# Population genetic modeling of genetic variation for complex traits and diseases

> **NIH NIH R35** · UNIVERSITY OF CHICAGO · 2024 · $392,592

## Abstract

Project Summary
Many phenotypes, as well as the risk of developing many diseases, are genetically complex, and
involve contributions from both genetic and non-genetic factors. Work in human genetics over the
past two decades have shown that this variation is the result of contributions from a very large
number of sites, on the order of thousands or tens of thousands. This presents challenges for
both the measurement and interpretation of genetic association studies, as real genetic effects
can be difficult to distinguish from the effects of confounding biases. On the other hand, biobank
scale resources represent a tremendous opportunity to learn about both the biology of complex
traits, and the evolutionary forces that have shaped modern patterns of variation. My group will
develop statistical methods to overcome several current challenges in the study of genetically
complex traits by apply tools from population, quantitative and statistical genetics.
First, we will develop tools to diagnose and correct for ancestry stratification biases in polygenic
scores. Even subtle stratification biases compound across loci to cause problems with polygenic
predictions, so methods of carefully accounting for these biases are needed. Second, we will
study the role of mutational pressure in maintaining complex disease and shaping its genetic
architecture. The increasingly availability of exome and genome-wide sequencing association
datasets make estimating the strength of mutational pressure toward increased disease risk
increasingly feasible. New theoretical development will be needed to make and interpret these
measurements. Third, we will develop models to study how mutation and selection jointly shape
the distribution of heritability for complex traits across genomic regions with different functions.
Current methods confound these two effects, so there is an opportunity for principled population
genetic modeling to provide clarity on the biology of complex traits. Finally, we will develop
improved methods for coalescent inference in population genetics. Recent breakthroughs in
coalescent inference have begun to reshape our ability to learn about evolutionary events from
genome sequencing data. However, these methods exhibit clear accuracy-scalability tradeoffs,
suggesting that a thoughtful approach to inference is needed if the benefits of these methods are
to be fully realized. My group will develop methods for accurately estimating coalescent times
from sequencing data.

## Key facts

- **NIH application ID:** 10914161
- **Project number:** 5R35GM151257-02
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Jeremy Jackson Berg
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $392,592
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10914161

## Citation

> US National Institutes of Health, RePORTER application 10914161, Population genetic modeling of genetic variation for complex traits and diseases (5R35GM151257-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10914161. Licensed CC0.

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