Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

Abstract

Deep-coverage whole genome sequencing at the population level is now feasible and offers potential advantages for locus discovery, particularly in the analysis rare mutations in non-coding regions. Here, we performed whole genome sequencing in 16,324 participants from four ancestries at mean depth >29X and analyzed correlations of genotypes with four quantitative traits – plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. We conducted a discovery analysis including common or rare variants in coding as well as non-coding regions and developed a framework to interpret genome sequence for dyslipidemia risk. Common variant association yielded loci previously described with the exception of a few variants not captured earlier by arrays or imputation. In coding sequence, rare variant association yielded known Mendelian dyslipidemia genes and, in non-coding sequence, we detected no rare variant association signals after application of four approaches to aggregate variants in non-coding regions. We developed a new, genome-wide polygenic score for LDL-C and observed that a high polygenic score conferred similar effect size to a monogenic mutation (~30 mg/dl higher LDL-C for each); however, among those with extremely high LDL-C, a high polygenic score was considerably more prevalent than a monogenic mutation (23% versus 2% of participants, respectively).

The influence of rare variants in circulating metabolic biomarkers

“…Given that high levels of small HDL particles have been previously associated with higher incidence of ischemic stroke [53] some of these variants could have protective effects. These results are in agreement with previous work on LDL-C [23] and HDL-C [54] that show that common polygenic signals seem to have a higher impact on the higher extremes of lipid traits whereas there is evidence for a higher prevalence of rare variation on the lower extremes [54]. This is also expected since the INTERVAL cohort consists of predominantly healthy blood donors and therefore might be depleted of individuals with rare "damaging" variants.…”

“…Genome-wide association studies (GWAS) focusing on traditionally measured lipid traits have greatly expanded our knowledge into lipid biology and to date more than 250 loci have been robustly associated with total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and/or triglycerides (TG) [15][16][17][18][19][20][21][22][23]. In addition to this, more detailed metabolic profiling using high resolution nuclear magnetic resonance (NMR) measurements has proven helpful to find additional lipid and small molecule metabolism-associated loci with smaller sample sizes, and to assess pleiotropic effects of previously established loci [24][25][26].…”

Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease

“…polygenic and monogenic) models can be used to provide additional information about disease genetic architecture. This approach has been fruitful in studying blood lipid levels and neurodevelopmental disorders in large number of individuals [82,97].…”

“…An exhaustive review of how to design and analyse data based on rare variants is beyond the scope of this manuscript and is specific to the study design and technology utilised. A number of conceptual frameworks for the design of rare variant association studies have been published [76,81,82]. Rare variant analyses, whether at the single variant or gene-wide level, require large sample sizes to provide the required statistical power for the genetic association analyses.…”

Candidate Regulators of Dyslipidemia in Chromosome 1 Substitution Lines Using Liver Co-expression Profiling Analysis

“…Recent genome wide association studies (GWAS) in human have linked hundreds of genetic loci to plasma lipids metabolism, including those pathogenic genes such as APOE, PCSK9 , CETP, LIPC, LPL APOA5 [2,3]. Further, several rare variants are also uncovered with next generation sequencing [4]. Although significant achievements have been made, the identified genetic loci only explained a small portion of the phenotypic variance, which suggests most of the genetic regulators remain unknown.…”