Benjamin F. Voight

Genome-Wide Association Analysis Identifies Variants Associated with Nonalcoholic Fatty Liver Disease That Have Distinct Effects on Metabolic Traits

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%–27%) in family-based Amish, Family Heart, and Framingham Heart Studies (nu200a=u200a880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p<5×10−8) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT–assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits.

Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia

Genome-wide association studies (GWAS) have identified multiple loci associated with plasma lipid concentrations. Common variants at these loci together explain <10% of variation in each lipid trait. Rare variants with large individual effects may also contribute to the heritability of lipid traits; however, the extent to which rare variants affect lipid phenotypes remains to be determined. Here we show an accumulation of rare variants, or a mutation skew, in GWAS-identified genes in individuals with hypertriglyceridemia (HTG). Through GWAS, we identified common variants in APOA5, GCKR, LPL and APOB associated with HTG. Resequencing of these genes revealed a significant burden of 154 rare missense or nonsense variants in 438 individuals with HTG, compared to 53 variants in 327 controls (P = 6.2 × 10−8), corresponding to a carrier frequency of 28.1% of affected individuals and 15.3% of controls (P = 2.6 × 10−5). Considering rare variants in these genes incrementally increased the proportion of genetic variation contributing to HTG.

Meta-analysis identifies six new susceptibility loci for atrial fibrillation

Atrial fibrillation is a highly prevalent arrhythmia and a major risk factor for stroke, heart failure and death. We conducted a genome-wide association study (GWAS) in individuals of European ancestry, including 6,707 with and 52,426 without atrial fibrillation. Six new atrial fibrillation susceptibility loci were identified and replicated in an additional sample of individuals of European ancestry, including 5,381 subjects with and 10,030 subjects without atrial fibrillation (P < 5 × 10−8). Four of the loci identified in Europeans were further replicated in silico in a GWAS of Japanese individuals, including 843 individuals with and 3,350 individuals without atrial fibrillation. The identified loci implicate candidate genes that encode transcription factors related to cardiopulmonary development, cardiac-expressed ion channels and cell signaling molecules.

Evaluation of the Metabochip Genotyping Array in African Americans and Implications for Fine Mapping of GWAS-Identified Loci: The PAGE Study

The Metabochip is a custom genotyping array designed for replication and fine mapping of metabolic, cardiovascular, and anthropometric trait loci and includes low frequency variation content identified from the 1000 Genomes Project. It has 196,725 SNPs concentrated in 257 genomic regions. We evaluated the Metabochip in 5,863 African Americans; 89% of all SNPs passed rigorous quality control with a call rate of 99.9%. Two examples illustrate the value of fine mapping with the Metabochip in African-ancestry populations. At CELSR2/PSRC1/SORT1, we found the strongest associated SNP for LDL-C to be rs12740374 (pu200a=u200a3.5×10−11), a SNP indistinguishable from multiple SNPs in European ancestry samples due to high correlation. Its distinct signal supports functional studies elsewhere suggesting a causal role in LDL-C. At CETP we found rs17231520, with risk allele frequency 0.07 in African Americans, to be associated with HDL-C (pu200a=u200a7.2×10−36). This variant is very rare in Europeans and not tagged in common GWAS arrays, but was identified as associated with HDL-C in African Americans in a single-gene study. Our results, one narrowing the risk interval and the other revealing an associated variant not found in Europeans, demonstrate the advantages of high-density genotyping of common and rare variation for fine mapping of trait loci in African American samples.

Delineating Signals from Association Studies

A central goal of genetic mapping efforts is to reveal pathways and relevant biology, e.g., ciliary dysfunction in Bardet-Biedl syndrome [1] . Using genome-wide approaches, researchers have discovered a remarkable number of common genetic variants reproducibly associated with a variety of human traits [2] . Owing to the nature of the complexity of the traits studied, for many loci, the underlying biological pathways they perturb along with the specific genes implicated remain elusive. For these loci, two initial barriers impede direct biological insight: first, pinpointing the causal genetic risk variant(s) from an initial genome-wide association signal; and second, identifying the specific causal gene(s) implicated by the association signal. In order to overcome these impediments, genetic locus mapping studies at a finer scale are often proposed. The designs of these studies using existing data are straightforward, and the associated caveats of these designs are relatively well understood [3] . However, recent innovations in high-throughput sequencing are already beginning to make new datasets and technologies available which will transform the design of these studies. In addition, due to the overwhelming number of possible targets of fine-mapping, and owing to the observation that not all loci are perfectly suited to extensive fine-mapping efforts, selecting the ideal candidates among the plethora of loci available is an important consideration in these designs. With these two factors along with others in mind, this chapter articulates the nature, objectives, and considerations for experiments designed to elucidate the association signals derived from whole-genome studies.