Dataset Information

Replicability analysis in genome-wide association studies via Cartesian hidden Markov models.

ABSTRACT: BACKGROUND:Replicability analysis which aims to detect replicated signals attracts more and more attentions in modern scientific applications. For example, in genome-wide association studies (GWAS), it would be of convincing to detect an association which can be replicated in more than one study. Since the neighboring single nucleotide polymorphisms (SNPs) often exhibit high correlation, it is desirable to exploit the dependency information among adjacent SNPs properly in replicability analysis. In this paper, we propose a novel multiple testing procedure based on the Cartesian hidden Markov model (CHMM), called repLIS procedure, for replicability analysis across two studies, which can characterize the local dependence structure among adjacent SNPs via a four-state Markov chain. RESULTS:Theoretical results show that the repLIS procedure can control the false discovery rate (FDR) at the nominal level ? and is shown to be optimal in the sense that it has the smallest false non-discovery rate (FNR) among all ?-level multiple testing procedures. We carry out simulation studies to compare our repLIS procedure with the existing methods, including the Benjamini-Hochberg (BH) procedure and the empirical Bayes approach, called repfdr. Finally, we apply our repLIS procedure and repfdr procedure in the replicability analyses of psychiatric disorders data sets collected by Psychiatric Genomics Consortium (PGC) and Wellcome Trust Case Control Consortium (WTCCC). Both the simulation studies and real data analysis show that the repLIS procedure is valid and achieves a higher efficiency compared with its competitors. CONCLUSIONS:In replicability analysis, our repLIS procedure controls the FDR at the pre-specified level ? and can achieve more efficiency by exploiting the dependency information among adjacent SNPs.

SUBMITTER: Wang P

PROVIDER: S-EPMC6423849 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Replicability analysis in genome-wide association studies via Cartesian hidden Markov models.

Wang Pengfei P Zhu Wensheng W

BMC bioinformatics 20190318 1

<h4>Background</h4>Replicability analysis which aims to detect replicated signals attracts more and more attentions in modern scientific applications. For example, in genome-wide association studies (GWAS), it would be of convincing to detect an association which can be replicated in more than one study. Since the neighboring single nucleotide polymorphisms (SNPs) often exhibit high correlation, it is desirable to exploit the dependency information among adjacent SNPs properly in replicability a ...[more]

PMID: 30885122

Similar Datasets

Project description:MotivationMultiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable.ResultsWe present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the other data series and estimate their effects on the odds of each transition in the given series. Following this, hidden states are inferred using a standard forward-backward algorithm, with the transition probabilities adjusted by the model at each position, which helps retain the order of computation close to fitting independent HMMs (iHMM). Hence, scHMM is a collection of inter-dependent non-homogeneous HMMs, capable of giving a close approximation to a fully multivariate HMM fit. A simulation study shows that scHMM achieves comparable sensitivity to the multivariate HMM fit at a much lower computational cost. The method was demonstrated in the joint analysis of 39 histone modifications, CTCF and RNA polymerase II in human CD4+ T cells. scHMM reported fewer high-confidence regions than iHMM in this dataset, but scHMM could recover previously characterized histone modifications in relevant genomic regions better than iHMM. In addition, the resulting combinatorial patterns from scHMM could be better mapped to the 51 states reported by the multivariate HMM method of Ernst and Kellis.AvailabilityThe scHMM package can be freely downloaded from http://sourceforge.net/p/schmm/ and is recommended for use in a linux environment.

Project description:BackgroundRecent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.ResultsIn this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.ConclusionsThese newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation.AvailabilityThe software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip.

Dataset Information

Replicability analysis in genome-wide association studies via Cartesian hidden Markov models.

Publications

Replicability analysis in genome-wide association studies via Cartesian hidden Markov models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets