Analysis of Brugada syndrome loci reveals that fine-mapping clustered GWAS hits enhances the annotation of disease-relevant variants.
Ontology highlight
ABSTRACT:
SUBMITTER: Pinsach-Abuin ML
PROVIDER: S-EPMC8080235 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:A recent genome-wide association study (GWAS) of central obesity identified 27 loci, from sex-combined analysis, associated with waist-to-hip ratio adjusted for body-mass index (WHRadjBMI) in European-ancestry individuals. Nevertheless, the identified variants may not be the biological causal ones due to the presence of linkage disequilibrium (LD). To better understand the mechanisms underlying the identified loci from the GWAS meta-analysis, we first imputed summary statistics at GWAS loci to increase genetic resolution, and then we applied a Bayesian statistical fine-mapping method through PAINTOR, incorporating LD structure and functional annotations to select and prioritize the most plausible causal variants across WHRadjBMI-associated regions. Using adipose tissue- and cell-specific annotations that showed significant associations with WHRadjBMI, we identified 33 single-nucleotide polymorphisms (SNPs) from 27 sex-combined fine-mapping loci with posterior probability of causality greater than 0.9. Six of the selected 33 SNPs belong to at least one of the top five identified annotations. SNPs rs1440372 (SMAD6) and rs12608504 (JUND) are particularly important since they not only have associated functional annotations but are also GWA hits in the original study. Incorporation of functional annotations helps identify additional plausible causal variants, such as rs2213731 (DNM3-PIGC) and rs4531856 (JUND), that did not reach genome-wide significance in GWAS. Our results provide promising candidates for future functional validation experiments.
| S-EPMC6117358 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-06-19 | GSE115961 | GEO
Project description:Aims/hypothesisType 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study.MethodsWe conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci.ResultsFour novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis.Conclusions/interpretationOur findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.Data availabilityFull summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).
| S-EPMC8810722 | biostudies-literature
Project description:We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.
| S-EPMC4666734 | biostudies-literature
Project description:Genome-wide association studies (GWASs) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes and functional annotation from external databases to prioritize the most plausible causal single-nucleotide polymorphisms (SNPs) in each trait-associated locus. Our proposed method improves upon previous fine-mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine-mapping studies helps to distinguish between variants in high LD and to identify promising targets for follow-up studies.
| S-EPMC8682766 | biostudies-literature
Project description:Despite high heritability of schizophrenia, genome-wide association studies (GWAS) have not yet revealed distinct combinations of single-nucleotide polymorphisms (SNPs), relevant for mental disease-related, quantifiable behavioral phenotypes. Here we propose an individual-based model to use genome-wide significant markers for extracting first genetic signatures of such behavioral continua. 'OTTO' (old Germanic=heritage) marks an individual characterized by a prominent phenotype and a particular load of phenotype-associated risk SNPs derived from GWAS that likely contributed to the development of his personal mental illness. This load of risk SNPs is shared by a small squad of 'similars' scattered under the genetically and phenotypically extremely heterogeneous umbrella of a schizophrenia end point diagnosis and to a variable degree also by healthy subjects. In a discovery sample of >1000 deeply phenotyped schizophrenia patients and several independent replication samples, including the general population, a gradual increase in the severity of 'OTTO's phenotype' expression is observed with an increasing share of 'OTTO's risk SNPs', as exemplified here by autistic and affective phenotypes. These data suggest a model in which the genetic contribution to dimensional behavioral traits can be extracted from combinations of GWAS SNPs derived from individuals with prominent phenotypes. Even though still in the 'model phase' owing to a world-wide lack of sufficiently powered, deeply phenotyped replication samples, the OTTO approach constitutes a conceptually novel strategy to delineate biological subcategories of mental diseases starting from GWAS findings and individual subjects.
| S-EPMC5794905 | biostudies-literature
Project description:Previous studies have identified 41 independent genome-wide significant psoriasis susceptibility loci. After our first psoriasis genome-wide association study, we designed a custom genotyping array to fine-map eight genome-wide significant susceptibility loci known at that time (IL23R, IL13, IL12B, TNIP1, MHC, TNFAIP3, IL23A and RNF114) enabling genotyping of 2269 single-nucleotide polymorphisms (SNPs) in the eight loci for 2699 psoriasis cases and 2107 unaffected controls of European ancestry. We imputed these data using the latest 1000 Genome reference haplotypes, which included both indels and SNPs, to increase the marker density of the eight loci to 49?239 genetic variants. Using stepwise conditional association analysis, we identified nine independent signals distributed across six of the eight loci. In the major histocompatibility complex (MHC) region, we detected three independent signals at rs114255771 (P = 2.94 × 10(-74)), rs6924962 (P = 3.21 × 10(-19)) and rs892666 (P = 1.11 × 10(-10)). Near IL12B we detected two independent signals at rs62377586 (P = 7.42 × 10(-16)) and rs918518 (P = 3.22 × 10(-11)). Only one signal was observed in each of the TNIP1 (rs17728338; P = 4.15 × 10(-13)), IL13 (rs1295685; P = 1.65 × 10(-7)), IL23A (rs61937678; P = 1.82 × 10(-7)) and TNFAIP3 (rs642627; P = 5.90 × 10(-7)) regions. We also imputed variants for eight HLA genes and found that SNP rs114255771 yielded a more significant association than any HLA allele or amino-acid residue. Further analysis revealed that the HLA-C*06-B*57 haplotype tagged by this SNP had a significantly higher odds ratio than other HLA-C*06-bearing haplotypes. The results demonstrate allelic heterogeneity at IL12B and identify a high-risk MHC class I haplotype, consistent with the existence of multiple psoriasis effectors in the MHC.
| S-EPMC4795047 | biostudies-literature
Project description:Genome-wide association studies (GWASs) have enabled unbiased identification of genetic loci contributing to common complex diseases. Because GWAS loci often harbor many variants and genes, it remains a major challenge to move from GWASs' statistical associations to the identification of causal variants and genes that underlie these association signals. Researchers have applied many statistical and functional fine-mapping strategies to prioritize genetic variants and genes as potential candidates. There is no gold standard in fine-mapping approaches, but consistent results across different approaches can improve confidence in the fine-mapping findings. Here, we combined text mining with a systematic review and formed a catalog of 85 studies with evidence of fine mapping for at least one autoimmune GWAS locus. Across all fine-mapping studies, we compiled 230 GWAS loci with allelic heterogeneity estimates and predictions of causal variants and trait-relevant genes. These 230 loci included 455 combinations of locus-by-disease association signals with 15 autoimmune diseases. Using these estimates, we assessed the probability of mediating disease risk associations across genes in GWAS loci and identified robust signals of causal disease biology. We predict that this comprehensive catalog of GWAS fine-mapping efforts in autoimmune disease will greatly help distill the plethora of information in the field and inform therapeutic strategies.
| S-EPMC8059376 | biostudies-literature