GWASeq: targeted re-sequencing follow up to GWAS

Abstract

BackgroundFor the last decade the conceptual framework of the Genome-Wide Association Study (GWAS) has dominated the investigation of human disease and other complex traits. While GWAS have been successful in identifying a large number of variants associated with various phenotypes, the overall amount of heritability explained by these variants remains small. This raises the question of how best to follow up on a GWAS, localize causal variants accounting for GWAS hits, and as a consequence explain more of the so-called “missing” heritability. Advances in high throughput sequencing technologies now allow for the efficient and cost-effective collection of vast amounts of fine-scale genomic data to complement GWAS.ResultsWe investigate these issues using a colon cancer dataset. After QC, our data consisted of 1993 cases, 899 controls. Using marginal tests of associations, we identify 10 variants distributed among six targeted regions that are significantly associated with colorectal cancer, with eight of the variants being novel to this study. Additionally, we perform so-called ‘SNP-set’ tests of association and identify two sets of variants that implicate both common and rare variants in the etiology of colorectal cancer.ConclusionsHere we present a large-scale targeted re-sequencing resource focusing on genomic regions implicated in colorectal cancer susceptibility previously identified in several GWAS, which aims to 1) provide fine-scale targeted sequencing data for fine-mapping and 2) provide data resources to address methodological questions regarding the design of sequencing-based follow-up studies to GWAS. Additionally, we show that this strategy successfully identifies novel variants associated with colorectal cancer susceptibility and can implicate both common and rare variants.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2459-y) contains supplementary material, which is available to authorized users.

Variants of genes encoding collagens and matrix metalloproteinase system increased the risk of aortic dissection

“…However, GWASs were performed using only known genetic markers involved in both cases and healthy controls, the ability to identify novel pathogenesis is limited compared with direct sequencing (Marian and Belmont, 2011;Salomon et al, 2016). It is clear that the imbalance of the homeostasis between collagens and MMPs/TIMPs system is a key responsible for extracellular matrix (ECM) degeneration and structure and functions of aorta, and therefore may contribute to the pathogenesis of AD (Barbour et al, 2007;Theruvath et al, 2012;Tsamis et al, 2013).…”

Cohort Profile: The Colon Cancer Family Registry Cohort (CCFRC)

“…This allows for the collection of DNA samples from caseprobands as well as their relatives, and the verification of reports of cancer by relatives rather than relying on selfreport only. This research includes the investigation of SNP associations, [43][44][45][46][47][48][49][50] genome panel testing 55 and statistical modelling of risk. 28,51,52 Many of these studies stemmed from the research within the CCFRC on syndromic classification of familial colorectal cancer, primarily the work leading to the description of 'familial colorectal cancer type X', the phenotype of MMRproficient colorectal cancer cases who fulfilled the Amsterdam Criteria for hereditary non-polyposis colon cancer, 13 which has become an accepted category of familial colorectal cancer.…”

Translating GWAS in rheumatic disease: approaches to establishing mechanism and function for genetic associations with ankylosing spondylitis

“…These include the use of genotyping arrays developed to study a specific set of SNPs, such as the Immunochip for immune-relevant variants, and statistical approaches that can define a small subset of statically likely casual SNPs, known as a credible set, that can then undergo functional annotations through the use of data sets and online predictors. Fine mapping typically involves imputation [ 52 ] and targeted re-sequencing [ 53 ] with stepwise conditional analysis to define independent association signals within a locus [ 54 ]. To narrow down the associations to particular variants, calculating a posterior probability in a Bayesian approach can define a credible set [ 54 ].…”