scholarly journals Linkage‐linkage disequilibrium dissection of the epigenetic quantitative trait loci (epiQTLs) underlying growth and wood properties in Populus

2019 ◽  
Vol 225 (3) ◽  
pp. 1218-1233 ◽  
Author(s):  
Wenjie Lu ◽  
Liang Xiao ◽  
Mingyang Quan ◽  
Qingshi Wang ◽  
Yousry A. El‐Kassaby ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Wood Properties ◽  
Trait Loci

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.

Joint Linkage and Linkage Disequilibrium Mapping of Quantitative Trait Loci in Natural Populations

Genetics ◽  
2002 ◽  
Vol 160 (2) ◽  
pp. 779-792 ◽  
Author(s):  
Rongling Wu ◽  
Chang-Xing Ma ◽  
George Casella
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Linkage Analysis ◽  
Quantitative Trait ◽  
Genome Mapping ◽  
Linkage Disequilibrium Mapping ◽  
Allelic Association ◽  
Mapping Strategy ◽  
Trait Loci ◽  
New Strategy

AbstractLinkage analysis and allelic association (also referred to as linkage disequilibrium) studies are two major approaches for mapping genes that control simple or complex traits in plants, animals, and humans. But these two approaches have limited utility when used alone, because they use only part of the information that is available for a mapping population. More recently, a new mapping strategy has been designed to integrate the advantages of linkage analysis and linkage disequilibrium analysis for genome mapping in outcrossing populations. The new strategy makes use of a random sample from a panmictic population and the open-pollinated progeny of the sample. In this article, we extend the new strategy to map quantitative trait loci (QTL), using molecular markers within the EM-implemented maximum-likelihood framework. The most significant advantage of this extension is that both linkage and linkage disequilibrium between a marker and QTL can be estimated simultaneously, thus increasing the efficiency and effectiveness of genome mapping for recalcitrant outcrossing species. Simulation studies are performed to test the statistical properties of the MLEs of genetic and genomic parameters including QTL allele frequency, QTL effects, QTL position, and the linkage disequilibrium of the QTL and a marker. The potential utility of our mapping strategy is discussed.

scholarly journals Comparing Linkage Disequilibrium-Based Methods for Fine Mapping Quantitative Trait Loci

Genetics ◽  
2004 ◽  
Vol 166 (3) ◽  
pp. 1561-1570 ◽  
Author(s):  
L. Grapes ◽  
J. C. M. Dekkers ◽  
M. F. Rothschild ◽  
R. L. Fernando
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Fine Mapping ◽  
Quantitative Trait ◽  
Trait Loci

scholarly journals Genome-wide Two-marker linkage disequilibrium mapping of quantitative trait loci

BMC Genetics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 20 ◽  
Author(s):  
Jie Yang ◽  
Wei Zhu ◽  
Jiansong Chen ◽  
Qiao Zhang ◽  
Song Wu
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Linkage Disequilibrium Mapping ◽  
Genome Wide ◽  
Trait Loci

Genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using a dense SNP map

2007 ◽  
Vol 2007 ◽  
pp. 6-6
Author(s):  
H.D. Daetwyler ◽  
F.S. Schenkel ◽  
M. Sargolzaei ◽  
J.A.B. Robinson
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Bovine Genome ◽  
Phenotypic Trait ◽  
Important Indicator ◽  
Trait Loci ◽  
Snp Map ◽  
First Time

Quantitative trait loci (QTL) are chromosome regions which are significantly associated with the expression of a phenotypic trait in a particular population. Detection of a QTL is carried out using association with a genetic marker, such as a single nucleotide polymorphism (SNP), which is in linkage disequilibrium (LD) with the QTL. The two main categories of association studies are linkage analyses (LA), which consider LD within families and linkage disequilibrium methods, which make use of LD across an entire population. The recent reduction in genotyping costs has allowed for testing individuals for a large number of SNP. This substantial increase in genotypic data has lead to denser marker distributions on the bovine genome thus potentially increasing the power of QTL detection studies. The objective of this study was to scan the bovine genome to detect QTL for 305 day lactation milk yield (MY), 305 day lactation fat yield (FY), 305 day lactation protein yield (PY), herd life (HL), somatic cell score (SCS), interval from calving to first service in cows (CTFS) and age at first service in heifers (AFS). HL is a measure of longevity measured in the number of lactations a cow stays in the herd. SCS refers to the amount of somatic cells a cow has in her milk and is an important indicator trait for mastitis. CTFS is the period from parturition to first insemination in days and AFS is the age in days at which a heifer was artificially inseminated for the first time. Fertility traits, such as CTFS and AFS, are indicators of reproductive efficiency.

scholarly journals Stability of quantitative trait loci for growth and wood properties across multiple pedigrees and environments inEucalyptus globulus

2013 ◽  
Vol 198 (4) ◽  
pp. 1121-1134 ◽  
Author(s):  
Jules S. Freeman ◽  
Brad M. Potts ◽  
Geoffrey M. Downes ◽  
David Pilbeam ◽  
Saravanan Thavamanikumar ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Wood Properties ◽  
Trait Loci
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