# Polygenic prediction and evolution of complex traits

> **NIH NIH R35** · UNIVERSITY OF PENNSYLVANIA · 2021 · $402,433

## Abstract

Project Summary
Human genetics research has made enormous progress in identifying the genetic basis of complex traits and
disease. An emerging consensus is that, since the effect of any individual variant on disease risk is typically
small, we need to take a “polygenic” approach to interpreting this information. That is, we need to consider
the cumulative effect of many variants in order to make useful prediction about individual disease risk,
and to make robust inference about evolution. The biggest limitation of current data is that most genomic
studies have been carried out in populations of European ancestry and their results may not be applicable
in non-European ancestry populations. This limits the utility of genomic medicine and risks exacerbating
existing global and national health disparities. Ideally, we would carry out large-scale genomic studies in
diverse populations across the world. However this is technically and economically difficult and, in some
cases, impossible. Instead, we aim to use the information that has already been collected from studies of
European-ancestry populations, and develop statistical and population genetic methods that allow us to use
this information in populations of non-European ancestry. We will infer ancestry-specific effects and apply
those to make prediction in populations of admixed ancestry. Using local ancestry inference we will test for
differences in trait architecture and genetic effects between populations.
In parallel, we aim to understand the biological and evolutionary basis for differences in genetic effects
and complex trait distributions between populations. Such differences can occur because of differences in
population structure or because of differences in demographic history and selective pressures. We aim to
use both simulations and data to understand the interaction between these forces and quantify their effect
on complex traits and genetic effects. By tracking complex trait evolution using ancient DNA, we add
a temporal dimension to our analysis, allowing us to identify changes over time in the genetic variation
underlying complex traits. As well as providing a direct window into human history and evolution, this
approach also helps to explain differences in complex traits and disease risk among present-day human
populations. In summary, this research program will extend our knowledge of the genetic basis of human
complex traits. It will increase our understanding of human history and evolution and help to explain present-
day trait distributions, to interpret the results of genome-wide association studies and to allow polygenic risk
prediction to be used in non-European ancestry populations.

## Key facts

- **NIH application ID:** 10189662
- **Project number:** 5R35GM133708-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Iain Neil Mathieson
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $402,433
- **Award type:** 5
- **Project period:** 2019-09-02 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189662

## Citation

> US National Institutes of Health, RePORTER application 10189662, Polygenic prediction and evolution of complex traits (5R35GM133708-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10189662. Licensed CC0.

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