scholarly journals A general mixture model approach for mapping quantitative trait loci from diverse cross designs involving multiple inbred lines

2000 ◽  
Vol 75 (3) ◽  
pp. 345-355 ◽  
Author(s):  
YUEFU LIU ◽  
ZHAO-BANG ZENG
Keyword(s):  
Quantitative Trait Loci ◽  
Statistical Method ◽  
Mixture Model ◽  
Quantitative Trait ◽  
Genetic Model ◽  
General Procedure ◽  
Inbred Lines ◽  
Design Matrix ◽  
Trait Loci ◽  
Parental Lines

Most current statistical methods developed for mapping quantitative trait loci (QTL) based on inbred line designs apply to crosses from two inbred lines. Analysis of QTL in these crosses is restricted by the parental genetic differences between lines. Crosses from multiple inbred lines or multiple families are common in plant and animal breeding programmes, and can be used to increase the efficiency of a QTL mapping study. A general statistical method using mixture model procedures and the EM algorithm is developed for mapping QTL from various cross designs of multiple inbred lines. The general procedure features three cross design matrices, W, that define the contribution of parental lines to a particular cross and a genetic design matrix, D, that specifies the genetic model used in multiple line crosses. By appropriately specifying W matrices, the statistical method can be applied to various cross designs, such as diallel, factorial, cyclic, parallel or arbitrary-pattern cross designs with two or multiple parental lines. Also, with appropriate specification for the D matrix, the method can be used to analyse different kinds of cross populations, such as F2 backcross, four-way cross and mixed crosses (e.g. combining backcross and F2). Simulation studies were conducted to explore the properties of the method, and confirmed its applicability to diverse experimental designs.

scholarly journals A Generalization of the Mixture Model in the Mapping of Quantitative Trait Loci for Progeny From a Biparental Cross of Inbred Lines

Genetics ◽  
1996 ◽  
Vol 143 (1) ◽  
pp. 571-577
Author(s):  
R D Fisch ◽  
M Ragot ◽  
G Gay
Keyword(s):  
Molecular Markers ◽  
Quantitative Trait Loci ◽  
Molecular Marker ◽  
Mixture Model ◽  
Quantitative Trait ◽  
Doubled Haploids ◽  
Inbred Lines ◽  
Quantitative Inheritance ◽  
Trait Loci ◽  
Biometrical Models

Abstract The recent advent of molecular markers has created a great potential for the understanding of quantitative inheritance. In parallel to rapid developments and improvements in molecular marker technologies, biometrical models have been constructed, refined and generalized for the mapping of quantitative trait loci (QTL). However, current models present restricitions in terms of breeding designs to which they apply. In this paper, we develop an approach for the generalization of the mixture model for progeny from a single bi-parental cross of inbred lines. Detailed derivations are given for genetic designs involving populations developed by selfing, i.e., where marker genotypes are obtained from Fx (x ≤ 2) individuals and where phenotypes are measured on Fy (y ≥ x) individuals or families. Extensions to designs involving doubled-haploids, backcrossderived individuals and random matings are outlined. The derivations presented here can easily be combined with current QTL mapping approaches.

Empirical Nonparametric Bootstrap Strategies in Quantitative Trait Loci Mapping: Conditioning on the Genetic Model

Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 525-535
Author(s):  
Claude M Lebreton ◽  
Peter M Visscher
Keyword(s):  
Quantitative Trait Loci ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Genetic Model ◽  
Original Data ◽  
Nonparametric Bootstrap ◽  
Test Protocol ◽  
Trait Loci

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

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.

scholarly journals Quantitative trait loci for lifespan of mated Drosophila melanogaster affect both sexes

2002 ◽  
Vol 80 (3) ◽  
pp. 225-230 ◽  
Author(s):  
SARAH G. REIWITCH ◽  
SERGEY V. NUZHDIN
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Specific Effects ◽  
Genome Wide ◽  
Evolution Of Life ◽  
Trait Loci ◽  
Males And Females ◽  
Qtls Mapping

The properties of alleles at quantitative trait loci (QTLs) contributing to variation in lifespan should be described to determine the mechanisms of evolution of life length and to predict its future changes. Previously, we and others conducted genome-wide screens for QTLs that segregate among one panel of recombinant inbred lines (RILs) using a dense molecular marker map. In non-stressful conditions, QTLs effecting the lifespans of virgin females and males were frequently sex specific. In an unrelated panel of RILs, the effects of QTLs in flies maintained in cages with mixed sexes were similar in both sexes. Here, we re-measured the lifespans of the former panel of RILs in cages with mixed sex cohorts. Lifespan declined owing to mating. The amount of decline correlated between sexes within lines. QTLs mapping to the intervals 15A–19C, 50B–57C, 63A–65A, and 96F–99B had similar effects on the lifespans of both males and females. These QTLs have previously been detected in virgin flies surveys and had sex- and/or environment-specific effects.

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