A robust clustering algorithm for identifying problematic samples in genome-wide association studies

Summary: High-throughput genotyping arrays provide an efficient way to survey single nucleotide polymorphisms (SNPs) across the genome in large numbers of individuals. Downstream analysis of the data, for example in genome-wide association studies (GWAS), often involves statistical models of genotype frequencies across individuals. The complexities of the sample collection process and the potential for errors in the experimental assay can lead to biases and artefacts in an individual's inferred genotypes. Rather than attempting to model these complications, it has become a standard practice to remove individuals whose genome-wide data differ from the sample at large. Here we describe a simple, but robust, statistical algorithm to identify samples with atypical summaries of genome-wide variation. Its use as a semi-automated quality control tool is demonstrated using several summary statistics, selected to identify different potential problems, and it is applied to two different genotyping platforms and sample collections

A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies

peer reviewe

Third European evidence-based consensus on diagnosis and management of ulcerative colitis. Part 2: Current management

info:eu-repo/semantics/publishedVersio

Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method

Background: Predicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn's disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach. Methods: We performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation. Results: On average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis. Conclusions: Our analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.Guo-Bo Chen, Sang Hong Lee, Grant W. Montgomery, Naomi R. Wray, Peter M. Visscher, Richard B. Gearry, Ian C. Lawrance, Jane M. Andrews, Peter Bampton, Gillian Mahy, Sally Bell, Alissa Walsh, Susan Connor, Miles Sparrow, Lisa M. Bowdler, Lisa A. Simms, Krupa Krishnaprasad, the International IBD Genetics Consortium, Graham L. Radford-Smith, and Gerhard Moser

Investigation of Multiple Susceptibility Loci for Inflammatory Bowel Disease in an Italian Cohort of Patients

BACKGROUND: Recent GWAs and meta-analyses have outlined about 100 susceptibility genes/loci for inflammatory bowel diseases (IBD). In this study we aimed to investigate the influence of SNPs tagging the genes/loci PTGER4, TNFSF15, NKX2-3, ZNF365, IFNG, PTPN2, PSMG1, and HLA in a large pediatric- and adult-onset IBD Italian cohort. METHODS: Eight SNPs were assessed in 1,070 Crohn's disease (CD), 1,213 ulcerative colitis (UC), 557 of whom being diagnosed at the age of ≤16 years, and 789 healthy controls. Correlations with sub-phenotypes and major variants of NOD2 gene were investigated. RESULTS: The SNPs tagging the TNFSF15, NKX2-3, ZNF365, and PTPN2 genes were associated with CD (P values ranging from 0.037 to 7×10(-6)). The SNPs tagging the PTGER4, NKX2-3, ZNF365, IFNG, PSMG1, and HLA area were associated with UC (P values 0.047 to 4×10(-5)). In the pediatric cohort the associations of TNFSF15, NKX2-3 with CD, and PTGER4, NKX2-3, ZNF365, IFNG, PSMG1 with UC, were confirmed. Association with TNFSF15 and pediatric UC was also reported. A correlation with NKX2-3 and need for surgery (P  =  0.038), and with HLA and steroid-responsiveness (P  =  0.024) in UC patients was observed. Moreover, significant association in our CD cohort with TNFSF15 SNP and colonic involvement (P  =  0.021), and with ZNF365 and ileal location (P  =  0.024) was demonstrated. CONCLUSIONS: We confirmed in a large Italian cohort the associations with CD and UC of newly identified genes, both in adult and pediatric cohort of patients, with some influence on sub-phenotypes

Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data

As custom arrays are cheaper than generic GWAS arrays, larger sample size is achievable for gene discovery. Custom arrays can tag more variants through denser genotyping of SNPs at associated loci, but at the cost of losing genome-wide coverage. Balancing this trade-off is important for maximizing experimental designs. We quantified both the gain in captured SNP-heritability at known candidate regions and the loss due to imperfect genome-wide coverage for inflammatory bowel disease using immunochip (iChip) and imputed GWAS data on 61 251 and 38 550 samples, respectively. For Crohn's disease (CD), the iChip and GWAS data explained 19 and 26% of variation in liability, respectively, and SNPs in the densely genotyped iChip regions explained 13% of the SNP-heritability for both the iChip and GWAS data. For ulcerative colitis (UC), the iChip and GWAS data explained 15 and 19% of variation in liability, respectively, and the dense iChip regions explained 10 and 9% of the SNP-heritability in the iChip and the GWAS data. From bivariate analyses, estimates of the genetic correlation in risk between CD and UC were 0.75 (SE 0.017) and 0.62 (SE 0.042) for the iChip and GWAS data, respectively. We also quantified the SNP-heritability of genomic regions that did or did not contain the previous 163 GWAS hits for CD and UC, and SNP-heritability of the overlapping loci between the densely genotyped iChip regions and the 163 GWAS hits. For both diseases, over different genomic partitioning, the densely genotyped regions on the iChip tagged at least as much variation in liability as in the corresponding regions in the GWAS data, however a certain amount of tagged SNP-heritability in the GWAS data was lost using the iChip due to the low coverage at unselected regions. These results imply that custom arrays with a GWAS backbone will facilitate more gene discovery, both at associated and novel loci

The molecular classification of the clinical manifestations of Crohn's disease

The clinical pattern of Crohn's disease may be defined by specific genotypes. This study may provide the basis for a future molecular classification of disease