The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.

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.

Author Correction: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.

Key Concepts in Genetic Epidemiology.

Genetic epidemiology is a discipline closely allied to traditional epidemiology that deals with the analysis of the familial distribution of traits. It emerged in the mid-1980s bringing together approaches and techniques developed in mathematical and quantitative genetics, medical and population genetics, statistics and epidemiology. The purpose of this chapter is to familiarize the reader with key concepts in genetic epidemiology as applied at present to unveil the familial and genetic determinants of disease and the joint effects of genes and environmental exposures.

Quality Control of Common and Rare Variants.

Thorough data quality control (QC) is a key step to the success of high-throughput genotyping approaches. Following extensive research several criteria and thresholds have been established for data QC at the sample and variant level. Sample QC is aimed at the identification and removal (when appropriate) of individuals with (1) low call rate, (2) discrepant sex or other identity-related information, (3) excess genome-wide heterozygosity and homozygosity, (4) relations to other samples, (5) ethnicity differences, (6) batch effects, and (7) contamination. Variant QC is aimed at identification and removal or refinement of variants with (1) low call rate, (2) call rate differences by phenotypic status, (3) gross deviation from Hardy-Weinberg Equilibrium (HWE), (4) bad genotype intensity plots, (5) batch effects, (6) differences in allele frequencies with published data sets, (7) very low minor allele counts (MAC), (8) low imputation quality score, (9) low variant quality score log-odds, and (10) few or low quality reads.

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.

Radiographic endophenotyping in hip osteoarthritis improves the precision of genetic association analysis.

OBJECTIVE: Osteoarthritis (OA) has a strong genetic component but the success of previous genome-wide association studies (GWAS) has been restricted due to insufficient sample sizes and phenotype heterogeneity. Our aim was to examine the effect of clinically relevant endophenotyping according to site of maximal joint space narrowing (maxJSN) and bone remodelling response on GWAS signal detection in hip OA. METHODS: A stratified GWAS meta-analysis was conducted in 2118 radiographically defined hip OA cases and 6500 population-based controls. Signals were followed up by analysing differential expression of proximal genes for bone remodelling endophenotypes in 33 pairs of macroscopically intact and OA-affected cartilage. RESULTS: We report suggestive evidence (p<5×10-6) of association at 6 variants with OA endophenotypes that would have been missed by using presence of hip OA as the disease end point. For example, in the analysis of hip OA cases with superior maxJSN versus cases with non-superior maxJSN we detected association with a variant in the LRCH1 gene (rs754106, p=1.49×10-7, OR (95% CIs) 0.70 (0.61 to 0.80)). In the comparison of hypertrophic with non-hypertrophic OA the most significant variant was located between STT3B and GADL1 (rs6766414, p=3.13×10-6, OR (95% CIs) 1.45 (1.24 to 1.69)). Both of these associations were fully attenuated in non-stratified analyses of all hip OA cases versus population controls (p>0.05). STT3B was significantly upregulated in OA-affected versus intact cartilage, particularly in the analysis of hypertrophic and normotrophic compared with atrophic bone remodelling pattern (p=4.2×10-4). CONCLUSIONS: Our findings demonstrate that stratification of OA cases into more homogeneous endophenotypes can identify genes of potential functional importance otherwise obscured by disease heterogeneity.

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome-wide association study data to identify single-nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P-value threshold. However, P-values do not account for differences in power, whereas Bayes' factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P-values of a decreasing type I error rate as study size increases for single-disease associations. Consequently, the overlap analysis of traits from different-sized studies encounters issues in fair P-value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P-values, particularly in low-power scenarios. Calibration tables between BFs and P-values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P-values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis.

Investigation of association between hip osteoarthritis susceptibility loci and radiographic proximal femur shape.

OBJECTIVE: To test whether previously reported hip morphology or osteoarthritis (OA) susceptibility loci are associated with proximal femur shape as represented by statistical shape model (SSM) modes and as univariate or multivariate quantitative traits. METHODS: We used pelvic radiographs and genotype data from 929 subjects with unilateral hip OA who had been recruited previously for the Arthritis Research UK Osteoarthritis Genetics Consortium genome-wide association study. We built 3 SSMs capturing the shape variation of the OA-unaffected proximal femur in the entire mixed-sex cohort and for male/female-stratified cohorts. We selected 41 candidate single-nucleotide polymorphisms (SNPs) previously reported as being associated with hip morphology (for replication analysis) or OA (for discovery analysis) and for which genotype data were available. We performed 2 types of analysis for genotype-phenotype associations between these SNPs and the modes of the SSMs: 1) a univariate analysis using individual SSM modes and 2) a multivariate analysis using combinations of SSM modes. RESULTS: The univariate analysis identified association between rs4836732 (within the ASTN2 gene) and mode 5 of the female SSM (P = 0.0016) and between rs6976 (within the GLT8D1 gene) and mode 7 of the mixed-sex SSM (P = 0.0003). The multivariate analysis identified association between rs5009270 (near the IFRD1 gene) and a combination of modes 3, 4, and 9 of the mixed-sex SSM (P = 0.0004). Evidence of associations remained significant following adjustment for multiple testing. All 3 SNPs had previously been associated with hip OA. CONCLUSION: These de novo findings suggest that rs4836732, rs6976, and rs5009270 may contribute to hip OA susceptibility by altering proximal femur shape.

Genetic characterization of Greek population isolates reveals strong genetic drift at missense and trait-associated variants.

Isolated populations are emerging as a powerful study design in the search for low-frequency and rare variant associations with complex phenotypes. Here we genotype 2,296 samples from two isolated Greek populations, the Pomak villages (HELIC-Pomak) in the North of Greece and the Mylopotamos villages (HELIC-MANOLIS) in Crete. We compare their genomic characteristics to the general Greek population and establish them as genetic isolates. In the MANOLIS cohort, we observe an enrichment of missense variants among the variants that have drifted up in frequency by more than fivefold. In the Pomak cohort, we find novel associations at variants on chr11p15.4 showing large allele frequency increases (from 0.2% in the general Greek population to 4.6% in the isolate) with haematological traits, for example, with mean corpuscular volume (rs7116019, P=2.3 × 10(-26)). We replicate this association in a second set of Pomak samples (combined P=2.0 × 10(-36)). We demonstrate significant power gains in detecting medical trait associations.

Meta-analysis identifies loci affecting levels of the potential osteoarthritis biomarkers sCOMP and uCTX-II with genome wide significance.

BACKGROUND: Research for the use of biomarkers in osteoarthritis (OA) is promising, however, adequate discrimination between patients and controls may be hampered due to innate differences. We set out to identify loci influencing levels of serum cartilage oligomeric protein (sCOMP) and urinary C-telopeptide of type II collagen (uCTX-II). METHODS: Meta-analysis of genome-wide association studies was applied to standardised residuals of sCOMP (N=3316) and uCTX-II (N=4654) levels available in 6 and 7 studies, respectively, from TreatOA. Effects were estimated using a fixed-effects model. Six promising signals were followed up by de novo genotyping in the Cohort Hip and Cohort Knee study (N = 964). Subsequently, their role in OA susceptibility was investigated in large-scale genome-wide association studies meta-analyses for OA. Differential expression of annotated genes was assessed in cartilage. RESULTS: Genome-wide significant association with sCOMP levels was found for a SNP within MRC1 (rs691461, p = 1.7 × 10(-12)) and a SNP within CSMD1 associated with variation in uCTX-II levels with borderline genome-wide significance (rs1983474, p = 8.5 × 10(-8)). Indication for association with sCOMP levels was also found for a locus close to the COMP gene itself (rs10038, p = 7.1 × 10(-6)). The latter SNP was subsequently found to be associated with hip OA whereas COMP expression appeared responsive to the OA pathophysiology in cartilage. CONCLUSIONS: We have identified genetic loci affecting either uCTX-II or sCOMP levels. The genome wide significant association of MRC1 with sCOMP levels was found likely to act independent of OA subtypes. Increased sensitivity of biomarkers with OA may be accomplished by taking genetic variation into account.

Assessment of osteoarthritis candidate genes in a meta-analysis of nine genome-wide association studies.

OBJECTIVE: To assess candidate genes for association with osteoarthritis (OA) and identify promising genetic factors and, secondarily, to assess the candidate gene approach in OA. METHODS: A total of 199 candidate genes for association with OA were identified using Human Genome Epidemiology (HuGE) Navigator. All of their single-nucleotide polymorphisms (SNPs) with an allele frequency of >5% were assessed by fixed-effects meta-analysis of 9 genome-wide association studies (GWAS) that included 5,636 patients with knee OA and 16,972 control subjects and 4,349 patients with hip OA and 17,836 control subjects of European ancestry. An additional 5,921 individuals were genotyped for significantly associated SNPs in the meta-analysis. After correction for the number of independent tests, P values less than 1.58 × 10(-5) were considered significant. RESULTS: SNPs at only 2 of the 199 candidate genes (COL11A1 and VEGF) were associated with OA in the meta-analysis. Two SNPs in COL11A1 showed association with hip OA in the combined analysis: rs4907986 (P = 1.29 × 10(-5) , odds ratio [OR] 1.12, 95% confidence interval [95% CI] 1.06-1.17) and rs1241164 (P = 1.47 × 10(-5) , OR 0.82, 95% CI 0.74-0.89). The sex-stratified analysis also showed association of COL11A1 SNP rs4908291 in women (P = 1.29 × 10(-5) , OR 0.87, 95% CI 0.82-0.92); this SNP showed linkage disequilibrium with rs4907986. A single SNP of VEGF, rs833058, showed association with hip OA in men (P = 1.35 × 10(-5) , OR 0.85, 95% CI 0.79-0.91). After additional samples were genotyped, association at one of the COL11A1 signals was reinforced, whereas association at VEGF was slightly weakened. CONCLUSION: Two candidate genes, COL11A1 and VEGF, were significantly associated with OA in this focused meta-analysis. The remaining candidate genes were not associated.

Genome-wide association study of sexual maturation in males and females highlights a role for body mass and menarche loci in male puberty.

Little is known about genes regulating male puberty. Further, while many identified pubertal timing variants associate with age at menarche, a late manifestation of puberty, and body mass, little is known about these variants' relationship to pubertal initiation or tempo. To address these questions, we performed genome-wide association meta-analysis in over 11 000 European samples with data on early pubertal traits, male genital and female breast development, measured by the Tanner scale. We report the first genome-wide significant locus for male sexual development upstream of myocardin-like 2 (MKL2) (P = 8.9 × 10(-9)), a menarche locus tagging a developmental pathway linking earlier puberty with reduced pubertal growth (P = 4.6 × 10(-5)) and short adult stature (p = 7.5 × 10(-6)) in both males and females. Furthermore, our results indicate that a proportion of menarche loci are important for pubertal initiation in both sexes. Consistent with epidemiological correlations between increased prepubertal body mass and earlier pubertal timing in girls, body mass index (BMI)-increasing alleles correlated with earlier breast development. In boys, some BMI-increasing alleles associated with earlier, and others with delayed, sexual development; these genetic results mimic the controversy in epidemiological studies, some of which show opposing correlations between prepubertal BMI and male puberty. Our results contribute to our understanding of the pubertal initiation program in both sexes and indicate that although mechanisms regulating pubertal onset in males and females may largely be shared, the relationship between body mass and pubertal timing in boys may be complex and requires further genetic studies.

Revisiting the thrifty gene hypothesis via 65 loci associated with susceptibility to type 2 diabetes.

We have investigated the evidence for positive selection in samples of African, European, and East Asian ancestry at 65 loci associated with susceptibility to type 2 diabetes (T2D) previously identified through genome-wide association studies. Selection early in human evolutionary history is predicted to lead to ancestral risk alleles shared between populations, whereas late selection would result in population-specific signals at derived risk alleles. By using a wide variety of tests based on the site frequency spectrum, haplotype structure, and population differentiation, we found no global signal of enrichment for positive selection when we considered all T2D risk loci collectively. However, in a locus-by-locus analysis, we found nominal evidence for positive selection at 14 of the loci. Selection favored the protective and risk alleles in similar proportions, rather than the risk alleles specifically as predicted by the thrifty gene hypothesis, and may not be related to influence on diabetes. Overall, we conclude that past positive selection has not been a powerful influence driving the prevalence of T2D risk alleles.

A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip.

OBJECTIVES: Osteoarthritis (OA) is the most common form of arthritis with a clear genetic component. To identify novel loci associated with hip OA we performed a meta-analysis of genome-wide association studies (GWAS) on European subjects. METHODS: We performed a two-stage meta-analysis on more than 78,000 participants. In stage 1, we synthesised data from eight GWAS whereas data from 10 centres were used for 'in silico' or 'de novo' replication. Besides the main analysis, a stratified by sex analysis was performed to detect possible sex-specific signals. Meta-analysis was performed using inverse-variance fixed effects models. A random effects approach was also used. RESULTS: We accumulated 11,277 cases of radiographic and symptomatic hip OA. We prioritised eight single nucleotide polymorphism (SNPs) for follow-up in the discovery stage (4349 OA cases); five from the combined analysis, two male specific and one female specific. One locus, at 20q13, represented by rs6094710 (minor allele frequency (MAF) 4%) near the NCOA3 (nuclear receptor coactivator 3) gene, reached genome-wide significance level with p=7.9×10(-9) and OR=1.28 (95% CI 1.18 to 1.39) in the combined analysis of discovery (p=5.6×10(-8)) and follow-up studies (p=7.3×10(-4)). We showed that this gene is expressed in articular cartilage and its expression was significantly reduced in OA-affected cartilage. Moreover, two loci remained suggestive associated; rs5009270 at 7q31 (MAF 30%, p=9.9×10(-7), OR=1.10) and rs3757837 at 7p13 (MAF 6%, p=2.2×10(-6), OR=1.27 in male specific analysis). CONCLUSIONS: Novel genetic loci for hip OA were found in this meta-analysis of GWAS.

The effect of FTO variation on increased osteoarthritis risk is mediated through body mass index: a Mendelian randomisation study.

OBJECTIVE: Variation in the fat mass and obesity-associated (FTO) gene influences susceptibility to obesity. A variant in the FTO gene has been implicated in genetic risk to osteoarthritis (OA). We examined the role of the FTO polymorphism rs8044769 in risk of knee and hip OA in cases and controls incorporating body mass index (BMI) information. METHODS: 5409 knee OA patients, 4355 hip OA patients and up to 5362 healthy controls from 7 independent cohorts from the UK and Australia were genotyped for rs8044769. The association of the FTO variant with OA was investigated in case/control analyses with and without BMI adjustment and in analyses matched for BMI category. A mendelian randomisation approach was employed using the FTO variant as the instrumental variable to evaluate the role of overweight on OA. RESULTS: In the meta-analysis of all overweight (BMI≥25) samples versus normal-weight controls irrespective of OA status the association of rs8044769 with overweight is highly significant (OR[CIs] for allele G=1.14 [01.08 to 1.19], p=7.5×10(-7)). A significant association with knee OA is present in the analysis without BMI adjustment (OR[CIs]=1.08[1.02 to 1.14], p=0.009) but the signal fully attenuates after BMI adjustment (OR[CIs]=0.99[0.93 to 1.05], p=0.666). We observe no evidence for association in the BMI-matched meta-analyses. Using mendelian randomisation approaches we confirm the causal role of overweight on OA. CONCLUSIONS: Our data highlight the contribution of genetic risk to overweight in defining risk to OA but the association is exclusively mediated by the effect on BMI. This is consistent with what is known of the biology of the FTO gene and supports the causative role of high BMI in OA.

A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates.

Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies, but the generalizability of such findings remains unknown. Here we genotype 1,267 individuals from a Greek population isolate on the Illumina HumanExome Beadchip, in search of functional coding variants associated with lipids traits. We find genome-wide significant evidence for association between R19X, a functional variant in APOC3, with increased high-density lipoprotein and decreased triglycerides levels. Approximately 3.8% of individuals are heterozygous for this cardioprotective variant, which was previously thought to be private to the Amish founder population. R19X is rare (<0.05% frequency) in outbred European populations. The increased frequency of R19X enables discovery of this lipid traits signal at genome-wide significance in a small sample size. This work exemplifies the value of isolated populations in successfully detecting transferable rare variant associations of high medical relevance.

In search of low-frequency and rare variants affecting complex traits.

The allelic architecture of complex traits is likely to be underpinned by a combination of multiple common frequency and rare variants. Targeted genotyping arrays and next-generation sequencing technologies at the whole-genome sequencing (WGS) and whole-exome scales (WES) are increasingly employed to access sequence variation across the full minor allele frequency (MAF) spectrum. Different study design strategies that make use of diverse technologies, imputation and sample selection approaches are an active target of development and evaluation efforts. Initial insights into the contribution of rare variants in common diseases and medically relevant quantitative traits point to low-frequency and rare alleles acting either independently or in aggregate and in several cases alongside common variants. Studies conducted in population isolates have been successful in detecting rare variant associations with complex phenotypes. Statistical methodologies that enable the joint analysis of rare variants across regions of the genome continue to evolve with current efforts focusing on incorporating information such as functional annotation, and on the meta-analysis of these burden tests. In addition, population stratification, defining genome-wide statistical significance thresholds and the design of appropriate replication experiments constitute important considerations for the powerful analysis and interpretation of rare variant association studies. Progress in addressing these emerging challenges and the accrual of sufficiently large data sets are poised to help the field of complex trait genetics enter a promising era of discovery.