Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.

Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.

Genome-wide association study of esophageal squamous cell cancer identifies shared and distinct risk variants in African and Chinese populations

Esophageal squamous cell carcinoma (ESCC) has a high disease burden in sub-Saharan Africa and has a very poor prognosis. Genome-wide association studies (GWASs) of ESCC in predominantly East Asian populations indicate a substantial genetic contribution to its etiology, but no genome-wide studies have been done in populations of African ancestry. Here, we report a GWAS in 1,686 African individuals with ESCC and 3,217 population-matched control individuals to investigate its genetic etiology. We identified a genome-wide-significant risk locus on chromosome 9 upstream of FAM120A (rs12379660, p = 4.58 × 10-8, odds ratio = 1.28, 95% confidence interval = 1.22-1.34), as well as a potential African-specific risk locus on chromosome 2 (rs142741123, p = 5.49 × 10-8) within MYO1B. FAM120A is a component of oxidative stress-induced survival signals, and the associated variants at the FAM120A locus co-localized with highly significant cis-eQTLs in FAM120AOS in both esophageal mucosa and esophageal muscularis tissue. A trans-ethnic meta-analysis was then performed with the African ESCC study and a Chinese ESCC study in a combined total of 3,699 ESCC-affected individuals and 5,918 control individuals, which identified three genome-wide-significant loci on chromosome 9 at FAM120A (rs12379660, pmeta = 9.36 × 10-10), chromosome 10 at PLCE1 (rs7099485, pmeta = 1.48 × 10-8), and chromosome 22 at CHEK2 (rs1033667, pmeta = 1.47 × 10-9). This indicates the existence of both shared and distinct genetic risk loci for ESCC in African and Asian populations. Our GWAS of ESCC conducted in a population of African ancestry indicates a substantial genetic contribution to ESCC risk in Africa.

Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.

The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions

Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and East Asian descent, identified 42 genome-wide significant loci comprising 49 distinct association signals. Effect sizes were largest for stage 3/4 disease, driven by ovarian endometriosis. Identified signals explained up to 5.01% of disease variance and regulated expression or methylation of genes in endometrium and blood, many of which were associated with pain perception/maintenance (SRP14/BMF, GDAP1, MLLT10, BSN and NGF). We observed significant genetic correlations between endometriosis and 11 pain conditions, including migraine, back and multisite chronic pain (MCP), as well as inflammatory conditions, including asthma and osteoarthritis. Multitrait genetic analyses identified substantial sharing of variants associated with endometriosis and MCP/migraine. Targeted investigations of genetically regulated mechanisms shared between endometriosis and other pain conditions are needed to aid the development of new treatments and facilitate early symptomatic intervention.

Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank

Chronic kidney disease (CKD) is a syndrome characterized by a gradual loss of kidney function with decreased estimated glomerular filtration rate (eGFR), which may be accompanied by an increase in the urine albumin-to-creatinine ratio (UACR). Although trans-ethnic genome-wide association studies (GWASs) have been conducted for kidney-related traits, there have been few analyses in the Japanese population, especially for the UACR trait. In this study, we conducted a GWAS to identify loci related to multiple kidney-related traits in Japanese individuals. First, to detect loci associated with CKD, eGFR, and UACR, we performed separate GWASs with the following two datasets: 475 cases of CKD diagnosed at seven university hospitals and 3471 healthy subjects (dataset 1) and 3664 cases of CKD-suspected individuals with eGFR <60 ml/min/1.73 m² or urinary protein ≥ 1+ and 5952 healthy subjects (dataset 2). Second, we performed a meta-analysis between these two datasets and detected the following associated loci: 10 loci for CKD, 9 loci for eGFR, and 22 loci for UACR. Among the loci detected, 22 have never been reported previously. Half of the significant loci for CKD were shared with those for eGFR, whereas most of the loci associated with UACR were different from those associated with CKD or eGFR. The GWAS of the Japanese population identified novel genetic components that were not previously detected. The results also suggest that the group primarily characterized by increased UACR possessed genetically different features from the group characterized by decreased eGFR.

Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P < 5 × 10-8) with GDM, mapping to/near MTNR1B (P = 4.3 × 10-54), TCF7L2 (P = 4.0 × 10-16), CDKAL1 (P = 1.6 × 10-14), CDKN2A-CDKN2B (P = 4.1 × 10-9) and HKDC1 (P = 2.9 × 10-8). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomization analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank

Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Meta-GWAS for quantitative trait loci identification in soybean

We report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker-trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed-related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

Meta-analysis of 208370 East Asians identifies 113 susceptibility loci for systemic lupus erythematosus

OBJECTIVE

Systemic lupus erythematosus (SLE), an autoimmune disorder, has been associated with nearly 100 susceptibility loci. Nevertheless, these loci only partially explain SLE heritability and their putative causal variants are rarely prioritised, which make challenging to elucidate disease biology. To detect new SLE loci and causal variants, we performed the largest genome-wide meta-analysis for SLE in East Asian populations.

METHODS

We newly genotyped 10 029 SLE cases and 180 167 controls and subsequently meta-analysed them jointly with 3348 SLE cases and 14 826 controls from published studies in East Asians. We further applied a Bayesian statistical approach to localise the putative causal variants for SLE associations.

RESULTS

We identified 113 genetic regions including 46 novel loci at genome-wide significance (p<5×10-8). Conditional analysis detected 233 association signals within these loci, which suggest widespread allelic heterogeneity. We detected genome-wide associations at six new missense variants. Bayesian statistical fine-mapping analysis prioritised the putative causal variants to a small set of variants (95% credible set size ≤10) for 28 association signals. We identified 110 putative causal variants with posterior probabilities ≥0.1 for 57 SLE loci, among which we prioritised 10 most likely putative causal variants (posterior probability ≥0.8). Linkage disequilibrium score regression detected genetic correlations for SLE with albumin/globulin ratio (rg=-0.242) and non-albumin protein (rg=0.238).

CONCLUSION

This study reiterates the power of large-scale genome-wide meta-analysis for novel genetic discovery. These findings shed light on genetic and biological understandings of SLE.

Effects of Observed Incubation Behavior on Egg Production in Laying Hens of a Commercial Chicken Breed and Detection of Single-Nucleotide Polymorphisms Associated with the Incubation Behavior

Upon contact with laid eggs, avians initiate incubation behavior and stop laying additional eggs. This phenomenon suggests that the productivity of laying hens in free-range facilities may decrease because of frequent contact with laid eggs. Here, we examined whether hens of a commercial breed exhibit incubation behavior in a free-range facility and whether egg productivity subsequently decreases. One-hour observations were performed twice weekly for 3 weeks, during which 9 of 129 hens (7.0%) exhibited incubation behavior (i.e., sitting on eggs) in the free-range facility and were defined as incubating hens. During 4 d of continuous behavioral observation, incubating and non-incubating hens laid the same number of eggs statistically (4.6 and 3.6, on average, respectively); however, incubating hens spent significantly more time on average incubating the eggs (2071.9 min) than did the non-incubating hens (20.9 min; P<0.05), indicating a clear behavioral difference. Subsequently, the incubation behavior and egg productivity of incubating hens and a Silkie Fowl breed hen, which is known to exhibit typical incubation behavior and cessation of laying, were continuously compared for 27 d. The average minutes spent incubating eggs during the observation period increased in both the incubating hens and Silkie Fowl hen and the total time was almost the same (18,088.5 and 23,092 min, respectively). However, the Silkie Fowl hen stopped laying on day 17 after laying 17 eggs, whereas the incubating hens continued laying throughout the observation period. Incubating hens laid an average of 24.5 eggs, indicating that some hens (at least those of the commercial breed used in our study) can continue laying while exhibiting incubation behavior. A single-nucleotide polymorphism associated with incubation behavior was detected on chromosome 4 through genome-wide association analysis.

Defining and predicting transdiagnostic categories of neurodegenerative disease

The prevalence of concomitant proteinopathies and heterogeneous clinical symptoms in neurodegenerative diseases hinders the identification of individuals who might be candidates for a particular intervention. Here, by applying an unsupervised clustering algorithm to post-mortem histopathological data from 895 patients with degeneration in the central nervous system, we show that six non-overlapping disease clusters can simultaneously account for tau neurofibrillary tangles, α-synuclein inclusions, neuritic plaques, inclusions of the transcriptional repressor TDP-43, angiopathy, neuron loss and gliosis. We also show that membership to the six transdiagnostic disease clusters, which explains more variance in cognitive phenotypes than can be explained by individual diagnoses, can be accurately predicted from scores of the Mini-Mental Status Exam, protein levels in cerebrospinal fluid, and genotype at the APOE and MAPT loci, via cross-validated multiple logistic regression. This combination of unsupervised and supervised data-driven tools provides a framework that could be used to identify latent disease subtypes in other areas of medicine.

Genome-wide association study of cerebral small vessel disease reveals established and novel loci

Intracerebral haemorrhage and small vessel ischaemic stroke (SVS) are the most acute manifestations of cerebral small vessel disease, with no established preventive approaches beyond hypertension management. Combined genome-wide association study (GWAS) of these two correlated diseases may improve statistical power to detect novel genetic factors for cerebral small vessel disease, elucidating underlying disease mechanisms that may form the basis for future treatments. Because intracerebral haemorrhage location is an adequate surrogate for distinct histopathological variants of cerebral small vessel disease (lobar for cerebral amyloid angiopathy and non-lobar for arteriolosclerosis), we performed GWAS of intracerebral haemorrhage by location in 1813 subjects (755 lobar and 1005 non-lobar) and 1711 stroke-free control subjects. Intracerebral haemorrhage GWAS results by location were meta-analysed with GWAS results for SVS from MEGASTROKE, using 'Multi-Trait Analysis of GWAS' (MTAG) to integrate summary data across traits and generate combined effect estimates. After combining intracerebral haemorrhage and SVS datasets, our sample size included 241 024 participants (6255 intracerebral haemorrhage or SVS cases and 233 058 control subjects). Genome-wide significant associations were observed for non-lobar intracerebral haemorrhage enhanced by SVS with rs2758605 [MTAG P-value (P) = 2.6 × 10-8] at 1q22; rs72932727 (P = 1.7 × 10-8) at 2q33; and rs9515201 (P = 5.3 × 10-10) at 13q34. In the GTEx gene expression library, rs2758605 (1q22), rs72932727 (2q33) and rs9515201 (13q34) are significant cis-eQTLs for PMF1 (P = 1 × 10-4 in tibial nerve), NBEAL1, FAM117B and CARF (P < 2.1 × 10-7 in arteries) and COL4A2 and COL4A1 (P < 0.01 in brain putamen), respectively. Leveraging S-PrediXcan for gene-based association testing with the predicted expression models in tissues related with nerve, artery, and non-lobar brain, we found that experiment-wide significant (P < 8.5 × 10-7) associations at three genes at 2q33 including NBEAL1, FAM117B and WDR12 and genome-wide significant associations at two genes including ICA1L at 2q33 and ZCCHC14 at 16q24. Brain cell-type specific expression profiling libraries reveal that SEMA4A, SLC25A44 and PMF1 at 1q22 and COL4A1 and COL4A2 at 13q34 were mainly expressed in endothelial cells, while the genes at 2q33 (FAM117B, CARF and NBEAL1) were expressed in various cell types including astrocytes, oligodendrocytes and neurons. Our cross-phenotype genetic study of intracerebral haemorrhage and SVS demonstrates novel genome-wide associations for non-lobar intracerebral haemorrhage at 2q33 and 13q34. Our replication of the 1q22 locus previous seen in traditional GWAS of intracerebral haemorrhage, as well as the rediscovery of 13q34, which had previously been reported in candidate gene studies with other cerebral small vessel disease-related traits strengthens the credibility of applying this novel genome-wide approach across intracerebral haemorrhage and SVS.

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.

Associations of autozygosity with a broad range of human phenotypes

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci

The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5-20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson's disease gene involved in dopamine regulation, PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations.

Genome-wide association study of type 2 diabetes in Africa

AIMS/HYPOTHESIS

Genome-wide association studies (GWAS) for type 2 diabetes have uncovered >400 risk loci, primarily in populations of European and Asian ancestry. Here, we aimed to discover additional type 2 diabetes risk loci (including African-specific variants) and fine-map association signals by performing genetic analysis in African populations.

METHODS

We conducted two type 2 diabetes genome-wide association studies in 4347 Africans from South Africa, Nigeria, Ghana and Kenya and meta-analysed both studies together. Likely causal variants were identified using fine-mapping approaches.

RESULTS

The most significantly associated variants mapped to the widely replicated type 2 diabetes risk locus near TCF7L2 (p = 5.3 × 10^-13). Fine-mapping of the TCF7L2 locus suggested one type 2 diabetes association signal shared between Europeans and Africans (indexed by rs7903146) and a distinct African-specific signal (indexed by rs17746147). We also detected one novel signal, rs73284431, near AGMO (p = 5.2 × 10^-9, minor allele frequency [MAF] = 0.095; monomorphic in most non-African populations), distinct from previously reported signals in the region. In analyses focused on 100 published type 2 diabetes risk loci, we identified 21 with shared causal variants in African and non-African populations.

CONCLUSIONS/INTERPRETATION

These results demonstrate the value of performing GWAS in Africans, provide a resource to larger consortia for further discovery and fine-mapping and indicate that additional large-scale efforts in Africa are warranted to gain further insight in to the genetic architecture of type 2 diabetes.

Discovery of common and rare genetic risk variants for colorectal cancer

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.

Bayesian multiple logistic regression for case-control GWAS

Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-enforcing prior on effect sizes is used to avoid overtraining and all effect sizes are integrated out for posterior inference. For binary traits, the logistic model has not yielded clear improvements over the linear model. For multi-SNP analysis, the logistic model required costly and technically challenging MCMC sampling to perform the integration. Here, we introduce the quasi-Laplace approximation to solve the integral and avoid MCMC sampling. We expect the logistic model to perform much better than multiple linear regression except when predicted disease risks are spread closely around 0.5, because only close to its inflection point can the logistic function be well approximated by a linear function. Indeed, in extensive benchmarks with simulated phenotypes and real genotypes, our Bayesian multiple LOgistic REgression method (B-LORE) showed considerable improvements (1) when regressing on many variants in multiple loci at heritabilities ≥ 0.4 and (2) for unbalanced case-control ratios. B-LORE also enables meta-analysis by approximating the likelihood functions of individual studies by multivariate normal distributions, using their means and covariance matrices as summary statistics. Our work should make sparse multiple logistic regression attractive also for other applications with binary target variables. B-LORE is freely available from: https://github.com/soedinglab/b-lore.

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

Biobank-driven genomic discovery yields new insight into atrial fibrillation biology

To identify genetic variation underlying atrial fibrillation, the most common cardiac arrhythmia, we performed a genome-wide association study of >1,000,000 people, including 60,620 atrial fibrillation cases and 970,216 controls. We identified 142 independent risk variants at 111 loci and prioritized 151 functional candidate genes likely to be involved in atrial fibrillation. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans (GATA4, MYH6, NKX2-5, PITX2, TBX5)¹, or near genes important for striated muscle function and integrity (for example, CFL2, MYH7, PKP2, RBM20, SGCG, SSPN). Pathway and functional enrichment analyses also suggested that many of the putative atrial fibrillation genes act via cardiac structural remodeling, potentially in the form of an 'atrial cardiomyopathy'², either during fetal heart development or as a response to stress in the adult heart.

Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio

Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure (e.g., a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects.

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.