Dominance on the human genome and non-additive polygenic models for predicting complex traits

人类基因组的优势和用于预测复杂性状的非加性多基因模型

• 批准号: 10456164
• 负责人: Xinjun Zhang
• 金额: $ 4.23万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456164
• 关键词: Accounting; Address; Admixture; Affect; Biological; Biology; Collaborations; Communities; Complex; Computing Methodologies; Data Analyses; Demography; Development; Disease; Ecology; European; Evolution; Genetic; Genome; Genomic Segment; Genomics; Human; Human Genome; Individual; Journals; Knowledge; Light; Machine Learning; Mentors; Methods; Minority Groups; Modeling; Modernization; Mutation; Outcome; Pattern; Phase; Phenotype; Play; Population; Population Genetics; Population Heterogeneity; Recording of previous events; Reporting; Research Personnel; Research Training; Resolution; Role; Series; Shapes; Statistical Methods; Study Subject; Testing; Training; Variant; Work; base; biobank; disorder risk; experience; fitness; functional genomics; genetic variant; genome wide association study; genome-wide; genomic data; human population genetics; improved; insight; machine learning method; novel; polygenic risk score; precision medicine; predictive modeling; risk prediction; simulation; training opportunity; trait

Project Abstract Dominance is one of the most fundamental concepts in genetics and has many key implications in population genetics, as it ultimately determines how selection manifests in a population. However, despite its unarguable importance, dominance is also one of the least characterized quantities in genetics, especially in humans, with the major challenge being current methods cannot distinguish dominance from the fitness effect of genomic variants. This proposed K99/R00 work will systematically address this longstanding problem from a dual- perspectives, by inferring dominance in humans and quantitatively model its role in shaping the phenotypes of complex traits and diseases. Specifically, in Aim1, I will develop a powerful machine learning-based method to infer the realistic distribution of dominance on the human genome in megabase-scale, leveraging archaic introgressed ancestry in non-African populations that is sensitive to dominance variation in genomic regions. In Aim 2, I will develop non-additive polygenic models accounting for dominance in full genomic regions to identify complex traits profiled in UK Biobank that deviate from additive models, improve the accuracy of phenotype and disease risk predictions, and contribute to an in-depth understanding of complex trait biology. Finally, in Aim 3 (R00 phase), I will extend these approaches to infer dominance variation in worldwide populations and investigate how dominance, combined with selection and admixture, determines complex trait phenotypes in diverse human populations. The mentored phase of this work will take place at the Department of Ecology and Evolutionary Biology at UCLA, where Dr. Zhang will have access to rich training opportunities and be supported by active scientific communities, including numerous seminar series, journal clubs, and networking activities. Dr. Kirk Lohmueller (primary mentor) and Dr. Sriram Sankararaman (co-mentor) will train Dr. Zhang in computational and statistical methods in population genetics, machine learning applications, and large-scale disease association data analysis. The research trainings, collaborations, and professional development during the K99 phase will assist Dr. Zhang in becoming an independent investigator in human population genetics.

项目摘要 显性是遗传学中最基本的概念之一，在种群中有许多重要的意义 遗传学，因为它最终决定了选择如何在种群中表现出来。然而，尽管其不可否认的 重要的是，显性也是遗传学中最不具特征的数量之一，特别是在人类中， 主要的挑战是目前的方法不能区分显性和基因组的适应性效应， 变体。这项拟议的K99/R 00工作将从双重角度系统地解决这一长期存在的问题- 观点，通过推断人类的优势，并定量建模其在塑造表型的作用， 复杂的性状和疾病。具体来说，在Aim 1中，我将开发一种强大的基于机器学习的方法， 推断出人类基因组在兆碱基尺度上的实际优势分布， 在非非洲种群中的渐渗祖先对基因组区域中的显性变异敏感。在 目的2，我将开发非加性多基因模型占优势，在全基因组区域，以确定 英国生物库中描述的复杂性状偏离加性模型，提高了表型的准确性， 疾病风险预测，并有助于深入了解复杂的性状生物学。最后，在Aim 3中 (R00阶段），我将扩展这些方法来推断世界范围内的种群优势变异， 研究显性，结合选择和混合，如何决定复杂的性状表型， 不同的人群。这项工作的指导阶段将在生态部进行， 在加州大学洛杉矶分校的进化生物学，张博士将获得丰富的培训机会，并得到支持 由活跃的科学界，包括许多研讨会系列，杂志俱乐部，和网络活动。博士 Kirk Lohmueller（主要导师）和Sriram Sankararaman博士（共同导师）将在计算方面培训张博士 人口遗传学、机器学习应用和大规模疾病中的统计方法 关联数据分析K99期间的研究培训，合作和专业发展 第三阶段将帮助张博士成为人类群体遗传学的独立研究者。

项目成果

Apportioning archaic variants among modern populations.

• DOI: 10.1098/rstb.2020.0411
• 发表时间: 2022-06-06
• 期刊: PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
• 影响因子: 6.3
• 作者: Witt, Kelsey E.;Villanea, Fernando;Loughran, Elle;Zhang, Xinjun;Huerta-Sanchez, Emilia
• 通讯作者: Huerta-Sanchez, Emilia

Denisovans and Homo sapiens on the Tibetan Plateau: dispersals and adaptations.

• DOI: 10.1016/j.tree.2021.11.004
• 发表时间: 2022-03
• 期刊: Trends in ecology & evolution
• 影响因子: 16.8
• 作者: Zhang P;Zhang X;Zhang X;Gao X;Huerta-Sanchez E;Zwyns N
• 通讯作者: Zwyns N