נגישות
menu      
Advanced Search
Syntax
Search...
Volcani treasures
About
Terms of use
Manage
Community:
אסיף מאגר המחקר החקלאי
Powered by ClearMash Solutions Ltd -
Selective genotyping to detect quantitative trait loci affecting multiple traits: Interval mapping analysis
Year:
1998
Source of publication :
Theoretical and Applied Genetics
Authors :
Weller, Joel Ira
;
.
Volume :
97
Co-Authors:
Ronin, Y.I., Institute of Evolution, University of Haifa, Mt Carmel, Haifa 31905, Israel
Korol, A.B., Institute of Evolution, University of Haifa, Mt Carmel, Haifa 31905, Israel
Weller, J.I., Animal Science Institute, ARO, Volcani Center, Israel
Facilitators :
From page:
1169
To page:
1178
(
Total pages:
10
)
Abstract:
Segregating quantitative trait loci can be detected via linkage to genetic markers. By selectively genotyping individuals with extreme phenotypes for the quantitative trait, the power per individual genotyped is increased at the expense of the power per individual phenotyped, but linear-model estimates of the quantitative-locus effect will be biased. The properties of single- and multiple-trait maximum-likelihood estimates of quantitative-loci parameters derived from selectively genotyped samples were investigated using Monte-Carlo simulations of backcross populations. All individuals with trait records were included in the analyses. All quantitative-locus parameters and the residual correlation were unbiasedly estimated by multiple-trait maximum-likelihood methodology. With single-trait maximum-likelihood, unbiased estimates for quantitative-locus effect and location, and the residual variance, were obtained for the trait under selection, but biased estimates were derived for a correlated trait that was analyzed separately. When an effect of the QTL was simulated only on the trait under selection, a 'ghost' effect was also found for the correlated trait. Furthermore, if an effect was simulated only for the correlated trait, then the statistical power was less than that obtained with a random sample of equal size, with multiple-trait analyses, the power of quantitative-trait locus detection was always greater with selective genotyping.
Note:
Related Files :
backcrossing
Interval mapping
interval mapping analysis
phenotype
quantitative trait
quantitative trait locus
Show More
Related Content
More details
DOI :
10.1007/s001220051006
Article number:
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
23402
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:59
You may also be interested in
Scientific Publication
Selective genotyping to detect quantitative trait loci affecting multiple traits: Interval mapping analysis
97
Ronin, Y.I., Institute of Evolution, University of Haifa, Mt Carmel, Haifa 31905, Israel
Korol, A.B., Institute of Evolution, University of Haifa, Mt Carmel, Haifa 31905, Israel
Weller, J.I., Animal Science Institute, ARO, Volcani Center, Israel
Selective genotyping to detect quantitative trait loci affecting multiple traits: Interval mapping analysis
Segregating quantitative trait loci can be detected via linkage to genetic markers. By selectively genotyping individuals with extreme phenotypes for the quantitative trait, the power per individual genotyped is increased at the expense of the power per individual phenotyped, but linear-model estimates of the quantitative-locus effect will be biased. The properties of single- and multiple-trait maximum-likelihood estimates of quantitative-loci parameters derived from selectively genotyped samples were investigated using Monte-Carlo simulations of backcross populations. All individuals with trait records were included in the analyses. All quantitative-locus parameters and the residual correlation were unbiasedly estimated by multiple-trait maximum-likelihood methodology. With single-trait maximum-likelihood, unbiased estimates for quantitative-locus effect and location, and the residual variance, were obtained for the trait under selection, but biased estimates were derived for a correlated trait that was analyzed separately. When an effect of the QTL was simulated only on the trait under selection, a 'ghost' effect was also found for the correlated trait. Furthermore, if an effect was simulated only for the correlated trait, then the statistical power was less than that obtained with a random sample of equal size, with multiple-trait analyses, the power of quantitative-trait locus detection was always greater with selective genotyping.
Scientific Publication
You may also be interested in