scholarly journals Prediction in high‐dimensional linear models and application to genomic selection under imperfect linkage disequilibrium

2021 ◽  
Author(s):  
Charles‐Elie Rabier ◽  
Simona Grusea
Keyword(s):  
Linkage Disequilibrium ◽  
Genomic Selection ◽  
Linear Models ◽  
High Dimensional

scholarly journals On the accuracy in high‐dimensional linear models and its application to genomic selection

2018 ◽  
Vol 46 (1) ◽  
pp. 289-313
Author(s):  
Charles‐Elie Rabier ◽  
Brigitte Mangin ◽  
Simona Grusea
Keyword(s):  
Genomic Selection ◽  
Linear Models ◽  
High Dimensional

scholarly journals Genomic selection in French dairy cattle

2012 ◽  
Vol 52 (3) ◽  
pp. 115 ◽  
Author(s):  
D. Boichard ◽  
F. Guillaume ◽  
A. Baur ◽  
P. Croiseau ◽  
M. N. Rossignol ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Genomic Selection ◽  
Reference Population ◽  
Specific Reference ◽  
Nucleotide Polymorphisms ◽  
Genomic Evaluation ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values ◽  
Restricted Use

Genomic selection is implemented in French Holstein, Montbéliarde, and Normande breeds (70%, 16% and 12% of French dairy cows). A characteristic of the model for genomic evaluation is the use of haplotypes instead of single-nucleotide polymorphisms (SNPs), so as to maximise linkage disequilibrium between markers and quantitative trait loci (QTLs). For each trait, a QTL-BLUP model (i.e. a best linear unbiased prediction model including QTL random effects) includes 300–700 trait-dependent chromosomal regions selected either by linkage disequilibrium and linkage analysis or by elastic net. This model requires an important effort to phase genotypes, detect QTLs, select SNPs, but was found to be the most efficient one among all tested ones. QTLs are defined within breed and many of them were found to be breed specific. Reference populations include 1800 and 1400 bulls in Montbéliarde and Normande breeds. In Holstein, the very large reference population of 18 300 bulls originates from the EuroGenomics consortium. Since 2008, ~65 000 animals have been genotyped for selection by Labogena with the 50k chip. Bulls genomic estimated breeding values (GEBVs) were made official in June 2009. In 2010, the market share of the young bulls reached 30% and is expected to increase rapidly. Advertising actions have been undertaken to recommend a time-restricted use of young bulls with a limited number of doses. In January 2011, genomic selection was opened to all farmers for females. Current developments focus on the extension of the method to a multi-breed context, to use all reference populations simultaneously in genomic evaluation.

scholarly journals Drawing inferences for high‐dimensional linear models: A selection‐assisted partial regression and smoothing approach

Biometrics ◽  
2019 ◽  
Vol 75 (2) ◽  
pp. 551-561
Author(s):  
Zhe Fei ◽  
Ji Zhu ◽  
Moulinath Banerjee ◽  
Yi Li
Keyword(s):  
Linear Models ◽  
High Dimensional ◽  
Partial Regression

The use of linkage disequilibrium to map quantitative trait loci

2005 ◽  
Vol 45 (8) ◽  
pp. 837 ◽  
Author(s):  
M. E. Goddard ◽  
T. H. E. Meuwissen
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Statistical Model ◽  
Quantitative Trait ◽  
Linear Models ◽  
Chromosome Segment ◽  
Identical By Descent ◽  
Trait Loci ◽  
Linkage Disequilibrium Information ◽  
Chromosome Segments

This paper reviews the causes of linkage disequilibrium and its use in mapping quantitative trait loci. The many causes of linkage disequilibrium can be understood as due to similarity in the coalescence tree of different loci. Consideration of the way this comes about allows us to divide linkage disequilibrium into 2 types: linkage disequilibrium between any 2 loci, even if they are unlinked, caused by variation in the relatedness of pairs of animals; and linkage disequilibrium due to the inheritance of chromosome segments that are identical by descent from a common ancestor. The extent of linkage disequilibrium due to the latter cause can be logically measured by the chromosome segment homozygosity which is the probability that chromosome segments taken at random from the population are identical by descent. This latter cause of linkage disequilibrium allows us to map quantitative trait loci to chromosome regions. The former cause of linkage disequilibrium can cause artefactual quantitative trait loci at any position in the genome. These artefacts can be avoided by fitting the relatedness of animals in the statistical model used to map quantitative trait loci. In the future it may be convenient to estimate this degree of relatedness between individuals from markers covering the whole genome. The statistical model for mapping quantitative trait loci also requires us to estimate the probability that 2 animals share quantitative trait loci alleles at a particular position because they have inherited a chromosome segment containing the quantitative trait loci identical by descent. Current methods to do this all involve approximations. Methods based on concepts of coalescence and chromosome segment homozygosity are useful, but improvements are needed for practical analysis of large datasets. Once these probabilities are estimated they can be used in flexible linear models that conveniently combine linkage and linkage disequilibrium information.

Variable selection in high-dimensional double generalized linear models

2012 ◽  
Vol 55 (2) ◽  
pp. 327-347 ◽  
Author(s):  
Dengke Xu ◽  
Zhongzhan Zhang ◽  
Liucang Wu
Keyword(s):  
Variable Selection ◽  
Generalized Linear Models ◽  
Linear Models ◽  
High Dimensional

scholarly journals Grouping strategies and thresholding for high dimensional linear models

2013 ◽  
Vol 143 (9) ◽  
pp. 1417-1438 ◽  
Author(s):  
Mathilde Mougeot ◽  
Dominique Picard ◽  
Karine Tribouley
Keyword(s):  
Linear Models ◽  
High Dimensional ◽  
Grouping Strategies

Optimal Estimation of Genetic Relatedness in High-Dimensional Linear Models

2018 ◽  
Vol 114 (525) ◽  
pp. 358-369 ◽  
Author(s):  
Zijian Guo ◽  
Wanjie Wang ◽  
T. Tony Cai ◽  
Hongzhe Li
Keyword(s):  
Linear Models ◽  
Genetic Relatedness ◽  
Optimal Estimation ◽  
High Dimensional
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