Genetic variation underlying pod size and color traits of common bean depends on quantitative trait loci with epistatic effects

2014 ◽  
Vol 33 (4) ◽  
pp. 939-952 ◽  
Author(s):  
Fernando J. Yuste-Lisbona ◽  
Ana M. González ◽  
Carmen Capel ◽  
Manuel García-Alcázar ◽  
Juan Capel ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Common Bean ◽  
Quantitative Trait ◽  
Epistatic Effects ◽  
Color Traits ◽  
Trait Loci

Epistatic Effects and Quantitative Trait Loci (QTL) x Environment (QE) Interaction Effects for Yield per Plot and Botanical Traits in Soybean

2014 ◽  
Vol 49 (3) ◽  
pp. 273
Author(s):  
Liang Huizhen ◽  
Yu Yongliang ◽  
Yang Hongqi ◽  
Zhang Haiyang ◽  
Dong Wei ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Interaction Effects ◽  
Epistatic Effects ◽  
Trait Loci

Quantitative trait loci define genes and pathways underlying genetic variation in longevity

2006 ◽  
Vol 41 (10) ◽  
pp. 1046-1054 ◽  
Author(s):  
Robert J. Shmookler Reis ◽  
Ping Kang ◽  
Srinivas Ayyadevara
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Trait Loci

Bayesian Model Choice and Search Strategies for Mapping Interacting Quantitative Trait Loci

Genetics ◽  
2003 ◽  
Vol 165 (2) ◽  
pp. 867-883 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu ◽  
David B Allison
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Model ◽  
Genetic Effects ◽  
Monte Carlo Algorithm ◽  
Data Set ◽  
Epistatic Effects ◽  
Trait Loci

AbstractMost complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.

Markers, maps and the detection of trait loci

1996 ◽  
Vol 1996 ◽  
pp. 50-50
Author(s):  
C.S. Haley
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Genetic Markers ◽  
Quantitative Trait ◽  
Coat Colour ◽  
Combined Effects ◽  
Potential Interest ◽  
Naturally Occurring ◽  
Trait Loci ◽  
Modern Breeding

Naturally occurring genetic variation is the basis for differences in performance and appearance between and within different breeds and lines of livestock. In a few instances (e.g. coat colour, polling) the genes (or loci) which control the variation between animals and breeds have a large enough effect to be individually recognisable. For many traits, however, the combined effects of many different genes act together to control quantitative differences between breeds and individuals within breeds (hence such genes are often referred to as quantitative trait loci or QTLs). Thus the dramatic successes of modern breeding result from generations of selection which has produced accumulated changes at a number of different loci. The genome contains up to 100,000 different genes and identifying those which contribute to variation in traits of interest is a difficult task. One first step is to identify regions of the genome containing loci of potential interest through their linkage to genetic markers.

Quantitative Trait Loci for Root Architecture Traits Correlated with Phosphorus Acquisition in Common Bean

Crop Science ◽  
2006 ◽  
Vol 46 (1) ◽  
pp. 413-423 ◽  
Author(s):  
Stephen E. Beebe ◽  
Marcela Rojas‐Pierce ◽  
Xiaolong Yan ◽  
Matthew W. Blair ◽  
Fabio Pedraza ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Common Bean ◽  
Quantitative Trait ◽  
Root Architecture ◽  
Phosphorus Acquisition ◽  
Trait Loci

Mapping quantitative trait loci conferring partial physiological resistance to white mold in the common bean RIL population Xana × Cornell 49242

2010 ◽  
Vol 29 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Elena Pérez-Vega ◽  
Aida Pascual ◽  
Ana Campa ◽  
Ramón Giraldez ◽  
Phillip N. Miklas ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Common Bean ◽  
Quantitative Trait ◽  
White Mold ◽  
Ril Population ◽  
Trait Loci ◽  
Physiological Resistance ◽  
The Common

Identification of quantitative trait loci with main and epistatic effects for plant architecture traits in Upland cotton (Gossypium hirsutumL.)

2014 ◽  
Vol 133 (3) ◽  
pp. 390-400 ◽  
Author(s):  
Cheng-Qi Li ◽  
Li Song ◽  
Hai-Hong Zhao ◽  
Qing-Lian Wang ◽  
Yuan-Zhi Fu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Upland Cotton ◽  
Plant Architecture ◽  
Epistatic Effects ◽  
Trait Loci

scholarly journals Two Independent Quantitative Trait Loci Are Responsible for Novel Resistance to Beet curly top virus in Common Bean Landrace G122

2010 ◽  
Vol 100 (10) ◽  
pp. 972-978 ◽  
Author(s):  
Richard C. Larsen ◽  
Chester J. Kurowski ◽  
Phillip N. Miklas
Keyword(s):  
Quantitative Trait Loci ◽  
Linkage Group ◽  
Common Bean ◽  
Quantitative Trait ◽  
Rapd Marker ◽  
The United States ◽  
Effective Control ◽  
Minor Effect ◽  
Beet Curly Top Virus ◽  
Trait Loci

Beet curly top virus, often referred to as Curly top virus (CTV), is an important virus disease of common bean in the semiarid regions of the United States, Canada, and Mexico and the only effective control is genetic resistance. Our objective was to determine if dry bean landrace G122, which lacks the Bct gene for resistance to CTV, contains novel resistance to the virus. Two populations, GT-A and GT-B, consisting of 98 F5:7 recombinant inbred lines (RILs) in total were derived from a cross between G122 and the susceptible variety Taylor Horticultural and evaluated for phenotypic response to natural CTV field infection. Genetic analyses revealed random amplified polymorphism DNA (RAPD) markers associated with a major-effect quantitative trait loci (QTL) from G122 which exhibited stable expression across 3 years in both populations. Phenotypic variation explained by the QTL in GT-A (37.6%) was greater than in GT-B (20.4%). RAPD marker Q14.973 was converted to a sequence-characterized amplified region (SCAR) and designated SQ14.973. The SCAR was used to locate the QTL on linkage group 6 of the Phaseolus core map. A survey of 74 common bean cultivars and breeding lines revealed SQ14.973 would be widely useful for marker-assisted selection of the QTL. An additional minor-effect QTL from G122 was detected on linkage group 7. G122 was determined to possess novel resistance to CTV conditioned by at least two genes, one with major the other minor effect.

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