Existing Genome-wide Association Studies Data Research Articles

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

BackgroundHeterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.ResultsHere, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.ConclusionPheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

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Detecting significant genotype\u2013phenotype association rules in bipolar disorder: market research meets complex genetics

BackgroundDisentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted.ResultsTwo of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings.ConclusionOur approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

Genome-wide compound heterozygosity analysis highlighted 4 novel susceptibility loci for congenital heart disease in Chinese population.

Genome-wide association studies (GWASs) have achieved great success in deciphering the genetic cause of congenital heart disease (CHD). However, the heritability of CHD remains to be clarified, and numerous genetic factors responsible for occurrence of CHD are yet unclear. In this study, we performed a genome-wide search for relaxed forms of compound heterozygosity (CH) in association with CHD using our existing GWAS data including 2265 individuals (957 CHD cases and 1308 controls). CollapsABEL was used to iteratively test the association between the CH genotype and the CHD phenotype in a sliding window manner. We highlighted 17 genetic loci showing suggestive CH-like associations with CHD (P < 5 × 10-8 ), among which 4 genetic loci had expression quantitative trait loci (eQTL) effects in blood (PeQTL < 0.01). After conditional association analysis, each loci had only 1 independently effective signal reaching the significance threshold (rs2071477/rs3129299 at 6p21.32, P = 2.47 × 10-10 ; rs10773097/rs2880921 at 12q24.31, P = 3.30 × 10-8 ; rs73032040/rs7259476 at 19q13.11, P = 1.14 × 10-8 ; rs10416386/rs4239517 at 19q13.31, P = 1.15 × 10-9 ), together explained 7.83% of the CHD variance. Among these 4 associated loci, outstanding candidates for CHD-associated genes included UBC, CFM2, ZNF302, LYPD3 and CADM4. Although replication studies with larger sample size are warranted, the first CH GWAS of CHD may extend our current knowledge of the genetic contributions to CHD in the Han Chinese population.

Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662075, associated with a twofold increased risk for T2D in males. rs146662075 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches.

The PF4/PPBP/CXCL5 Gene Cluster Is Associated with Periodontitis

Periodontitis is a common dysbiotic inflammatory disease with an estimated heritability of 50%. Due to the limited sample size of available periodontitis cohorts and the underlying trait heterogeneity, genome-wide association studies (GWAS) of chronic periodontitis (CP) have been unsuccessful in discovering susceptibility factors. A strategy that combines agnostic GWAS with a well-powered candidate-gene approach has the potential to discover novel loci. We combined RNA-seq data from gingival tissues with quantitative trait loci (QTLs) that were identified in a F2-cross of mice resistant and susceptible to infection with oral bacterial pathogens. Four genes, which were located within the mapped QTLs, showed differential expression. The chromosomal regions across the human orthologous were interrogated for putative periodontitis-associated variants using existing GWAS data from a German case-control sample of aggressive periodontitis (AgP; 651 cases, 4,001 controls), the most severe and early onset form of periodontitis. Two haplotype blocks, one upstream to the coding region of UGT2A1 (rs146712414, P = 9.1 × 10−5; odds ratio [OR], 1.34; 95% confidence interval [CI], 1.16–1.56) and one downstream of the genes PF4/PPBP/CXCL5 (rs1595009, P = 1.3 × 10−4; OR, 1.32; 95% CI, 1.15–1.52), were associated with AgP. The association of rs1595009 was validated in an independent cohort of CP of European Americans (1,961 cases and 1,864 controls; P = 0.03; OR, 1.45; 95% CI, 1.01–1.29). This association was further replicated in another sample of 399 German CP cases (disease onset <60 y of age) and 1,633 controls (P = 0.03; OR, 1.75; 95% CI, 1.06–2.90). The combined estimates of association from all samples were P = 2.9 × 10−5 (OR, 1.2; 95% CI, 1.1–1.3). This study shows the strength of combining QTL mapping and RNA-Seq data from a mouse model with association studies in human case-control samples to identify genetic risk variants of periodontitis.

Systematic analysis of chromatin interactions at disease associated loci links novel candidate genes to inflammatory bowel disease.

BackgroundGenome-wide association studies (GWAS) have revealed many susceptibility loci for complex genetic diseases. For most loci, the causal genes have not been identified. Currently, the identification of candidate genes is predominantly based on genes that localize close to or within identified loci. We have recently shown that 92 of the 163 inflammatory bowel disease (IBD)-loci co-localize with non-coding DNA regulatory elements (DREs). Mutations in DREs can contribute to IBD pathogenesis through dysregulation of gene expression. Consequently, genes that are regulated by these 92 DREs are to be considered as candidate genes. This study uses circular chromosome conformation capture-sequencing (4C-seq) to systematically analyze chromatin-interactions at IBD susceptibility loci that localize to regulatory DNA.ResultsUsing 4C-seq, we identify genomic regions that physically interact with the 92 DRE that were found at IBD susceptibility loci. Since the activity of regulatory elements is cell-type specific, 4C-seq was performed in monocytes, lymphocytes, and intestinal epithelial cells. Altogether, we identified 902 novel IBD candidate genes. These include genes specific for IBD-subtypes and many noteworthy genes including ATG9A and IL10RA. We show that expression of many novel candidate genes is genotype-dependent and that these genes are upregulated during intestinal inflammation in IBD. Furthermore, we identify HNF4α as a potential key upstream regulator of IBD candidate genes.ConclusionsWe reveal many novel and relevant IBD candidate genes, pathways, and regulators. Our approach complements classical candidate gene identification, links novel genes to IBD and can be applied to any existing GWAS data.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1100-3) contains supplementary material, which is available to authorized users.

Abstract 3132: Genome-wide scan for genetic markers modifying survival of pancreatic cancer patients

Abstract Background: Genetic factors that are associated with pancreatic cancer survival have previously been examined in Genome-Wide Association Studies (GWAS), but the results are inconsistent due to limited sample size. Method: We performed a two-stage study on pancreatic cancer survival. The discovery phase took advantage of the existing GWAS data of 868 patients with European descendent from a case-control study conducted at MD Anderson. The validation study on ten top hits was conducted in another 820 white patients with Taqman. The missing genotypes were imputed using IMPUTE2 based on 1000 Genomes Project. Cox regression model was fitted to calculate allele-specific hazard ratios (HR) and 95% confidence intervals (95% CI) with adjustment for demographics, tumor stage, resection, and chemotherapy. Results: After imputation, 7,738,399 SNPs with a Quality Score &amp;gt;0.03, minor allele frequency &amp;gt;0.01, and Hardy-Weinberg equilibrium were obtained for analysis. Three loci in complete LD at chromosome 2 were associated with overall survival (OS) at genome-wide significance (P = 1 ×): rs113988120 in PAIP2B (poly(A) binding protein interacting protein 2B), rs112493246 and rs138529893 in DYSF (dysferlin). The variant allele was associated with 3.02 fold higher risk of dying (95% CI: 2.07-4.40). SNP rs113988120 and nine others were examined in the validation study and rs113988120 remained nominally significant (P = 2.85 ×; HR: 1.45, 95% CI: 1.04-2.03). In the combined datasets of 1688 patients, this SNP was significantly associated with OS (P = 1.46 × and HR = 1.80 (95% CI: 1.42-2.30). Conclusion: These observations support our hypothesis that genetic variations to a certain degree influence patient survival in pancreatic cancer. Key words: Pancreatic cancer, GWAS, Survival Citation Format: Hongwei Tang. Genome-wide scan for genetic markers modifying survival of pancreatic cancer patients. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3132.

Fine mapping the MHC region identified four independent variants modifying susceptibility to chronic hepatitis B in Han Chinese.

Several genome-wide association studies (GWAS) have demonstrated the association between genetic variants in the major histocompatibility complex (MHC) region and chronic hepatitis B (CHB) virus infection, but it is still unknown about the disease-causing loci and potential mechanisms owing to the complicated linkage disequilibrium for this region. To systematically characterize the MHC variations in relation to the CHB infection, we fine mapped the MHC region on our existing GWAS data with SNP2HLA taken the Pan-Asian panel as reference and finally identified four independent associations. The HLA-DPβ1 amino acid positions 84-87, which drove the effect of reported single nucleotide polymorphisms rs9277535 and rs3077, showed the most significant association (OR = 0.65, P = 2.03 × 10(-8)). The Leu-15 of HLA-C, conferring the effect of rs3130542, increased the risk of CHB infection independently (OR = 1.61, P = 3.42 × 10(-7)). The HLA-DRβ1*13, in perfect LD with glutamic at site 71, and rs400488, an expression quantitative trait locus for HLA-J, were newly identified to be associated with CHB infection independently (OR = 1.84, P = 3.84 × 10(-9); OR = 0.28, P = 6.27 × 10(-7), respectively). HLA-DPβ1 positions 84-87 and HLA-DRβ1 position 71 implicated the P1 and P4 in the antigen-binding groove, whereas HLA-C position 15 affected the signal peptide. These four independent loci together can explain ∼ 6% of the phenotypic variance for CHB infection, accounting for 72.94% of that explained by known genetic variations. We fine mapped the MHC region and identified four loci that independently drove the chronic HBV infection. The results provided a deeper understanding of the GWAS signals and identified additional susceptibility loci which were missed in previous association studies.

Genetic variants associated with serum thyroid stimulating hormone (TSH) levels in European Americans and African Americans from the eMERGE Network.

Thyroid stimulating hormone (TSH) hormone levels are normally tightly regulated within an individual; thus, relatively small variations may indicate thyroid disease. Genome-wide association studies (GWAS) have identified variants in PDE8B and FOXE1 that are associated with TSH levels. However, prior studies lacked racial/ethnic diversity, limiting the generalization of these findings to individuals of non-European ethnicities. The Electronic Medical Records and Genomics (eMERGE) Network is a collaboration across institutions with biobanks linked to electronic medical records (EMRs). The eMERGE Network uses EMR-derived phenotypes to perform GWAS in diverse populations for a variety of phenotypes. In this report, we identified serum TSH levels from 4,501 European American and 351 African American euthyroid individuals in the eMERGE Network with existing GWAS data. Tests of association were performed using linear regression and adjusted for age, sex, body mass index (BMI), and principal components, assuming an additive genetic model. Our results replicate the known association of PDE8B with serum TSH levels in European Americans (rs2046045 p = 1.85×10−17, β = 0.09). FOXE1 variants, associated with hypothyroidism, were not genome-wide significant (rs10759944: p = 1.08×10−6, β = −0.05). No SNPs reached genome-wide significance in African Americans. However, multiple known associations with TSH levels in European ancestry were nominally significant in African Americans, including PDE8B (rs2046045 p = 0.03, β = −0.09), VEGFA (rs11755845 p = 0.01, β = −0.13), and NFIA (rs334699 p = 1.50×10−3, β = −0.17). We found little evidence that SNPs previously associated with other thyroid-related disorders were associated with serum TSH levels in this study. These results support the previously reported association between PDE8B and serum TSH levels in European Americans and emphasize the need for additional genetic studies in more diverse populations.

Systematic fine-mapping of association with BMI and type 2 diabetes at the FTO locus by integrating results from multiple ethnic groups.

Background/ObjectiveThe 16q12.2 locus in the first intron of FTO has been robustly associated with body mass index (BMI) and type 2 diabetes in genome-wide association studies (GWAS). To improve the resolution of fine-scale mapping at FTO, we performed a systematic approach consisting of two parts.MethodsThe first part is to partition the associated variants into linkage disequilibrium (LD) clusters, followed by conditional and haplotype analyses. The second part is to filter the list of potential causal variants through trans-ethnic comparison.ResultsWe first examined the LD relationship between FTO SNPs showing significant association with type 2 diabetes in Japanese GWAS and between those previously reported in European GWAS. We could partition all the assayed or imputed SNPs showing significant association in the target FTO region into 7 LD clusters. Assaying 9 selected SNPs in 4 Asian-descent populations—Japanese, Vietnamese, Sri Lankan and Chinese (n≤26,109 for BMI association and n≤24,079 for type 2 diabetes association), we identified a responsible haplotype tagged by a cluster of SNPs and successfully narrowed the list of potential causal variants to 25 SNPs, which are the smallest in number among the studies conducted to date for FTO.ConclusionsOur data support that the power to resolve the causal variants from those in strong LD increases consistently when three distant populations—Europeans, Asians and Africans—are included in the follow-up study. It has to be noted that this fine-mapping approach has the advantage of applicability to the existing GWAS data set in combination with direct genotyping of selected variants.

VTET: a variable threshold exact test for identifying disease-associated copy number variations enriched in short genomic regions

Copy number variations (CNVs) constitute a major source of genetic variations in human populations and have been reported to be associated with complex diseases. Methods have been developed for detecting CNVs and testing CNV associations in genome-wide association studies (GWAS) based on SNP arrays. Commonly used two-step testing procedures work well only for long CNVs while direct CNV association testing methods work only for recurrent CNVs. Assuming that short CNVs disrupting any part of a given genomic region increase disease risk, we developed a variable threshold exact test (VTET) for testing disease associations of CNVs randomly distributed in the genome using intensity data from SNP arrays. By extensive simulations, we found that VTET outperformed two-step testing procedures based on existing CNV calling algorithms for short CNVs and that the performance of VTET was robust to the length of the genomic region. In addition, VTET had a comparable performance with CNVtools for testing the association of recurrent CNVs. Thus, we expect VTET to be useful for testing disease associations of both recurrent and randomly distributed CNVs using existing GWAS data. We applied VTET to a lung cancer GWAS and identified a genome-wide significant region on chromosome 18q22.3 for lung squamous cell carcinoma.

Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants

Recent developments in sequencing technologies have made it possible to uncover both rare and common genetic variants. Genome-wide association studies (GWASs) can test for the effect of common variants, whereas sequence-based association studies can evaluate the cumulative effect of both rare and common variants on disease risk. Many groupwise association tests, including burden tests and variance-component tests, have been proposed for this purpose. Although such tests do not exclude common variants from their evaluation, they focus mostly on testing the effect of rare variants by upweighting rare-variant effects and downweighting common-variant effects and can therefore lose substantial power when both rare and common genetic variants in a region influence trait susceptibility. There is increasing evidence that the allelic spectrum of risk variants at a given locus might include novel, rare, low-frequency, and common genetic variants. Here, we introduce several sequence kernel association tests to evaluate the cumulative effect of rare and common variants. The proposed tests are computationally efficient and are applicable to both binary and continuous traits. Furthermore, they can readily combine GWAS and whole-exome-sequencing data on the same individuals, when available, and are also applicable to deep-resequencing data of GWAS loci. We evaluate these tests on data simulated under comprehensive scenarios and show that compared with the most commonly used tests, including the burden and variance-component tests, they can achieve substantial increases in power. We next show applications to sequencing studies for Crohn disease and autism spectrum disorders. The proposed tests have been incorporated into the software package SKAT.

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.

Post-GWAS Functional Characterization of Susceptibility Variants for Chronic Lymphocytic Leukemia

Recent genome-wide association studies (GWAS) have identified several gene variants associated with sporadic chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). Many of these CLL/SLL susceptibility loci are located in non-coding or intergenic regions, posing a significant challenge to determine their potential functional relevance. Here, we review the literature of all CLL/SLL GWAS and validation studies, and apply eQTL analysis to identify putatively functional SNPs that affect gene expression that may be causal in the pathogenesis of CLL/SLL. We tested 12 independent risk loci for their potential to alter gene expression through cis-acting mechanisms, using publicly available gene expression profiles with matching genotype information. Sixteen SNPs were identified that are linked to differential expression of SP140, a putative tumor suppressor gene previously associated with CLL/SLL. Three additional SNPs were associated with differential expression of DACT3 and GNG8, which are involved in the WNT/β-catenin- and G protein-coupled receptor signaling pathways, respectively, that have been previously implicated in CLL/SLL pathogenesis. Using in silico functional prediction tools, we found that 14 of the 19 significant eQTL SNPs lie in multiple putative regulatory elements, several of which have prior implications in CLL/SLL or other hematological malignancies. Although experimental validation is needed, our study shows that the use of existing GWAS data in combination with eQTL analysis and in silico methods represents a useful starting point to screen for putatively causal SNPs that may be involved in the etiology of CLL/SLL.

‘Next-generation’ genome wide association studies

The first wave of cancer genome-wide association studies (GWAS) have revealed tens of independent loci marked by common variants of unknown or likely no functional significance that explain about 5-10% of familial risk for the particular disease. The approach taken to date has been conservative, and only a fraction of information has yet to be extracted from these expensive enterprises. For example, the Bonferroni procedure for selecting candidate phase II SNPs ignores many SNPs that happen to fail an extremely low p-value threshold. While this procedure does guarantee control of false positives, it seems counterintuitive to the purpose of phase I, which is to generate hypotheses based on promising candidates. Researchers have generally combined data from the discovery phase I and other phases and used ‘genome-wide thresholds’ based on assuming all SNPs are independent. Linkage disequilibrium (LD) makes it problematic to differentiate a real signal from highly correlated proxy signals. Most published GWAS do not examine SNP interactions due to: (a) the high computational complexity of computing pvalues for the interaction terms, and (b) the typically low power to detect significant interactions. It is plausible that more information should be extracted if: (i) higher order interactions are fitted, (ii) highly selected cases and controls are used in phase I, (iii) large replication studies are used, especially if involving existing GWAS data, (iv) the non-independence of SNPs is taken into account using, e.g. BEAGLE CALL or haplotype analyses, (v) focus is on candidate gene pathways, and/or functional SNPs, and (vi) rarer and more SNPs, such as is available from the Illumina 5M SNP chip, are used. We will illustrate these ideas using data from a GWAS of early-onset breast cancers, enriched for those with a family history, and a GWAS using extremes sample of extremes for mammographic density. We will also discuss the design of a large international breast cancer GWAS using the Illumina 5M SNP chip, phase I cases enriched for family history, population-based phase II cases and controls, population-based family study of candidate SNPs, and GxG analyses using ‘massively parallel’ super computing.