Insights into the Genetic Architecture Underlying Plasma Lipids and Related Phenotypes from Genome-wide Human Genetic Variation.

genome-wide association studies;meta-analysis;lipids;whole-genome sequencing;structural variation;Genetics;Science;Bioinformatics

Schmidt, Ellen MarieBurmeister, Margit ;

University of Michigan

关键词: genome-wide association studies;    meta-analysis;    lipids;    whole-genome sequencing;    structural variation;    Genetics;    Science;    Bioinformatics;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/120821/schellen_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Complex traits are multifactorial, often with risk contributions from numerous common and rare genetic mutations. The considerable challenges in understanding complex human phenotypes have prompted genome-wide association studies (GWAS), which generally compare large samples of unrelated individuals to test the relationship between genetic markers or nearby linked alleles and modulation of a trait or disease risk. Heritable levels of plasma lipids can influence heart disease risk, highlighting lipid-associated genetic variants as effective therapeutic targets. In collaboration with the Global Lipids Genetics Consortium, I present a follow-up study of approximately 100,000 individuals genotyped on a custom Metabochip array in the largest meta-analysis for lipids to-date. I report 62 novel genetic loci associated with lipids and present downstream bioinformatics analyses to support the role of these loci in lipid regulation.Many of the GWAS-identified lipid loci are non-protein-coding, suggesting a role in transcriptional regulation. This regulatory role can involve altering the DNA sequence at which proteins bind, ultimately affecting gene expression levels in particular cell types. I developed an open source tool called GREGOR (Genomic Regulatory Elements and Gwas Overlap AlgoRithm) to evaluate enrichment of GWAS variants in tissue-specific regulatory features defined by experimental approaches such as chromatin immunoprecipitation followed by high-throughput DNA sequencing (ChIP-seq). I report strong evidence for enrichment in DNase hypersensitive sites of biologically relevant tissues for 5 phenotypes including lipids, coronary artery disease, blood pressure, body mass index, and type 2 diabetes. In addition, I evaluate regulatory feature overlap of linked variants at a set of individual lipid-associated loci to predict the functionality of particular variants, and present experimental results to support my computational predictions.Lastly, I perform discovery and genotyping of structural variation (SV) from low-pass whole genome sequence data of 2,202 Norwegian cases with early-onset myocardial infarction (MI) and matched controls. I use complementary and established SV detection algorithms to call deletions, duplications, and inversions, and perform association analyses with MI disease risk and lipid levels. I observe a deletion in strong linkage disequilibrium with a known MI-associated single variant at the WDR12 locus, suggesting its plausibility as a functional variant at that locus.