Genomic discovery and prediction for quantitative traits with complex genetic mechanisms

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 regions with important epistasis effects will be subjected to fine mapping using increased sample size and high SNP density by imputing. Aim 2: Evaluation of the contributions of complex genetics effects to the phenotypic variance and the accuracy of genomic prediction. Genomic heritability of each type of genetic effects will be estimated as a measure of the contribution to the phenotypic variance. Observed prediction accuracy from validation studies is used as an objective measure for the relevance of any type of genetic effects to the accuracy of genomic prediction, and any genetic effect affecting prediction accuracy is considered relevant to the phenotype. The combination of this genomic estimation and prediction under complex model with the GWAS approach will yield uniquely high confidence results of multigenic mechanisms underlying quantitative traits. Aim 3: Evaluation of prediction accuracy of complex models for traits that benefit from any or a combination of dominance, global epistasis and locally high-order epistasis effects using large sample validation studies. This process will lead to recommendations for routine applications of the prediction models with complex genetic effects in genomic evaluation. BROADER IMPACTS The novel discoveries in multigenic mechanisms of quantitative traits in Holstein cattle are expected to advance the understanding of complex genetic mechanism of quantitative traits in diploid species and benefit the scientific community in research, teaching and training. The research approach will facilitate opening new direction for studying and utilizing multigenic mechanisms of quantitative traits. New methods for genomic prediction with complex genetic mechanism may increase the efficiency of genetic selection for some of the most difficult traits facing the dairy industry such as fertility and health. Solutions from this project will enhance collaboration between academics and U.S. dairy industry, and increased prediction accuracy of genomic prediction using complex genetic effects may translate into substantial economic benefits for U.S. dairy industry. CREATIVITY, ORIGINALITY, MECHANISM TO ASSESS SUCCESS This is the first large-scale complex genetic analysis using the most complex models ever attempted for many traits. Creative and original ideas include the integration of the large-sample GWAS for detecting epistasis effects with genomic OMB No. 0925-0001/0002 (Rev. 03/2020 Approved Through 02/28/2023) Page Continuation Format Page