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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...

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.

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

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.

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).

Key challenges include: - Extracting directional, quantitative effects of _genes_ on traits from GWAS data - Choosing cell types and conditions and types of perturbations in screens - Choosing the right level of abstraction for modeling the path from variant --> trait

We discuss all of this and more in the piece. It was a lot of fun thinking through how to use the data that we have and to try to crystallize what the goal of human genetics should be. We're hoping to spark a conversation so please let us know you're thoughts. doi.org/10.5281/zeno...

This piece grew out of many conversations about where the field is going, sparked by work from @jengreitz.bsky.social's group, and from @jkpritch.bsky.social's group in collaboration with @marsonlab.bsky.social (led by @minetoota.bsky.social). Thank you all for all of your insights!