# Accurate and robust inference of mutational bias across complex traits and diseases

> **NIH NIH F31** · UNIVERSITY OF CHICAGO · 2023 · $23,924

## Abstract

PROJECT SUMMARY/ABSTRACT
A long-standing question in human genetics is why common diseases continue to persist in the population
despite potential fitness consequences. Naively, we would expect selection to remove disease from the
population, suggesting that it must be maintained by a countervailing force. A natural null hypothesis is that
disease is maintained in the population at least partially by a mutational bias towards the disease state. To
date, the degree of mutational across complex diseases remains unexplored. However, over the past decade
and a half genome-wide association studies (GWAS) have identified genetic loci associated with complex
diseases providing insight into the underlying genetic architecture of complex diseases. With the development
of the right tools, this large influx of GWAS data will allow us to measure mutational bias across traits. Here I
propose to develop methods that accurately and robustly infer mutational bias and apply them to GWAS
datasets. Population genetic theory indicates that the average frequency of causal risk alleles is a sensitive
measure of mutational bias. In Aim 1, I will first demonstrate that calculating the average frequency of
associated risk alleles, as identified in GWAS, is also an accurate measure of mutational bias. I will then
conduct a broad screen for evidence of mutational bias across the 7,221 phenotypes available in the Pan-UK
Biobank. In Aim 2, I will first demonstrate that population stratification in GWAS summary statistics can
potentially mimic signals of mutational bias and then develop a method to correct for the effects of stratification
using the evolutionary status of an allele as an instrumental variable. Finally, in Aim 3 I will extend cutting-edge
LD based statistical methods for estimating and partitioning heritability to provide the first genome-wide
estimates of the degree of mutational bias across complex traits and within functional genomic categories.
Together my results will provide insight into the evolutionary forces driving complex trait evolution and generate
novel methods with application beyond my direct research question. A better understanding of the evolutionary
mechanisms driving disease etiology will not only help answer the question of disease persistence but also
uncover biological processes driving disease susceptibility.

## Key facts

- **NIH application ID:** 10655302
- **Project number:** 5F31HG011821-03
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Jennifer G Blanc
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $23,924
- **Award type:** 5
- **Project period:** 2021-07-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10655302

## Citation

> US National Institutes of Health, RePORTER application 10655302, Accurate and robust inference of mutational bias across complex traits and diseases (5F31HG011821-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10655302. Licensed CC0.

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