Thymic stromal lymphopoietin (TSLP), an IL7-like cytokine produced by bronchial epithelial cells is upregulated in asthma and induces dendritic cell maturation supporting a Th2 response. Environmental pollutants, including tobacco smoke and diesel exhaust particles upregulate TSLP suggesting that TSLP may be an interface between environmental pollution and immune responses in asthma. Since asthma is prevalent in urban communities, variants in the TSLP gene may be important in asthma susceptibility in these populations.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist.

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.

The minor allele of the R620W missense single-nucleotide polymorphism (SNP) (rs2476601) in the hematopoietic-specific protein tyrosine phosphatase gene, PTPN22, has been associated with multiple autoimmune diseases, including rheumatoid arthritis (RA). These genetic data, combined with biochemical evidence that this SNP affects PTPN22 function, suggest that this phosphatase is a key regulator of autoimmunity. To determine whether other genetic variants in PTPN22 contribute to the development of RA, we sequenced the coding regions of this gene in 48 white North American patients with RA and identified 15 previously unreported SNPs, including 2 coding SNPs in the catalytic domain. We then genotyped 37 SNPs in or near PTPN22 in 475 patients with RA and 475 individually matched controls (sample set 1) and selected a subset of markers for replication in an additional 661 patients with RA and 1,322 individually matched controls (sample set 2). Analyses of these results predict 10 common (frequency >1%) PTPN22 haplotypes in white North Americans. The sole haplotype found to carry the previously identified W620 risk allele was strongly associated with disease in both sample sets, whereas another haplotype, identical at all other SNPs but carrying the R620 allele, showed no association. R620W, however, does not fully explain the association between PTPN22 and RA, since significant differences between cases and controls persisted in both sample sets after the haplotype data were stratified by R620W. Additional analyses identified two SNPs on a single common haplotype that are associated with RA independent of R620W, suggesting that R620W and at least one additional variant in the PTPN22 gene region influence RA susceptibility.

Many lines of evidence implicate mitochondria in phenotypic variation: (a) rare mutations in mitochondrial proteins cause metabolic, neurological, and muscular disorders; (b) alterations in oxidative phosphorylation are characteristic of type 2 diabetes, Parkinson disease, Huntington disease, and other diseases; and (c) common missense variants in the mitochondrial genome (mtDNA) have been implicated as having been subject to natural selection for adaptation to cold climates and contributing to "energy deficiency" diseases today. To test the hypothesis that common mtDNA variation influences human physiology and disease, we identified all 144 variants with frequency >1% in Europeans from >900 publicly available European mtDNA sequences and selected 64 tagging single-nucleotide polymorphisms that efficiently capture all common variation (except the hypervariable D-loop). Next, we evaluated the complete set of common mtDNA variants for association with type 2 diabetes in a sample of 3,304 diabetics and 3,304 matched nondiabetic individuals. Association of mtDNA variants with other metabolic traits (body mass index, measures of insulin secretion and action, blood pressure, and cholesterol) was also tested in subsets of this sample. We did not find a significant association of common mtDNA variants with these metabolic phenotypes. Moreover, we failed to identify any physiological effect of alleles that were previously proposed to have been adaptive for energy metabolism in human evolution. More generally, this comprehensive association-testing framework can readily be applied to other diseases for which mitochondrial dysfunction has been implicated.

To discover new rheumatoid arthritis (RA) risk loci, we systematically examined 370 SNPs from 179 independent loci with P < 0.001 in a published meta-analysis of RA genome-wide association studies (GWAS) of 3,393 cases and 12,462 controls. We used Gene Relationships Across Implicated Loci (GRAIL), a computational method that applies statistical text mining to PubMed abstracts, to score these 179 loci for functional relationships to genes in 16 established RA disease loci. We identified 22 loci with a significant degree of functional connectivity. We genotyped 22 representative SNPs in an independent set of 7,957 cases and 11,958 matched controls. Three were convincingly validated: CD2-CD58 (rs11586238, P = 1 x 10(-6) replication, P = 1 x 10(-9) overall), CD28 (rs1980422, P = 5 x 10(-6) replication, P = 1 x 10(-9) overall) and PRDM1 (rs548234, P = 1 x 10(-5) replication, P = 2 x 10(-8) overall). An additional four were replicated (P < 0.0023): TAGAP (rs394581, P = 0.0002 replication, P = 4 x 10(-7) overall), PTPRC (rs10919563, P = 0.0003 replication, P = 7 x 10(-7) overall), TRAF6-RAG1 (rs540386, P = 0.0008 replication, P = 4 x 10(-6) overall) and FCGR2A (rs12746613, P = 0.0022 replication, P = 2 x 10(-5) overall). Many of these loci are also associated to other immunologic diseases.

Accounting for population genetic substructure is important in reducing type 1 errors in genetic studies of complex disease. As efforts to understand complex genetic disease are expanded to different continental populations the understanding of genetic substructure within these continents will be useful in design and execution of association tests. In this study, population differentiation (Fst) and Principal Components Analyses (PCA) are examined using >200 K genotypes from multiple populations of East Asian ancestry. The population groups included those from the Human Genome Diversity Panel [Cambodian, Yi, Daur, Mongolian, Lahu, Dai, Hezhen, Miaozu, Naxi, Oroqen, She, Tu, Tujia, Naxi, Xibo, and Yakut], HapMap [ Han Chinese (CHB) and Japanese (JPT)], and East Asian or East Asian American subjects of Vietnamese, Korean, Filipino and Chinese ancestry. Paired Fst (Wei and Cockerham) showed close relationships between CHB and several large East Asian population groups (CHB/Korean, 0.0019; CHB/JPT, 00651; CHB/Vietnamese, 0.0065) with larger separation with Filipino (CHB/Filipino, 0.014). Low levels of differentiation were also observed between Dai and Vietnamese (0.0045) and between Vietnamese and Cambodian (0.0062). Similarly, small Fst's were observed among different presumed Han Chinese populations originating in different regions of mainland of China and Taiwan (Fst's <0.0025 with CHB). For PCA, the first two PC's showed a pattern of relationships that closely followed the geographic distribution of the different East Asian populations. PCA showed substructure both between different East Asian groups and within the Han Chinese population. These studies have also identified a subset of East Asian substructure ancestry informative markers (EASTASAIMS) that may be useful for future complex genetic disease association studies in reducing type 1 errors and in identifying homogeneous groups that may increase the power of such studies.

Case-control genetic studies of complex human diseases can be confounded by population stratification. This issue can be addressed using panels of ancestry informative markers (AIMs) that can provide substantial population substructure information. Previously, we described a panel of 128 SNP AIMs that were designed as a tool for ascertaining the origins of subjects from Europe, Sub-Saharan Africa, Americas, and East Asia.

For admixture mapping studies in Mexican Americans (MAM), we define a genomewide single-nucleotide-polymorphism (SNP) panel that can distinguish between chromosomal segments of Amerindian (AMI) or European (EUR) ancestry. These studies used genotypes for >400,000 SNPs, defined in EUR and both Pima and Mayan AMI, to define a set of ancestry-informative markers (AIMs). The use of two AMI populations was necessary to remove a subset of SNPs that distinguished genotypes of only one AMI subgroup from EUR genotypes. The AIMs set contained 8,144 SNPs separated by a minimum of 50 kb with only three intermarker intervals >1 Mb and had EUR/AMI FST values >0.30 (mean FST = 0.48) and Mayan/Pima FST values <0.05 (mean FST < 0.01). Analysis of a subset of these SNP AIMs suggested that this panel may also distinguish ancestry between EUR and other disparate AMI groups, including Quechuan from South America. We show, using realistic simulation parameters that are based on our analyses of MAM genotyping results, that this panel of SNP AIMs provides good power for detecting disease-associated chromosomal segments for genes with modest ethnicity risk ratios. A reduced set of 5,287 SNP AIMs captured almost the same admixture mapping information, but smaller SNP sets showed substantial drop-off in admixture mapping information and power. The results will enable studies of type 2 diabetes, rheumatoid arthritis, and other diseases among which epidemiological studies suggest differences in the distribution of ancestry-associated susceptibility.

Primary biliary cholangitis (PBC) is an autoimmune liver disease with a strong hereditary component. Here, we report a genome-wide association study that included 1,122 PBC cases and 4,036 controls of Han Chinese descent, with subsequent replication in a separate cohort of 907 PBC cases and 2,127 controls. Our results show genome-wide association of 14 PBC risk loci including previously identified 6p21 (HLA-DRA and DPB1), 17q12 (ORMDL3), 3q13.33 (CD80), 2q32.3 (STAT1/STAT4), 3q25.33 (IL12A), 4q24 (NF-κB) and 22q13.1 (RPL3/SYNGR1). We also identified variants in IL21, IL21R, CD28/CTLA4/ICOS, CD58, ARID3A and IL16 as novel PBC risk loci. These new findings and histochemical studies showing enhanced expression of IL21 and IL21R in PBC livers (particularly in the hepatic portal tracks) support a disease mechanism in which the deregulation of the IL21 signalling pathway, in addition to CD4 T-cell activation and T-cell co-stimulation are critical components in the development of PBC.

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Inflammatory signals associated with cardiac diseases trigger trans-differentiation of cardiac fibroblasts to cardiac myofibroblasts. Cardiac myofibroblasts are the main cell type involved in the development of cardiac fibrosis, a diffuse and disproportionate accumulation of collagen in the myocardium. Although the role of the scavenger like-lectin receptor LOX-1 was previously investigated in cardiac fibroblasts and fibrosis, the involvement of the LOX-1 ligand -oxidized low-density lipoprotein (oxLDL)- on cardiac myofibroblast function still remains unexplored. In the present work, we investigated the effect of oxLDL/LOX-1 on fibrotic markers and cardiac myofibroblast function. Our in vitro results showed that oxLDL increased cardiac myofibroblast proliferation, triggered an increase in the synthesis of collagen type I and fibronectin containing extra domain A, and stimulated collagen type I secretion. oxLDL also decreased cardiac myofibroblast migration, collagen gel contraction and cell area, without modifying α-smooth muscle actin protein levels. These effects were dependent on LOX-1, because LOX-1 knockdown abolished oxLDL effects. Collectively these data showed that oxLDL has important modulatory effects on cardiac myofibroblast function.

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Hyperinsulinaemic androgen excess (HIAE) in prepubertal and pubertal girls usually precedes a broader pathological phenotype in adulthood that is associated with anovulatory infertility, metabolic syndrome and type 2 diabetes. The metabolic derangements that determine these long-term health risks remain to be clarified. Here we use NMR and MS-based metabolomics to show that serum levels of methionine sulfoxide in HIAE girls are an indicator of the degree of oxidation of methionine-148 residue in apolipoprotein-A1. Oxidation of apo-A1 in methionine-148, in turn, leads to an impaired maturation of high-density lipoproteins (HDL) that is reflected in a decline of large HDL particles. Notably, such metabolic alterations occur in the absence of impaired glucose tolerance, hyperglycemia and hypertriglyceridemia, and were partially restored after 18 months of treatment with a low-dose combination of pioglitazone, metformin and flutamide.

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.

We conducted a genome-wide association study of rheumatoid arthritis in 2,418 cases and 4,504 controls from North America and identified an association at the REL locus, encoding c-Rel, on chromosome 2p13 (rs13031237, P = 6.01 x 10(-10)). Replication in independent case-control datasets comprising 2,604 cases and 2,882 controls confirmed this association, yielding an allelic OR = 1.25 (P = 3.08 x 10(-14)) for marker rs13031237 and an allelic OR = 1.21 (P = 2.60 x 10(-11)) for marker rs13017599 in the combined dataset. The combined dataset also provides definitive support for associations at both CTLA4 (rs231735; OR = 0.85; P = 6.25 x 10(-9)) and BLK (rs2736340; OR = 1.19; P = 5.69 x 10(-9)). c-Rel is an NF-kappaB family member with distinct functional properties in hematopoietic cells, and its association with rheumatoid arthritis suggests disease pathways that involve other recently identified rheumatoid arthritis susceptibility genes including CD40, TRAF1, TNFAIP3 and PRKCQ.

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.