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Jeff Spence
@jeffspence.github.io

It's been 30 years (!!!) since the conceptualization of GWAS. GWAS has driven tons of discoveries, but with recent experimental developments (CRISPR, single-cell omics), we thought it was time to think about how to drive the next 30 years of human genetics. doi.org/10.5281/zeno...

doi.orgTrait Causal Models: from GWAS, perturbations, and omics to predictive models of human biologyA central goal of human genetics is to understand the molecular pathways that link genes to functions in cells and tissues, and ultimately to their roles in health and disease. Genome-wide association...

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Steve Pittelli, MD
@stevepittelli.bsky.social

Consider whether GWAS, particularly in behavioral genetics, but I imagine also with other traits, are creating mostly spurious results, and whether at this point, they have served their purpose (like “candidate genes” before them), to keep the dream alive that genes will be found for traits.

Jeff Spence
@jeffspence.github.io

We argue that the goal of human genetics should be to build *Trait Causal Models*. TCMs are quantitative, predictive models that trace the biological processes from genes --> traits.

Jeff Spence
@jeffspence.github.io

TCMs should be: - Quantitative: we should move beyond just cataloging associations and instead we should estimate _by how much_ and _in which direction_ a variant or a perturbation would affect the trait

Jeff Spence
@jeffspence.github.io

TCMs should be: - Mechanistic: a model capturing the mechanisms by which genes affect a trait should be able to convert knowledge about how a perturbation or variant affects an intermediate phenotype into predictions about how it would affect the trait.

Jeff Spence
@jeffspence.github.io

To build TCMs will require 3 distinct "data pillars", each with complementary advantages 1. Genetic association data (e.g., GWAS) 2. Perturbations in model systems (e.g., perturb-seq in cell lines) 3. Human subjects omics profiling (e.g., scRNA-seq from cases and controls).