Human Translational Bioinformatics Core

人类转化生物信息学核心

• 批准号: 10713763
• 负责人: Hooman Allayee
• 金额: $ 9.17万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713763
• 关键词: Basic Science; Bioinformatics; Biological; Biological Markers; Biological Models; Candidate Disease Gene; Cardiometabolic Disease; Cells; Clinical; Clinical Research; Communities; Computer Analysis; Computer software; Coronary Arteriosclerosis; Data; Data Set; Databases; Disease; Ensure; Epidemiology; Genes; Genetic; Genetic Complementation Test; Genetic study; Genomics; Genotype; Goals; Health; Hospitals; Human; Human Activities; Human Resources; In Vitro; Inbred Strains Mice; Intuition; Knowledge; Leadership; Molecular; Multiomic Data; Mus; Online Systems; Outcome; Pathway interactions; Population; Proteomics; Publishing; Research; Research Personnel; Resources; Risk Factors; Role; Sample Size; Series; Sex Differences; System; Systems Biology; Translating; Update; Validation; Variant; Visualization software; Woman; analytical tool; biomarker identification; candidate identification; cardiometabolic risk; cardiometabolism; cohort; computer infrastructure; cost effective; experience; experimental study; genetic association; genome wide association study; human data; in silico; large scale data; men; metabolomics; mouse model; multiple data sources; online resource; open source tool; phenome; programs; searchable database; sexual dimorphism; success; targeted biomarker; tool; trait; transcriptomics; web based interface

PROJECT SUMMARY The overall objective of this SCORE is to elucidate sex differences in cardiometabolic disease and its risk factors and treatments using genetically modified mouse models, inbred mouse strains, and in vitro or cell-based experiments. However, another important component of the SCORE is to translate findings from model systems to humans. The Human Translational Bioinformatics Core will fulfill this goal by serving as the hub for computational and in silico analyses that translate findings from the three Projects for association and relevance to cardiometabolic traits in humans, thereby enhancing the overall research results of the SCORE. The Core will be led by Dr. Hooman Allayee, whose own research program has developed numerous bioinformatics and analytical pipelines to efficiently and systematically evaluate the role of candidate gene/molecular/biomarker targets in cardiometabolic diseases using large human datasets. Furthermore, the computational infrastructure is in place and access to a wide array of 'omics data is already available from numerous multi-ancestry cohorts. Taken together, these tools have been successfully applied in prior studies and will serve as the paradigm for how the Core will support the three SCORE Projects. Specific Aim 1 will use these resources to investigate sexually dimorphic candidate gene/pathway, transcriptomic, proteomic, and metabolomic biomarkers identified by the three Projects for relevance to human populations. These analyses will leverage primary-level data (i.e. genotypes, clinical outcomes, and biomarker levels) from several population- and hospital-based cohorts with a combined sample size of >500K subjects. As such, epidemiological, genetic, and causal association analyses can be carried out with increased granularity. Specific Aim 2 will complement this approach by leveraging publicly available summary-level data for various `omics data from large-scale genome-wide association studies (GWAS). This strategy will provide even greater power and expand the bioinformatics analyses to additional clinical, proteomic, and metabolomic traits due to the large number of subjects and diverse outcomes in the studies from which the data are drawn. Finally, Specific Aim 3 will create a web-based resource that houses the internally driven and publicly available data from the first two Aims, which will be made available to the three SCORE Projects and, in a controlled manner, to the broader research community. The resource will be developed with PheWeb, an easily implementable and widely used open-source tool for visualizing, navigating, and sharing GWAS and phenome-wide association study (PheWAS) results. The goal will be to provide an intuitive and searchable interface for exploring genetic associations of candidate genes/variants with `omics traits in humans. The culmination of Core activities may, in turn, reveal additional biological information and lead to new hypotheses that can then be investigated mechanistically by the same basic science approaches being used by the three Projects. The collective experience of its leadership/personnel in carrying out the proposed bioinformatics analyses ensures an important contribution from the Core to the overall success of this SCORE.

项目总结