Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.

Using ~60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

A substantial genetic contribution to systemic lupus erythematosus (SLE) risk is conferred by major histocompatibility complex (MHC) gene(s) on chromosome 6p21. Previous studies in SLE have lacked statistical power and genetic resolution to fully define MHC influences. We characterized 1,610 Caucasian SLE cases and 1,470 parents for 1,974 MHC SNPs, the highly polymorphic HLA-DRB1 locus, and a panel of ancestry informative markers. Single-marker analyses revealed strong signals for SNPs within several MHC regions, as well as with HLA-DRB1 (global p = 9.99 x 10(-16)). The most strongly associated DRB1 alleles were: *0301 (odds ratio, OR = 2.21, p = 2.53 x 10(-12)), *1401 (OR = 0.50, p = 0.0002), and *1501 (OR = 1.39, p = 0.0032). The MHC region SNP demonstrating the strongest evidence of association with SLE was rs3117103, with OR = 2.44 and p = 2.80 x 10(-13). Conditional haplotype and stepwise logistic regression analyses identified strong evidence for association between SLE and the extended class I, class I, class III, class II, and the extended class II MHC regions. Sequential removal of SLE-associated DRB1 haplotypes revealed independent effects due to variation within OR2H2 (extended class I, rs362521, p = 0.006), CREBL1 (class III, rs8283, p = 0.01), and DQB2 (class II, rs7769979, p = 0.003, and rs10947345, p = 0.0004). Further, conditional haplotype analyses demonstrated that variation within MICB (class I, rs3828903, p = 0.006) also contributes to SLE risk independent of HLA-DRB1*0301. Our results for the first time delineate with high resolution several MHC regions with independent contributions to SLE risk. We provide a list of candidate variants based on biologic and functional considerations that may be causally related to SLE risk and warrant further investigation.

Crohn's disease (CD) and celiac disease (CelD) are chronic intestinal inflammatory diseases, involving genetic and environmental factors in their pathogenesis. The two diseases can co-occur within families, and studies suggest that CelD patients have a higher risk to develop CD than the general population. These observations suggest that CD and CelD may share common genetic risk loci. Two such shared loci, IL18RAP and PTPN2, have already been identified independently in these two diseases. The aim of our study was to explicitly identify shared risk loci for these diseases by combining results from genome-wide association study (GWAS) datasets of CD and CelD. Specifically, GWAS results from CelD (768 cases, 1,422 controls) and CD (3,230 cases, 4,829 controls) were combined in a meta-analysis. Nine independent regions had nominal association p-value <1.0 x 10⁻⁵ in this meta-analysis and showed evidence of association to the individual diseases in the original scans (p-value < 1 x 10⁻² in CelD and < 1 x 10⁻³ in CD). These include the two previously reported shared loci, IL18RAP and PTPN2, with p-values of 3.37 x 10⁻⁸ and 6.39 x 10⁻⁹, respectively, in the meta-analysis. The other seven had not been reported as shared loci and thus were tested in additional CelD (3,149 cases and 4,714 controls) and CD (1,835 cases and 1,669 controls) cohorts. Two of these loci, TAGAP and PUS10, showed significant evidence of replication (Bonferroni corrected p-values <0.0071) in the combined CelD and CD replication cohorts and were firmly established as shared risk loci of genome-wide significance, with overall combined p-values of 1.55 x 10⁻¹⁰ and 1.38 x 10⁻¹¹ respectively. Through a meta-analysis of GWAS data from CD and CelD, we have identified four shared risk loci: PTPN2, IL18RAP, TAGAP, and PUS10. The combined analysis of the two datasets provided the power, lacking in the individual GWAS for single diseases, to detect shared loci with a relatively small effect.

Coronary heart disease (CHD) is the leading cause of mortality in African Americans. To identify common genetic polymorphisms associated with CHD and its risk factors (LDL- and HDL-cholesterol (LDL-C and HDL-C), hypertension, smoking, and type-2 diabetes) in individuals of African ancestry, we performed a genome-wide association study (GWAS) in 8,090 African Americans from five population-based cohorts. We replicated 17 loci previously associated with CHD or its risk factors in Caucasians. For five of these regions (CHD: CDKN2A/CDKN2B; HDL-C: FADS1-3, PLTP, LPL, and ABCA1), we could leverage the distinct linkage disequilibrium (LD) patterns in African Americans to identify DNA polymorphisms more strongly associated with the phenotypes than the previously reported index SNPs found in Caucasian populations. We also developed a new approach for association testing in admixed populations that uses allelic and local ancestry variation. Using this method, we discovered several loci that would have been missed using the basic allelic and global ancestry information only. Our conclusions suggest that no major loci uniquely explain the high prevalence of CHD in African Americans. Our project has developed resources and methods that address both admixture- and SNP-association to maximize power for genetic discovery in even larger African-American consortia.

Long-chain n-3 polyunsaturated fatty acids (PUFAs) can derive from diet or from α-linolenic acid (ALA) by elongation and desaturation. We investigated the association of common genetic variation with plasma phospholipid levels of the four major n-3 PUFAs by performing genome-wide association studies in five population-based cohorts comprising 8,866 subjects of European ancestry. Minor alleles of SNPs in FADS1 and FADS2 (desaturases) were associated with higher levels of ALA (p = 3 x 10⁻⁶⁴) and lower levels of eicosapentaenoic acid (EPA, p = 5 x 10⁻⁵⁸) and docosapentaenoic acid (DPA, p = 4 x 10⁻¹⁵⁴). Minor alleles of SNPs in ELOVL2 (elongase) were associated with higher EPA (p = 2 x 10⁻¹²) and DPA (p = 1 x 10⁻⁴³) and lower docosahexaenoic acid (DHA, p = 1 x 10⁻¹⁵). In addition to genes in the n-3 pathway, we identified a novel association of DPA with several SNPs in GCKR (glucokinase regulator, p = 1 x 10⁻⁸). We observed a weaker association between ALA and EPA among carriers of the minor allele of a representative SNP in FADS2 (rs1535), suggesting a lower rate of ALA-to-EPA conversion in these subjects. In samples of African, Chinese, and Hispanic ancestry, associations of n-3 PUFAs were similar with a representative SNP in FADS1 but less consistent with a representative SNP in ELOVL2. Our findings show that common variation in n-3 metabolic pathway genes and in GCKR influences plasma phospholipid levels of n-3 PUFAs in populations of European ancestry and, for FADS1, in other ancestries.

Genome-wide association studies (GWAS) for type 1 diabetes (T1D) have successfully identified more than 40 independent T1D associated tagging single nucleotide polymorphisms (SNPs). However, owing to technical limitations of copy number variants (CNVs) genotyping assays, the assessment of the role of CNVs has been limited to the subset of these in high linkage disequilibrium with tag SNPs. The contribution of untagged CNVs, often multi-allelic and difficult to genotype using existing assays, to the heritability of T1D remains an open question. To investigate this issue, we designed a custom comparative genetic hybridization array (aCGH) specifically designed to assay untagged CNV loci identified from a variety of sources. To overcome the technical limitations of the case control design for this class of CNVs, we genotyped the Type 1 Diabetes Genetics Consortium (T1DGC) family resource (representing 3,903 transmissions from parents to affected offspring) and used an association testing strategy that does not necessitate obtaining discrete genotypes. Our design targeted 4,309 CNVs, of which 3,410 passed stringent quality control filters. As a positive control, the scan confirmed the known T1D association at the INS locus by direct typing of the 5' variable number of tandem repeat (VNTR) locus. Our results clarify the fact that the disease association is indistinguishable from the two main polymorphic allele classes of the INS VNTR, class I-and class III. We also identified novel technical artifacts resulting into spurious associations at the somatically rearranging loci, T cell receptor, TCRA/TCRD and TCRB, and Immunoglobulin heavy chain, IGH, loci on chromosomes 14q11.2, 7q34 and 14q32.33, respectively. However, our data did not identify novel T1D loci. Our results do not support a major role of untagged CNVs in T1D heritability.

Inflammation, which is directly regulated by interleukin-6 (IL-6) signaling, is implicated in the etiology of several chronic diseases. Although a common, non-synonymous variant in the IL-6 receptor gene (IL6R Asp358Ala; rs2228145 A>C) is associated with the risk of several common diseases, with the 358Ala allele conferring protection from coronary heart disease (CHD), rheumatoid arthritis (RA), atrial fibrillation (AF), abdominal aortic aneurysm (AAA), and increased susceptibility to asthma, the variant's effect on IL-6 signaling is not known. Here we provide evidence for the association of this non-synonymous variant with the risk of type 1 diabetes (T1D) in two independent populations and confirm that rs2228145 is the major determinant of the concentration of circulating soluble IL-6R (sIL-6R) levels (34.6% increase in sIL-6R per copy of the minor allele 358Ala; rs2228145 [C]). To further investigate the molecular mechanism of this variant, we analyzed expression of IL-6R in peripheral blood mononuclear cells (PBMCs) in 128 volunteers from the Cambridge BioResource. We demonstrate that, although 358Ala increases transcription of the soluble IL6R isoform (P = 8.3×10⁻²²) and not the membrane-bound isoform, 358Ala reduces surface expression of IL-6R on CD4+ T cells and monocytes (up to 28% reduction per allele; P≤5.6×10⁻²²). Importantly, reduced expression of membrane-bound IL-6R resulted in impaired IL-6 responsiveness, as measured by decreased phosphorylation of the transcription factors STAT3 and STAT1 following stimulation with IL-6 (P≤5.2×10⁻⁷). Our findings elucidate the regulation of IL-6 signaling by IL-6R, which is causally relevant to several complex diseases, identify mechanisms for new approaches to target the IL-6/IL-6R axis, and anticipate differences in treatment response to IL-6 therapies based on this common IL6R variant.

Using a genome-wide single nucleotide polymorphism (SNP) panel, we observed population structure in a diverse group of Europeans and European Americans. Under a variety of conditions and tests, there is a consistent and reproducible distinction between "northern" and "southern" European population groups: most individual participants with southern European ancestry (Italian, Spanish, Portuguese, and Greek) have >85% membership in the "southern" population; and most northern, western, eastern, and central Europeans have >90% in the "northern" population group. Ashkenazi Jewish as well as Sephardic Jewish origin also showed >85% membership in the "southern" population, consistent with a later Mediterranean origin of these ethnic groups. Based on this work, we have developed a core set of informative SNP markers that can control for this partition in European population structure in a variety of clinical and genetic studies.

Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.

Recently it has become clear that only a small percentage (7%) of disease-associated single nucleotide polymorphisms (SNPs) are located in protein-coding regions, while the remaining 93% are located in gene regulatory regions or in intergenic regions. Thus, the understanding of how genetic variations control the expression of non-coding RNAs (in a tissue-dependent manner) has far-reaching implications. We tested the association of SNPs with expression levels (eQTLs) of large intergenic non-coding RNAs (lincRNAs), using genome-wide gene expression and genotype data from five different tissues. We identified 112 cis-regulated lincRNAs, of which 45% could be replicated in an independent dataset. We observed that 75% of the SNPs affecting lincRNA expression (lincRNA cis-eQTLs) were specific to lincRNA alone and did not affect the expression of neighboring protein-coding genes. We show that this specific genotype-lincRNA expression correlation is tissue-dependent and that many of these lincRNA cis-eQTL SNPs are also associated with complex traits and diseases.

It is known that genetic variants can affect gene expression, but it is not yet completely clear through what mechanisms genetic variation mediate this expression. We therefore compared the cis-effect of single nucleotide polymorphisms (SNPs) on gene expression between blood samples from 1,240 human subjects and four primary non-blood tissues (liver, subcutaneous, and visceral adipose tissue and skeletal muscle) from 85 subjects. We characterized four different mechanisms for 2,072 probes that show tissue-dependent genetic regulation between blood and non-blood tissues: on average 33.2% only showed cis-regulation in non-blood tissues; 14.5% of the eQTL probes were regulated by different, independent SNPs depending on the tissue of investigation. 47.9% showed a different effect size although they were regulated by the same SNPs. Surprisingly, we observed that 4.4% were regulated by the same SNP but with opposite allelic direction. We show here that SNPs that are located in transcriptional regulatory elements are enriched for tissue-dependent regulation, including SNPs at 3' and 5' untranslated regions (P = 1.84×10(-5) and 4.7×10(-4), respectively) and SNPs that are synonymous-coding (P = 9.9×10(-4)). SNPs that are associated with complex traits more often exert a tissue-dependent effect on gene expression (P = 2.6×10(-10)). Our study yields new insights into the genetic basis of tissue-dependent expression and suggests that complex trait associated genetic variants have even more complex regulatory effects than previously anticipated.

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

For many complex traits, genetic variants have been found associated. However, it is still mostly unclear through which downstream mechanism these variants cause these phenotypes. Knowledge of these intermediate steps is crucial to understand pathogenesis, while also providing leads for potential pharmacological intervention. Here we relied upon natural human genetic variation to identify effects of these variants on trans-gene expression (expression quantitative trait locus mapping, eQTL) in whole peripheral blood from 1,469 unrelated individuals. We looked at 1,167 published trait- or disease-associated SNPs and observed trans-eQTL effects on 113 different genes, of which we replicated 46 in monocytes of 1,490 different individuals and 18 in a smaller dataset that comprised subcutaneous adipose, visceral adipose, liver tissue, and muscle tissue. HLA single-nucleotide polymorphisms (SNPs) were 10-fold enriched for trans-eQTLs: 48% of the trans-acting SNPs map within the HLA, including ulcerative colitis susceptibility variants that affect plausible candidate genes AOAH and TRBV18 in trans. We identified 18 pairs of unlinked SNPs associated with the same phenotype and affecting expression of the same trans-gene (21 times more than expected, P<10(-16)). This was particularly pronounced for mean platelet volume (MPV): Two independent SNPs significantly affect the well-known blood coagulation genes GP9 and F13A1 but also C19orf33, SAMD14, VCL, and GNG11. Several of these SNPs have a substantially higher effect on the downstream trans-genes than on the eventual phenotypes, supporting the concept that the effects of these SNPs on expression seems to be much less multifactorial. Therefore, these trans-eQTLs could well represent some of the intermediate genes that connect genetic variants with their eventual complex phenotypic outcomes.

Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases-as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple-but not all-immune-mediated diseases (SNP-wise P(CPMA)<0.01). We also show that distinct groups of interacting proteins are encoded near SNPs which predispose to the same subsets of diseases; we propose these as the mechanistic basis of shared disease risk. We are thus able to leverage genetic data across diseases to construct biological hypotheses about the underlying mechanism of pathogenesis.

Recent advances in the identification of susceptibility genes and environmental exposures provide broad support for a post-infectious autoimmune basis for narcolepsy/hypocretin (orexin) deficiency. We genotyped loci associated with other autoimmune and inflammatory diseases in 1,886 individuals with hypocretin-deficient narcolepsy and 10,421 controls, all of European ancestry, using a custom genotyping array (ImmunoChip). Three loci located outside the Human Leukocyte Antigen (HLA) region on chromosome 6 were significantly associated with disease risk. In addition to a strong signal in the T cell receptor alpha (TRA@), variants in two additional narcolepsy loci, Cathepsin H (CTSH) and Tumor necrosis factor (ligand) superfamily member 4 (TNFSF4, also called OX40L), attained genome-wide significance. These findings underline the importance of antigen presentation by HLA Class II to T cells in the pathophysiology of this autoimmune disease.