A catalog of GWAS fine-mapping efforts in autoimmune disease.
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ABSTRACT:
SUBMITTER: Caliskan M
PROVIDER: S-EPMC8059376 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4(+) T-cell subsets, regulatory T cells, CD8(+) T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.
| S-EPMC4336207 | biostudies-literature
Project description:BACKGROUND:Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. RESULTS:We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability. CONCLUSIONS:Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.
| S-EPMC6958677 | biostudies-literature
Project description:Genetic factors play a major role in the etiology of autoimmune thyroid disease (AITD) including Graves' disease (GD) and Hashimoto's thyroiditis (HT). We have previously identified three loci on chromosomes 10q, 12q, and 14q that showed strong linkage with AITD, HT, and GD, respectively.The objective of the study was to identify the AITD susceptibility genes at the 10q, 12q, and 14q loci.Three hundred forty North American Caucasian AITD patients and 183 healthy controls were studied. The 10q, 12q, and 14q loci were fine mapped by genotyping densely spaced single-nucleotide polymorphisms (SNPs) using the Illumina GoldenGate genotyping platform. Case control association analyses were performed using the UNPHASED computer package. Associated SNPs were reanalyzed in a replication set consisting of 238 AITD patients and 276 controls.Fine mapping of the AITD locus, 10q, showed replicated association of the AITD phenotype (both GD and HT) with SNP rs6479778. This SNP was located within the ARID5B gene recently reported to be associated with rheumatoid arthritis and GD in Japanese. Fine mapping of the GD locus, 14q, revealed replicated association of the GD phenotype with two markers, rs12147587 and rs2284720, located within the NRXN3 and TSHR genes, respectively.Fine mapping of three linked loci identified novel susceptibility genes for AITD. The discoveries of new AITD susceptibility genes will engender a new understanding of AITD etiology.
| S-EPMC3537111 | biostudies-literature
Project description:IntroductionGenome-wide association studies (GWAS) have confirmed the leucine-rich repeat kinase 2 (LRRK2) gene as a susceptibility locus for idiopathic Parkinson's disease (PD) in Caucasians. Though the rs1491942 and rs76904798 variants have shown the strongest associations, the causal variant(s) remains unresolved. Therefore, the aim of this study was to identify variants that may be driving the LRRK2 GWAS signal by sequencing the entire LRRK2 gene in Caucasian PD patients and controls.MethodsA discovery series (287 PD patients, 294 controls) and replication series (362 PD patients, 168 controls) were included. The entire LRRK2 gene as well as 10 Kb upstream/downstream was sequenced. Candidate potential causal variants were considered to be those that (a) were in at least weak linkage disequilibrium with the two GWAS-nominated variants (rs1491942 and rs76904798), and (b) displayed an association odds ratio (OR) that is stronger than the two GWAS variants.ResultsThirty-four candidate variants (all intronic/intergenic) that may drive the LRRK2 PD GWAS signal were identified in the discovery series. However, examination of the replication series for these variants did not reveal any with a consistently stronger OR than both PD GWAS variants. Evaluation of public databases to determine which candidate variants are most likely to have a direct functional effect on LRRK2 expression was inconclusive.ConclusionThough our findings provide novel insights into the LRRK2 GWAS association, a clear causal variant was not identified. The identified candidate variants can form the basis for future experiments and functional studies that can more definitively assess causal LRRK2 variants.
| S-EPMC9310432 | biostudies-literature
Project description:[Figure: see text].
| S-EPMC8260472 | biostudies-literature
Project description:The growing availability of high-quality genomic annotation has increased the potential for mechanistic insights when the specific variants driving common genome-wide association signals are accurately localized. A range of fine-mapping strategies have been advocated, and specific successes reported, but the overall performance of such approaches, in the face of the extensive linkage disequilibrium that characterizes the human genome, is not well understood. Using simulations based on sequence data from the 1000 Genomes Project, we quantify the extent to which fine-mapping, here conducted using an approximate Bayesian approach, can be expected to lead to useful improvements in causal variant localization. We show that resolution is highly variable between loci, and that performance is severely degraded as the statistical power to detect association is reduced. We confirm that, where causal variants are shared between ancestry groups, further improvements in performance can be obtained in a trans-ethnic fine-mapping design. Finally, using empirical data from a recently published genome-wide association study for ankylosing spondylitis, we provide empirical confirmation of the behaviour of the approximate Bayesian approach and demonstrate that seven of twenty-six loci can be fine-mapped to fewer than ten variants.
| S-EPMC4583479 | biostudies-literature
Project description:IntroductionLarge-scale genome-wide association studies (GWASs) have identified 97 chromosomal loci associated with increased body mass index in population-based studies on adults. One of these SNPs, rs7359397, tags a large region (approx. 1MB) with high linkage disequilibrium (r2>0.7), which comprises five genes (SH2B1, APOBR, sulfotransferases: SULT1A1 and SULT1A2, TUFM). We had previously described a rare mutation in SH2B1 solely identified in extremely obese individuals but not in lean controls.MethodsThe coding regions of the genes APOBR, SULT1A1, SULT1A2, and TUFM were screened for mutations (dHPLC, SSCP, Sanger re-sequencing) in 95 extremely obese children and adolescents. Detected non-synonymous variants were genotyped (TaqMan SNP Genotyping, MALDI TOF, PCR-RFLP) in independent large study groups (up to 3,210 extremely obese/overweight cases, 485 lean controls and 615 obesity trios). In silico tools were used for the prediction of potential functional effects of detected variants.ResultsExcept for TUFM we detected non-synonymous variants in all screened genes. Two polymorphisms rs180743 (APOBR p.Pro428Ala) and rs3833080 (APOBR p.Gly369_Asp370del9) showed nominal association to (extreme) obesity (uncorrected p = 0.003 and p = 0.002, respectively). In silico analyses predicted a functional implication for rs180743 (APOBR p.Pro428Ala). Both APOBR variants are located in the repetitive region with unknown function.ConclusionVariants in APOBR contributed as strongly as variants in SH2B1 to the association with extreme obesity in the chromosomal region chr16p11.2. In silico analyses implied no functional effect of several of the detected variants. Further in vitro or in vivo analyses on the functional implications of the obesity associated variants are warranted.
| S-EPMC4425372 | biostudies-literature
Project description:Genome-wide association studies (GWASs) are instrumental in identifying loci harboring common single-nucleotide variants (SNVs) that affect human traits and diseases. GWAS hits emerge in clusters, but the focus is often on the most significant hit in each trait- or disease-associated locus. The remaining hits represent SNVs in linkage disequilibrium (LD) and are considered redundant and thus frequently marginally reported or exploited. Here, we interrogate the value of integrating the full set of GWAS hits in a locus repeatedly associated with cardiac conduction traits and arrhythmia, SCN5A-SCN10A. Our analysis reveals 5 common 7-SNV haplotypes (Hap1-5) with 2 combinations associated with life-threatening arrhythmia-Brugada syndrome (the risk Hap1/1 and protective Hap2/3 genotypes). Hap1 and Hap2 share 3 SNVs; thus, this analysis suggests that assuming redundancy among clustered GWAS hits can lead to confounding disease-risk associations and supports the need to deconstruct GWAS data in the context of haplotype composition.
| S-EPMC8080235 | biostudies-literature
Project description:Background & aimsAssociation studies have greatly refined the important role of the major histocompatibility complex (MHC) region in autoimmune hepatitis (AIH). However, the effects of human leucocyte antigen (HLA) polymorphisms on AIH are not well established. The aim of this study is to systematically characterise the association of MHC variants with AIH in our well-defined cohort of patients.MethodsWe performed an imputation-based analysis on the extensive association observed within the MHC region using the Han-MHC reference panel, and tested the comprehensive associations of HLA polymorphisms with AIH in 1622 Chinese AIH type 1 patients and 10,466 population controls.ResultsA total of 588 HLA variants were significantly associated with AIH, with HLA-B∗35:01 (p = 8.17 × 10-304; odds ratio [OR] = 7.32) contributing the strongest signal. Stepwise conditional analysis revealed additional independent signals at HLA-B∗08:01 (p = 1.35 × 10-33; OR = 4.26) and rs7765379 (p = 5.08 × 10-18; OR = 1.66). A strong link between the lead HLA variant and clinical phenotypes of AIH was observed: patients with HLA-B∗35:01 were less frequently positive for ANA and tended to have higher serum AST and ALT levels at diagnosis, but lower serum IgG levels.ConclusionsOur study reveals three novel and independent variants at HLA-B∗35:01, HLA-B∗08:01, and rs7765379 associated with AIH across the whole MHC region in the Han Chinese population. The findings illustrate the value of the MHC region in AIH and provide a new perspective for the immunogenetics of AIH.Impact and implicationsThis study revealed three novel and independent variants associated with autoimmune hepatitis across the whole major histocompatibility complex region in the Han Chinese population. These findings are significant in identifying autoantigens, providing insights into the activation of the autoimmune processes, and further advancing our understanding of the immunogenetic basis underlying autoimmune hepatitis.
| S-EPMC10711477 | biostudies-literature
Project description:MotivationFine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.ResultsSimulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).Availability and implementationThe HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
| S-EPMC5544112 | biostudies-literature