NDLI: An Integrated Approach to Empirical Bayesian Whole Genome Prediction Modeling

Content Provider	Springer Nature Link
Author	Chen, C. Tempelman, R. J.
Copyright Year	2015
Abstract	Computational efficiency is an increasing concern for whole genome prediction (WGP) based on denser genetic marker panels such that algorithms other than Markov Chain Monte Carlo (MCMC) warrant greater consideration, particularly for hierarchical models that flexibly confer either heavy-tailed (e.g., BayesA) or stochastic search and variable selection (SSVS) instead of Gaussian specifications on marker effect distributions. The expectation maximization (EM) algorithm is one attractive alternative; however, recently proposed hierarchical model implementations of EM have not addressed formal estimation of underlying hyperparameters even though their specifications are known to impact WGP accuracy. Furthermore, EM can be sensitive to starting values. We develop and explore the properties of an empirical Bayes strategy by conditioning EM implementations of BayesA or SSVS WGP models on marginal modal estimation of variance components and other key hyperparameters. These empirical Bayes implementations are compared against their MCMC counterparts for estimation of hyperparameters and WGP accuracy, both within the context of a simulation study and application to a loblolly pine dataset. In all cases, starting values were deemed to be important for EM-based estimates. Starting values based on MCMC posterior means were preferable, whereas those based on setting all marker effects equal to zero generally led to inferior performance. Nevertheless, a recently proposed regularization procedure was useful in alleviating the impact of starting values in the EM implementation of the SSVS model, as was modifying the expectation step in the BayesA model to be based on relative variances rather than on relative precisions.
Starting Page	491
Ending Page	511
Page Count	21
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	20
Issue Number	4
e-ISSN	15372693
Language	English
Publisher	Springer US
Publisher Date	2015-09-28
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computational efficiency Expectation–maximization Genomic prediction Hierarchical Bayesian Variance component estimation Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach

Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions

A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys

Bayesian Calibration of Blue Crab (Callinectes sapidus) Abundance Indices Based on Probability Surveys

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

Genomic Prediction Models for Count Data

Hierarchical Bayesian Spatio-Temporal Conway–Maxwell Poisson Models with Dynamic Dispersion

A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

An Integrated Approach to Empirical Bayesian Whole Genome Prediction Modeling

Similar Documents

Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach

Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions

A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys

Bayesian Calibration of Blue Crab (Callinectes sapidus) Abundance Indices Based on Probability Surveys

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

Genomic Prediction Models for Count Data

Hierarchical Bayesian Spatio-Temporal Conway–Maxwell Poisson Models with Dynamic Dispersion

A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

An Integrated Approach to Empirical Bayesian Whole Genome Prediction Modeling