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Methods for Integrative Genomic Data Analysis

综合基因组数据分析方法

基本信息

批准号：
10734227
负责人：
Hongzhe Lee
金额：
$ 45.26万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10734227
关键词：
Address African ancestry Algorithmic Software Algorithms Alzheimer&apos s Disease Area Benchmarking Biological Biological Process Chronic Kidney Failure Collaborations Complex Computer software Computing Methodologies Data Data Analyses Data Set Dependence Development Disease Documentation Ethnic Origin Ethnic Population Functional disorder Gene Expression Gene Proteins Genes Genetic Genetic Risk Genome Genomics Genotype Genotype-Tissue Expression Project Individual Kidney Kidney Diseases Learning Linear Models Maps Methodology Methods Minority Groups Modeling Pathway interactions Pennsylvania Phenotype Population Public Health Publishing Research Personnel Sampling Signal Transduction Software Tools Statistical Methods Statistical Models Structure Syndrome System Tissue-Specific Gene Expression Tissues Universities Variant Work cardiometabolism causal variant computerized tools data complexity disorder risk epigenomics experimental study gene discovery genetic association genetic variant genome wide association study genome-wide genome-wide analysis genomic data high dimensionality high throughput technology human disease human genomics improved insight learning strategy machine learning method metabolomics next generation sequencing novel polygenic risk score predictive modeling programs protein expression risk prediction risk prediction model simulation statistics theories trait transcriptome transfer learning treatment response

项目摘要

Abstract The broad, long-term objective of this project concerns the development of novel statistical methods, theory and computational tools for statistical modeling of large-scale multiple high-dimensional genomic data motivated by im- portant biological questions and experiments. New high-throughput technologies and next generation sequencing are generating various types of very high-dimensional genetics, genomic, epigenomics, metabolomics data in order to obtain an integrative understanding of various complex phenotypes. Integrative analysis of genomic data from differ- ent populations and tissues can potentially increase the power of detecting disease associated genetic variants and genes, and provide the possibility of making causal inference in genomic studies, eventually leading to understanding of the disease causal pathways and genomics-based risk prediction. The speciﬁc aims of the current project are to develop new statistical models and methods for polygenic risk score (PRS) prediction and for integrative analysis of eQTL and genome wide genetic association (GWAS) data for identiﬁcation of possible causal genes and pathways of complex diseases. In order to effectively utilize data across different ethnicity groups and different tissues, this project will develop several novel transfer learning methods in order to achieve better estimate of polygenic risk scores and to increase the power of detecting trait associated variants in minority populations. The project will also develop method of meta-learning to predict ethnicity- and tissue-speciﬁc gene expressions in order to increase the power of transcriptome-wide association analysis (TWAS). Finally, statistical methods for genome-wide co-localization analysis that can effectively integrate GTEx data with GWAS association summary statistics will be developed in order to identify possible causal disease genes and pathways. These methods hinge on novel integration of methods for multiple re- lated high-dimensional regressions, high-dimensional Gaussian sequence models and subspace estimation. The new methods can be applied to different types of genomic data and will ideally help facilitate the identiﬁcation of genes as well as the biological pathways underlying various complex human diseases and genomics-based disease risk predic- tion. The work proposed here will contribute statistical methodology and theory for transfer learning and meta-learning in high-dimensional genomic data to study complex phenotypes and to offer insights into each of the biological areas represented by the various data sets, including Alzheimer's disease, cardiometabolic syndrome, and chronic kidney disease. All algorithms, software tools and the resulting polygenic risk score models and tissue-speciﬁc gene expres- sion prediction models together with detailed documentation will be made available on the GitHub.

摘要