scholarly journals A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery

2021 ◽  
Author(s):  
Leonardo Bottolo ◽  
Marco Banterle ◽  
Sylvia Richardson ◽  
Mika Ala‐Korpela ◽  
Marjo‐Riitta Järvelin ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
High Dimensional ◽  
Computationally Efficient ◽  
Seemingly Unrelated Regressions ◽  
Trait Loci

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]

scholarly journals R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations

Genetics ◽  
2018 ◽  
Vol 211 (2) ◽  
pp. 495-502 ◽  
Author(s):  
Karl W. Broman ◽  
Daniel M. Gatti ◽  
Petr Simecek ◽  
Nicholas A. Furlotte ◽  
Pjotr Prins ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Trait Loci

scholarly journals R/qtl2: software for mapping quantitative trait loci with high-dimensional data and multi-parent populations

2018 ◽  
Author(s):  
Karl W. Broman ◽  
Daniel M. Gatti ◽  
Petr Simecek ◽  
Nicholas A. Furlotte ◽  
Pjotr Prins ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Programming Languages ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
High Dimensional ◽  
Interactive Software ◽  
Software Environment ◽  
Trait Loci ◽  
Basic Features

AbstractR/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multi-parent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2 comes with a test framework and is free and open source software written in the R and C++ programming languages.

Identification of cis-regulated genes in quantitative trait loci driving liver fibrosis

2012 ◽  
Vol 50 (08) ◽  
Author(s):  
R Hall ◽  
R Müllenbach ◽  
S Huss ◽  
R Alberts ◽  
K Schughart ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Liver Fibrosis ◽  
Quantitative Trait ◽  
Trait Loci

Identification of NASH Quantitative Trait Loci (QTLs) using in silico mapping analysis in inbred mice

2004 ◽  
Vol 42 (08) ◽  
Author(s):  
F Lammert ◽  
S Hillebrandt ◽  
S Matern ◽  
K Spatz ◽  
RM Green
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
In Silico ◽  
Inbred Mice ◽  
In Silico Mapping ◽  
Mapping Analysis ◽  
Trait Loci

Quantitative trait loci polymorphism in the reserve population of dual-purpose chickens

2018 ◽  
Vol 20 (4) ◽  
pp. 74-80
Author(s):  
L. V. Shulika ◽  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Dual Purpose ◽  
Trait Loci
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