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.

Exome sequencing and analysis of 454,787 UK Biobank participants.

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.

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.

Polygenic risk of social isolation behavior and its influence on psychopathology and personality.

Social isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a genome-wide associations study (GWAS) was carried out to identify genetic variants that contribute specifically to social isolation behavior (SIB) in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP-based heritability estimate. Using the SIB GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with self-reported friendship scores, comprising items related to friendship quality and quantity, at age 12 and 18 to determine whether genetic predisposition manifests during childhood development. At age 18, friendship scores were associated with the SIB PRS, demonstrating that the genetic factors can predict related social traits in late adolescence. Linkage disequilibrium (LD) score correlation using the SIB GWAS demonstrated genetic correlations with autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), educational attainment, extraversion, and loneliness. However, no evidence of causality was found using a conservative Mendelian randomization approach between SIB and any of the traits in either direction. Genomic Structural Equation Modeling (SEM) revealed a common factor contributing to SIB, neuroticism, loneliness, MDD, and ASD, weakly correlated with a second common factor that contributes to psychiatric and psychotic traits. Our results show that SIB contributes a small heritable component, which is associated genetically with other social traits such as friendship as well as psychiatric disorders.

Impact of schizophrenia GWAS loci converge onto distinct pathways in cortical interneurons vs glutamatergic neurons during development.

Remarkable advances have been made in schizophrenia (SCZ) GWAS, but gleaning biological insight from these loci is challenging. Genetic influences on gene expression (e.g., eQTLs) are cell type-specific, but most studies that attempt to clarify GWAS loci's influence on gene expression have employed tissues with mixed cell compositions that can obscure cell-specific effects. Furthermore, enriched SCZ heritability in the fetal brain underscores the need to study the impact of SCZ risk loci in specific developing neurons. MGE-derived cortical interneurons (cINs) are consistently affected in SCZ brains and show enriched SCZ heritability in human fetal brains. We identified SCZ GWAS risk genes that are dysregulated in iPSC-derived homogeneous populations of developing SCZ cINs. These SCZ GWAS loci differential expression (DE) genes converge on the PKC pathway. Their disruption results in PKC hyperactivity in developing cINs, leading to arborization deficits. We show that the fine-mapped GWAS locus in the ATP2A2 gene of the PKC pathway harbors enhancer marks by ATACseq and ChIPseq, and regulates ATP2A2 expression. We also generated developing glutamatergic neurons (GNs), another population with enriched SCZ heritability, and confirmed their functionality after transplantation into the mouse brain. Then, we identified SCZ GWAS risk genes that are dysregulated in developing SCZ GNs. GN-specific SCZ GWAS loci DE genes converge on the ion transporter pathway, distinct from those for cINs. Disruption of the pathway gene CACNA1D resulted in deficits of Ca2+ currents in developing GNs, suggesting compromised neuronal function by GWAS loci pathway deficits during development. This study allows us to identify cell type-specific and developmental stage-specific mechanisms of SCZ risk gene function, and may aid in identifying mechanism-based novel therapeutic targets.

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.

Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways.

Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.

Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders.

Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium-schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

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.

Collagenous Colitis Is Associated With HLA Signature and Shares Genetic Risks With Other Immune-Mediated Diseases.

BACKGROUND & AIMS: Collagenous colitis (CC) is an inflammatory bowel disorder with unknown etiopathogenesis involving HLA-related immune-mediated responses and environmental and genetic risk factors. We carried out an array-based genetic association study in a cohort of patients with CC and investigated the common genetic basis between CC and Crohn's disease (CD), ulcerative colitis (UC), and celiac disease. METHODS: DNA from 804 CC formalin-fixed, paraffin-embedded tissue samples was genotyped with Illumina Immunochip. Matching genotype data on control samples and CD, UC, and celiac disease cases were provided by the respective consortia. A discovery association study followed by meta-analysis with an independent cohort, polygenic risk score calculation, and cross-phenotype analyses were performed. Enrichment of regulatory expression quantitative trait loci among the CC variants was assessed in hemopoietic and intestinal cells. RESULTS: Three HLA alleles (HLA-B∗08:01, HLA-DRB1∗03:01, and HLA-DQB1∗02:01), related to the ancestral haplotype 8.1, were significantly associated with increased CC risk. We also identified an independent protective effect of HLA-DRB1∗04:01 on CC risk. Polygenic risk score quantifying the risk across multiple susceptibility loci was strongly associated with CC risk. An enrichment of expression quantitative trait loci was detected among the CC-susceptibility variants in various cell types. The cross-phenotype analysis identified a complex pattern of polygenic pleiotropy between CC and other immune-mediated diseases. CONCLUSIONS: In this largest genetic study of CC to date with histologically confirmed diagnosis, we strongly implicated the HLA locus and proposed potential non-HLA mechanisms in disease pathogenesis. We also detected a shared genetic risk between CC, celiac disease, CD, and UC, which supports clinical observations of comorbidity.

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.

Genome-wide non-HLA donor-recipient genetic differences influence renal allograft survival via early allograft fibrosis.

Donor-recipient (D-R) differences at human leukocyte antigen (HLA) loci are currently incorporated into organ sharing, allocation and immunosuppression decisions. However, while acute rejection episodes have substantially diminished, progressive histologic damage occurs in allografts and improved long-term survival remains an unrealized goal among kidney recipients. Here we tested the hypothesis that non-HLA dependent, genome-wide D-R genetic differences could contribute to unchecked alloimmunity with histologic and functional consequences, culminating in long-term allograft failure. Genome-wide single nucleotide polymorphism (SNP) array data, excluding the HLA region, was utilized from 385 transplants to study the role of D-R differences upon serial histology and allograft survival. ADMIXTURE analysis was performed to quantitatively estimate ancestry in each D-R pair and PLINK was used to estimate the proportion of genome-shared identity-by-descent (pIBD) between D-R pairs. Subsequently, quantitative measures of recipient ancestry based on non-HLA SNPs was associated with death-censored allograft survival in adjusted Cox models. In D-R pairs of similar ancestry, pIBD was significantly associated with allograft survival independent of HLA mismatches in 224 transplants. Surprisingly, pIBD and recipient ancestry were not associated with clinical or subclinical rejection at any time post-transplant. Significantly, in multivariable analysis, pIBD inversely correlated with vascular intimal fibrosis in 160 biopsies obtained less than one year which in turn was significantly associated with allograft survival. Thus, our novel data show that non-HLA D-R differences associate with early vascular intimal fibrosis and allograft survival.

A polygenic burden of rare disruptive mutations in schizophrenia.

Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease.

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.

A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data.

A common goal in data-analysis is to sift through a large data-matrix and detect any significant submatrices (i.e., biclusters) that have a low numerical rank. We present a simple algorithm for tackling this biclustering problem. Our algorithm accumulates information about 2-by-2 submatrices (i.e., 'loops') within the data-matrix, and focuses on rows and columns of the data-matrix that participate in an abundance of low-rank loops. We demonstrate, through analysis and numerical-experiments, that this loop-counting method performs well in a variety of scenarios, outperforming simple spectral methods in many situations of interest. Another important feature of our method is that it can easily be modified to account for aspects of experimental design which commonly arise in practice. For example, our algorithm can be modified to correct for controls, categorical- and continuous-covariates, as well as sparsity within the data. We demonstrate these practical features with two examples; the first drawn from gene-expression analysis and the second drawn from a much larger genome-wide-association-study (GWAS).

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.

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study.

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (ß = 16.1, CI(ß) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (ß = 4.86,CI(ß) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest.

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci.

Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies.