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Thousands of genetic variants are associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. Both lack of power, and joint tagging of two or more distinct causal variants by a single non-causal SNP, lead to inaccuracies in fine-mapping, with stochastic search more robust than stepwise. We develop a computationally efficient multinomial fine-mapping (MFM) approach that borrows information between diseases in a Bayesian framework. We show that MFM has greater accuracy than single disease analysis when shared causal variants exist, and negligible loss of precision otherwise. MFM analysis of six immune-mediated diseases reveals causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4 + T cells from genotype-selected individuals. MFM has the potential to increase fine-mapping resolution in related diseases enabling the identification of associated cellular and molecular phenotypes.

Shared genetic aetiology may explain the co-occurrence of diseases in individuals more often than expected by chance. On identifying associated variants shared between two traits, one objective is to determine whether such overlap may be explained by specific genomic characteristics (eg, functional annotation). In clinical studies, inter-rater agreement approaches assess concordance among expert opinions on the presence/absence of a complex disease for each subject. We adapt a two-stage inter-rater agreement model to the genetic association setting to identify features predictive of overlap variants, while accounting for their marginal trait associations. The resulting corrected overlap and marginal enrichment test (COMET) also assesses enrichment at the individual trait level. Multiple categories may be tested simultaneously and the method is computationally efficient, not requiring permutations to assess significance. In an extensive simulation study, COMET identifies features predictive of enrichment with high power and has well-calibrated type I error. In contrast, testing for overlap with a single-trait enrichment test has inflated type I error. COMET is applied to three glycaemic traits using a set of functional annotation categories as predictors, followed by further analyses that focus on tissue-specific regulatory variants. The results support previous findings that regulatory variants in pancreatic islets are enriched for fasting glucose-associated variants, and give insight into differences/similarities between characteristics of variants associated with glycaemic traits. Also, despite regulatory variants in pancreatic islets being enriched for variants that are marginally associated with fasting glucose and fasting insulin, there is no enrichment of shared variants between the traits., Competing Interests: The authors declare no conflict of interest.

Early studies of breast cancer microarray data used linear models to quantify the relationship between measures of gene expression (GE) and copy number (CN) obtained from tumour samples. Motivated by a study of women with axillary node-negative breast cancer, we propose a regression-based scan statistic to identify within-chromosome clusters of genetic probes that exhibit association between GE and CN, while accounting for tumour characteristics known to be prognostic for clinical outcome. As a measure of the association between GE and CN, for each genetic probe available from a microarray we regress GE on CN, and include subject-specific covariates. In the development of the scan statistic, the within-chromosome spatial distribution of the subset of probes with a statistically significant association is approximated by a Poisson process. By incorporating the distance between the probe positions, the scan statistic accounts for the spatial nature of CN alterations. Regions identified as clusters of significant associations are hypothesized to harbour genes involved in breast cancer progression. Using simulations, we examine the sensitivity of the method to certain factors, and to address issues of repeatability, we consider reappearance probabilities for each probe within detected regions and assess the utility of a quantity estimated by bootstrap sample frequencies. Applications of the proposed method to joint analysis of GE and CN in breast tumours, with and without an informative covariate, and comparisons with alternative methods suggest that inclusion of covariates and the use of a regional test statistic can serve to refine regions for further investigation including the analysis of their association with outcome., (Copyright © 2011 John Wiley & Sons, Ltd.)