Similar Datasets
Project description:We investigated whether intersecting functional genomic data (ATAC-seq + promoter focused Capture C) with increasingly powered publically available GWAS for Body Mass Index and Waist to Hip Ratio could identify additional true postiive subsignficant signals (5x10-8< P value < 5x10-4) without increasing the GWAS sample size
2021-01-16 | GSE164911 | GEO
Project description:We investigated whether intersecting functional genomic data (ATAC-seq + promoter focused Capture C) with increasingly powered publically available GWAS for Body Mass Index and Waist to Hip Ratio could identify additional true postiive subsignficant signals (5x10-8< P value < 5x10-4) without increasing the GWAS sample size
2021-01-16 | GSE164745 | GEO
Project description:To uncover novel significant association signals (p<5×10-8), genome-wide association studies (GWAS) requires increasingly larger sample sizes to overcome statistical correction for multiple testing. As an alternative, we aimed to identify associations among suggestive signals (5 × 10-8≤p<5×10-4) in increasingly powered GWAS efforts using chromatin accessibility and direct contact with gene promoters as biological constraints. We conducted retrospective analyses of three GIANT BMI GWAS efforts using ATAC-seq and promoter-focused Capture C data from human adipocytes and embryonic stem cell (ESC)-derived hypothalamic-like neurons. This approach, with its extremely low false-positive rate, identified 15 loci at p<5×10-5 in the 2010 GWAS, of which 13 achieved genome-wide significance by 2018, including at NAV1, MTIF3, and ADCY3. Eighty percent of constrained 2015 loci achieved genome-wide significance in 2018. We observed similar results in waist-to-hip ratio analyses. In conclusion, biological constraints on sub-significant GWAS signals can reveal potentially true-positive loci for further investigation in existing data sets without increasing sample size.
| S-EPMC7815306 | biostudies-literature
Project description:Biological constraints on GWAS SNPs at suggestive significance thresholds reveal true BMI loci.
| PRJNA692436 | ENA
Project description:Biological constraints on GWAS SNPs at suggestive significance thresholds reveal true BMI loci [hMSC_adipocytes_captureC]
| PRJNA692438 | ENA
Project description:Biological constraints on GWAS SNPs at suggestive significance thresholds reveal true BMI loci [hMSC_adipocytes_atacseq]
| PRJNA691887 | ENA
Project description:Meta-analyses of genome-wide association studies (GWAS) have improved our understanding of the genetic foundations of a number of diseases, including diabetes. However, single nucleotide polymorphisms (SNPs) that are identified by GWAS, especially those that fall outside of gene regions, do not always clearly link to the underlying biology. Despite this, these SNPs have often been validated through re-sequencing efforts as not just tag SNPs, but as causative SNPs, and so must play a role in disease development or progression. In this study, we show how the 3D genome (spatial connections) and trans-expression Quantitative Trait Loci connect diabetes loci from different GWAS meta-analyses, informing the backbone of regulatory networks. Our findings include a three-way functional-spatial connection between the TM6SF2, CTRB1-BCAR1, and CELSR2-PSRC1 loci (rs201189528, rs7202844, and rs7202844, respectively) connected through the KCNIP3 and BCAR1/BCAR3 loci, respectively. These spatial hubs serve as an example of how loci in genes with little biological connection to disease come together to contribute to the diabetes phenotype.
| S-EPMC4490250 | biostudies-literature
Project description:An important issue affecting genome-wide association studies with deep phenotyping (multiple correlated phenotypes) is determining the suitable family-wise significance threshold. Straightforward family-wise correction (Bonferroni) of p < 0.05 for 4.3 million genotypes and 335 phenotypes would give a threshold of p < 3.46E-11. This would be too conservative because it assumes all tests are independent. The effective number of tests, both phenotypic and genotypic, must be adjusted for the correlations between them. Spectral decomposition of the phenotype matrix and LD-based correction of the number of tested SNPs are currently used to determine an effective number of tests. In this paper, we compare these calculated estimates with permutation-determined family-wise significance thresholds. Permutations are performed by shuffling individual IDs of the genotype vector for this dataset, to preserve correlation of phenotypes. Our results demonstrate that the permutation threshold is influenced by minor allele frequency (MAF) of the SNPs, and by the number of individuals tested. For the more common SNPs (MAF > 0.1), the permutation family-wise threshold was in close agreement with spectral decomposition methods. However, for less common SNPs (0.05 < MAF ≤ 0.1), the permutation threshold calculated over all SNPs was off by orders of magnitude. This applies to the number of individuals studied (here 777) but not to very much larger numbers. Based on these findings, we propose that the threshold to find a particular level of family-wise significance may need to be established using separate permutations of the actual data for several MAF bins.
| S-EPMC7429341 | biostudies-literature
Project description:A number of studies have found that BMI in early life influences the risk of developing type 2 diabetes later in life. Our goal was to investigate if any type 2 diabetes variants uncovered through genome-wide association studies (GWAS) impact BMI in childhood.Using data from an ongoing GWAS of pediatric BMI in our cohort, we investigated the association of pediatric BMI with 20 single nucleotide polymorphisms at 18 type 2 diabetes loci uncovered through GWAS, consisting of ADAMTS9, CDC123-CAMK1D, CDKAL1, CDKN2A/B, EXT2, FTO, HHEX-IDE, IGF2BP2, the intragenic region on 11p12, JAZF1, KCNQ1, LOC387761, MTNR1B, NOTCH2, SLC30A8, TCF7L2, THADA, and TSPAN8-LGR5. We randomly partitioned our cohort exactly in half in order to have a discovery cohort (n = 3,592) and a replication cohort (n = 3,592).Our data show that the major type 2 diabetes risk-conferring G allele of rs7923837 at the HHEX-IDE locus was associated with higher pediatric BMI in both the discovery (P = 0.0013 and survived correction for 20 tests) and replication (P = 0.023) sets (combined P = 1.01 x 10(-4)). Association was not detected with any other known type 2 diabetes loci uncovered to date through GWAS except for the well-established FTO.Our data show that the same genetic HHEX-IDE variant, which is associated with type 2 diabetes from previous studies, also influences pediatric BMI.
| S-EPMC2828649 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have implicated 58 loci in coronary artery disease (CAD). However, the biological basis for these associations, the relevant genes, and causative variants often remain uncertain. Since the vast majority of GWAS loci reside outside coding regions, most exert regulatory functions. Here we explore the complexity of each of these loci, using tissue specific RNA sequencing data from GTEx to identify genes that exhibit altered expression patterns in the context of GWAS-significant loci, expanding the list of candidate genes from the 75 currently annotated by GWAS to 245, with almost half of these transcripts being non-coding. Tissue specific allelic expression imbalance data, also from GTEx, allows us to uncover GWAS variants that mark functional variation in a locus, e.g., rs7528419 residing in the SORT1 locus, in liver specifically, and rs72689147 in the GUYC1A1 locus, across a variety of tissues. We consider the GWAS variant rs1412444 in the LIPA locus in more detail as an example, probing tissue and transcript specific effects of genetic variation in the region. By evaluating linkage disequilibrium (LD) between tissue specific eQTLs, we reveal evidence for multiple functional variants within loci. We identify 3 variants (rs1412444, rs1051338, rs2250781) that when considered together, each improve the ability to account for LIPA gene expression, suggesting multiple interacting factors. These results refine the assignment of 58 GWAS loci to likely causative variants in a handful of cases and for the remainder help to re-prioritize associated genes and RNA isoforms, suggesting that ncRNAs maybe a relevant transcript in almost half of CAD GWAS results. Our findings support a multi-factorial system where a single variant can influence multiple genes and each genes is regulated by multiple variants.
| S-EPMC8863290 | biostudies-literature