An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts

Euphytica ◽  
2005 ◽  
Vol 142 (1-2) ◽  
pp. 169-196 ◽  
Author(s):  
B. C. Y. Collard ◽  
M. Z. Z. Jahufer ◽  
J. B. Brouwer ◽  
E. C. K. Pang
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Marker Assisted Selection ◽  
Crop Improvement ◽  
Trait Loci ◽  
Basic Concepts ◽  
Selection For

scholarly journals Quantitative trait loci controlling kernel discoloration in barley (Hordeum vulgare L.)

2003 ◽  
Vol 54 (12) ◽  
pp. 1251 ◽  
Author(s):  
C. D. Li ◽  
R. C. M. Lance ◽  
H. M. Collins ◽  
A. Tarr ◽  
S. Roumeliotis ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Marker Assisted Selection ◽  
Major Gene ◽  
Heading Date ◽  
Major Qtls ◽  
Trait Loci ◽  
Selection For ◽  
Kernel Discoloration

Barley kernel discoloration (KD) leads to substantial annual loss in value through downgrading and discounting of malting barley. KD is a difficult trait to introgress into elite varieties as it is controlled by multiple genes and strongly influenced by environment and maturity. As the first step towards marker assisted selection for KD tolerance, we mapped quantitative trait loci (QTLs) controlling KD measured by grain brightness [Minolta L; (Min L)], redness (Min a), and yellowness (Min b) in 7 barley populations. One to 3 QTLs were detected for grain brightness in various populations, and one QTL could account for 5–31% of the phenotypic variation. The QTL located around the centromere region of chromosome 2H was consistently detected in 6 of the 7 populations, explaining up to 28% of the phenotypic variation. In addition, QTLs for grain brightness were most frequently identified on chromosomes 3H and 7H in various populations. Australian varieties Galleon, Chebec, and Sloop contribute an allele to increase grain brightness on chromosome 7H in 3 different populations. A major gene effect was detected for grain redness. One QTL on chromosome 4H explained 54% of the phenotypic variation in the Sloop/Halcyon population, and was associated with the blue aleurone trait. A second QTL was detected on the long arm of chromosome 2H in 3 populations, accounting for 23–47% of the phenotypic variation. The major QTLs for grain yellowness were mapped on chromosomes 2H and 5H. There were strong associations between the QTLs for heading date, grain brightness, and yellowness. The molecular markers linked with the major QTLs should be useful for marker assisted selection for KD.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

scholarly journals Bayesian Model Selection for Genome-Wide Epistatic Quantitative Trait Loci Analysis

Genetics ◽  
2005 ◽  
Vol 170 (3) ◽  
pp. 1333-1344 ◽  
Author(s):  
Nengjun Yi ◽  
Brian S. Yandell ◽  
Gary A. Churchill ◽  
David B. Allison ◽  
Eugene J. Eisen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Model Selection ◽  
Quantitative Trait ◽  
Bayesian Model ◽  
Bayesian Model Selection ◽  
Genome Wide ◽  
Trait Loci ◽  
Selection For

Investigating temperament traits in cattle for quantitative trait loci (QTL) identification

2002 ◽  
Vol 2002 ◽  
pp. 66-66
Author(s):  
N. Ball ◽  
M.J. Haskell ◽  
J.L. Williams ◽  
J.M. Deag
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Farm Animals ◽  
Behavioural Responses ◽  
Trait Loci ◽  
Breeding Programmes ◽  
Behavioural Phenotypes ◽  
Selection For ◽  
Temperament Traits ◽  
Behavioural Tests

Farm animals show individual variation in their behavioural responses to handling and management systems on farms. These behavioural responses are presumed to reflect underlying temperament traits such as fear or aggression. Information about the location of genes that influence temperament traits could be used in selective breeding programmes to improve animal welfare, as selection for desirable behavioural responses would increase the ability of animals to cope with stressors encountered on farms. The aims of this study were to obtain reliable temperament measurements in cattle using behavioural tests, and to use this data to localise the genes (quantitative trait loci) that are involved in such traits.Behavioural data obtained in temperament tests must be shown to reflect underlying traits by demonstrating intra-animal repeatability, inter-animal variability and validity. The objectives of this experiment were i) to carry out four behaviour tests on a group of heifers, and examine the repeatability, variability and validity of the results obtained; ii) to correlate the behavioural data with genotyping data from a large number of heifers to look for associations between behavioural phenotypes and genetic markers. Associations localise quantitative trait loci (QTLs), or regions of the genome, that are involved in these traits.

Letter to the Editor: Methods for mapping quantitative trait loci in autotetraploid species

2020 ◽  
Author(s):  
Jing Chen ◽  
Lindsey J Leach ◽  
Zewei Luo
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Statistical Methods ◽  
Quantitative Trait ◽  
Important Species ◽  
Letter To The Editor ◽  
Trait Loci ◽  
Points Of View

Abstract Mapping quantitative trait loci (QTL) in autotetraploid species represents a timely and challenging task. Two papers published by Wu and his colleagues proposed statistical methods for QTL mapping in these evolutionarily and economically important species. In this Letter to the Editor, we present critical comments on the fundamental conceptual errors involved, from both statistical and genetic points of view.

scholarly journals Identification of Quantitative Trait Loci Relating to Flowering Time, Flag Leaf and Awn Characteristics in a Novel Triticum dicoccum Mapping Population

Plants ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 829
Author(s):  
Tally I.C. Wright ◽  
Angela C. Burnett ◽  
Howard Griffiths ◽  
Maxime Kadner ◽  
James S. Powell ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Quantitative Trait Loci ◽  
Flowering Time ◽  
Quantitative Trait ◽  
Marker Assisted Selection ◽  
Mapping Population ◽  
Flag Leaf ◽  
Snp Array ◽  
Leaf Length ◽  
Trait Loci

Tetraploid landraces of wheat harbour genetic diversity that could be introgressed into modern bread wheat with the aid of marker-assisted selection to address the genetic diversity bottleneck in the breeding genepool. A novel bi-parental Triticum turgidum ssp. dicoccum Schrank mapping population was created from a cross between two landrace accessions differing for multiple physiological traits. The population was phenotyped for traits hypothesised to be proxies for characteristics associated with improved photosynthesis or drought tolerance, including flowering time, awn length, flag leaf length and width, and stomatal and trichome density. The mapping individuals and parents were genotyped with the 35K Wheat Breeders’ single nucleotide polymorphism (SNP) array. A genetic linkage map was constructed from 104 F4 individuals, consisting of 2066 SNPs with a total length of 3295 cM and an average spacing of 1.6 cM. Using the population, 10 quantitative trait loci (QTLs) for five traits were identified in two years of trials. Three consistent QTLs were identified over both trials for awn length, flowering time and flag leaf width, on chromosomes 4A, 7B and 5B, respectively. The awn length and flowering time QTLs correspond with the major loci Hd and Vrn-B3, respectively. The identified marker-trait associations could be developed for marker-assisted selection, to aid the introgression of diversity from a tetraploid source into modern wheat for potential physiological trait improvement.

Sign in / Sign up

Export Citation Format

Share Document