect them to prioritize genes similarly. We find that their prioritizations of genes are surprisingly discordant (even when trying to get them to match as much as possible!) 2/n

If GWAS and burden rank genes differently, then how exactly are they ranking genes? More fundamentally, if we could measure whatever we wanted about genes, how SHOULD we rank genes? We propose two possible criteria: trait importance and trait specificity. 3/n

Trait importance is how much of an effect a gene has on the study trait. Genes with big effects should be ranked highly. But what if a gene has huge effects across a ton of traits? Should a gene that affects everything still be ranked highly? 4/n

Maybe instead, one might want to rank genes by their trait specificity – how important they are for the study trait relative to how important they are across all traits. A trait-specific gene might better capture how “core” it is to trait biology. 5/n

We developed theory to understand what determines burden test gene ranking: surprisingly, these rank by SPECIFICITY! We confirmed this in practice in UKB. This is surprising! Burden tests don’t rank genes by their importance! 6/n

The intuition is surprisingly simple! See our mini-thread here 7/n bsky.app/profile/jeff...

What about GWAS? GWAS prioritize genes near trait-specific VARIANTS. This is profoundly different from prioritizing trait-specific GENES. Variants can be specific because they act on trait-specific genes, or because they act on pleiotropic genes in a context-specific way. 8/n

We find that both of these ways of being specific contribute to variants being ranked highly in GWAS: 1) Coding variants in specifically-expressed genes are more highly ranked 2) Non-coding variants in tissue-specific ATAC peaks are more highly ranked 9/n

To recap so far: burden tests prioritize trait-specific genes; GWAS prioritize genes near trait-specific variants. Height GWAS and burden tests give a couple of really nice examples: NPR2 and HHIP. 10/n

NPR2 is the second most significant gene in the burden tests, but is in only the 243rd most significant GWAS locus. Homozygous LoFs in NPR2 cause severe short stature, but don’t affect intelligence, facial features, etc… NPR2 is height specific! 11/n

In contrast, HHIP (hedgehog interacting protein) has tons of GWAS hits near it, and basically no burden signal. HHIP makes sense as a height hit, but it’s also been implicated in COPD, pituitary hormone deficiency, etc… It’s not height specific! 12/n

GWAS prioritize individual variants, and sometimes variants just happen to get lucky and drift to high frequencies. This plays a surprisingly large role in prioritization in simulated GWAS 14/n

This randomness in variant frequencies also results in what seems a paradox: top GWAS hits are both more trait specific AND more likely to be hits for other traits! 15/n

Basically, when a variant gets lucky and drifts to high frequency, it is more likely to be a hit for every trait that it affects. This makes top hits APPEAR more pleiotropic, when in fact they’re actually LESS pleiotropic on average. 16/n