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      Genome-wide association analysis of diverticular disease points towards neuromuscular, connective tissue and epithelial pathomechanisms

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      Gut

      BMJ

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          Abstract

          Objective

          Diverticular disease is a common complex disorder characterised by mucosal outpouchings of the colonic wall that manifests through complications such as diverticulitis, perforation and bleeding. We report the to date largest genome-wide association study (GWAS) to identify genetic risk factors for diverticular disease.

          Design

          Discovery GWAS analysis was performed on UK Biobank imputed genotypes using 31 964 cases and 419 135 controls of European descent. Associations were replicated in a European sample of 3893 cases and 2829 diverticula-free controls and evaluated for risk contribution to diverticulitis and uncomplicated diverticulosis. Transcripts at top 20 replicating loci were analysed by real-time quatitative PCR in preparations of the mucosal, submucosal and muscular layer of colon. The localisation of expressed protein at selected loci was investigated by immunohistochemistry.

          Results

          We discovered 48 risk loci, of which 12 are novel, with genome-wide significance and consistent OR in the replication sample. Nominal replication (p<0.05) was observed for 27 loci, and additional 8 in meta-analysis with a population-based cohort. The most significant novel risk variant rs9960286 is located near CTAGE1 with a p value of 2.3×10 −10 and 0.002 (OR allelic=1.14 (95% CI 1.05 to 1.24)) in the replication analysis. Four loci showed stronger effects for diverticulitis, PHGR1 (OR 1.32, 95% CI 1.12 to 1.56), FAM155A-2 (OR 1.21, 95% CI 1.04 to 1.42), CALCB (OR 1.17, 95% CI 1.03 to 1.33) and S100A10 (OR 1.17, 95% CI 1.03 to 1.33).

          Conclusion

          In silico analyses point to diverticulosis primarily as a disorder of intestinal neuromuscular function and of impaired connective fibre support, while an additional diverticulitis risk might be conferred by epithelial dysfunction.

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          Most cited references 51

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Is Open Access

            Second-generation PLINK: rising to the challenge of larger and richer datasets

             ,  ,   (2014)
            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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              LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

              Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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                Author and article information

                Journal
                Gut
                Gut
                BMJ
                0017-5749
                1468-3288
                April 08 2019
                May 2019
                May 2019
                January 19 2019
                : 68
                : 5
                : 854-865
                Article
                10.1136/gutjnl-2018-317619
                © 2019

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