Integrating genomics and multi‐platform metabolomics enables metabolite QTL detection in breeding‐relevant apple germplasm

2021 ◽  
Author(s):  
Emma A. Bilbrey ◽  
Kathryn Williamson ◽  
Emmanuel Hatzakis ◽  
Diane Doud Miller ◽  
Jonathan Fresnedo‐Ramírez ◽  
...  
Keyword(s):  
Qtl Detection ◽  
Metabolite Qtl

scholarly journals Alternative models for QTL detection in livestock. II. Likelihood approximations and sire marker genotype estimations

1999 ◽  
Vol 31 (3) ◽  
pp. 225 ◽  
Author(s):  
Brigitte Mangin ◽  
Bruno Goffinet ◽  
Pascale Le Roy ◽  
Didier Boichard ◽  
Jean-Michel Elsen
Keyword(s):  
Marker Genotype ◽  
Qtl Detection ◽  
Alternative Models

scholarly journals Power and false-positive rate in QTL detection with near-isogenic line libraries

Heredity ◽  
2010 ◽  
Vol 106 (4) ◽  
pp. 576-584 ◽  
Author(s):  
K C Falke ◽  
M Frisch
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Isogenic Line ◽  
Near Isogenic Line ◽  
Qtl Detection ◽  
Positive Rate

Fruit shape morphometric analysis and QTL detection in a set of eggplant introgression lines

2021 ◽  
Vol 282 ◽  
pp. 110006
Author(s):  
Giulio Mangino ◽  
Santiago Vilanova ◽  
Mariola Plazas ◽  
Jaime Prohens ◽  
Pietro Gramazio
Keyword(s):  
Morphometric Analysis ◽  
Fruit Shape ◽  
Introgression Lines ◽  
Qtl Detection

scholarly journals QTL detection for growth and latex production in a full-sib rubber tree population cultivated under suboptimal climate conditions

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
João Ricardo Bachega Feijó Rosa ◽  
Camila Campos Mantello ◽  
Dominique Garcia ◽  
Lívia Moura de Souza ◽  
Carla Cristina da Silva ◽  
...  
Keyword(s):  
Rubber Tree ◽  
Qtl Detection ◽  
Climate Conditions ◽  
Tree Population ◽  
Latex Production

From QTL Detection to Gene Identification

1999 ◽  
Author(s):  
Marie-Pierre Moisan
Keyword(s):  
Gene Identification ◽  
Qtl Detection

scholarly journals Assessing the effect of phenotyping scoring systems and SNP calling and filtering methods on detection of QTL associated with reaction of Brassica napus to Sclerotinia sclerotiorum

2021 ◽  
Author(s):  
Fereshteh Shahoveisi ◽  
Atena Oladzad ◽  
Luis E. del Rio Mendoza ◽  
Seyedali Hosseinirad ◽  
Susan Ruud ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Missing Data ◽  
Sclerotinia Sclerotiorum ◽  
Scoring Systems ◽  
Mortality Data ◽  
Lesion Length ◽  
Data Sets ◽  
Qtl Detection ◽  
Snp Calling ◽  
The Impact

The polyploid nature of canola (Brassica napus) represents a challenge for the accurate identification of single nucleotide polymorphisms (SNPs) and the detection of quantitative trait loci (QTL). In this study, combinations of eight phenotyping scoring systems and six SNP calling and filtering parameters were evaluated for their efficiency in detection of QTL associated with response to Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, in two doubled haploid (DH) canola mapping populations. Most QTL were detected in lesion length, relative areas under the disease progress curve (rAUDPC) for lesion length, and binomial-plant mortality data sets. Binomial data derived from lesion size were less efficient in QTL detection. Inclusion of additional phenotypic sets to the analysis increased the numbers of significant QTL by 2.3-fold; however, the continuous data sets were more efficient. Between two filtering parameters used to analyze genotyping by sequencing (GBS) data, imputation of missing data increased QTL detection in one population with a high level of missing data but not in the other. Inclusion of segregation-distorted SNPs increased QTL detection but did not impact their R2 values significantly. Twelve of the 16 detected QTL were on chromosomes A02 and C01, and the rest were on A07, A09, and C03. Marker A02-7594120, associated with a QTL on chromosome A02 was detected in both populations. Results of this study suggest the impact of genotypic variant calling and filtering parameters may be population dependent while deriving additional phenotyping scoring systems such as rAUDPC datasets and mortality binary may improve QTL detection efficiency.

State of QTL Detection and Marker-Assisted Selection in Wheat Improvement

2009 ◽  
pp. 309-326 ◽  
Author(s):  
Daryl J. Somers ◽  
Gavin Humphreys
Keyword(s):  
Marker Assisted Selection ◽  
Qtl Detection ◽  
Wheat Improvement
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