Journal ArticleDOI
Regression based fast multi-trait genome-wide QTL analysis
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TLDR
A new approach is introduced (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations that can identify almost the same QTL positions as those identified by the existing methods.Abstract:
Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance-covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.read more
Citations
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Journal ArticleDOI
Robust regression based genome-wide multi-trait QTL analysis.
Md. Jahangir Alam,Janardhan Mydam,Janardhan Mydam,Md. Ripter Hossain,S. M. Shahinul Islam,Md. Nurul Haque Mollah +5 more
TL;DR: In this article, an LRM-RobMtQTL approach was proposed for the backcross population based on the robust estimation of regression parameters by maximizing the βlikelihood function induced from the β-divergence with multivariate normal distribution.
References
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Journal ArticleDOI
Mapping mendelian factors underlying quantitative traits using rflp linkage maps
Eric S. Lander,David Botstein +1 more
TL;DR: In this paper, a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs) are described, and explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.
Journal ArticleDOI
Multipoint Quantitative-Trait Linkage Analysis in General Pedigrees
Laura Almasy,John Blangero +1 more
TL;DR: It is shown how variance-component linkage methods can be used in pedigrees of arbitrary size and complexity, and a general framework for multipoint identity-by-descent (IBD) probability calculations is developed.
Journal ArticleDOI
A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.
Chris Haley,Sara Knott +1 more
TL;DR: Methods for mapping QTL based on multiple regression which can be applied using any general statistical package are developed and it is shown that these regression methods produce very similar results to those obtained using maximum likelihood.
Journal ArticleDOI
Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci
Changjian Jiang,Zhao-Bang Zeng +1 more
TL;DR: In this article, the authors present a model and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method, taking into account the correlated structure of multiple traits, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation.
Journal ArticleDOI
Quantitative trait locus effects and environmental interaction in a sample of North American barley germ plasm.
Patrick M. Hayes,Ben Hui Liu,Steven J. Knapp,F. Chen,B. L. Jones,Tom Blake,Jerome D. Franckowiak,D Rasmusson,Mark E. Sorrells,Steven E. Ullrich,D. Wesenberg,Andris Kleinhofs +11 more
TL;DR: Quantitative trait locus (QTL) and QTL x environment (E) interaction effects for agronomic and malting quality traits were measured using a 123-point linkage map and multi-environment phenotype data from an F1-derived doubled haploid population of barley.
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