III: Small: Novel Statistical Data Analysis Approaches for Mining Human Genetics Datasets

III：小型：挖掘人类遗传学数据集的新颖统计数据分析方法

• 批准号: 1715202
• 负责人: Petros Drineas
• 金额: $ 50万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715202&HistoricalAwards=false
• 关键词: III; Small; Novel; Statistical; Data

The advent of modern genotyping and sequencing technologies has revolutionized human genetics research, allowing researchers to truly understand how different we are from one another. Large datasets describing the common patterns of human genetic variation may be easily thought of as matrices, with the rows representing individuals and the columns representing loci in the genome that correspond to common polymorphisms. The broader impact of such datasets cannot be overemphasized: they are a key resource for researchers to use to find genes affecting health, disease, and responses to drugs and environmental factors, as well as understanding the evolutionary and biological history of our species. Extracting useful information from such datasets promotes the progress of science and, at the same time, advances national health, prosperity and welfare. This project will bridge the gap between state-of-the-art algorithms for data analysis developed in the theoretical computer science and applied mathematics communities and the application of such algorithms to the analysis of the increasingly larger volume of datasets in the human genetics community.In the context of this project, first, from an algorithmic perspective, the project team will design and analyze novel algorithms for three prototypical, fundamental research topics that combine linear algebra and randomization, namely sparse Principal Components Analysis, matrix completion, and linear (or kernel) discriminant analysis. All three topics have been widely popular in the theoretical computer science, machine learning, and applied mathematics communities. Yet these research topics have been essentially overlooked by the population genetics community. Second, from a population genetics perspective, the team will apply the developed algorithms to gain novel insights regarding population structure, ancestry informative markers, and natural selection, as well as improve imputation methods and Genome-Wide Association Studies (GWAS) data analysis. All three methods will be evaluated on population genetics datasets that are available to the PIs. The project will train graduate students and will disseminate the results of the research to a broad community of applied mathematicians, theoretical computer scientists, and population geneticists.

现代基因分型和测序技术的出现彻底改变了人类遗传学研究，使研究人员能够真正了解我们彼此之间的不同。描述人类遗传变异的常见模式的大型数据集可以很容易地将其视为矩阵，而行代表个体和代表基因组中基因座的柱，与常见的多态性相对应。不能过分强调此类数据集的更广泛的影响：它们是研究人员使用影响健康，疾病以及对药物和环境因素反应的基因的关键资源，并了解我们物种的进化和生物学史。从此类数据集中提取有用的信息促进了科学的进步，同时促进了国家健康，繁荣和福利。该项目将弥合在理论计算机科学和应用数学社区中开发的数据分析的最新算法之间的差距结合线性代数和随机化，即稀疏主成分分析，矩阵完成和线性（或内核）判别分析。这三个主题在理论计算机科学，机器学习和应用数学社区中都广泛流行。然而，这些研究主题本质上被人群遗传学界忽略了。其次，从人口遗传学的角度来看，该团队将应用开发的算法来获得有关人口结构，祖先信息标记和自然选择的新见解，并改善了插补方法和全基因组范围的关联研究（GWAS）数据分析。所有三种方法将对PIS可用的人群遗传数据集进行评估。该项目将培训研究生，并将研究结果传播给广泛的应用数学家，理论计算机科学家和人口遗传学家。

项目成果

Genetic history of the population of Crete

克里特岛人口的遗传史

• DOI: 10.1111/ahg.12328
• 发表时间: 2019
• 期刊: Annals of Human Genetics
• 影响因子: 1.9
• 作者: Drineas, Petros;Tsetsos, Fotis;Plantinga, Anna;Lazaridis, Iosif;Yannaki, Evangelia;Razou, Anna;Kanaki, Katerina;Michalodimitrakis, Manolis;Perez‐Jimenez, Francisco;De Silvestro, Giustina
• 通讯作者: De Silvestro, Giustina

De Novo Sequence and Copy Number Variants Are Strongly Associated with Tourette Disorder and Implicate Cell Polarity in Pathogenesis.

• DOI: 10.1016/j.celrep.2018.08.082
• 发表时间: 2018-09-25
• 期刊: Cell reports
• 影响因子: 8.8
• 作者: Wang S;Mandell JD;Kumar Y;Sun N;Morris MT;Arbelaez J;Nasello C;Dong S;Duhn C;Zhao X;Yang Z;Padmanabhuni SS;Yu D;King RA;Dietrich A;Khalifa N;Dahl N;Huang AY;Neale BM;Coppola G;Mathews CA;Scharf JM;Tourette International Collaborative Genetics Study (TIC Genetics);Tourette Syndrome Genetics Southern and Eastern Europe Initiative (TSGENESEE);Tourette Association of America International Consortium for Genetics (TAAICG);Fernandez TV;Buxbaum JD;De Rubeis S;Grice DE;Xing J;Heiman GA;Tischfield JA;Paschou P;Willsey AJ;State MW
• 通讯作者: State MW

CluStrat: A Structure Informed Clustering Strategy for Population Stratification

CluStrat：用于人口分层的结构知情聚类策略

• DOI: 10.1007/978-3-030-45257-5_19
• 发表时间: 2020
• 期刊: Research in Computational Molecular Biology
• 作者: Bose, Aritra;Burch, Myson;Chowdhury, Agniva;Paschou, Peristera;Drineas, Petros
• 通讯作者: Drineas, Petros

Genome-Wide Association Study Points to Novel Locus for Gilles de la Tourette Syndrome

• DOI: 10.1016/j.biopsych.2023.01.023
• 发表时间: 2024-06-24
• 期刊: BIOLOGICAL PSYCHIATRY
• 影响因子: 10.6
• 作者: Tsetsos，Fotis;Topaloudi，Apostolia;Paschou，Peristera
• 通讯作者: Paschou，Peristera

TeraPCA: a fast and scalable software package to study genetic variation in tera-scale genotypes

• DOI: 10.1093/bioinformatics/btz157
• 发表时间: 2019-10-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Bose, Aritra;Kalantzis, Vassilis;Drineas, Petros
• 通讯作者: Drineas, Petros