Whole-Exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated with LDL Cholesterol

Abstract

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.

The effect of phenotypic outliers and non-normality on rare-variant association testing

“…1 Recently, there has been a deepening interest in evaluating the extent to which rare variants contribute to variation in complex traits and diseases. [2][3][4][5][6][7][8][9] This has motivated development of statistical methods for testing rare-variant associations at the gene level. [10][11][12][13][14][15][16] Although these methods are useful for increasing statistical power to detect associations relative to single-variant analyses, valid well-powered statistical analyses are contingent on careful examination of phenotypes and underlying assumptions.…”

“…For instance, the LDLR gene harbors multiple rare variants that are strongly associated with circulating low-density lipoprotein (LDL)-cholesterol levels. 7 The genetic effects of these rare variants are so strong that individuals carrying certain LDLR mutations appear as outliers in population level summaries of LDL-cholesterol levels. 7 Rare-variant association studies of complex traits are particularly interested in phenotypic outliers because they may harbor rare variants with strong genetic effects.…”

“…7 The genetic effects of these rare variants are so strong that individuals carrying certain LDLR mutations appear as outliers in population level summaries of LDL-cholesterol levels. 7 Rare-variant association studies of complex traits are particularly interested in phenotypic outliers because they may harbor rare variants with strong genetic effects. Furthermore, many rare-variant association tests rely on asymptotics that work best with normally (or approximately normally) distributed phenotypes.…”

Phenotypic extremes in rare variant study designs

“…8,9 The power to detect association with the various proposed gene-based methods is dependent on the underlying genetic architecture of the gene. [10][11][12] The relative power of different study designs for CVASs has been well established. 13 For all genetic studies, selecting the extremes of the phenotype distribution improves power; a concept in genetics that can be traced back to seminal work by Lander and Botstein.…”

From exomes to genomes: challenges and solutions in population-based genetic association studies

“…10,11 There are multiple examples of new rarevariant associations being discovered from extreme samples drawn from large, population-based studies. [12][13][14] Though extreme trait sampling represents a powerful approach, they may not be suitable for every type of study. First, conclusions from extreme trait designs may be difficult to generalize due to differences in genetic architecture at the extremes of a quantitative trait.…”