scholarly journals Testing the Robustness of the Likelihood-Ratio Test in a Variance-Component Quantitative-Trait Loci–Mapping Procedure

1999 ◽  
Vol 65 (2) ◽  
pp. 531-544 ◽  
Author(s):  
David B. Allison ◽  
Michael C. Neale ◽  
Raffaella Zannolli ◽  
Nicholas J. Schork ◽  
Christopher I. Amos ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Quantitative Trait ◽  
Variance Component ◽  
Ratio Test ◽  
Quantitative Trait Loci Mapping ◽  
Mapping Procedure ◽  
Trait Loci

scholarly journals Testing the Robustness of the New Haseman-Elston Quantitative-Trait Loci–Mapping Procedure

2000 ◽  
Vol 67 (1) ◽  
pp. 249-252 ◽  
Author(s):  
David B. Allison ◽  
José R. Fernández ◽  
Moonseong Heo ◽  
T. Mark Beasley
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Quantitative Trait Loci Mapping ◽  
Mapping Procedure ◽  
Trait Loci

scholarly journals Bayesian Mapping of Quantitative Trait Loci Under the Identity-by-Descent-Based Variance Component Model

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 411-422 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Posterior Distribution ◽  
Quantitative Trait ◽  
Variance Component ◽  
Threshold Model ◽  
Variance Component Model ◽  
Identity By Descent ◽  
Mapping Procedure ◽  
Trait Loci ◽  
Bayesian Mapping

AbstractVariance component analysis of quantitative trait loci (QTL) is an important strategy of genetic mapping for complex traits in humans. The method is robust because it can handle an arbitrary number of alleles with arbitrary modes of gene actions. The variance component method is usually implemented using the proportion of alleles with identity-by-descent (IBD) shared by relatives. As a result, information about marker linkage phases in the parents is not required. The method has been studied extensively under either the maximum-likelihood framework or the sib-pair regression paradigm. However, virtually all investigations are limited to normally distributed traits under a single QTL model. In this study, we develop a Bayes method to map multiple QTL. We also extend the Bayesian mapping procedure to identify QTL responsible for the variation of complex binary diseases in humans under a threshold model. The method can also treat the number of QTL as a parameter and infer its posterior distribution. We use the reversible jump Markov chain Monte Carlo method to infer the posterior distributions of parameters of interest. The Bayesian mapping procedure ends with an estimation of the joint posterior distribution of the number of QTL and the locations and variances of the identified QTL. Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families.

Major Quantitative Trait Loci Mapping for Tassel Branch Number and Construction of qTBN5 Near-isogenic Lines in Maize (Zea mays L.)

2018 ◽  
Vol 44 (8) ◽  
pp. 1127
Author(s):  
Zi-Ju DAI ◽  
Xin-Tao WANG ◽  
Qing YANG ◽  
Yan WANG ◽  
Ying-Ying ZHANG ◽  
...  
Keyword(s):  
Zea Mays ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Zea Mays L ◽  
Quantitative Trait Loci Mapping ◽  
Near Isogenic Lines ◽  
Branch Number ◽  
Isogenic Lines ◽  
Tassel Branch Number ◽  
Trait Loci

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.

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

Quantitative Trait Loci Mapping of Resistance to Laodelphax striatellus (Homoptera: Delphacidae) in Rice Using Recombinant Inbred Lines

2007 ◽  
Vol 100 (4) ◽  
pp. 1450-1455 ◽  
Author(s):  
Can-Xing Duan ◽  
Jian-Min Wan ◽  
Hu-Qu Zhai ◽  
Qing Chen ◽  
Jian-Kang Wang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Recombinant Inbred ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Quantitative Trait Loci Mapping ◽  
Laodelphax Striatellus ◽  
Trait Loci

scholarly journals Mapping quantitative trait loci in Gallus gallus using principal components

2010 ◽  
Vol 39 (11) ◽  
pp. 2434-2441 ◽  
Author(s):  
Luís Fernando Batista Pinto ◽  
Irineu Umberto Packer ◽  
Mônica Corrêa Ledur ◽  
Ana Silvia Alves Meira Tavares Moura ◽  
Kátia Nones ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Principal Components ◽  
Quantitative Trait ◽  
Live Weight ◽  
Gallus Gallus ◽  
Heart Weight ◽  
Ratio Test ◽  
Linear Combinations ◽  
Significant Qtls ◽  
Trait Loci

This study aimed at mapping QTL (quantitative trait loci) using linear combinations of characteristics of economical interest in Gallus gallus. A total of 350 F2 chickens from an initial crossing among males from a broiler line (TT) with females from a layer line (CC) were used. It was conducted a QTL mapping in chromosomes of Gallus gallus (GGA1, GGA3, GGA5, GGA8, GGA11, and GGA13) for 20 performance and carcass traits. For detecting QTL, it was used the likelihood ratio test between a reduced model (including fixed effects of sex, hatch and random effect of infinitesimal genetic value) and a full model (including all the previous effects plus QTL effects). When original characterists were analyzed, that is, before the formation of linear combinations, six significant QTLs were mapped at 1% in the genome, four in the GGA1 (live weight at 35 days of age and at 42 days of age, abdominal fat and heart weight); and two on GGA3 (live weight at 35 and 42 days of age); three significant QTLs at 5% in the genome, one on GGA1 (head weight), one on GGA3 (wings weight), and one on GGA8 (gizzard weight); besides seven suggestive linkages for several traits. When QTLs were mapped for principal components, many mapped QTLs were confirmed for original traits, in addition to finding three QTLs and eight suggestive linkages not mapped for the original traits.

Quantitative trait loci mapping for meat quality and muscle fiber traits in a Japanese wild boar × Large White intercross

2005 ◽  
Vol 83 (2) ◽  
pp. 308-315 ◽  
Author(s):  
M. Nii ◽  
T. Hayashi ◽  
S. Mikawa ◽  
F. Tani ◽  
A. Niki ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Wild Boar ◽  
Meat Quality ◽  
Muscle Fiber ◽  
Quantitative Trait ◽  
Quantitative Trait Loci Mapping ◽  
Large White ◽  
Trait Loci ◽  
Japanese Wild Boar ◽  
Fiber Traits
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