# Genomic discovery and prediction for quantitative traits with complex genetic mechanisms

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2022 · $241,537

## Abstract

Program Director/Principal Investigator (Da, Yang):
Project Description
MOTIVATION AND OBJECTIVES
 Complex genetic mechanism of quantitative traits may include gene interaction effects commonly referred to as
epistasis and multiple genetic factors with small effects. This is among the most difficult genetic areas due to difficulties
to discover and the need of large samples to detect many small effects. The U.S. Holstein cattle have the largest genomic
evaluation program in the world with 3,852,580 genotyped cattle by March 2021, and the number of genotyped cattle
increased at a pace of ~600,000 per year. Among the genotyped cows, phenotypic records were available for 43 traits
covering production, reproduction, health, longevity, and body shape and structure. Majority of these traits have been
collected and evaluated for decades. In addition, more new traits may become available continuously. The unprecedented
sample sizes of the genomic selection data of U.S. Holstein cattle provide an unprecedented opportunity for understanding
and utilizing complex genetic mechanisms of quantitative traits. Preliminary results using 294,076 Holstein cows for 8
traits already had interesting discovery that would have been unimaginable, including a single chromosome region
interacting with all chromosomes, intra-chromosome epistasis covering an entire chromosome, and nearly exclusively
inter-chromosome epistasis for one trait. With methods and computing tools to study complex genetics developed by PI’s
group as well as encouraging preliminary results, this proposed research is an unprecedented large-scale study on genomic
discovery and prediction for 43 traits mostly with one million cows using complex multigenic models that have never been
attempted before, are expected to generate many new discoveries, and have potential to advance multigenic knowledge to
a new level. The long-term goal of this project is to identify multigenetic factors underlying quantitative traits, to
understand how multigenetic factors affect phenotypes, and to apply multigenetic mechanisms and factors to predict
phenotypes. Specific aims are as follows.
 Aim 1: Large-scale discovery of global pairwise epistasis effects for 43 traits covering production, reproduction,
health, and body shape and structure by testing four types of epistasis effects per SNP pair, additive × additive, additive ×
dominance, dominance × additive, and dominance × dominance using million cow genome-wide association study
(GWAS) for most of the 43 traits. These tests will identify the most important epistasis type underlying each trait, and
chromosome regions and genes with the most significant epistasis effects for epistasis network with unprecedented
statistical confidence. All four types of epistasis effects will be further analyzed as intra- and inter-chromosome epistasis
effects to investigate their potential association with the trait heritability and response to genetic selection. Selected
chromosome regio...

## Key facts

- **NIH application ID:** 10447843
- **Project number:** 1R01HG012425-01
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** YANG DA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $241,537
- **Award type:** 1
- **Project period:** 2022-02-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10447843

## Citation

> US National Institutes of Health, RePORTER application 10447843, Genomic discovery and prediction for quantitative traits with complex genetic mechanisms (1R01HG012425-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10447843. Licensed CC0.

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