Title: Incorporating survey-derived information in human heritability estimates

Abstract: In the era of massive, biobank-scale human datasets (i.e., hundreds of thousands to millions of individuals), trait modeling requires new approaches. In addition to genetic data, these datasets contain data from participant-completed surveys that were designed to capture trait-specific relevant environmental information. However, this survey data is typically noisy and characterized by missingness, making the goal of integrating it with genetic data to model human traits challenging. Additionally, environmental factors vary between populations, further complicating cross-population comparisons. Here, we consider a key trait-modeling task: heritability estimation from population samples. We first apply dimensionality reduction techniques to the health- and lifestyle-related survey data from the All of Us Research Program. We then include these survey summaries in heritability models of biomarkers and anthropometric measurements, several of which are used to diagnose common diseases. We find that including survey summaries as covariates reduces heritability to an extent that varies by population and by trait, indicating a context-specific role of survey data in trait modeling. We further find that these survey summaries are themselves heritable, indicating their overlap with genetic information and the heritability of environmental factors.

Bio: Dr. Shaila Musharoff is a statistical and population geneticist studying human disease architecture. Shaila uses population genetic approaches to assess how demographic and cultural factors, such as admixture and assortative mating, affect patterns of genetic and phenotypic variation. Shaila uses these insights to develop statistical methods to interrogate disease and predict traits in human populations, with the goal of incorporating environmental and other trait-relevant information. Current projects in the lab include investigating the extent to which gene-by-environment interactions drive disease risk, accounting for genetic and environmental heterogeneity when estimating heritability in biobank-scale population samples, and estimating the extent of socially-induced assortative mating in human populations.

Shaila joined the Cornell Department of Computational Biology in January 2023. Previously, Shaila earned a Ph.D. in Genetics advised by Dr. Carlos Bustamante at Stanford University, and then completed Postdoctoral positions with Dr. Noah Zaitlen at UCSF and Dr. Jonathan Pritchard at Stanford University.