Bayesian semiparametric meta-analysis for genetic association studies

Abstract

We present a Bayesian semiparametric model for the meta-analysis of candidate gene studies with a binary outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping genetic markers in the same genetic region. Meta-analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequilibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta-analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian semiparametric model which models the observed genotype group frequencies conditional to the case/control status and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach allows borrowing of strength across studies and across markers. The analysis is based on a mixture of Dirichlet processes model as the underlying semiparametric model. Full posterior inference is performed through Markov chain Monte Carlo algorithms. The approach is demonstrated on simulated and real data.

Bayesian methods for genetic association analysis with heterogeneous subgroups: From meta-analyses to gene–environment interactions

“…Thus, although there has been previous work on Bayesian methods for meta-analysis (e.g. Sutton and Abrams (2001); Stangl and Berry (2000); DuMouchel and Harris (1983); Whitehead and Whitehead (1991); Li and Begg (1994); Eddy, Hasselblad and Schachter (1990); Givens, Smith and Tweedie (1997); Verzilli et al (2008); De Iorio et al (2011); Burgess et al (2010); Mila and Ngugi (2011)), the nature of our applications calls for a different focus. Specifically our applications need tools for association testing and model comparison, via Bayes Factors (BFs), rather than for estimation.…”

A simple-to-use method incorporating genomic markers into prostate cancer risk prediction tools facilitated future validation

“…Therefore, a method is needed for incorporating LD from an external source into the analysis. Recently, a study showed how LD information from the HapMap or elsewhere could be incorporated into an SNPphenotype association analysis [46].…”

Μεθοδολογία Γενετικής Επιδημιολογίας Και Μετα-Ανάλυσης Γενετικών Δεδομένων

“…αλγορίθμων MCMC. Πρόσφατα, έχει προταθεί μια μπεϋζιανή ημιπαραμετρική προσέγγιση όπου μοντελοποιούνται οι παρατηρηθείσες συχνότητες των γονοτύπων δοθείσης της παρουσίας/απουσίας της νόσου και χρησιμοποιούνται οι εκτιμήσεις της ανισορροπίας σύνδεσης των πολυμορφισμών σαν εκ των προτέρων πληροφορία για την εξαγωγή των εκ των υστέρων εκτιμήσεων (De Iorio,Newcombe et al 2011).από κοινού γονοτύπων, κάτι που σπανίως είναι διαθέσιμο. Έτσι, έχει προταθεί ο υπολογισμός των πιθανοτήτων των από κοινού γονοτύπων με τη χρήση μιας εκτίμησης του βαθμού της ανισορροπίας σύνδεσης υπό τη θεώρηση ότι υπάρχει ισορροπίαHardy-Weinberg.…”