Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Wu, Xiaolin; Sun, Chuanyu; Beissinger, Timothy; Rosa, Guilherme J. M.; Weigel, K.A.; Gatti, Natalia de Leon; Gianola, Daniel

doi:10.1186/1297-9686-44-29

Cited by 18 publications

(21 citation statements)

References 30 publications

(41 reference statements)

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“…ALDsuite retains computation efficiency as the number and density of markers increases by analyzing PCs of small chromosomal regions. Additional computational efficiency can be achieved in multicore environments with support for the parallelization of ALDsuite using a distributed MCMC approach in which a separate analysis, or chain, is run for each parallel process [ 37 , 38 ]. In order to avoid unnecessary duplication of effort during the burn-in phase, each chain reports back to the main process after each iteration, where a remote proposal of each parameter is calculated based on the average of all parallel chains.…”

Section: Methodsmentioning

confidence: 99%

ALDsuite: Dense marker MALD using principal components of ancestral linkage disequilibrium

Johnson

Nelson

Zagury³

et al. 2015

BMC Genet

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BackgroundMapping by admixture linkage disequilibrium (MALD) is a whole genome gene mapping method that uses LD from extended blocks of ancestry inherited from parental populations among admixed individuals to map associations for diseases, that vary in prevalence among human populations. The extended LD queried for marker association with ancestry results in a greatly reduced number of comparisons compared to standard genome wide association studies. As ancestral population LD tends to confound the analysis of admixture LD, the earliest algorithms for MALD required marker sets sufficiently sparse to lack significant ancestral LD between markers. However current genotyping technologies routinely provide dense SNP data, which convey more information than sparse sets, if this information can be efficiently used. There are currently no software solutions that offer both local ancestry inference using dense marker data and disease association statistics.ResultsWe present here an R package, ALDsuite, which accounts for local LD using principal components of haplotypes from surrogate ancestral population data, and includes tools for quality control of data, MALD, downstream analysis of results and visualization graphics.ConclusionsALDsuite offers a fast, accurate estimation of global and local ancestry and comes bundled with the tools needed for MALD, from data quality control through mapping of and visualization of disease genes.

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Section: Methodsmentioning

confidence: 99%

ALDsuite: Dense marker MALD using principal components of ancestral linkage disequilibrium

Johnson

Nelson

Zagury³

et al. 2015

BMC Genet

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“…Ridge regression is also originated to handle the problem of inverting a nearly singular matrix and Cholesky decomposition is usually adopted to speed up the matrix inversion. Some high-performance Gibbs samplers and M-H sampling algorithms have been developed [71]. A straightforward solution is to run multiple chains simultaneously (see Gelman and Rubin [72]) on multiple virtual machines in computer clusters or using Cloud Computing platforms.…”

Section: Methodsmentioning

confidence: 99%

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

et al. 2013

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The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

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“…In the meantime, researchers continue to strive to improve computational efficiency of MCMC sampling schemes in animal breeding (Shariati and Sorensen 2008;Shariati et al 2009;Yang et al 2015). Animal breeding scientists have been particularly astute in developing computational enhancements for WGP models including parallel computing Wu et al 2012), Gauss Seidel residual updating (Legarra and Misztal 2008), or other right-hand-side updating strategies (Calus 2014). Although most code created for public domain use is written using efficient low-level programming languages such as FORTRAN, C or C++ Fernando 2009), often with convenient R wrapper packages (Endelman 2011;Pérez and de los Campos 2014;Wimmer et al 2012), the animal breeding community nevertheless has also been conscientious to provide lucid R code for pedagogical purposes as well (Gondro et al 2013) in an attempt to train and engage future researchers to further pursue these challenges.…”

Section: Improving Mcmc Schemesmentioning

confidence: 99%

Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding

Tempelman

2015

JABES

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Whole genome prediction (WGP) modeling and genome-wide association (GWA) analyses are big data issues in agricultural quantitative genetics. Both areas require meaningful input from the statistical scholarly community in order to further improve the accuracy of prediction of genetic merit and inference on putative causal variants as well as improving the computational efficiency of existing methods and algorithms. These concerns have become increasingly critical as new sequencing technologies will only exacerbate current model dimensionality problems. We focus primarily on mixed model and hierarchical Bayesian analyses which have been most commonly pursued by animal and plant breeders for WGP thus far. We draw attention to our observation that many such previous analyses have not carefully inferred upon hyperparameters defined at the top levels of the Bayesian model hierarchy, but simply arbitrarily specify their values. We also reassess previous discussions on WGP model dimensionality, believing that useful data augmentation schemes utilized in various Markov Chain Monte Carlo (MCMC) schemes have led to a general misunderstanding that heavy-tailed or variable selection-based WGP models may be highly parameterized relative to more standard mixed model representations. Computational efficiency is addressed with respect to MCMC and competitive, albeit approximate, alternatives. Furthermore, GWA analyses are reassessed, encouraging a greater reliance on shrinkage-based inferences based on critically chosen priors, instead of potentially nonreproducible fixed effects P valuebased inference.

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Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Cited by 18 publications

References 30 publications

ALDsuite: Dense marker MALD using principal components of ancestral linkage disequilibrium

ALDsuite: Dense marker MALD using principal components of ancestral linkage disequilibrium

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding

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