No large-effect low-frequency coding variation found for myocardial infarction

The HUNT study: A population-based cohort for genetic research

The Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. Approximately 88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during four community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.

Meta-analysis of exome array data identifies six novel genetic loci for lung function

Background: Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. Methods: We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV 1), forced vital capacity (FVC) and the ratio of FEV 1 to FVC (FEV 1/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. Results: We identified significant (P<2·8x10 -7) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs ( SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including TYRO3 and PLAU. Conclusions: Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.

Meta-analysis of exome array data identifies six novel genetic loci for lung function

Background: Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. Methods: We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV ₁), forced vital capacity (FVC) and the ratio of FEV ₁ to FVC (FEV ₁/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. Results: We identified significant (P<2·8x10 ^-7) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs ( SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including TYRO3 and PLAU. Conclusions: Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.

Meta-analysis of exome array data identifies six novel genetic loci for lung function

Background: Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. Methods: We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV 1), forced vital capacity (FVC) and the ratio of FEV 1 to FVC (FEV 1/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. Results: We identified significant (P<2·8x10 -7) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs ( SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including TYRO3 and PLAU. Conclusions: Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.

Analysis with the exome array identifies multiple new independent variants in lipid loci

It has been hypothesized that low frequency (1-5% minor allele frequency (MAF)) and rare (<1% MAF) variants with large effect sizes may contribute to the missing heritability in complex traits. Here, we report an association analysis of lipid traits (total cholesterol, LDL-cholesterol, HDL-cholesterol triglycerides) in up to 27 312 individuals with a comprehensive set of low frequency coding variants (ExomeChip), combined with conditional analysis in the known lipid loci. No new locus reached genome-wide significance. However, we found a new lead variant in 26 known lipid association regions of which 16 were >1000-fold more significant than the previous sentinel variant and not in close LD (six had MAF <5%). Furthermore, conditional analysis revealed multiple independent signals (ranging from 1 to 5) in a third of the 98 lipid loci tested, including rare variants. Addition of our novel associations resulted in between 1.5- and 2.5-fold increase in the proportion of heritability explained for the different lipid traits. Our findings suggest that rare coding variants contribute to the genetic architecture of lipid traits.

Exome genotyping arrays to identify rare and low frequency variants associated with epithelial ovarian cancer risk

Rare and low frequency variants are not well covered in most germline genotyping arrays and are understudied in relation to epithelial ovarian cancer (EOC) risk. To address this gap, we used genotyping arrays targeting rarer protein-coding variation in 8,165 EOC cases and 11,619 controls from the international Ovarian Cancer Association Consortium (OCAC). Pooled association analyses were conducted at the variant and gene level for 98,543 variants directly genotyped through two exome genotyping projects. Only common variants that represent or are in strong linkage disequilibrium (LD) with previously-identified signals at established loci reached traditional thresholds for exome-wide significance (P < 5.0 × 10 -  7). One of the most significant signals (Pall histologies = 1.01 × 10 -  13;Pserous = 3.54 × 10 -  14) occurred at 3q25.31 for rs62273959, a missense variant mapping to the LEKR1 gene that is in LD (r2 = 0.90) with a previously identified 'best hit' (rs7651446) mapping to an intron of TIPARP. Suggestive associations (5.0 × 10 -  5 > P≥5.0 ×10 -  7) were detected for rare and low-frequency variants at 16 novel loci. Four rare missense variants were identified (ACTBL2 rs73757391 (5q11.2), BTD rs200337373 (3p25.1), KRT13 rs150321809 (17q21.2) and MC2R rs104894658 (18p11.21)), but only MC2R rs104894668 had a large effect size (OR = 9.66). Genes most strongly associated with EOC risk included ACTBL2 (PAML = 3.23 × 10 -  5; PSKAT-o = 9.23 × 10 -  4) and KRT13 (PAML = 1.67 × 10 -  4; PSKAT-o = 1.07 × 10 -  5), reaffirming variant-level analysis. In summary, this large study identified several rare and low-frequency variants and genes that may contribute to EOC susceptibility, albeit with possible small effects. Future studies that integrate epidemiology, sequencing, and functional assays are needed to further unravel the unexplained heritability and biology of this disease.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.

Assessing the Power of Exome Chips

Genotyping chips for rare and low-frequent variants have recently gained popularity with the introduction of exome chips, but the utility of these chips remains unclear. These chips were designed using exome sequencing data from mainly American-European individuals, enriched for a narrow set of common diseases. In addition, it is well-known that the statistical power of detecting associations with rare and low-frequent variants is much lower compared to studies exclusively involving common variants. We developed a simulation program adaptable to any exome chip design to empirically evaluate the power of the exome chips. We implemented the main properties of the Illumina HumanExome BeadChip array. The simulated data sets were used to assess the power of exome chip based studies for varying effect sizes and causal variant scenarios. We applied two widely-used statistical approaches for rare and low-frequency variants, which collapse the variants into genetic regions or genes. Under optimal conditions, we found that a sample size between 20,000 to 30,000 individuals were needed in order to detect modest effect sizes (0.5% < PAR > 1%) with 80% power. For small effect sizes (PAR <0.5%), 60,000–100,000 individuals were needed in the presence of non-causal variants. In conclusion, we found that at least tens of thousands of individuals are necessary to detect modest effects under optimal conditions. In addition, when using rare variant chips on cohorts or diseases they were not originally designed for, the identification of associated variants or genes will be even more challenging.

A multiancestral genome-wide exome array study of Alzheimer disease, frontotemporal dementia, and progressive supranuclear palsy

IMPORTANCE

Previous studies have indicated a heritable component of the etiology of neurodegenerative diseases such as Alzheimer disease (AD), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP). However, few have examined the contribution of low-frequency coding variants on a genome-wide level.

OBJECTIVE

To identify low-frequency coding variants that affect susceptibility to AD, FTD, and PSP.

DESIGN, SETTING, AND PARTICIPANTS

We used the Illumina HumanExome BeadChip array to genotype a large number of variants (most of which are low-frequency coding variants) in a cohort of patients with neurodegenerative disease (224 with AD, 168 with FTD, and 48 with PSP) and in 224 control individuals without dementia enrolled between 2005-2012 from multiple centers participating in the Genetic Investigation in Frontotemporal Dementia and Alzheimer's Disease (GIFT) Study. An additional multiancestral replication cohort of 240 patients with AD and 240 controls without dementia was used to validate suggestive findings. Variant-level association testing and gene-based testing were performed.

MAIN OUTCOMES AND MEASURES

Statistical association of genetic variants with clinical diagnosis of AD, FTD, and PSP.

RESULTS

Genetic variants typed by the exome array explained 44%, 53%, and 57% of the total phenotypic variance of AD, FTD, and PSP, respectively. An association with the known AD gene ABCA7 was replicated in several ancestries (discovery P=.0049, European P=.041, African American P=.043, and Asian P=.027), suggesting that exonic variants within this gene modify AD susceptibility. In addition, 2 suggestive candidate genes, DYSF (P=5.53×10(-5)) and PAXIP1 (P=2.26×10(-4)), were highlighted in patients with AD and differentially expressed in AD brain. Corroborating evidence from other exome array studies and gene expression data points toward potential involvement of these genes in the pathogenesis of AD.

CONCLUSIONS AND RELEVANCE

Low-frequency coding variants with intermediate effect size may account for a significant fraction of the genetic susceptibility to AD and FTD. Furthermore, we found evidence that coding variants in the known susceptibility gene ABCA7, as well as candidate genes DYSF and PAXIP1, confer risk for AD.

Contribution of Rare and Common Genetic Variants to Plasma Lipid Levels and Carotid Stiffness and Geometry: A Substudy of the Paris Prospective Study 3

BACKGROUND

We assess the contribution of common and rare putatively functional genetic variants (most of them coding) present on the Illumina exome Beadchip to the variability of plasma lipids and stiffness of the common carotid artery.

METHODS AND RESULTS

Measurements were obtained from 2283 men and 1398 women, and after filtering and exclusion of monomorphic variants, 32,827 common (minor allele frequency >0.01) and 68,770 rare variants were analyzed. A large fraction of the heritability of plasma lipids is attributable to variants present on the array, especially for triglycerides (fraction of variance attributable to measured genotypes: V(G)/V(p)=31.4%, P<3.1×10(-11)) and high-density lipoprotein cholesterol (V(G)/V(p)=26.4%, P<4.2×10(-12)). Plasma lipids were associated with common variants located in known candidate genes, but no implication of rare variants could be established. Gene sets for plasma lipids, blood pressure, and coronary artery disease were defined on the basis of recent meta-analyses of genome-wide association studies. We observed a strong association between the plasma lipids gene set and plasma lipid variables, but none of the 3 genome-wide association studies gene sets was associated with the carotid parameters. Significant V(G)/V(p) ratios were observed for external (14.5%, P<2.7×10(-5)) and internal diameter (13.4%, P<4.3×10(-4)), stiffness (12.5%, P<8.0×10(-4)), intima-media thickness (10.6%, P<7.9×10(-4)), and wall cross-sectional area (13.2%, P<2.4×10(-5)). A significant association was observed between the common rs2903692 polymorphism of the CLEC16A gene and the internal diameter (P<4.3×10(-7)).

CONCLUSIONS

These results suggest an involvement of CLEC16A, a gene that has been reported to be associated with immune disorders, in the modulation of carotid vasodilatation.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.