Genomic discovery and prediction for quantitative traits with complex genetic mechanisms

项目主任/首席调查员(大、杨)： 项目说明 动机和目标 数量性状的复杂遗传机制可能包括基因互作效应，通常称为 上位性，遗传因素多，影响小。由于困难，这是最困难的遗传区之一。 要发现和需要大样本才能检测出许多小效果。美国荷斯坦牛拥有最大的基因组 到2021年3月，世界上有3852,580头分型牛的评估计划，以及分型牛的数量 以每年约600,000人的速度增长。在基因分型的奶牛中，有43个性状的表型记录 涵盖生产、生殖、健康、长寿、身体形态和结构。这些特征中的大多数都是 几十年来收集和评估的数据。此外，更多的新特征可能会不断出现。史无前例的 美国荷斯坦牛基因组选择数据的样本量提供了一个前所未有的机会 并利用数量性状的复杂遗传机制。294,076头荷斯坦奶牛饲养8头的初步结果 特征已经有了难以想象的有趣发现，包括一个单一的染色体区域 与所有染色体相互作用，覆盖整个染色体的染色体内上位性，并且几乎是排他性的 一个性状的染色体间上位性。用Pi‘s开发的方法和计算工具研究复杂遗传学 除了令人鼓舞的初步结果，这项拟议的研究是一项史无前例的大规模基因组研究 使用前所未有的复杂多基因模型发现和预测43个性状，其中大部分是100万头奶牛 以前尝试过的，有望产生许多新的发现，并有可能将多基因知识推进到 更上一层楼。这个项目的长期目标是确定数量性状背后的多基因因素，以 了解多基因因素如何影响表型，并应用多基因机制和因素来预测 表型。具体目标如下。 目标1：大规模发现43个性状的全球成对上位性效应，包括生产，繁殖， 通过检验每对SNP的四种上位性效应，即加性×加性、加性×加性 百万头奶牛全基因组关联的显性、显性×加性和显性×显性研究 对43个性状中的大多数性状进行了遗传分析。这些测试将确定每个性状背后最重要的上位性类型，以及 具有史无前例的上位性网络的最显著上位性效应的染色体区域和基因 统计上的可信度。所有四种类型的上位性效应都将进一步分析为染色体内和染色体间的上位性 研究它们与性状遗传力和对遗传选择的反应的潜在联系。已选择 具有重要上位性效应的染色体区域将通过增加样本量和 通过注入高SNP浓度。 目标2：评估复杂的遗传效应对表型变异的贡献和 基因组预测。每种类型的遗传效应的基因组遗传力将被估计为贡献的衡量标准 对表型变异的影响。从验证研究中观察到的预测准确性被用作 任何类型的遗传效应与基因组预测的准确性的相关性，以及任何影响预测的遗传效应 准确性被认为与表型有关。这种基因组估计和预测的组合在 使用GWAS方法的复杂模型将产生独特的高置信度的多基因机制的结果 数量性状。 目标3：对受益于以下任何一个或其组合的性状的复杂模型的预测准确性进行评估 使用大样本验证研究的优势、全局上位性和局部高阶上位性效应。这一过程将 对基因组中具有复杂遗传效应的预测模型的常规应用提出建议 评估。 更广泛的影响 荷斯坦牛数量性状多基因机制的新发现有望推动 了解二倍体物种数量性状的复杂遗传机制并造福科学界 在研究、教学和培训方面。这种研究方法将有助于开辟研究和利用的新方向 数量性状的多基因机制。具有复杂遗传机制的基因组预测的新方法可能 提高对乳品业面临的一些最困难的性状(如生育能力)的遗传选择效率 和健康。该项目的解决方案将加强学术界和美国乳制品行业之间的合作，以及 利用复杂的遗传效应提高基因组预测的预测精度可能会转化为可观的经济 给美国乳制品行业带来的好处。 创造力、原创性、评估成功的机制 这是第一次使用最复杂的模型对许多性状进行大规模复杂的遗传分析。 创造性和原创性的想法包括将大样本GWAS用于检测上位效应与基因组的整合 OMB编号0925-0001/0002(批准的第03/2020版至2023年2月28日)续页格式页

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