Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa.

Genomic studies in African populations provide unique opportunities to understand disease etiology, human diversity, and population history. In the largest study of its kind, comprising genome-wide data from 6,400 individuals and whole-genome sequences from 1,978 individuals from rural Uganda, we find evidence of geographically correlated fine-scale population substructure. Historically, the ancestry of modern Ugandans was best represented by a mixture of ancient East African pastoralists. We demonstrate the value of the largest sequence panel from Africa to date as an imputation resource. Examining 34 cardiometabolic traits, we show systematic differences in trait heritability between European and African populations, probably reflecting the differential impact of genes and environment. In a multi-trait pan-African GWAS of up to 14,126 individuals, we identify novel loci associated with anthropometric, hematological, lipid, and glycemic traits. We find that several functionally important signals are driven by Africa-specific variants, highlighting the value of studying diverse populations across the region.

Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis.

Multimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases and robust evidence for association-signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals and identify high-confidence effector genes, including FTO and IRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.

Integration of genetic fine-mapping and multi-omics data reveals candidate effector genes for hypertension.

Genome-wide association studies of blood pressure (BP) have identified >1,000 loci, but the effector genes and biological pathways at these loci are mostly unknown. Using published association summary statistics, we conducted annotation-informed fine-mapping incorporating tissue-specific chromatin segmentation and colocalization to identify causal variants and candidate effector genes for systolic BP, diastolic BP, and pulse pressure. We observed 532 distinct signals associated with ≥2 BP traits and 84 with all three. For >20% of signals, a single variant accounted for >75% posterior probability, 65 were missense variants in known (SLC39A8, ADRB2, and DBH) and previously unreported BP candidate genes (NRIP1 and MMP14). In disease-relevant tissues, we colocalized >80 and >400 distinct signals for each BP trait with cis-eQTLs and regulatory regions from promoter capture Hi-C, respectively. Integrating mouse, human disorder, gene expression and tissue abundance data, and literature review, we provide consolidated evidence for 436 BP candidate genes for future functional validation and discover several potential drug targets.

Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Identification of type 2 diabetes loci in 433,540 East Asian individuals.

Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues4-6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.

Blood copper and risk of cardiometabolic diseases: a Mendelian randomization study.

Observational evidence links higher blood levels of copper with higher risk of cardiovascular diseases. However, whether those associations reflect causal links or can be attributed to confounding is still not fully clear. We investigated causal effects of copper on the risk of cardiometabolic endpoints (stroke, coronary artery disease [CAD] and type 2 diabetes) and cardiometabolic risk factors in two-sample Mendelian randomization (MR) studies. The selection of genetic instruments for blood copper levels relied on meta-analysis of genome-wide association studies in three independent studies (European Prospective Investigation into Cancer and Nutrition-Potsdam study, Prospective investigation of the Vasculature in Uppsala Seniors study, Queensland Institute of Medical Research studies). For the selected instruments, outcome associations were drawn from large public genetic consortia on the respective disease endpoints (MEGASTROKE, Cardiogram, DIAGRAM) and cardiometabolic risk factors. MR results indicate an inverse association for genetically higher copper levels with risk of CAD (odds ratio [95% confidence interval] = 0.92 [0.86-0.99], P = 0.022) and systolic blood pressure (beta [standard error (SE)] = -0.238 [0.121]; P = 0.049). Multivariable MR incorporating copper and systolic blood pressure into one model suggested systolic blood pressure as mediating factor between copper and CAD risk. In contrast to previous observational evidence establishing higher blood copper levels as risk-increasing factor for cardiometabolic diseases, this study suggests that higher levels of genetically predicted copper might play a protective role for the development of CAD and systolic blood pressure.

Methods for multiancestry genome-wide association study meta-analysis.

Genome-wide association studies (GWAS) have significantly enhanced our understanding of the genetic basis of complex diseases. Despite the technological advancements, gaps in our understanding remain, partly due to small effect sizes and inadequate coverage of genetic variation. Multiancestry GWAS meta-analysis (MAGMA) addresses these challenges by integrating genetic data from diverse populations, thereby increasing power to detect loci and improving fine-mapping resolution to identify causal variants across different ancestry groups. This review provides an overview of the protocols, statistical methods, and software of MAGMA, as well as highlighting some challenges associated with this approach.

Fine mapping of candidate effector genes for heart rate.

An elevated resting heart rate (RHR) is associated with increased cardiovascular mortality. Genome-wide association studies (GWAS) have identified > 350 loci. Uniquely, in this study we applied genetic fine-mapping leveraging tissue specific chromatin segmentation and colocalization analyses to identify causal variants and candidate effector genes for RHR. We used RHR GWAS summary statistics from 388,237 individuals of European ancestry from UK Biobank and performed fine mapping using publicly available genomic annotation datasets. High-confidence causal variants (accounting for > 75% posterior probability) were identified, and we collated candidate effector genes using a multi-omics approach that combined evidence from colocalisation with molecular quantitative trait loci (QTLs), and long-range chromatin interaction analyses. Finally, we performed druggability analyses to investigate drug repurposing opportunities. The fine mapping pipeline indicated 442 distinct RHR signals. For 90 signals, a single variant was identified as a high-confidence causal variant, of which 22 were annotated as missense. In trait-relevant tissues, 39 signals colocalised with cis-expression QTLs (eQTLs), 3 with cis-protein QTLs (pQTLs), and 75 had promoter interactions via Hi-C. In total, 262 candidate genes were highlighted (79% had promoter interactions, 15% had a colocalised eQTL, 8% had a missense variant and 1% had a colocalised pQTL), and, for the first time, enrichment in nervous system pathways. Druggability analyses highlighted ACHE, CALCRL, MYT1 and TDP1 as potential targets. Our genetic fine-mapping pipeline prioritised 262 candidate genes for RHR that warrant further investigation in functional studies, and we provide potential therapeutic targets to reduce RHR and cardiovascular mortality.

Integrated proteomics and genomics analysis of paradoxical eczema in psoriasis patients treated with biologics.

BACKGROUND: Few studies have explored the immunology and genetic risk of paradoxical eczema occurring as an adverse event of biologic therapy in patients with psoriasis. OBJECTIVES: We sought to describe the systemic inflammatory signature of paradoxical eczema using proteomics and explore whether this is genetically mediated. METHODS: This study used the Olink Target 96 Inflammation panel on 256 serum samples from 71 patients with psoriasis and paradoxical eczema, and 75 controls with psoriasis to identify differentially expressed proteins and enriched gene sets. Case samples from 1 or more time points (T1 prebiologic, T2 postbiologic, and T3 postparadoxical eczema) were matched 1:1 with control samples. Genes contributing to enriched gene sets were selected, and functional single nucleotide polymorphisms used to create polygenic risk scores in a genotyped cohort of 88 paradoxical eczema cases and 3124 psoriasis controls. RESULTS: STAMBP expression was lower in cases at T1 than in controls (log-fold change: -0.44; adjusted P = .022); no other proteins reached statistical significance at equivalent time points. Eleven gene sets including cytokine and chemokine pathways were enriched in cases at T2 and 10 at T3. Of the 39 proteins contributing to enriched gene sets, the majority are associated with the atopic dermatitis serum proteome. A polygenic risk score including 38 functional single nucleotide polymorphisms linked to enriched gene sets was associated with paradoxical eczema (adjusted P = .046). CONCLUSIONS: The paradoxical eczema systemic inflammatory proteome trends toward atopic dermatitis at a gene-set level and is detectable before onset of the phenotype. This signature could be genetically determined.

Dog ownership in infancy is protective for persistent wheeze in 17q21 asthma-risk carriers.

BACKGROUND: Asthma-associated single nucleotide polymorphisms from large genome-wide association studies only explain a fraction of genetic heritability. Likely causes of the missing heritability include broad phenotype definitions and gene-environment interactions (GxE). The mechanisms underlying GxE in asthma are poorly understood. Previous GxE studies on pet ownership showed discordant results. OBJECTIVES: We sought to study the GxE between the 17q12-21 locus and pet ownership in infancy in relation to wheeze. METHODS: Wheezing classes derived from 5 UK-based birth cohorts (latent class analysis) were used to study GxE between the 17q12-21 asthma-risk variant rs2305480 and dog and cat ownership in infancy, using multinomial logistic regression. A total of 9149 children had both pet ownership and genotype data available. Summary statistics from individual analyses were meta-analyzed. RESULTS: rs2305480 G allele was associated with increased risk of persistent wheeze (additive model odds ratio, 1.37; 95% CI, 1.25-1.51). There was no evidence of an association between dog or cat ownership and wheeze. We found significant evidence of a GxE interaction between rs2305480 and dog ownership (P = 8.3 × 10-4) on persistent wheeze; among dog owners, the G allele was no longer associated with an increased risk of persistent wheeze (additive model odds ratio, 0.95; 95% CI, 0.73-1.24). For those without pets, G allele was associated with increased risk of persistent wheeze (odds ratio, 1.61; 95% CI, 1.40-1.86). Among cat owners, no such dampening of the genetic effect was observed. CONCLUSIONS: Among dog owners, rs2305480 G was no longer associated with an increased risk of persistent wheeze (or asthma). Early-life environmental exposures may therefore attenuate likelihood of asthma in those carrying 17q12-21 risk alleles.

Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits.

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

Assessing the impact of alcohol consumption on the genetic contribution to mean corpuscular volume.

The relationship between the genetic loci that influence mean corpuscular volume (MCV) and those associated with excess alcohol drinking is unknown. We used white British participants from the UK Biobank (n = 362 595) to assess the association between alcohol consumption and MCV, and whether this was modulated by genetic factors. Multivariable regression was applied to identify predictors of MCV. GWAS, with and without stratification for alcohol consumption, determined how genetic variants influence MCV. SNPs in ADH1B, ADH1C and ALDH1B were used to construct a genetic score to test the assumption that acetaldehyde formation is an important determinant of MCV. Additional investigations using Mendelian randomization and phenome-wide association analysis were conducted. Increasing alcohol consumption by 40 g/week resulted in a 0.30% [95% confidence interval CI: 0.30-0.31%] increase in MCV (P < 1.0 × 10-320). Unstratified (irrespective of alcohol intake) GWAS identified 212 loci associated with MCV, of which 108 were novel. There was no heterogeneity of allelic effects by drinking status. No association was found between MCV and the genetic score generated from alcohol metabolizing genes. Mendelian randomization demonstrated a causal effect for alcohol on MCV. Seventy-one SNP-outcome pairs reached statistical significance in phenome-wide association analysis, with evidence of shared genetic architecture for MCV and thyroid dysfunction, and mineral metabolism disorders. MCV increases linearly with alcohol intake in a causal manner. Many genetic loci influence MCV, with new loci identified in this analysis that provide novel biological insights. However, there was no interaction between alcohol consumption and the allelic variants associated with MCV.

Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans.

Genome-wide association studies (GWAS) of kidney function have uncovered hundreds of loci, primarily in populations of European ancestry. We have undertaken the first continental African GWAS of estimated glomerular filtration rate (eGFR), a measure of kidney function used to define chronic kidney disease (CKD). We conducted GWAS of eGFR in 3288 East Africans from the Uganda General Population Cohort (GPC) and replicated in 8224 African Americans from the Women's Health Initiative. Loci attaining genome-wide significant evidence for association (P < 5 × 10-8) were followed up with Bayesian fine-mapping to localize potential causal variants. The predictive power of a genetic risk score (GRS) constructed from previously reported trans-ancestry eGFR lead single nucleotide polymorphism (SNPs) was evaluated in the Uganda GPC. We identified and validated two eGFR loci. At the glycine amidinotransferase (GATM) locus, the association signal (lead SNP rs2433603, P = 1.0 × 10-8) in the Uganda GPC GWAS was distinct from previously reported signals at this locus. At the haemoglobin beta (HBB) locus, the association signal (lead SNP rs141845179, P = 3.0 × 10-8) has been previously reported. The lead SNP at the HBB locus accounted for 88% of the posterior probability of causality after fine-mapping, but did not colocalise with kidney expression quantitative trait loci. The trans-ancestry GRS of eGFR was not significantly predictive into the Ugandan population. In the first GWAS of eGFR in continental Africa, we validated two previously reported loci at GATM and HBB. At the GATM locus, the association signal was distinct from that previously reported. These results demonstrate the value of performing GWAS in continental Africans, providing a rich genomic resource to larger consortia for further discovery and fine-mapping. The study emphasizes that additional large-scale efforts in Africa are warranted to gain further insight into the genetic architecture of CKD.

Do genetics contribute to TNF inhibitor response prediction in Psoriatic Arthritis?

Psoriatic arthritis (PsA) is a heterogeneous chronic musculoskeletal disease, affecting up to 30% of people with psoriasis. Research into PsA pathogenesis has led to the development of targeted therapies, including Tumor Necrosis Factor inhibitors (TNF-i). Good response is only achieved by ~60% of patients leading to 'trial and error' drug management approaches, adverse reactions and increasing healthcare costs. Robust and well-validated biomarker identification, and subsequent development of sensitive and specific assays, would facilitate the implementation of a stratified approach into clinical care. This review will summarise potential genetic biomarkers for TNF-i (adalimumab, etanercept and infliximab) response that have been reported to date. It will also comment upon the importance of managing clinical confounders when understanding drug response prediction. Variants in multiple gene regions including TNF-A, FCGR2A, TNFAIP3, TNFR1/TNFR1A/TNFRSF1A, TRAIL-R1/TNFRSF10A, FCGR3A have been reported to correlate with TNF-i response at various levels of statistical significance in patients with PsA. However, results were often from heterogenous and underpowered cohorts and none are currently implemented into clinical practice. External validation of genetic biomarkers in large, well-documented cohorts is required, and assessment of the predictive value of combining multiple genetic biomarkers with clinical measures is essential to clinically embed pharmacogenomics into PsA drug management.

Non-trough serum drug levels of adalimumab and etanercept are associated with response in patients with psoriatic arthritis.

OBJECTIVES: Up to 40% of psoriatic arthritis (PsA) patients experience first-line Tumour Necrosis Factor inhibitors (TNF-i) failure. Lower serum drug levels (SDL) have been associated with lower response in autoimmune conditions. This study aimed to: (i) establish the relationship between adalimumab (ADL) and etanercept (ETN) SDL and 3-month response; and (ii) identify optimal non-trough SDL thresholds in PsA. METHODS: PsA patients commencing ADL or ETN were recruited to the UK observational study OUTPASS. Patients were seen pre-TNF-i and at 3 months when response was measured, and non-trough serum samples collected. Response was defined according to the PsARC or EULAR criteria. Descriptive statistics and concentration-effect curves established differences in SDL based on response. Receiver operating characteristics and regression identified optimal SDL thresholds. RESULTS: PsA ETN (n = 97) PsARC and EULAR good responders had significantly higher 3-month SDL compared with non-responders (p= 0.006 and p= 0.020 respectively). Non-trough 3-month ETN SDL discriminated PsARC responders from non-responders (AUC = 0.70), with a threshold of 1.8 µg/ml being 63% specific and 69% sensitive. EULAR good and non-/moderate responders were discriminated with an AUC of 0.65 with a threshold of 2.0 µg/ml being 57% specific and 69% sensitive. ADL prescribed (n = 104) EULAR good responders had significantly higher 3-month SDL (p= 0.049). Non-trough 3-month ADL SDL discriminated EULAR good and non-/moderate responders (AUC = 0.63) with a threshold of 3.6 µg/ml being 48% specific and 81% sensitive. CONCLUSION: Higher 3-month SDL were detected in responders. Interventions to optimise SDL may improve treatment response earlier. This study suggests 3-month SDL thresholds which may be useful in clinical practice to optimise treatment response.

Genome-Wide Gene-Set Analysis Identifies Molecular Mechanisms Associated with ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal late-onset motor neuron disease characterized by the loss of the upper and lower motor neurons. Our understanding of the molecular basis of ALS pathology remains elusive, complicating the development of efficient treatment. Gene-set analyses of genome-wide data have offered insight into the biological processes and pathways of complex diseases and can suggest new hypotheses regarding causal mechanisms. Our aim in this study was to identify and explore biological pathways and other gene sets having genomic association to ALS. Two cohorts of genomic data from the dbGaP repository were combined: (a) the largest available ALS individual-level genotype dataset (N = 12,319), and (b) a similarly sized control cohort (N = 13,210). Following comprehensive quality control pipelines, imputation and meta-analysis, we assembled a large European descent ALS-control cohort of 9244 ALS cases and 12,795 healthy controls represented by genetic variants of 19,242 genes. Multi-marker analysis of genomic annotation (MAGMA) gene-set analysis was applied to an extensive collection of 31,454 gene sets from the molecular signatures database (MSigDB). Statistically significant associations were observed for gene sets related to immune response, apoptosis, lipid metabolism, neuron differentiation, muscle cell function, synaptic plasticity and development. We also report novel interactions between gene sets, suggestive of mechanistic overlaps. A manual meta-categorization and enrichment mapping approach is used to explore the overlap of gene membership between significant gene sets, revealing a number of shared mechanisms.

Kidney omics in hypertension: from statistical associations to biological mechanisms and clinical applications.

Hypertension is a major cardiovascular disease risk factor and contributor to premature death globally. Family-based investigations confirmed a significant heritable component of blood pressure (BP), whereas genome-wide association studies revealed >1000 common and rare genetic variants associated with BP and/or hypertension. The kidney is not only an organ of key relevance to BP regulation and the development of hypertension, but it also acts as the tissue mediator of genetic predisposition to hypertension. The identity of kidney genes, pathways, and related mechanisms underlying the genetic associations with BP has started to emerge through integration of genomics with kidney transcriptomics, epigenomics, and other omics as well as through applications of causal inference, such as Mendelian randomization. Single-cell methods further enabled mapping of BP-associated kidney genes to cell types, and in conjunction with other omics, started to illuminate the biological mechanisms underpinning associations of BP-associated genetic variants and kidney genes. Polygenic risk scores derived from genome-wide association studies and refined on kidney omics hold the promise of enhanced diagnostic prediction, whereas kidney omics-informed drug discovery is likely to contribute new therapeutic opportunities for hypertension and hypertension-mediated kidney damage.

Fine-scale population structure in the UK Biobank: implications for genome-wide association studies.

The UK Biobank is a prospective study of more than 500 000 participants, which has aggregated data from questionnaires, physical measures, biomarkers, imaging and follow-up for a wide range of health-related outcomes, together with genome-wide genotyping supplemented with high-density imputation. Previous studies have highlighted fine-scale population structure in the UK on a North-West to South-East cline, but the impact of unmeasured geographical confounding on genome-wide association studies (GWAS) of complex human traits in the UK Biobank has not been investigated. We considered 368 325 white British individuals from the UK Biobank and performed GWAS of their birth location. We demonstrate that widely used approaches to adjust for population structure, including principal component analysis and mixed modelling with a random effect for a genetic relationship matrix, cannot fully account for the fine-scale geographical confounding in the UK Biobank. We observe significant genetic correlation of birth location with a range of lifestyle-related traits, including body-mass index and fat mass, hypertension and lung function, even after adjustment for population structure. Variants driving associations with birth location are also strongly associated with many of these lifestyle-related traits after correction for population structure, indicating that there could be environmental factors that are confounded with geography that have not been adequately accounted for. Our findings highlight the need for caution in the interpretation of lifestyle-related trait GWAS in UK Biobank, particularly in loci demonstrating strong residual association with birth location.