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

基本信息

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

项目摘要

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有多么不同。描述人类遗传变异常见模式的大型数据集可以很容易地被认为是矩阵，其中行代表个体，列代表基因组中与常见多态性相对应的基因座。此类数据集的更广泛影响怎么强调都不为过：它们是研究人员用来寻找影响健康、疾病以及对药物和环境因素反应的基因以及了解我们物种的进化和生物学历史的关键资源。从这些数据集中提取有用的信息可以促进科学进步，同时促进国民健康、繁荣和福利。该项目将弥合理论计算机科学和应用数学界开发的最先进的数据分析算法与此类算法在人类遗传学界日益增长的数据集分析中的应用之间的差距。在这个项目中，首先，从算法的角度来看，项目团队将为结合线性代数和随机化的三个典型的基础研究主题设计和分析新颖的算法，即稀疏主成分分析、矩阵完成和线性（或核）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，这些研究课题基本上被群体遗传学界忽视了。其次，从群体遗传学的角度来看，该团队将应用开发的算法来获得有关群体结构、祖先信息标记和自然选择的新见解，并改进插补方法和全基因组关联研究（GWAS）数据分析。所有三种方法都将在 PI 可用的群体遗传学数据集上进行评估。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群体遗传学家。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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