Statistical Methods in Trans-Omics Chronic Disease Research

跨组学慢性病研究的统计方法

• 批准号: 10329975
• 负责人: DANYU LIN
• 金额: $ 30.52万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10329975
• 关键词: Accounting; Address; Algorithms; Applied Research; Biological; Cardiovascular Diseases; Characteristics; Chronic Disease; Communities; Complex; Computer software; DNA Sequence; Data; Data Set; Derivation procedure; Diagnosis; Dimensions; Disease; Documentation; Equation; Formulation; Gene Expression; Genes; Genetic Code; Genetic Transcription; Genomics; Goals; Grant; Information Networks; Institution; Inter-tumoral heterogeneity; Joints; Knowledge; Malignant Neoplasms; Mathematics; Measurement; Medical; Medicine; Mental disorders; Methods; Methylation; Modeling; Modernization; Molecular; Molecular Abnormality; Molecular Profiling; Mutation; Mutation Analysis; National Human Genome Research Institute; North Carolina; Patients; Pattern; Precision Medicine Initiative; Prevention; Procedures; Process; Property; Public Health; Research; Research Personnel; Resources; Somatic Mutation; Statistical Methods; Symptoms; System; Tail; Technology; Testing; The Cancer Genome Atlas; Trans-Omics for Precision Medicine; United States; United States National Institutes of Health; Universities; Work; base; detection limit; disease phenotype; driver mutation; experience; gene interaction; genome sequencing; high dimensionality; innovation; machine learning method; metabolomics; multidimensional data; multiple omics; novel; open source; outcome prediction; personalized care; precision medicine; programs; protein expression; research and development; semiparametric; simulation; sound; statistical learning; statistics; theories; tool; tumor; tumor heterogeneity; user-friendly

Project Summary The broad, long-term objectives of this research are the development of novel and high-impact statistical methods for medical studies of chronic diseases, with a focus on trans-omics precision medicine research. The specific aims of this competing renewal application include: (1) derivation of efficient and robust statistics for integrative association analysis of multiple omics platforms (DNA sequences, RNA expressions, methylation profiles, protein expressions, metabolomics profiles, etc.) with arbitrary patterns of missing data and with detection limits for quantitative measurements; (2) exploration of statistical learning approaches for handling multiple types of high- dimensional omics variables with structural associations and with substantial missing data; and (3) construction of a multivariate regression model of the effects of somatic mutations on gene expressions in cancer tumors for discovery of subject-specific driver mutations, leveraging gene interaction network information and accounting for inter-tumor heterogeneity in mutational effects. All these aims have been motivated by the investigators' applied research experience in trans-omics studies of cancer and cardiovascular diseases. The proposed solutions are based on likelihood and other sound statistical principles. The theoretical properties of the new statistical methods will be rigorously investigated through innovative use of advanced mathematical arguments. Computationally efficient and numerically stable algorithms will be developed to implement the inference procedures. The new methods will be evaluated extensively with simulation studies that mimic real data and applied to several ongoing trans-omics precision medicine projects, most of which are carried out at the University of North Carolina at Chapel Hill. Their scientific merit and computational feasibility are demonstrated by preliminary simulation results and real examples. Efficient, reliable, and user-friendly open-source software with detailed documentation will be produced and disseminated to the broad scientific community. The proposed work will advance the field of statistical genomics and facilitate trans-omics precision medicine studies of chronic diseases.

项目摘要 这项研究的广泛，长期目标是开发新颖和高影响力的统计方法 用于慢性病的医学研究，重点是反式组学精准医学研究。规格 这一竞争性更新应用的目的包括：（1）为综合应用导出有效和可靠的统计数据。 多个组学平台的关联分析（DNA序列、RNA表达、甲基化特征、蛋白质 表达、代谢组学特征等）具有任意模式的缺失数据和检测极限， 定量测量;（2）探索处理多种类型高- 具有结构关联和大量缺失数据的维度组学变量;以及（3）构建 的多变量回归模型的影响，体细胞突变的基因表达，在癌症肿瘤， 发现受试者特异性驱动突变，利用基因相互作用网络信息， 突变效应的肿瘤间异质性。所有这些目标都是由研究者的应用所激发的 在癌症和心血管疾病的反式组学研究方面的研究经验。建议的解决方案是 基于可能性和其他合理的统计原则。新统计方法的理论性质 将通过创新使用先进的数学论证进行严格的调查。计算上 将开发有效和数值稳定的算法来执行推理程序。新 将通过模拟真实的数据的模拟研究对这些方法进行广泛的评估，并将其应用于几个正在进行的项目 trans-omics精准医学项目，其中大部分是在北卡罗来纳州大学进行的， 查佩尔山。初步的模拟结果证明了它们的科学价值和计算的可行性 和真实的例子。高效、可靠、用户友好的开源软件，以及详细的文档， 制作并传播给广大科学界。拟议的工作将推进以下领域： 统计基因组学和促进慢性病的跨组学精确医学研究。

项目成果

Inherited causes of clonal haematopoiesis in 97,691 whole genomes.

• DOI: 10.1038/s41586-020-2819-2
• 发表时间: 2020-10
• 期刊: Nature
• 影响因子: 64.8
• 作者: Bick AG;Weinstock JS;Nandakumar SK;Fulco CP;Bao EL;Zekavat SM;Szeto MD;Liao X;Leventhal MJ;Nasser J;Chang K;Laurie C;Burugula BB;Gibson CJ;Lin AE;Taub MA;Aguet F;Ardlie K;Mitchell BD;Barnes KC;Moscati A;Fornage M;Redline S;Psaty BM;Silverman EK;Weiss ST;Palmer ND;Vasan RS;Burchard EG;Kardia SLR;He J;Kaplan RC;Smith NL;Arnett DK;Schwartz DA;Correa A;de Andrade M;Guo X;Konkle BA;Custer B;Peralta JM;Gui H;Meyers DA;McGarvey ST;Chen IY;Shoemaker MB;Peyser PA;Broome JG;Gogarten SM;Wang FF;Wong Q;Montasser ME;Daya M;Kenny EE;North KE;Launer LJ;Cade BE;Bis JC;Cho MH;Lasky-Su J;Bowden DW;Cupples LA;Mak ACY;Becker LC;Smith JA;Kelly TN;Aslibekyan S;Heckbert SR;Tiwari HK;Yang IV;Heit JA;Lubitz SA;Johnsen JM;Curran JE;Wenzel SE;Weeks DE;Rao DC;Darbar D;Moon JY;Tracy RP;Buth EJ;Rafaels N;Loos RJF;Durda P;Liu Y;Hou L;Lee J;Kachroo P;Freedman BI;Levy D;Bielak LF;Hixson JE;Floyd JS;Whitsel EA;Ellinor PT;Irvin MR;Fingerlin TE;Raffield LM;Armasu SM;Wheeler MM;Sabino EC;Blangero J;Williams LK;Levy BD;Sheu WH;Roden DM;Boerwinkle E;Manson JE;Mathias RA;Desai P;Taylor KD;Johnson AD;NHLBI Trans-Omics for Precision Medicine Consortium;Auer PL;Kooperberg C;Laurie CC;Blackwell TW;Smith AV;Zhao H;Lange E;Lange L;Rich SS;Rotter JI;Wilson JG;Scheet P;Kitzman JO;Lander ES;Engreitz JM;Ebert BL;Reiner AP;Jaiswal S;Abecasis G;Sankaran VG;Kathiresan S;Natarajan P
• 通讯作者: Natarajan P

Efficient Estimation for Semiparametric Structural Equation Models With Censored Data.

具有删失数据的半参数结构方程模型的有效估计。

• DOI: 10.1080/01621459.2017.1299626
• 发表时间: 2018
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Wong,KinYau;Zeng,Donglin;Lin,DY
• 通讯作者: Lin,DY

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants.

• DOI: 10.1016/j.xhgg.2022.100163
• 发表时间: 2023-01-12
• 期刊: HUMAN GENETICS AND GENOMICS ADVANCES
• 作者: Young, Kristin L.;Fisher, Virginia;Deng, Xuan;Brody, Jennifer A.;Graff, Misa;Lim, Elise;Lin, Bridget M.;Xu, Hanfei;Amin, Najaf;An, Ping;Aslibekyan, Stella;Fohner, Alison E.;Hidalgo, Bertha;Lenzini, Petra;Kraaij, Robert;Medina-Gomez, Carolina;Prokic, Ivana;Rivadeneira, Fernando;Sitlani, Colleen;Tao, Ran;van Rooij, Jeroen;Zhang, Di;Broome, Jai G.;Buth, Erin J.;Heavner, Benjamin D.;Jain, Deepti;Smith, Albert, V;Barnes, Kathleen;Boorgula, Meher Preethi;Chavan, Sameer;Darbar, Dawood;De Andrade, Mariza;Guo, Xiuqing;Haessler, Jeffrey;Irvin, Marguerite R.;Kalyani, Rita R.;Kardia, Sharon L. R.;Kooperberg, Charles;Kim, Wonji;Mathias, Rasika A.;McDonald, Merry-Lynn;Mitchell, Braxton D.;Peyser, Patricia A.;Regan, Elizabeth A.;Redline, Susan;Reiner, Alexander P.;Rich, Stephen S.;Rotter, Jerome I.;Smith, Jennifer A.;Weiss, Scott;Wiggins, Kerri L.;Yanek, Lisa R.;Arnett, Donna;Heard-Costa, Nancy L.;Leal, Suzanne;Lin, Danyu;McKnight, Barbara;Province, Michael;van Duijn, Cornelia M.;North, Kari E.;Cupples, L. Adrienne;Liu, Ching-Ti
• 通讯作者: Liu, Ching-Ti

Sample size/power calculation for stratified case-cohort design.

分层病例队列设计的样本量/功效计算。

• DOI: 10.1002/sim.6215
• 发表时间: 2014
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Hu,Wenrong;Cai,Jianwen;Zeng,Donglin
• 通讯作者: Zeng,Donglin

SEMIPARAMETRIC TRANSFORMATION MODELS WITH RANDOM EFFECTS FOR CLUSTERED FAILURE TIME DATA.

• 发表时间: 2008
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: D. Zeng;Danyu Lin;Xihong Lin