Welcome Trust Case Control Consortium Research Articles

A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping

BackgroundBayesian mixture models in which the effects of SNP are assumed to come from normal distributions with different variances are attractive for simultaneous genomic prediction and QTL mapping. These models are usually implemented with Monte Carlo Markov Chain (MCMC) sampling, which requires long compute times with large genomic data sets. Here, we present an efficient approach (termed HyB_BR), which is a hybrid of an Expectation-Maximisation algorithm, followed by a limited number of MCMC without the requirement for burn-in.ResultsTo test prediction accuracy from HyB_BR, dairy cattle and human disease trait data were used. In the dairy cattle data, there were four quantitative traits (milk volume, protein kg, fat% in milk and fertility) measured in 16,214 cattle from two breeds genotyped for 632,002 SNPs. Validation of genomic predictions was in a subset of cattle either from the reference set or in animals from a third breeds that were not in the reference set. In all cases, HyB_BR gave almost identical accuracies to Bayesian mixture models implemented with full MCMC, however computational time was reduced by up to 1/17 of that required by full MCMC. The SNPs with high posterior probability of a non-zero effect were also very similar between full MCMC and HyB_BR, with several known genes affecting milk production in this category, as well as some novel genes. HyB_BR was also applied to seven human diseases with 4890 individuals genotyped for around 300 K SNPs in a case/control design, from the Welcome Trust Case Control Consortium (WTCCC). In this data set, the results demonstrated again that HyB_BR performed as well as Bayesian mixture models with full MCMC for genomic predictions and genetic architecture inference while reducing the computational time from 45 h with full MCMC to 3 h with HyB_BR.ConclusionsThe results for quantitative traits in cattle and disease in humans demonstrate that HyB_BR can perform equally well as Bayesian mixture models implemented with full MCMC in terms of prediction accuracy, but with up to 17 times faster than the full MCMC implementations. The HyB_BR algorithm makes simultaneous genomic prediction, QTL mapping and inference of genetic architecture feasible in large genomic data sets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3082-7) contains supplementary material, which is available to authorized users.

Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.

Replication of Identified Inflammatory Bowel Diseases Genetic Associations: A Case–Control Study in the Tunisian Population

Inflammatory bowel diseases (IBD) — Crohn’s disease (CD) and ulcerative colitis (UC) — are chronic gastrointestinal inflammatory disorders with a complex genetic background. A genome wide association scan by the Welcome Trust Case Control Consortium (WTCCC) recently identified several novel susceptibility loci. We performed a replication study in 107 IBD patients (39 CD and 68 UC) and 162 controls. In total, 19 single nucleotide polymorphisms (SNPs) from previously identified susceptibility genes PTPN11, TNF α, IL23R, PTPN2, PTPN22, IL2 and IL10 were studied. In UC, we confirmed the association with PTPN2 (rs254215, GG, P=0.013, Pcorr=0.039; OR= 6.23 (1.18; 32.95)). In CD, we confirmed a marginal association with ((rs11066320, GG, P=0.018, Pcorr=0.054, OR= 0.4 (0.19; 0.82)). No significant association was found at the allele and genotype levels of SNPs in TNF α, IL23R, PTPN22, IL2, and IL10. However, on the haplotype analysis the AG haplotype of TNFα was more frequent in CD patients compared to controls (23.1% vs. 13.7%; P = 0.039; OR= 1.89 (1.02; 3.5)). The PTPN11ATG haplotype was also more frequent in CD patients compared to controls (32.1% vs 21.3%; P = 0.04; OR= 1.75 (1.01; 3.01). These results reveal limited replication in Tunisian population and indicate differences in genetic architecture between populations.

High-Order Interactions in Rheumatoid Arthritis Detected by Bayesian Method using Genome-Wide Association Studies Data

Problem statement: In order to reveal the missing genetic component of Rheumatoid Arthritis (RA) susceptibility, we carried out a genome-wide high-order epistatic interaction study for RA. Approach: A powerful Bayesian strategy was applied to analyze the data of Genome-Wide Association Studies (GWAS) from the Welcome Trust Case Control Consortium (WTCCC), where 319 high-order interactions were found across the whole genome and many of which were validated by the GWAS data from the North American Rheumatoid Arthritis Consortium (NARAC). Results: This is the first study intensively searching for high-order epistatic interactions genome-widely for RA. Conclusion: Our results suggest that high-order interactions might explain a big proportion of missing genetic component of RA. In the meanwhile, synapse, calcium ion binding and membrane part likely have interactive associations with RA. This finding implies that not only autoimmune system but also nervous system can play an important role in RA.

68 Rare alleles in genetic predisposition to coronary artery disease: insights from the novel analysis of gene-centric array

BackgroundGenome-wide association studies have been successful in identifying association between several common variants and coronary artery disease (CAD). However, collectively these variants explain only a small proportion of CAD heritability....

Transferrin receptor‐1 gene polymorphisms are associated with type 2 diabetes

Iron is involved in oxidative stress and type 2 diabetes (T2D). Transferrin receptor (TFRC) constitutes the major receptor by which most cells take up iron. The aim of this study was to evaluate whether TFRC gene polymorphisms are associated with T2D. We evaluated TFRC gene polymorphism (rs3817672, 210AG, S142G) in a sample of T2D patients and nondiabetic controls (n = 722), and 39 SNPs within the TFRC genomic region analysed by the Welcome Trust Case Control Consortium (WTCCC) (1921 T2D subjects and 3000 controls). In a subset of subjects, glucose tolerance and insulin sensitivity were also studied. The frequency of the G allele at the position 210 of the TFRC gene was significantly higher in T2D patients. Both GG and GA genotypes had a 69% (P < 0.01) greater risk of developing T2D estimated under a dominant model. The increased prevalence of the G allele run in parallel to increased sex-adjusted log-serum ferritin and slightly increased soluble transferrin receptor among patients with T2D. Furthermore, post-load glucose and insulin sensitivity were significantly associated with circulating soluble transferrin receptor, and insulin sensitivity was significantly associated with serum ferritin among G allele carriers, (r = -0.33, P = 0.001) but not in AA homozygotes. Sixteen other TFRC SNPs were also associated to T2D according to the Welcome Trust Case Control Consortium data. TFRC gene variants are associated with T2D.

Association of three polymorphisms selected from a genome-wide association study with coronary heart disease in the Tunisian population

Despite extensive exploration of many genes, strong evidence of a molecular genetic association with coronary heart disease (CHD) or myocardial infarction (MI) remains to be obtained. Recently, significant interest has emerged in mapping genetic susceptibility for complex traits through whole-genome studies association generating promoting data that will determine the genetic contribution to common human diseases such as coronary heart disease. The aim of the present case-control study including 324 healthy controls and 296 patients with coronary heart disease from Tunisia, was to assess relation between three polymorphisms previously reported to be strongly associated with coronary heart disease in the Welcome Trust Case Control Consortium (WTCCC) and the German myocardial infarction family studies: locus 9p21.3 (rs 1333049), locus 6q25.1 (rs6922269) and 2q36.3 (rs2943634). By single locus analysis, no differences in genotype distribution and allelic frequency were found between the two groups of study. The risk allele (C) for rs2943634 was less frequent among Tunisian population than in controls from the WTCCC and German studies (57% vs 65%). The three SNPs previously reported to be associated with CHD were not replicated in our small sample.

Genome-wide scanning versus candidate gene approach in the genetic architecture of common diseases.

AbstractIn the Nature 7 June issue, researchers from the Welcome Trust Case Control Consortium (WTCCC) reported the findings of a large genome-wide association (GWA) study of 14,000 cases of common diseases and 3000 controls studies revealing the role of several loci in the genetic risk of common diseases. We wish to emphasize that by making public the access to databases, consortia allow other investigators to search for association between candidate genes and T2D-related phenotypes. As an example we found, in a pilot study enrolling 1100 individuals that SNPs in the CLOCK (i.e. rs1554483 and rs6843722) were associated with hypertension (p value &lt; 0.01, and 0.01, respectively). Interestingly, the same association was found by the WTCCC study (rs1554483, rs4580704, rs6843722 and rs4864548 with p&lt;0.039, 0.014, 0.049 and 0.019). However, these findings were not mentioned by the authors because of the stringent p value criteria logically used by them in the face of a WGA.