Dissection of a complex disease susceptibility region using a Bayesian stochastic search approach to fine mapping

Abstract

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1Dassociated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r 2 ⋍ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4 + T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data. Author SummaryGenetic association studies have identified many DNA sequence variants that associate with disease risk. By exploiting the known correlation that exists between neighbouring variants in the genome, inference can be extended beyond those individual variants tested to identify sets within which a causal variant is likely to reside. However, this correlation, particularly in the presence of multiple disease causing variants in relative proximity, makes disentangling the specific causal variants difficult. Statistical approaches to this fine mapping problem have traditionally taken a stepwise search approach, beginning with the most associated variant in a region, then iteratively attempting to find additional associated variants. We adapted a stochastic search approach that avoids this stepwise process and is explicitly designed for dealing with highly correlated predictors to the fine mapping problem. We showed in simulated data that it outperforms its stepwise counterpart and other variable selection strategies such as the lasso. We applied our approach to understand the association of two immune-mediated dis...

A simple new approach to variable selection in regression, with application to genetic fine-mapping

“…Our approach builds on previous work on Bayesian variable selection in regression (BVSR) (Mitchell and Beauchamp, 1988;George and McCulloch, 1997), which has already been widely applied to genetic fine-mapping and related applications (e.g., Meuwissen et al, 2001;Sillanpää and Bhattacharjee, 2005;Servin and Stephens, 2007;Hoggart et al, 2008;Stephens and Balding, 2009;Logsdon et al, 2010;Guan and Stephens, 2011;Bottolo et al, 2011;Maller et al, 2012;Carbonetto and Stephens, 2012;Zhou et al, 2013;Hormozdiari et al, 2014;Chen et al, 2015;Wallace et al, 2015;Moser et al, 2015;Wen et al, 2016;Lee et al, 2018). BVSR is an attractive approach to these problems because it can, in principle, assess uncertainty in which variables to select, even when the variables are highly correlated.…”

“…However, applying BVSR in practice remains difficult for at least two reasons. First, BVSR is computationally challenging, often requiring implementation of sophisticated Markov chain Monte Carlo or stochastic search algorithms (e.g., Bottolo and Richardson, 2010;Bottolo et al, 2011;Guan and Stephens, 2011;Wallace et al, 2015;Benner et al, 2016;Wen et al, 2016;Lee et al, 2018). Second, and perhaps more importantly, the output from BVSR methods is typically a complex posterior distribution -or samples approximating the posterior distribution -and this can be difficult to distill into results that are easily interpretable.…”

A flexible and parallelizable approach to genome‐wide polygenic risk scores

“…Various extensions to the original method have been successfully applied in genomics to explore multi‐single nucleotide polymorphism (SNP) models of disease (Vignal, Bansal, & Balding, ; Wu, Chen, Hastie, Sobel, & Lange, ) or to search for master predictors (Peng, Zhu, & Bergamaschi, ). Bayesian versions of the LASSO have also been described (Griffin & Brown, ; Park & Casella, ) and used for efficient variable selection in genetics (Bottolo et al, ; Newcombe, Conti, & Richardson, ; Servin & Stephens, ; Tachmazidou, Johnson, & De Iorio, ; Wallace et al, ). Attractive features of Bayesian sparse regression include inference of posterior probabilities for each predictor, posterior inference on competing combinations, and, potentially most importantly, the possibility of incorporating prior information into the analysis.…”

Sharing information between related diseases using Bayesian joint fine mapping increases accuracy and identifies novel associations in six immune mediated diseases

“…It has been noted that the SNP with the 28 smallest p value need not be causal, especially if it is in LD with two causal SNPs. 5 Alternative 29 Bayesian fine mapping methods have been developed which use a stochastic search instead of 30 stepwise search [6][7][8] . Stepwise and stochastic search results may disagree 8 and although stochastic 31 search generally demonstrates improved accuracy 9 these techniques have not yet been widely 32 adopted.…”

“…5 Alternative 29 Bayesian fine mapping methods have been developed which use a stochastic search instead of 30 stepwise search [6][7][8] . Stepwise and stochastic search results may disagree 8 and although stochastic 31 search generally demonstrates improved accuracy 9 these techniques have not yet been widely 32 adopted. 33 34 Here, we systematically compare stepwise and stochastic approaches by application to dense 35 genotype data for six IMD.…”