Interrogating Type 2 Diabetes Genome-Wide Association Data Using a Biological Pathway-Based Approach

OBJECTIVE-Recent genome-wide association Studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow its to identify additional risk loci. RESEARCH DESIGN AND METHODS-We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases. RESULTS-After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top P-adj = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas. CONCLUSIONS-Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more Successful for some complex traits than others, depending on the nature of the underlying disease physiology. Diabetes 58:1463-1467, 200

A novel variant in GLIS3 is associated with osteoarthritis

Objectives Osteoarthritis (OA) is a complex disease, but its genetic aetiology remains poorly characterised. To identify novel susceptibility loci for OA, we carried out a genome-wide association study (GWAS) in individuals from the largest UK-based OA collections to date. Methods We carried out a discovery GWAS in 5414 OA individuals with knee and/or hip total joint replacement (TJR) and 9939 population-based controls. We followed-up prioritised variants in OA subjects from the interim release of the UK Biobank resource (up to 12 658 cases and 50 898 controls) and our lead finding in operated OA subjects from the full release of UK Biobank (17 894 cases and 89 470 controls). We investigated its functional implications in methylation, gene expression and proteomics data in primary chondrocytes from 12 pairs of intact and degraded cartilage samples from patients undergoing TJR. Results We detect a genome-wide significant association at rs10116772 with TJR (P=3.7×10−8; for allele A: OR (95% CI) 0.97 (0.96 to 0.98)), an intronic variant in GLIS3, which is expressed in cartilage. Variants in strong correlation with rs10116772 have been associated with elevated plasma glucose levels and diabetes. Conclusions We identify a novel susceptibility locus for OA that has been previously implicated in diabetes and glycaemic traits

The effects of chronic cobalt and chromium exposure after metal-on-metal hip resurfacing: An epigenome-wide association pilot study.

Metal-on-metal (MOM) hip resurfacing has recently been a popular prosthesis choice for the treatment of symptomatic arthritis, but results in the release of cobalt and chromium ions into the circulation that can be associated with adverse clinical effects. The mechanism underlying these effects remains unclear. While chromosomal aneuploidy and translocations are associated with this exposure, the presence of subtle structural epigenetic modifications in patients with MOM joint-replacements remains unexplored. Consequently, we analysed whole blood DNA methylation in 34 OA patients with MOM hip resurfacing (MOM HR) compared to 34 OA patients with non-MOM total hip replacements (non-MOM THR), using the genome-wide Illumina HumanMethylation 450k BeadChip. No probes showed differential methylation significant at 5% false-discovery rate (FDR). We also tested association of probe methylation levels with blood chromium and cobalt levels directly; there were no significant associations at 5% FDR. Finally, we used the 'epigenetic clock' to compare estimated to actual age at sample for all individuals. We found no significant difference between MOM HR and non-MOM THR, and no correlation of age acceleration with blood metal levels. Our results suggest the absence of large methylation differences systemically following metal exposure, however, larger sample sizes will be required to identify potential small effects. Any DNA methylation changes that may occur in the local periprosthetic tissues remain to be elucidated. This article is protected by copyright. All rights reserved

ProxECAT: Proxy External Controls Association Test. A new case-control gene region association test using allele frequencies from public controls.

A primary goal of the recent investment in sequencing is to detect novel genetic associations in health and disease improving the development of treatments and playing a critical role in precision medicine. While this investment has resulted in an enormous total number of sequenced genomes, individual studies of complex traits and diseases are often smaller and underpowered to detect rare variant genetic associations. Existing genetic resources such as the Exome Aggregation Consortium (>60,000 exomes) and the Genome Aggregation Database (~140,000 sequenced samples) have the potential to be used as controls in these studies. Fully utilizing these and other existing sequencing resources may increase power and could be especially useful in studies where resources to sequence additional samples are limited. However, to date, these large, publicly available genetic resources remain underutilized, or even misused, in large part due to the lack of statistical methods that can appropriately use this summary level data. Here, we present a new method to incorporate external controls in case-control analysis called ProxECAT (Proxy External Controls Association Test). ProxECAT estimates enrichment of rare variants within a gene region using internally sequenced cases and external controls. We evaluated ProxECAT in simulations and empirical analyses of obesity cases using both low-depth of coverage (7x) whole-genome sequenced controls and ExAC as controls. We find that ProxECAT maintains the expected type I error rate with increased power as the number of external controls increases. With an accompanying R package, ProxECAT enables the use of publicly available allele frequencies as external controls in case-control analysis

The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies

Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ∼25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary

The 2018 Otto Aufranc Award: How does genome-wide variation affect osteolysis risk after THA?

BACKGROUND: Periprosthetic osteolysis resulting in aseptic loosening is a leading cause of THA revision. Individuals vary in their susceptibility to osteolysis and heritable factors may contribute to this variation. However, the overall contribution that such variation makes to osteolysis risk is unknown. QUESTIONS/PURPOSES: We conducted two genome-wide association studies to (1) identify genetic risk loci associated with susceptibility to osteolysis; and (2) identify genetic risk loci associated with time to prosthesis revision for osteolysis. METHODS: The Norway cohort comprised 2624 patients after THA recruited from the Norwegian Arthroplasty Registry, of whom 779 had undergone revision surgery for osteolysis. The UK cohort included 890 patients previously recruited from hospitals in the north of England, 317 who either had radiographic evidence of and/or had undergone revision surgery for osteolysis. All participants had received a fully cemented or hybrid THA using a small-diameter metal or ceramic-on-conventional polyethylene bearing. Osteolysis susceptibility case-control analyses and quantitative trait analyses for time to prosthesis revision (a proxy measure of the speed of osteolysis onset) in those patients with osteolysis were undertaken in each cohort separately after genome-wide genotyping. Finally, a meta-analysis of the two independent cohort association analysis results was undertaken. RESULTS: Genome-wide association analysis identified four independent suggestive genetic signals for osteolysis case-control status in the Norwegian cohort and 11 in the UK cohort (p ≤ 5 x 10). After meta-analysis, five independent genetic signals showed a suggestive association with osteolysis case-control status at p ≤ 5 x 10 with the strongest comprising 18 correlated variants on chromosome 7 (lead signal rs850092, p = 1.13 x 10). Genome-wide quantitative trait analysis in cases only showed a total of five and nine independent genetic signals for time to revision at p ≤ 5 x 10, respectively. After meta-analysis, 11 independent genetic signals showed suggestive evidence of an association with time to revision at p ≤ 5 x 10 with the largest association block comprising 174 correlated variants in chromosome 15 (lead signal rs10507055, p = 1.40 x 10). CONCLUSIONS: We explored the heritable biology of osteolysis at the whole genome level and identify several genetic loci that associate with susceptibility to osteolysis or with premature revision surgery. However, further studies are required to determine a causal association between the identified signals and osteolysis and their functional role in the disease. CLINICAL RELEVANCE: The identification of novel genetic risk loci for osteolysis enables new investigative avenues for clinical biomarker discovery and therapeutic intervention in this disease

Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip

Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fails to properly account for the effects of sample size, the genetic model of the disease, and linkage disequilibrium between SNPs. In this paper, we argue that the statistical power to detect a causative variant should be the major criterion in study design. Because of the complicated pattern of linkage disequilibrium (LD) in the human genome, power cannot be calculated analytically and must instead be assessed by simulation. We describe in detail a method of simulating case-control samples at a set of linked SNPs that replicates the patterns of LD in human populations, and we used it to assess power for a comprehensive set of available genotyping chips. Our results allow us to compare the performance of the chips to detect variants with different effect sizes and allele frequencies, look at how power changes with sample size in different populations or when using multi-marker tags and genotype imputation approaches, and how performance compares to a hypothetical chip that contains every SNP in HapMap. A main conclusion of this study is that marked differences in genome coverage may not translate into appreciable differences in power and that, when taking budgetary considerations into account, the most powerful design may not always correspond to the chip with the highest coverage. We also show that genotype imputation can be used to boost the power of many chips up to the level obtained from a hypothetical “complete” chip containing all the SNPs in HapMap. Our results have been encapsulated into an R software package that allows users to design future association studies and our methods provide a framework with which new chip sets can be evaluated

Association between Type 2 Diabetes Loci and Measures of Fatness

Background: Type 2 diabetes (T2D) is a metabolic disorder characterized by disturbances of carbohydrate, fat and protein metabolism and insulin resistance. The majority of T2D patients are obese and obesity by itself may be a cause of insulin resistance. Our aim was to evaluate whether the recently identified T2D risk alleles are associated with human measures of fatness as characterized with Dual Energy X-ray Absorptiometry (DEXA). Methodology/Principal Findings: Genotypes and phenotypes of approximately 3,000 participants from cross-sectional ERF study were analyzed. Nine single nucleotide polymorphisms (SNPs) in CDKN2AB, CDKAL1, FTO, HHEX, IGF2BP2, KCNJ11, PPARG, SLC30A8 and TCF7L2 were genotyped. We used linear regression to study association between individual SNPs and the combined allelic risk score with body mass index (BMI), fat mass index (FMI), fat percentage (FAT), waist circumference (WC) and waist to hip ratio (WHR). Significant association was observed between rs8050136 (FTO) and BMI (p = 0.003), FMI (p = 0.007) and WC (p = 0.03); fat percentage was borderline significant (p = 0.053). No other SNPs alone or combined in a risk score demonstrated significant association to the measures of fatness. Conclusions/Significance: From the recently identified T2D risk variants only the risk variant of the FTO gene (rs8050136) showed statistically significant association with BMI, FMI, and WC

Type 2 Diabetes Risk Alleles Are Associated With Reduced Size at Birth

OBJECTIVE: Low birth weight is associated with an increased risk of type 2 diabetes. The mechanisms underlying this association are unknown and may represent intrauterine programming or two phenotypes of one genotype. The fetal insulin hypothesis proposes that common genetic variants that reduce insulin secretion or action may predispose to type 2 diabetes and also reduce birth weight, since insulin is a key fetal growth factor. We tested whether common genetic variants that predispose to type 2 diabetes also reduce birth weight. RESEARCH DESIGN AND METHODS: We genotyped single-nucleotide polymorphisms (SNPs) at five recently identified type 2 diabetes loci (CDKAL1, CDKN2A/B, HHEX-IDE, IGF2BP2, and SLC30A8) in 7,986 mothers and 19,200 offspring from four studies of white Europeans. We tested the association between maternal or fetal genotype at each locus and birth weight of the offspring. RESULTS: We found that type 2 diabetes risk alleles at the CDKAL1 and HHEX-IDE loci were associated with reduced birth weight when inherited by the fetus (21 g [95% CI 11-31], P = 2 x 10(-5), and 14 g [4-23], P = 0.004, lower birth weight per risk allele, respectively). The 4% of offspring carrying four risk alleles at these two loci were 80 g (95% CI 39-120) lighter at birth than the 8% carrying none (P(trend) = 5 x 10(-7)). There were no associations between birth weight and fetal genotypes at the three other loci or maternal genotypes at any locus. CONCLUSIONS: Our results are in keeping with the fetal insulin hypothesis and provide robust evidence that common disease-associated variants can alter size at birth directly through the fetal genotype