# Statistical Methods for Modeling Polygenic Architecture in Association and Re-sequencing Studies

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $339,696

## Abstract

Project Summary
Array- and sequencing-based association studies have identified many loci harboring genetic variants
associated with complex traits and common diseases. Altogether, these associated variants only explain a
small proportion of heritability, suggesting that most traits and diseases have a polygenic background and are
influenced by many variants with small effects. Early attempts to model polygenic complex traits, notably via
the linear mixed models (LMMs) and the best linear unbiased predictor (BLUP), have shown promising
outcomes for estimating chip heritability, identifying causal variants, and predicting disease risks. However,
statistical methods for modeling polygenic architecture remain in their infancy. In particular, existing methods
rely on simple effect size assumptions, are not flexible nor adaptive to the underlying genetic architecture of a
given trait or disease, and hence cannot take full advantage of the polygenic natural of most traits and
diseases.
 To increase the power of association test and enable more precise phenotype and risk prediction, I propose
to develop a suite of novel statistical methods to accurately and flexibly model the polygenic architecture.
These new methods will facilitate evaluation and integration of variant functional annotations, multiple
phenotype association mapping, and phenotype and risk prediction in association studies. In particular, we will
(1) develop methods to evaluate and integrate variant genomic functional annotations to better understand the
polygenic architecture of traits and diseases, and enable powerful association mapping; (2) develop strategies
for association mapping with multiple correlated phenotypes to identify pleiotropic associations by taking
advantage of the shared polygenic background among phenotypes; and (3) develop methods to flexibly model
polygenic architecture and use all variants jointly to achieve accurate phenotype and risk prediction. We will
develop efficient algorithms to accompany these methods and implement them in free open-source software.
We will perform rigorous simulations and comparisons to evaluate our methods. Finally, we will perform in-
depth analysis on several large-scale real data sets, including data from the Global Lipids Genetics
Consortium, T2D-GENES and METSIM projects, to demonstrate the power of the proposed methods.

## Key facts

- **NIH application ID:** 10159307
- **Project number:** 5R01HG009124-05
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Xiang Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $339,696
- **Award type:** 5
- **Project period:** 2017-06-14 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10159307

## Citation

> US National Institutes of Health, RePORTER application 10159307, Statistical Methods for Modeling Polygenic Architecture in Association and Re-sequencing Studies (5R01HG009124-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10159307. Licensed CC0.

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