# Quantitative Methods for Genetic Epidemiology

> **NIH NIH R35** · MAYO CLINIC ROCHESTER · 2022 · $397,500

## Abstract

Project Summary
The advancements of genomic technologies and assemblies of large disparate sets of biological and health
data have outpaced the ability to integrate these different sources of information. Powerful statistical methods
and software are needed to fill this gap in order to provide novel understandings of biological processes, as
well as provide better predictions of human diseases to achieve the vision of personalized medicine. The broad
goals of this project are to advance genetic epidemiology studies of human traits and diseases by expanding
our development of statistical analytic methods and software encompassing four main areas: 1) multivariate
methods to decipher genetic contributions; 2) statistical fine-mapping of genetic variants; 3) causal mediation
methods; 4) polygenic risk scores (PRS) for predicting disease. Although these areas might appear broad and
disparate, there is pressing need to build more integrative methods across these domains. For example,
because molecular pleiotropy is pervasive, multivariate analysis is essential to identify shared genetic factors
acting through common biological mechanisms of multiple traits, and when using PRS to predict disease,
complex traits are often better predicted when multivariate correlated traits are used. And, the methods used
for statistical fine-mapping, including use of annotation, are relevant for creating PRS to predict disease. Our
team, involving statistical geneticists, computational biologists, genetic epidemiologist and clinical
investigators, has decades of experience and will capitalize on the extensive resources and collaborations we
have developed. Our novel methods will be applied to a broad range of diseases, with ultimate aims to better
understand disease etiology and improved disease prediction across different ethnic groups to reduce health
disparities. User-friendly software will be distributed with open access to the scientific community. We will take
advantage of rapidly evolving technologies, biologic and computational insights from multiple fields, and
evolving public health and clinical unmet needs to inform our science.

## Key facts

- **NIH application ID:** 10396017
- **Project number:** 5R35GM140487-02
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Daniel J. Schaid
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $397,500
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10396017

## Citation

> US National Institutes of Health, RePORTER application 10396017, Quantitative Methods for Genetic Epidemiology (5R35GM140487-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10396017. Licensed CC0.

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