Computational and statistical models for human genetics

人类遗传学的计算和统计模型

• 批准号: 9062478
• 负责人: Goncalo Abecasis
• 金额: $ 61.91万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-18 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9062478
• 关键词: Affect; Chromosome Mapping; Complex; Computer Simulation; Computer software; Computing Methodologies; Data; Development; Disease; Evaluation; Exhibits; Genetic Markers; Genetic Variation; Genetic study; Genotype; Haplotypes; Human; Human Genetics; Human Genome; Individual; Laboratories; Lead; Linkage Disequilibrium; Maps; Meta-Analysis; Methods; Molecular; Population Control; Reading; Research Design; Role; Sampling; Staging; Statistical Methods; Statistical Models; Structure; Technology; Variant; analytical method; base; computer studies; computerized tools; cost; cost effective; design; exome; exome sequencing; follow-up; genetic analysis; genetic association; genome sequencing; genome-wide analysis; genotyping technology; human genome sequencing; improved; new technology; next generation; rare variant; tool; trait; whole genome

DESCRIPTION (provided by applicant): In the past five years, genetic association studies have evaluated the contribution of common SNP variation to complex traits at an unprecedented level of detail. These genome wide studies relied not only on advances in genotyping technologies but also on improved study designs and advances in statistical and computational methods - ranging from the development of cost-effective two stage designs, to new strategies to control for population structure, to methods and software for genotype imputation and for cross study meta-analyses. In the next five years, great advances are again expected in genotyping and sequencing technologies. Effectively using these technologies to further our understanding of complex traits will require continued advances in methods for the design and analysis of genetic studies. In this application, we build on our record of developing practical useful analytical methods, computational tools, and study designs for human genetic studies. We set out to develop computational and statistical methods that will enable studies of complex traits in humans to effectively exploit these new technologies. Specifically, we will develop new methods and computational tools for genotype imputation and for the interpretation of short read sequence data, evaluate sequence and genotyping based design strategies for complex trait studies, and develop statistical methods that facilitate the prioritization of likely functional variants in genetic association studies.

描述（由申请人提供）：在过去的五年中，遗传关联研究以前所未有的细节水平评估了共同SNP变异对复杂性状的贡献。这些全基因组研究不仅依赖于基因分型技术的进步，而且还依赖于改进的研究设计和统计和计算方法的进步——从具有成本效益的两阶段设计的发展，到控制种群结构的新策略，再到基因型输入和交叉研究荟萃分析的方法和软件。在未来的五年里，基因分型和测序技术有望再次取得巨大进步。有效地利用这些技术来进一步了解复杂性状，将需要在遗传研究的设计和分析方法上不断取得进展。在这个应用程序中，我们建立在我们开发实用有用的分析方法，计算工具和人类遗传研究设计的记录上。我们着手开发计算和统计方法，使人类复杂特征的研究能够有效地利用这些新技术。具体来说，我们将开发新的方法和计算工具，用于基因型插入和短读序列数据的解释，评估复杂性状研究中基于序列和基因分型的设计策略，并开发统计方法，以促进遗传关联研究中可能功能变异的优先级。

项目成果

Genotype calling and haplotyping in parent-offspring trios.

• DOI: 10.1101/gr.142455.112
• 发表时间: 2013-01
• 期刊: Genome research
• 影响因子: 7
• 作者: Chen W;Li B;Zeng Z;Sanna S;Sidore C;Busonero F;Kang HM;Li Y;Abecasis GR
• 通讯作者: Abecasis GR

A likelihood-based framework for variant calling and de novo mutation detection in families.

• DOI: 10.1371/journal.pgen.1002944
• 发表时间: 2012
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Li B;Chen W;Zhan X;Busonero F;Sanna S;Sidore C;Cucca F;Kang HM;Abecasis GR
• 通讯作者: Abecasis GR

Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers.

• DOI: 10.1038/ng.3368
• 发表时间: 2015-11
• 期刊: Nature genetics
• 影响因子: 30.8
• 作者: Sidore C;Busonero F;Maschio A;Porcu E;Naitza S;Zoledziewska M;Mulas A;Pistis G;Steri M;Danjou F;Kwong A;Ortega Del Vecchyo VD;Chiang CWK;Bragg-Gresham J;Pitzalis M;Nagaraja R;Tarrier B;Brennan C;Uzzau S;Fuchsberger C;Atzeni R;Reinier F;Berutti R;Huang J;Timpson NJ;Toniolo D;Gasparini P;Malerba G;Dedoussis G;Zeggini E;Soranzo N;Jones C;Lyons R;Angius A;Kang HM;Novembre J;Sanna S;Schlessinger D;Cucca F;Abecasis GR
• 通讯作者: Abecasis GR

An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data.

• DOI: 10.1101/gr.176552.114
• 发表时间: 2015-06
• 期刊: Genome research
• 影响因子: 7
• 作者: Jun G;Wing MK;Abecasis GR;Kang HM
• 通讯作者: Kang HM

Bayesian model selection in complex linear systems, as illustrated in genetic association studies.

• DOI: 10.1111/biom.12112
• 发表时间: 2014-03
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Wen X