Uncovering sources of human gene expression variation in a globally diverse cohort

揭示全球多样化群体中人类基因表达变异的来源

• 批准号: 10607411
• 负责人: Dylan James Taylor
• 金额: $ 4.77万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-09-26
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10607411
• 关键词: Accounting; Address; Admixture; Affect; Automobile Driving; Awareness; Binding Sites; Biological; Complex; Data; Data Set; Disease; Equity; Ethnic Origin; European; European ancestry; Event; Evolution; Gene Expression; Gene Expression Profile; Gene Frequency; Genealogy; Genetic Enhancer Element; Genetic Recombination; Genetic Variation; Genetic study; Genome; Genomics; Genotype; Goals; Graph; Health; Human; Human Genetics; Human Genome; Individual; Knowledge; Length; Link; Linkage Disequilibrium; Maps; Measures; Methods; Modeling; Molecular; Participant; Pattern; Phenotype; Phylogenetic Analysis; Population; Population Control; Population Genetics; Quantitative Trait Loci; RNA Splicing; Research; Research Design; Resolution; Resources; Sampling; Series; Site; Societies; Source; Stratification; Structure; Tissues; Trees; Underrepresented Populations; Variant; Work; causal variant; cohort; data structure; differential expression; diverse data; epigenomics; gene discovery; gene expression variation; genetic variant; health care disparity; human RNA sequencing; improved; lymphoblastoid cell line; novel; tool; trait; transcriptome sequencing; variant of interest

PROJECT SUMMARY Genetic variation affecting gene expression level and splicing accounts for a large proportion of phenotypic variation between humans, including health and disease. The variants that underlie these phenotypic changes are often discovered by associating individuals’ gene expression data with their genotypes. These methods can be confounded by population structure in the sample, which leads to false positive and negative errors. As such, samples are often selected from relatively homogenous populations. However, this limits the applicability of results to populations not included in the study, and limits the resolution at which potentially causal variants can be identified. Previous work has shown that controlling for population structure locally across the genome in association studies of diverse samples serves to reduce error. However, these methods assign individuals to one of a few ancestral populations and do not fully capture the relatedness between included samples. To extend the results of association studies to diverse cohorts, I will develop a method to control for local relatedness between samples in association studies. The Ancestral Recombination Graph (ARG) is a data structure which encodes the genealogical relationships between samples at each locus along the genome. In Aim 1, I will develop a linear mixed model approach for association mapping that utilizes a similarity matrix derived from the ARG to control for local relatedness between samples. One barrier in extending the results of association studies investigating gene expression is that the majority of data currently available is from individuals of European descent. To address this limitation, I recently generated gene expression data for a large, globally diverse human sample. In Aim 2, I will use the method developed in Aim 1 to map expression level- and splicing-associated variation in this sample. I will then investigate enrichment of epigenomic features near associated variants to determine the functional mechanisms by which they may be driving transcription differences, and I will intersect my findings with previously discovered disease associations. Using this globally diverse dataset, I will also explore the diversity and evolution of human gene expression, elucidating the extent to which patterns of gene expression are partitioned within versus between populations and the sources of such stratification. Extending association studies to diverse cohorts requires not only diverse datasets, but also tools that can appropriately control for patterns of population structure within those datasets; the research proposed here addresses both goals. This will allow the discovery of associations in previously underrepresented groups and will also serve to improve confidence in discovering causal variants. Together, this proposed work will characterize the functional mechanisms linking genetic variation and phenotypic differences in a globally diverse human cohort.

项目总结