scholarly journals Multiple Quantitative Trait Loci Mapping With Cofactors and Application of Alternative Variants of the False Discovery Rate in an Enlarged Granddaughter Design

Genetics ◽  
2004 ◽  
Vol 168 (2) ◽  
pp. 1019-1027 ◽  
Author(s):  
Jörn Bennewitz ◽  
Norbert Reinsch ◽  
Volker Guiard ◽  
Sebastien Fritz ◽  
Hauke Thomsen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
False Discovery Rate ◽  
Quantitative Trait ◽  
Quantitative Trait Loci Mapping ◽  
False Discovery ◽  
Trait Loci

Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits

Genetics ◽  
2002 ◽  
Vol 161 (2) ◽  
pp. 905-914 ◽  
Author(s):  
Hakkyo Lee ◽  
Jack C M Dekkers ◽  
M Soller ◽  
Massoud Malek ◽  
Rohan L Fernando ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
False Discovery Rate ◽  
Error Rate ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Error Rates ◽  
Multiple Traits ◽  
Test Statistic ◽  
False Discovery ◽  
Trait Loci

Abstract Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genomewise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.

scholarly journals Quantitative Trait Loci Analysis Using the False Discovery Rate

Genetics ◽  
2005 ◽  
Vol 171 (2) ◽  
pp. 783-790 ◽  
Author(s):  
Yoav Benjamini ◽  
Daniel Yekutieli
Keyword(s):  
Quantitative Trait Loci ◽  
False Discovery Rate ◽  
Quantitative Trait ◽  
False Discovery ◽  
Trait Loci

Quantitative trait loci mapping for biomass yield traits in a Lolium inbred line derived F2 population

Euphytica ◽  
2009 ◽  
Vol 170 (1-2) ◽  
pp. 99-107 ◽  
Author(s):  
U. C. M. Anhalt ◽  
J. S. Heslop-Harrison (Pat) ◽  
H. P. Piepho ◽  
S. Byrne ◽  
S. Barth
Keyword(s):  
Quantitative Trait Loci ◽  
Inbred Line ◽  
Quantitative Trait ◽  
Biomass Yield ◽  
Yield Traits ◽  
Quantitative Trait Loci Mapping ◽  
F2 Population ◽  
Trait Loci

Genes and Quantitative Trait Loci Mapping for Major Agronomic Traits in Brassica napus L.

2018 ◽  
pp. 41-85 ◽  
Author(s):  
Régine Delourme ◽  
Anne Laperche ◽  
Anne-Sophie Bouchet ◽  
Mélanie Jubault ◽  
Sophie Paillard ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Quantitative Trait Loci Mapping ◽  
Brassica Napus L ◽  
Trait Loci

scholarly journals Quantitative Trait Loci Mapping for Lameness Associated Phenotypes in Holstein–Friesian Dairy Cattle

2019 ◽  
Vol 10 ◽  
Author(s):  
Enrique Sánchez-Molano ◽  
Veysel Bay ◽  
Robert F. Smith ◽  
Georgios Oikonomou ◽  
Georgios Banos
Keyword(s):  
Quantitative Trait Loci ◽  
Dairy Cattle ◽  
Quantitative Trait ◽  
Quantitative Trait Loci Mapping ◽  
Holstein Friesian ◽  
Trait Loci
Sign in / Sign up

Export Citation Format

Share Document