Collaborative Research: DMS/NIGSM 1: Statistical methods for estimating direct genetic effect

This project addresses a fundamental challenge in understanding how genetics influences human health and behavior: distinguishing true biological genetic effects from influences that arise through family environment. Human genetic studies often ignore environmental factors, yet growing evidence suggests that parental characteristics can shape the family environment and confound genetic associations. As a result, common analytic approaches may overstate genetic contributions or misidentify biological mechanisms. This project develops new statistical tools to separate direct genetic effects from environmentally mediated influences, enabling more accurate interpretation of genetic association findings. The results will improve the reliability of genetic risk prediction through biotechnology, inform precision medicine, and support evidence-based public health and policy decisions. The project will also produce open-source software and provide interdisciplinary training opportunities at the interface of statistics, biostatistics, genetics, and data science. The research develops a unified statistical framework grounded in causal inference and high-dimensional data analysis. First, it introduces a new definition of heritability based on counterfactual comparisons between individuals with identical environments, allowing direct genetic contributions to be isolated and bounded under realistic assumptions. Second, it develops methods to estimate direct genetic effects by combining large population-based studies with smaller family-based studies using summary-level data. This approach leverages the strengths of both study designs to produce unbiased and statistically efficient estimates without requiring extensive family data. The methods will be supported by theoretical guarantees, scalable algorithms, and applications to large genomic datasets. By providing principled tools for disentangling genetic and environmental effects, the project advances both statistical methodology and the scientific understanding of complex traits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2553817 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Hongyuan Cao | Institution: Florida State University, TALLAHASSEE, FL | Award Amount: $300,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553817 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2553817.html