Resolving and understanding the genomic basis of heterogeneous complex traits and disease

解决和理解异质复杂性状和疾病的基因组基础

• 批准号: 9764395
• 负责人: Arjun Krishnan
• 金额: $ 33.61万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9764395
• 关键词: Address; Age; Animal Model; Cardiomyopathies; Catalogs; Cell physiology; Clinical; Collaborations; Collection; Complex; Coronary Arteriosclerosis; Data; Data Collection; Disease; Eating Disorders; Female; Gastrointestinal Diseases; Genes; Genomics; Genotype; Heterogeneity; Individual; Intervention; Knowledge; Life; Link; Methods; Outcome; Pathway interactions; Phenotype; Physiological; Physiology; Population; Prevalence; Sex Bias; System; Testing; Tissues; Variant; advanced disease; analog; autism spectrum disorder; clinical phenotype; computational suite; computer framework; disease diagnosis; disorder risk; disorder subtype; genetic variant; genomic data; genomic profiles; genomic signature; genomic variation; human disease; human model; in vivo; insight; male; novel; precision medicine; pregnancy disorder; sex; trait

ABSTRACT Over the last decade, numerous large-scale biomedical studies have helped catalog hundreds of genomic variants and physiological-clinical phenotypes associated with a range of complex traits and diseases. These catalogs are now exposing wide chasms in our understanding of the mechanistic relationships between genomic variation, cellular processes, tissue function, and trait variation – knowledge that is crucial for advancing disease diagnosis and intervention. We develop and apply computational data-driven approaches to bridge these gaps and help resolve, understand, and tackle the heterogeneity of complex traits and diseases. We are specifically focusing on three key questions: 1) Each disease is not a single well-defined condition. Can we deconvolve complex disorders into subtypes defined by shared functional dysregulations, and characterize novel genes/mechanisms underlying each subtype? 2) Most diseases vary in prevalence and impact between males and females, and across life stages. Can we delineate the genomic basis of differences in tissue physiology and disease between sexes and across ages? 3) Choosing the right in vivo system to study human diseases is hard due to murky relationships between phenotypes/genes in humans and model species. Can we systematically identify functionally `analogous' genes, phenotypes, and conditions in model organisms for studying specific facets of complex traits/diseases? To address these critical questions across diseases, we will develop a suite of computational frameworks that integrate genomic data collections, fragmented prior knowledge, and individual-/population-level genotypes-phenotypes. We will use this approach to systematically unravel genomic signatures, pathways, and networks that help characterize mechanistic subtypes, age/sex biases, and cross-species analogs of a wide range of diseases. We have established collaborations for experimentally following-up our predictions for specific test cases including autism, gastrointestinal disorder, coronary artery disease, cardiomyopathies, abnormal pregnancy, and eating disorders. Together, this concerted effort will help us gain insights into the multi-scale mechanisms underlying heterogeneous traits and diseases. In the long-term, our frameworks and mechanistic insights will enable us to link an individual's genomic profiles to a precise assessment of her/his physiological traits, disease risks, and clinical outcomes.

ABSTRACT Over the last decade, numerous large-scale biomedical studies have helped catalog hundreds of genomic variants and physiological-clinical phenotypes associated with a range of complex traits and diseases. These catalogs are now exposing wide chasms in our understanding of the mechanistic relationships between genomic variation, cellular processes, tissue function, and trait variation – knowledge that is crucial for advancing disease diagnosis and intervention. We develop and apply computational data-driven approaches to bridge these gaps and help resolve, understand, and tackle the heterogeneity of complex traits and diseases. We are specifically focusing on three key questions: 1) Each disease is not a single well-defined condition. Can we deconvolve complex disorders into subtypes defined by shared functional dysregulations, and characterize novel genes/mechanisms underlying each subtype? 2) Most diseases vary in prevalence and impact between males and females, and across life stages. Can we delineate the genomic basis of differences in tissue physiology and disease between sexes and across ages? 3) Choosing the right in vivo system to study human diseases is hard due to murky relationships between phenotypes/genes in humans and model species. Can we systematically identify functionally `analogous' genes, phenotypes, and conditions in model organisms for studying specific facets of complex traits/diseases? To address these critical questions across diseases, we will develop a suite of computational frameworks that integrate genomic data collections, fragmented prior knowledge, and individual-/population-level genotypes-phenotypes. We will use this approach to systematically unravel genomic signatures, pathways, and networks that help characterize mechanistic subtypes, age/sex biases, and cross-species analogs of a wide range of diseases. We have established collaborations for experimentally following-up our predictions for specific test cases including autism, gastrointestinal disorder, coronary artery disease, cardiomyopathies, abnormal pregnancy, and eating disorders. Together, this concerted effort will help us gain insights into the multi-scale mechanisms underlying heterogeneous traits and diseases. In the long-term, our frameworks and mechanistic insights will enable us to link an individual's genomic profiles to a precise assessment of her/his physiological traits, disease risks, and clinical outcomes.