Bayesian Composite Model Space Approach for Mapping Quantitative Trait Loci in Variance Component Model

2009 ◽  
Vol 39 (3) ◽  
pp. 337-346 ◽  
Author(s):  
Ming Fang ◽  
ShengCai Liu ◽  
Dan Jiang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Variance Component ◽  
Model Space ◽  
Composite Model ◽  
Component Model ◽  
Variance Component Model ◽  
Trait Loci ◽  
Space Approach

scholarly journals Genome-wide evaluation for quantitative trait loci under the variance component model

Genetica ◽  
2010 ◽  
Vol 138 (9-10) ◽  
pp. 1099-1109 ◽  
Author(s):  
Lide Han ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Variance Component ◽  
Component Model ◽  
Variance Component Model ◽  
Genome Wide ◽  
Trait Loci

A new Bayesian automatic model selection approach for mapping quantitative trait loci under variance component model

Genetica ◽  
2008 ◽  
Vol 135 (3) ◽  
pp. 429-437 ◽  
Author(s):  
Ming Fang ◽  
Dan Jiang ◽  
Huijiang Gao ◽  
Dongxiao Sun ◽  
Runqing Yang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Model Selection ◽  
Quantitative Trait ◽  
Variance Component ◽  
Component Model ◽  
Variance Component Model ◽  
Automatic Model Selection ◽  
Model Selection Approach ◽  
Selection Approach ◽  
Trait Loci

scholarly journals Bayesian Mapping of Quantitative Trait Loci Under the Identity-by-Descent-Based Variance Component Model

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 411-422 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Posterior Distribution ◽  
Quantitative Trait ◽  
Variance Component ◽  
Threshold Model ◽  
Variance Component Model ◽  
Identity By Descent ◽  
Mapping Procedure ◽  
Trait Loci ◽  
Bayesian Mapping

AbstractVariance component analysis of quantitative trait loci (QTL) is an important strategy of genetic mapping for complex traits in humans. The method is robust because it can handle an arbitrary number of alleles with arbitrary modes of gene actions. The variance component method is usually implemented using the proportion of alleles with identity-by-descent (IBD) shared by relatives. As a result, information about marker linkage phases in the parents is not required. The method has been studied extensively under either the maximum-likelihood framework or the sib-pair regression paradigm. However, virtually all investigations are limited to normally distributed traits under a single QTL model. In this study, we develop a Bayes method to map multiple QTL. We also extend the Bayesian mapping procedure to identify QTL responsible for the variation of complex binary diseases in humans under a threshold model. The method can also treat the number of QTL as a parameter and infer its posterior distribution. We use the reversible jump Markov chain Monte Carlo method to infer the posterior distributions of parameters of interest. The Bayesian mapping procedure ends with an estimation of the joint posterior distribution of the number of QTL and the locations and variances of the identified QTL. Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families.

scholarly journals How to deal with genotype uncertainty in variance component quantitative trait loci analyses

2011 ◽  
Vol 93 (5) ◽  
pp. 333-342 ◽  
Author(s):  
XIA SHEN ◽  
LARS RÖNNEGÅRD ◽  
ÖRJAN CARLBORG
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Variance Component ◽  
Large Animal ◽  
Trait Loci ◽  
Sib Families ◽  
Variance Component Estimates ◽  
Variance Component Models ◽  
Component Models

SummaryDealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

scholarly journals Testing the Robustness of the Likelihood-Ratio Test in a Variance-Component Quantitative-Trait Loci–Mapping Procedure

1999 ◽  
Vol 65 (2) ◽  
pp. 531-544 ◽  
Author(s):  
David B. Allison ◽  
Michael C. Neale ◽  
Raffaella Zannolli ◽  
Nicholas J. Schork ◽  
Christopher I. Amos ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Quantitative Trait ◽  
Variance Component ◽  
Ratio Test ◽  
Quantitative Trait Loci Mapping ◽  
Mapping Procedure ◽  
Trait Loci

Mapping Quantitative Trait Loci in Complex Pedigrees: A Two-Step Variance Component Approach

Genetics ◽  
2000 ◽  
Vol 156 (4) ◽  
pp. 2081-2092 ◽  
Author(s):  
Andrew W George ◽  
Peter M Visscher ◽  
Chris S Haley
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Variance Component ◽  
Simulated Data ◽  
Statistical Techniques ◽  
Animal Populations ◽  
Trait Loci ◽  
Component Approach ◽  
Complex Relationships ◽  
Variance Component Approach

Abstract There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

scholarly journals A computationally efficient Bayesian Seemingly Unrelated Regressions model for high-dimensional Quantitative Trait Loci discovery

2018 ◽  
Author(s):  
L. Bottolo ◽  
M. Banterle ◽  
S. Richardson ◽  
M. Ala-Korpela ◽  
M-R. Järvelin ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Model Space ◽  
High Dimensional ◽  
Dependence Structure ◽  
Nucleotide Polymorphisms ◽  
Computationally Efficient ◽  
Seemingly Unrelated Regressions ◽  
Trait Loci ◽  
Graphical Structure

AbstractMotivationOur work is motivated by the search for metabolite Quantitative Trait Loci (QTL) in a cohort of more than 5,000 people. There are 158 metabolites measured by NMR spectroscopy measured in the 31-year follow-up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high-throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci.ResultsWe present a computationally efficient Bayesian Seemingly Unrelated Regressions (SUR) model for high-dimensional data, with cell-sparse variable selection and sparse graphical structure for covariance selection. Cell-sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies between phenotype variables. To achieve feasible computation of the large model space, we exploit a factorisation of the covariance matrix. Applying the model to the NFBC66 data with 9,000 directly-genotyped Single Nucleotide Polymorphisms, we are able to simultaneously estimate genotype-phenotype associations and the residual dependence structure amongst the metabolites at the same time.Availability and implementationThe R package BayesSUR with full documentation is available at https://cran.r-project.org/web/packages/BayesSUR/[email protected]

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