Genomic discovery and prediction for quantitative traits with complex genetic mechanisms

具有复杂遗传机制的数量性状的基因组发现和预测

• 批准号: 10557153
• 负责人: YANG DA
• 金额: $ 24.62万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10557153
• 关键词: Affect; Area; Cattle; Chromosomes; Collaborations; Communities; Complex; Creativeness; Dairying; Data; Diploidy; Economics; Educational process of instructing; Evaluation; Fertility; Genes; Genetic; Genetic Epistasis; Genomics; Genotype; Goals; Health; Heritability; Industry; Knowledge; Longevity; Maps; Measures; Methods; Modeling; Motivation; Phenotype; Principal Investigator; Process; Production; Program Evaluation; Recommendation; Records; Reproduction; Research; Sample Size; Sampling; Shapes; Structure; Testing; Training; Translating; density; gene interaction; genetic analysis; genetic selection; genome wide association study; genomic predictors; novel; originality; predictive modeling; programs; response; success; tool; trait; validation studies

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

项目负责人/主要研究者（Da，Yang）： 项目描述 动机和动机 数量性状的复杂遗传机制可能包括基因互作效应， 上位性和多遗传因素作用小。这是其中最困难的遗传领域，由于困难 发现和需要大样本来检测许多小的影响。美国荷斯坦牛拥有世界上最大的 到2021年3月，全球有3，852，580头基因分型牛的评估计划， 以每年约60万人的速度增长。在基因分型的奶牛中，有43个性状的表型记录可用 包括生产、生殖、健康、长寿和身体形态和结构。这些特征中的大多数 收集和评估了几十年。此外，更多的新特性可能会不断出现。前所未有的 美国荷斯坦牛基因组选择数据的样本量为理解 并利用数量性状的复杂遗传机制。使用294，076头荷斯坦奶牛进行8次试验的初步结果 已经有了一些有趣的发现，包括一个染色体区域， 与所有染色体相互作用，染色体内上位性覆盖整个染色体，并且几乎完全 染色体间上位性。用PI开发的方法和计算工具来研究复杂的遗传学 小组以及令人鼓舞的初步结果，这项拟议的研究是一个前所未有的大规模研究基因组 发现和预测43个性状，主要是使用复杂的多基因模型， 以前尝试过，预计将产生许多新的发现，并有可能推进多基因知识， 一个新的水平。该项目的长期目标是确定数量性状的多遗传因素， 了解多遗传因素如何影响表型，并应用多遗传机制和因素来预测 表型具体目标如下。 目的1：大规模发现43个性状的全局成对上位性效应，包括生产，繁殖， 通过测试每对SNP的四种类型的上位性效应，加性×加性，加性× 利用百万头奶牛全基因组关联研究的显性、显性×加性和显性×显性 （GWAS）的43个性状中的大多数。这些测试将确定每个特征下最重要的上位性类型， 染色体区域和基因具有最显着的上位性效应的上位性网络， 统计置信度。所有四种类型的上位性效应将进一步分析为染色体内和染色体间上位性 影响，以探讨其与性状遗传力和对遗传选择的反应的潜在关联。选择 具有重要上位效应的染色体区域将使用增加的样本量进行精细作图， 高SNP密度。 目的2：评估复杂遗传效应对表型方差的贡献和 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作为贡献的量度。 表型方差的关系从验证研究中观察到的预测准确度被用作对 任何类型的遗传效应与基因组预测准确性的相关性，以及任何影响预测的遗传效应 准确性被认为与表型相关。这种基因组估计和预测的结合， 使用GWAS方法的复杂模型将产生多基因机制的独特高置信度结果， 数量性状 目标3：评估复杂模型对受益于以下任一或组合的性状的预测准确性： 优势，整体上位性和局部高阶上位性效应，使用大样本验证研究。这一进程将 导致建议的常规应用程序的预测模型与复杂的遗传效应，在基因组 评价 更宽的阻抗 荷斯坦牛数量性状多基因机制的新发现，有望推动奶牛数量性状多基因调控机制的研究。 了解二倍体物种数量性状的复杂遗传机制，并使科学界受益 研究、教学和培训。该研究方法将为研究和利用开辟新的方向 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 提高遗传选择的效率，以解决乳制品行业面临的一些最困难的性状，如生育力。 与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， 使用复杂遗传效应的基因组预测的增加的预测准确性可以转化为实质性的经济效益。 对美国乳制品行业的影响。 创造力、独创性、评估成功的机制 这是第一次使用有史以来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用于检测上位性效应的大样本GWAS与基因组 OMB编号0925-0001/0002（修订版03/2020批准至02/28/2023）页码继续格式页码