# Traits on trees: Population genomics for understanding complex phenotypes

> **NIH NIH R35** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $412,500

## Abstract

PROJECT SUMMARY
There are now hundreds of thousands of people enrolled in biobank studies, with
genome-wide genotypes and rich phenotype data recorded. These data are an
unprecedented resource for learning about human genetic and phenotypic variation. At
the same time, population geneticists are developing advanced tools for representing
large genetic datasets as ancestral recombination graphs, which concisely encode the
genealogical relationships among genomic segments from different people in the
dataset. Combining biobank-scale data with these advanced computational tools
presents a tremendous opportunity, but we need new statistical methods to realize the
possible benefits. My research group will develop and apply such statistical methods to
complex biomedical traits, drawing on expertise in population-genetic theory, statistical
genetics, and computation. First, we will leverage our new methods and large public
datasets to study the evolution of genetic variants associated with biomedically relevant
traits, providing important clues about disease etiology. Ancestral recombination graphs
can encode all historical information available in a sample of contemporary genomes,
so they are a rich basis for evolutionary inference. Second, we will also develop
methods for enhancing genome-wide association studies (GWAS) aimed at discovering
trait-associated genetic variants. Many key GWAS goals that remain challenging
today—adjustment for population stratification and assortative mating, fine mapping of
causal variants, and others—hinge critically on parsing correlations among both
neighboring and distant genetic loci. Ancestral recombination graphs represent such
correlations naturally, and so emerging tools present a variety of novel possibilities for
clarifying the genetic basis of trait variation, including heritable differences in disease
susceptibility. Finally, a third leg of our research program will be aimed at protecting the
privacy of participants in large genetic databases. The assembly of large genetic
datasets is critical to biomedical research and also hinges on public trust that privacy
can be ensured. We will consider a set of new privacy threats that will arise as genetic
research advances, particularly as genotype–phenotype associations are better
understood and as applications of genetic genealogy become more prevalent.

## Key facts

- **NIH application ID:** 10023459
- **Project number:** 1R35GM137758-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Michael Donald Edge
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $412,500
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10023459

## Citation

> US National Institutes of Health, RePORTER application 10023459, Traits on trees: Population genomics for understanding complex phenotypes (1R35GM137758-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10023459. Licensed CC0.

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