权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

The pursuit of genetic causal mechanisms

追求遗传因果机制

基本信息

批准号：
10291186
负责人：
CHIARA SABATTI
金额：
$ 45.53万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10291186
关键词：
Algorithms All of Us Research Program Attention Awareness Biological Characteristics Chromatin Cohort Studies Complex Computer software Counseling DNA Resequencing DNA Sequence Alteration Data Data Analyses Data Set Dependence Development Disease Drug Targeting Environment Environmental Exposure Ethnic Origin Evaluation Family Gene Frequency Gene Mutation Generations Genes Genetic Genetic Determinism Genetic Polymorphism Genetic Predisposition to Disease Genetic Risk Genetic Variation Genome Genomics Genotype Heterogeneity Individual Linear Models Link Linkage Disequilibrium Literature Measures Mediating Medical Medical Records Methodology Methods Modeling Nature Noise Pathway interactions Patients Performance Phenotype Population Positioning Attribute Prevention Probability Research Research Personnel Research Project Grants Resolution Resources Risk Risk Assessment Role Sample Size Sampling Scientist Side Solid Specificity Statistical Data Interpretation Statistical Methods Structure Testing Time Training Variant Veterans base causal variant computer science drug development flexibility genetic architecture genetic variant genome sequencing genome wide association study genome-wide genomic locus graduate student human disease improved insight interest large datasets machine learning algorithm non-genetic novel novel strategies personalized medicine polygenic risk score prediction algorithm programs rare variant sex software development statistics tool trait virtual whole genome

项目摘要

Project Summary Recent years have witnessed the development of large research projects that involve genotyping hundreds of thousands of individuals, on which we have available detailed medical records. Examples include the All of us research project, the Million Veteran Program, and the UKBiobank resource. Often, whole-genome sequencing data is also available for a substantial fraction of the individuals. These large samples, with their precise genotypic and phenotypic information, give us the opportunity to bring our understanding of the relations between genetic variation and traits of medical interest to the next level. While the initial small sample sizes available for genome wide association studies (GWAS) motivated analyses that were approximative in nature, we are now in the position to probe more closely the genetic causal mechanisms underlying medically relevant phenotypes. We can aspire to distinguish variants that have causal effects from those that are associated because of linkage disequilibrium or population structure. Indeed, we need to pay even greater attention to the implications of hidden confounders: even small effects become significant when sample sizes are large enough. Increasing the resolution with which we can describe causal mechanisms will result in the identification of clearer targets for drug development. It will also improve the precision of personalized risk evaluations based on genotypes: if we can construct risk scores using variants that are truly causal, their performance will remain solid across ethnicities and environmental exposures. To zoom in on genetic variants with causal effects, this project will leverage a set of new statistical methodologies that the investigators have recently introduced. These new approaches are remarkably flexible, in that they do not rely on specific assumptions of how phenotypes are linked to genetic variants. Indeed, they allow researchers to capitalize on powerful machine learning algorithms and, crucially, equip their results with precise replicability guarantees. We have assembled a diverse and complementary team, including experts in statistical genomics, methodological statistics and computer science, with a strong record both of software development and genetic data analysis. A postdoctoral scholar and two graduate students will contribute to the research program, and the interdisciplinary training they will acquire in statistics, computation and genetics will add another substantial benefit.

项目总结