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Detection and parameter estimation for quantitative trait loci using regression models and multiple markers

A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consid...

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Detalles Bibliográficos
Autores principales: Da, Yang, VanRaden, Paul M, Schook, Lawrence B
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2000
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2706850/
https://www.ncbi.nlm.nih.gov/pubmed/14736383
http://dx.doi.org/10.1186/1297-9686-32-4-357
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author Da, Yang
VanRaden, Paul M
Schook, Lawrence B
author_facet Da, Yang
VanRaden, Paul M
Schook, Lawrence B
author_sort Da, Yang
collection PubMed
description A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1 000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.
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spelling pubmed-27068502009-07-08 Detection and parameter estimation for quantitative trait loci using regression models and multiple markers Da, Yang VanRaden, Paul M Schook, Lawrence B Genet Sel Evol Research A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1 000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL. BioMed Central 2000-07-15 /pmc/articles/PMC2706850/ /pubmed/14736383 http://dx.doi.org/10.1186/1297-9686-32-4-357 Text en Copyright © 2000 INRA, EDP Sciences
spellingShingle Research
Da, Yang
VanRaden, Paul M
Schook, Lawrence B
Detection and parameter estimation for quantitative trait loci using regression models and multiple markers
title Detection and parameter estimation for quantitative trait loci using regression models and multiple markers
title_full Detection and parameter estimation for quantitative trait loci using regression models and multiple markers
title_fullStr Detection and parameter estimation for quantitative trait loci using regression models and multiple markers
title_full_unstemmed Detection and parameter estimation for quantitative trait loci using regression models and multiple markers
title_short Detection and parameter estimation for quantitative trait loci using regression models and multiple markers
title_sort detection and parameter estimation for quantitative trait loci using regression models and multiple markers
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2706850/
https://www.ncbi.nlm.nih.gov/pubmed/14736383
http://dx.doi.org/10.1186/1297-9686-32-4-357
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