Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations.

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control.

High serum urate is a prerequisite for gout and associated with metabolic disease. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control; however, there has been little progress in understanding the molecular basis of the associated loci. Here, we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify 10 new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci. By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Functional fine mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs. This systematic analysis of serum urate GWAS loci identified candidate causal genes at 24 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.

Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS.

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.

Characterisation of age and polarity at onset in bipolar disorder.

BACKGROUND: Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools. AIMS: To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics. METHOD: Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts. RESULTS: Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (ß = -0.34 years, s.e. = 0.08), major depression (ß = -0.34 years, s.e. = 0.08), schizophrenia (ß = -0.39 years, s.e. = 0.08), and educational attainment (ß = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO. CONCLUSIONS: AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry.

Schizophrenia is a common, chronic and debilitating neuropsychiatric syndrome affecting tens of millions of individuals worldwide. While rare genetic variants play a role in the etiology of schizophrenia, most of the currently explained liability is within common variation, suggesting that variation predating the human diaspora out of Africa harbors a large fraction of the common variant attributable heritability. However, common variant association studies in schizophrenia have concentrated mainly on cohorts of European descent. We describe genome-wide association studies of 6152 cases and 3918 controls of admixed African ancestry, and of 1234 cases and 3090 controls of Latino ancestry, representing the largest such study in these populations to date. Combining results from the samples with African ancestry with summary statistics from the Psychiatric Genomics Consortium (PGC) study of schizophrenia yielded seven newly genome-wide significant loci, and we identified an additional eight loci by incorporating the results from samples with Latino ancestry. Leveraging population differences in patterns of linkage disequilibrium, we achieve improved fine-mapping resolution at 22 previously reported and 4 newly significant loci. Polygenic risk score profiling revealed improved prediction based on trans-ancestry meta-analysis results for admixed African (Nagelkerke's R2 = 0.032; liability R2 = 0.017; P < 10-52), Latino (Nagelkerke's R2 = 0.089; liability R2 = 0.021; P < 10-58), and European individuals (Nagelkerke's R2 = 0.089; liability R2 = 0.037; P < 10-113), further highlighting the advantages of incorporating data from diverse human populations.

Rare protein-coding variants implicate genes involved in risk of suicide death.

Identification of genetic factors leading to increased risk of suicide death is critical to combat rising suicide rates, however, only a fraction of the genetic variation influencing risk has been accounted for. To address this limitation, we conducted the first comprehensive analysis of rare genetic variation in suicide death leveraging the largest suicide death biobank, the Utah Suicide Genetic Risk Study (USGRS). We conducted a single-variant association analysis of rare (minor allele frequency <1%) putatively functional single-nucleotide polymorphisms (SNPs) present on the Illumina PsychArray genotyping array in 2,672 USGRS suicide deaths of non-Finnish European (NFE) ancestry and 51,583 NFE controls from the Genome Aggregation Database. Secondary analyses used an independent control sample of 21,324 NFE controls from the Psychiatric Genomics Consortium. Five novel, high-impact, rare SNPs were identified with significant associations with suicide death (SNAPC1, rs75418419; TNKS1BP1, rs143883793; ADGRF5, rs149197213; PER1, rs145053802; and ESS2, rs62223875). 119 suicide decedents carried these high-impact SNPs. Both PER1 and SNAPC1 have other supporting gene-level evidence of suicide risk, and psychiatric associations exist for PER1 (bipolar disorder, schizophrenia), and for TNKS1BP1 and ESS2 (schizophrenia). Three of the genes (PER1, TNKS1BP1, and ADGRF5), together with additional genes implicated by genome-wide association studies on suicidal behavior, showed significant enrichment in immune system, homeostatic and signal transduction processes. No specific diagnostic phenotypes were associated with the subset of suicide deaths with the identified rare variants. These findings suggest an important role for rare variants in suicide risk and implicate genes and gene pathways for targeted replication.

Genetics of rheumatoid arthritis contributes to biology and drug discovery.

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn's disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.

Mitochondrial genetic variation and gout in Maori and Pacific people living in Aotearoa New Zealand.

OBJECTIVE: Mitochondria have an important role in the induction of the NLRP3 inflammasome response central in gout. The objective was to test whether mitochondrial genetic variation and copy number in New Zealand Maori and Pacific (Polynesian) people in Aotearoa New Zealand associate with susceptibility to gout. METHODS: 437 whole mitochondrial genomes from Maori and Pacific people (predominantly men) from Aotearoa New Zealand (327 people with gout, 110 without gout) were sequenced. Mitochondrial DNA copy number variation was determined by assessing relative read depth using data produced from whole genome sequencing (32 cases, 43 controls) and targeted resequencing of urate loci (151 cases, 222 controls). Quantitative PCR was undertaken for replication of copy number findings in an extended sample set of 1159 Maori and Pacific men and women (612 cases, 547 controls). RESULTS: There was relatively little mitochondrial genetic diversity, with around 96% of those sequenced in this study belonging to the B4a1a and derived sublineages. A B haplogroup heteroplasmy in hypervariable region I was found to associate with a higher risk of gout among the mitochondrial sequenced sample set (position 16181: OR=1.57, P=0.001). Increased copies of mitochondrial DNA were found to protect against gout risk with the effect being consistent when using hyperuricaemic controls across each of the three independent sample sets (OR=0.89, P=0.007; OR=0.90, P=0.002; OR=0.76, P=0.03). Paradoxically, an increase of mitochondrial DNA also associated with an increase in gout flare frequency in people with gout in the two larger sample sets used for the copy number analysis (ß=0.003, P=7.1×10-7; ß=0.08, P=1.2×10-4). CONCLUSION: Association of reduced copy number with gout in hyperuricaemia was replicated over three Polynesian sample sets. Our data are consistent with emerging research showing that mitochondria are important for the colocalisation of the NLRP3 and ASC inflammasome subunits, a process essential for the generation of interleukin-1ß in gout.

Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.

Genome-wide association study identifies SESTD1 as a novel risk gene for lithium-responsive bipolar disorder.

Lithium is the mainstay prophylactic treatment for bipolar disorder (BD), but treatment response varies considerably across individuals. Patients who respond well to lithium treatment might represent a relatively homogeneous subtype of this genetically and phenotypically diverse disorder. Here, we performed genome-wide association studies (GWAS) to identify (i) specific genetic variations influencing lithium response and (ii) genetic variants associated with risk for lithium-responsive BD. Patients with BD and controls were recruited from Sweden and the United Kingdom. GWAS were performed on 2698 patients with subjectively defined (self-reported) lithium response and 1176 patients with objectively defined (clinically documented) lithium response. We next conducted GWAS comparing lithium responders with healthy controls (1639 subjective responders and 8899 controls; 323 objective responders and 6684 controls). Meta-analyses of Swedish and UK results revealed no significant associations with lithium response within the bipolar subjects. However, when comparing lithium-responsive patients with controls, two imputed markers attained genome-wide significant associations, among which one was validated in confirmatory genotyping (rs116323614, P=2.74 × 10(-8)). It is an intronic single-nucleotide polymorphism (SNP) on chromosome 2q31.2 in the gene SEC14 and spectrin domains 1 (SESTD1), which encodes a protein involved in regulation of phospholipids. Phospholipids have been strongly implicated as lithium treatment targets. Furthermore, we estimated the proportion of variance for lithium-responsive BD explained by common variants ('SNP heritability') as 0.25 and 0.29 using two definitions of lithium response. Our results revealed a genetic variant in SESTD1 associated with risk for lithium-responsive BD, suggesting that the understanding of BD etiology could be furthered by focusing on this subtype of BD.

Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid arthritis.

The extent to which variants in the protein-coding sequence of genes contribute to risk of rheumatoid arthritis (RA) is unknown. In this study, we addressed this issue by deep exon sequencing and large-scale genotyping of 25 biological candidate genes located within RA risk loci discovered by genome-wide association studies (GWASs). First, we assessed the contribution of rare coding variants in the 25 genes to the risk of RA in a pooled sequencing study of 500 RA cases and 650 controls of European ancestry. We observed an accumulation of rare nonsynonymous variants exclusive to RA cases in IL2RA and IL2RB (burden test: p = 0.007 and p = 0.018, respectively). Next, we assessed the aggregate contribution of low-frequency and common coding variants to the risk of RA by dense genotyping of the 25 gene loci in 10,609 RA cases and 35,605 controls. We observed a strong enrichment of coding variants with a nominal signal of association with RA (p < 0.05) after adjusting for the best signal of association at the loci (p(enrichment) = 6.4 × 10(-4)). For one locus containing CD2, we found that a missense variant, rs699738 (c.798C>A [p.His266Gln]), and a noncoding variant, rs624988, reside on distinct haplotypes and independently contribute to the risk of RA (p = 4.6 × 10(-6)). Overall, our results indicate that variants (distributed across the allele-frequency spectrum) within the protein-coding portion of a subset of biological candidate genes identified by GWASs contribute to the risk of RA. Further, we have demonstrated that very large sample sizes will be required for comprehensively identifying the independent alleles contributing to the missing heritability of RA.

Quantifying missing heritability at known GWAS loci.

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 x more heritability than GWAS-associated SNPs on average (P=3.3 x 10⁻5). For some diseases, this increase was individually significant: 2.07 x for Multiple Sclerosis (MS) (P=6.5 x 10⁻9) and 1.48 x for Crohn's Disease (CD) (P = 1.3 x 10⁻³); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 x more MS heritability than known MS SNPs (P < 1.0 x 10⁻¹6 and 2.20 x more CD heritability than known CD SNPs (P = 6.1 x 10⁻9), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 x more heritability from all SNPs at GWAS loci (P = 2.3 x 10⁻6) and 5.33 x more heritability from all autoimmune disease loci (P < 1 x 10⁻¹6 compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis.

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8 × 10(-8)), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3' UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1 × 10(-11) in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry.

Use of a multiethnic approach to identify rheumatoid- arthritis-susceptibility loci, 1p36 and 17q12.

We have previously shown that rheumatoid arthritis (RA) risk alleles overlap between different ethnic groups. Here, we utilize a multiethnic approach to show that we can effectively discover RA risk alleles. Thirteen putatively associated SNPs that had not yet exceeded genome-wide significance (p < 5 × 10(-8)) in our previous RA genome-wide association study (GWAS) were analyzed in independent sample sets consisting of 4,366 cases and 17,765 controls of European, African American, and East Asian ancestry. Additionally, we conducted an overall association test across all 65,833 samples (a GWAS meta-analysis plus the replication samples). Of the 13 SNPs investigated, four were significantly below the study-wide Bonferroni corrected p value threshold (p < 0.0038) in the replication samples. Two SNPs (rs3890745 at the 1p36 locus [p = 2.3 × 10(-12)] and rs2872507 at the 17q12 locus [p = 1.7 × 10(-9)]) surpassed genome-wide significance in all 16,659 RA cases and 49,174 controls combined. We used available GWAS data to fine map these two loci in Europeans and East Asians, and we found that the same allele conferred risk in both ethnic groups. A series of bioinformatic analyses identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as the genes most likely associated with RA. These findings demonstrate empirically that a multiethnic approach is an effective strategy for discovering RA risk loci, and they suggest that combining GWASs across ethnic groups represents an efficient strategy for gaining statistical power.

Meta-analysis of genome-wide association studies in celiac disease and rheumatoid arthritis identifies fourteen non-HLA shared loci.

Epidemiology and candidate gene studies indicate a shared genetic basis for celiac disease (CD) and rheumatoid arthritis (RA), but the extent of this sharing has not been systematically explored. Previous studies demonstrate that 6 of the established non-HLA CD and RA risk loci (out of 26 loci for each disease) are shared between both diseases. We hypothesized that there are additional shared risk alleles and that combining genome-wide association study (GWAS) data from each disease would increase power to identify these shared risk alleles. We performed a meta-analysis of two published GWAS on CD (4,533 cases and 10,750 controls) and RA (5,539 cases and 17,231 controls). After genotyping the top associated SNPs in 2,169 CD cases and 2,255 controls, and 2,845 RA cases and 4,944 controls, 8 additional SNPs demonstrated P<5 × 10(-8) in a combined analysis of all 50,266 samples, including four SNPs that have not been previously confirmed in either disease: rs10892279 near the DDX6 gene (P(combined) =  1.2 × 10(-12)), rs864537 near CD247 (P(combined) =  2.2 × 10(-11)), rs2298428 near UBE2L3 (P(combined) =  2.5 × 10(-10)), and rs11203203 near UBASH3A (P(combined) =  1.1 × 10(-8)). We also confirmed that 4 gene loci previously established in either CD or RA are associated with the other autoimmune disease at combined P<5 × 10(-8) (SH2B3, 8q24, STAT4, and TRAF1-C5). From the 14 shared gene loci, 7 SNPs showed a genome-wide significant effect on expression of one or more transcripts in the linkage disequilibrium (LD) block around the SNP. These associations implicate antigen presentation and T-cell activation as a shared mechanism of disease pathogenesis and underscore the utility of cross-disease meta-analysis for identification of genetic risk factors with pleiotropic effects between two clinically distinct diseases.

Polygenic heritability estimates in pharmacogenetics: focus on asthma and related phenotypes.

Although accurate measures of heritability are required to understand the pharmacogenetic basis of drug treatment response, these are generally not available, as it is unfeasible to give medications to individuals for which treatment is not indicated. Using a polygenic linear mixed modeling approach, we estimated lower bounds on the heritability of asthma and the heritability of two related drug-response phenotypes, bronchodilator response and airway hyperreactivity, using genome-wide single nucleotide polymorphism (SNP) data from existing asthma cohorts. Our estimate of the heritability for bronchodilator response is 28.5% (SE 16%, P=0.043) and airway hyperresponsiveness is 51.1% (SE 34%, P=0.064), whereas we estimate asthma genetic liability at 61.5% (SE 16%, P<0.001). Our results agree with the previously published estimates of the heritability of these traits, suggesting that the linear mixed modeling method is useful for computing the heritability of other pharmacogenetic traits. Furthermore, our results indicate that multiple SNP main effects, including SNPs as yet unidentified by genome-wide association study methods, together explain a sizable portion of the heritability of these traits.

Genome-wide association analysis of anti-TNF drug response in patients with rheumatoid arthritis.

BACKGROUND: Treatment strategies blocking tumour necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA). However, a significant subset of patients does not respond for unknown reasons. Currently, there are no means of identifying these patients before treatment. This study was aimed at identifying genetic factors predicting anti-TNF treatment outcome in patients with RA using a genome-wide association approach. METHODS: We conducted a multistage, genome-wide association study with a primary analysis of 2 557 253 single-nucleotide polymorphisms (SNPs) in 882 patients with RA receiving anti-TNF therapy included through the Dutch Rheumatoid Arthritis Monitoring (DREAM) registry and the database of Apotheekzorg. Linear regression analysis of changes in the Disease Activity Score in 28 joints after 14 weeks of treatment was performed using an additive model. Markers with p<10(-3) were selected for replication in 1821 patients from three independent cohorts. Pathway analysis including all SNPs with p<10(-3) was performed using Ingenuity. RESULTS: 772 markers showed evidence of association with treatment outcome in the initial stage. Eight genetic loci showed improved p value in the overall meta-analysis compared with the first stage, three of which (rs1568885, rs1813443 and rs4411591) showed directional consistency over all four cohorts studied. We were unable to replicate markers previously reported to be associated with anti-TNF outcome. Network analysis indicated strong involvement of biological processes underlying inflammatory response and cell morphology. CONCLUSIONS: Using a multistage strategy, we have identified eight genetic loci associated with response to anti-TNF treatment. Further studies are required to validate these findings in additional patient collections.

The extent of linkage disequilibrium in Arabidopsis thaliana.

Linkage disequilibrium (LD), the nonrandom occurrence of alleles in haplotypes, has long been of interest to population geneticists. Recently, the rapidly increasing availability of genomic polymorphism data has fueled interest in LD as a tool for fine-scale mapping, in particular for human disease loci. The chromosomal extent of LD is crucial in this context, because it determines how dense a map must be for associations to be detected and, conversely, limits how finely loci may be mapped. Arabidopsis thaliana is expected to harbor unusually extensive LD because of its high degree of selfing. Several polymorphism studies have found very strong LD within individual loci, but also evidence of some recombination. Here we investigate the pattern of LD on a genomic scale and show that in global samples, LD decays within approximately 1 cM, or 250 kb. We also show that LD in local populations may be much stronger than that of global populations, presumably as a result of founder events. The combination of a relatively high level of polymorphism and extensive haplotype structure bodes well for developing a genome-wide LD map in A. thaliana.