CAREER: Flexible and efficient mixed models to infer the genetic architecture of complex phenotypes

职业：灵活高效的混合模型来推断复杂表型的遗传结构

• 批准号: 1943497
• 负责人: Sriram Sankararaman
• 金额: $ 68.6万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943497&HistoricalAwards=false
• 关键词: CAREER; Flexible; efficient; mixed; models

Complex phenotypes, i.e., observable characteristics, are a central focus of biology and medicine. Growing collections of data that survey how genes and phenotypes vary across individuals present the tantalizing opportunity to systematically understand the genetic architecture of complex phenotypes. Drawing inferences about genetic architecture from these large collections of high-dimensional genomic data will elucidate the sets of rules that predict an organism’s phenotype. Methods based on mixed models, that model the joint effects of large numbers of genetic variants, have emerged as an important tool in this endeavor. Mixed model methods, however, rely on a number of simplifying modeling assumptions that can lead to biased inferences. Further, applying these methods to large-scale genetic datasets is computationally impractical. The project will develop novel, scalable methods that can characterize genetic architecture of complex traits. The application of these methods to large genetic datasets available will lead to novel insights into genes that underlie variation in complex phenotypes, which leads to further uncover rules of life. The inter-disciplinary aspect of the project will bring together researchers and students from computer science, statistics, bioinformatics and human genetics and will lead to cross-fertilization and closer interactions across these communities. The project will develop new computational mixed model methods that are flexible and efficient and to apply these methods to obtain novel insights into genetic architecture. Specifically, the project will develop 1) linear mixed models that provide accurate estimates of heritability across a wide range of genetic architectures, 2) non-linear mixed models that estimate the contribution of gene-gene and gene-environment interactions, and 3) multi-trait mixed models that estimate the genetic component shared across traits. Importantly, the proposed methods are designed to scale to datasets that contain millions of individuals. To demonstrate their utility, we will apply these methods to large genetic datasets to obtain novel insights into heritability, its distribution across the genome, its correlation with other traits, the contribution of gene-gene and gene-environment interactions, and the impact of natural selection.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

复杂的表型，即，可观察到的特征是生物学和医学的中心焦点。越来越多的调查基因和表型如何在个体之间变化的数据集合为系统地了解复杂表型的遗传结构提供了诱人的机会。从这些大量的高维基因组数据中推断遗传结构，将阐明预测生物体表型的规则集。基于混合模型的方法（对大量遗传变异的联合影响进行建模）已成为这一奋进的重要工具。然而，混合模型方法依赖于许多简化的建模假设，这可能导致有偏见的推断。此外，将这些方法应用于大规模遗传数据集在计算上是不切实际的。该项目将开发新的，可扩展的方法，可以表征复杂性状的遗传结构。将这些方法应用于现有的大型遗传数据集，将导致对复杂表型变异背后的基因的新见解，从而进一步揭示生命规则。该项目的跨学科方面将汇集来自计算机科学、统计学、生物信息学和人类遗传学的研究人员和学生，并将导致这些社区之间的相互促进和更密切的互动。该项目将开发新的计算混合模型方法，这些方法是灵活和有效的，并应用这些方法来获得新的见解遗传结构。具体来说，该项目将开发1）线性混合模型，提供广泛的遗传结构的遗传力的准确估计，2）非线性混合模型，估计基因-基因和基因-环境相互作用的贡献，以及3）多性状混合模型，估计性状之间共享的遗传成分。重要的是，所提出的方法旨在扩展到包含数百万个个体的数据集。为了证明它们的实用性，我们将把这些方法应用于大型遗传数据集，以获得对遗传力的新见解，它在基因组中的分布，它与其他性状的相关性，基因-基因和基因-环境相互作用的贡献，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

项目成果

Efficient variance components analysis across millions of genomes

• DOI: 10.1038/s41467-020-17576-9
• 发表时间: 2020-08-11
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Pazokitoroudi, Ali;Wu, Yue;Sankararaman, Sriram
• 通讯作者: Sankararaman, Sriram

An efficient linear mixed model framework for meta-analytic association studies across multiple contexts.

用于跨多个上下文的元分析关联研究的有效线性混合模型框架。

• 发表时间: 2016
• 期刊: LIPIcs : Leibniz international proceedings in informatics
• 作者: Jew,Brandon;Li,Jiajin;Sankararaman,Sriram;Sul,JaeHoon
• 通讯作者: Sul,JaeHoon

The lingering effects of Neanderthal introgression on human complex traits.

• DOI: 10.7554/elife.80757
• 发表时间: 2023-03-20
• 期刊: eLife
• 影响因子: 7.7
• 作者: Wei X;Robles CR;Pazokitoroudi A;Ganna A;Gusev A;Durvasula A;Gazal S;Loh PR;Reich D;Sankararaman S
• 通讯作者: Sankararaman S

Quantifying the contribution of dominance deviation effects to complex trait variation in biobank-scale data.

• DOI: 10.1016/j.ajhg.2021.03.018
• 发表时间: 2021-05-06
• 期刊: American journal of human genetics
• 影响因子: 9.8
• 作者: Pazokitoroudi A;Chiu AM;Burch KS;Pasaniuc B;Sankararaman S
• 通讯作者: Sankararaman S

Cross-trait assortative mating is widespread and inflates genetic correlation estimates.

跨性向分类的交配是广泛的，并且膨胀了遗传相关估计。

• DOI: 10.1126/science.abo2059
• 发表时间: 2022-11-18
• 期刊: SCIENCE
• 影响因子: 56.9
• 作者: Border, Richard;Athanasiadis, Georgios;Buil, Alfonso;Schork, Andrew J.;Cai, Na;Young, Alexander I.;Werge, Thomas;Flint, Jonathan;Kendler, Kenneth S.;Sankararaman, Sriram;Dahl, Andy W.;Zaitlen, Noah A.
• 通讯作者: Zaitlen, Noah A.