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פותח על ידי קלירמאש פתרונות בע"מ -
Designs and solutions to multiple trait comparisons
Year:
1997
Source of publication :
Animal Biotechnology
Authors :
ולר, יהודה
;
.
Volume :
8
Co-Authors:
Weller, J.I., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel
Song, J.Z., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel
Ronin, Y.I., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel, Institute of Evolution, University of Haifa, Mount Carmel, Haifa, 31905, Israel
Korol, A.B., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel, Institute of Evolution, University of Haifa, Mount Carmel, Haifa, 31905, Israel
Facilitators :
From page:
107
To page:
122
(
Total pages:
16
)
Abstract:
Multiple trait detection and analysis of quantitative trait loci via linkage to genetic markers is problematic, first because of the increase in the number of comparisons tested, second because of possible multitrait loci effects, and third because of biases due to selection based on phenotypic trait values. Nearly all studies that considered multiple traits have analyzed each trait separately. Two methods have been proposed for multitrait experiments; maximum likelihood multivariate analysis, and canonical transformation to a set of uncorrelated variables. If individuals are selected for genotyping based on a single trait, parameter estimates for other correlated traits will be biased using single trait analyses, and significance levels will be incorrect. With multivariate analysis, unbiased estimates of QTL effects can be derived even with selective genotyping. Furthermore, power is increased per individual genotyped, even if selective genotyping is relative to a trait unaffected by the segregating locus. For a preliminary multiple trait analysis, controlling the false discovery rate rather than the experimentwisetype-I error allows for greater statistical power to detect true effects. In the absence of any true effects the two methods are equivalent. An example is given using granddaughter design data.
Note:
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DOI :
Article number:
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Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
31417
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 01:02
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Scientific Publication
Designs and solutions to multiple trait comparisons
8
Weller, J.I., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel
Song, J.Z., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel
Ronin, Y.I., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel, Institute of Evolution, University of Haifa, Mount Carmel, Haifa, 31905, Israel
Korol, A.B., Department of Genetics, Inst. of Animal Sciences A. R. O., Volcani Center, Bet Dagan 50250, Israel, Institute of Evolution, University of Haifa, Mount Carmel, Haifa, 31905, Israel
Designs and solutions to multiple trait comparisons
Multiple trait detection and analysis of quantitative trait loci via linkage to genetic markers is problematic, first because of the increase in the number of comparisons tested, second because of possible multitrait loci effects, and third because of biases due to selection based on phenotypic trait values. Nearly all studies that considered multiple traits have analyzed each trait separately. Two methods have been proposed for multitrait experiments; maximum likelihood multivariate analysis, and canonical transformation to a set of uncorrelated variables. If individuals are selected for genotyping based on a single trait, parameter estimates for other correlated traits will be biased using single trait analyses, and significance levels will be incorrect. With multivariate analysis, unbiased estimates of QTL effects can be derived even with selective genotyping. Furthermore, power is increased per individual genotyped, even if selective genotyping is relative to a trait unaffected by the segregating locus. For a preliminary multiple trait analysis, controlling the false discovery rate rather than the experimentwisetype-I error allows for greater statistical power to detect true effects. In the absence of any true effects the two methods are equivalent. An example is given using granddaughter design data.
Scientific Publication
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