Mapping quantitative trait loci for complex binary traits in outbred populations

Heredity ◽  
1999 ◽  
Vol 82 (6) ◽  
pp. 668-676 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Binary Traits ◽  
Trait Loci ◽  
Outbred Populations

A Random Model Approach to Mapping Quantitative Trait Loci for Complex Binary Traits in Outbred Populations

Genetics ◽  
1999 ◽  
Vol 153 (2) ◽  
pp. 1029-1040 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Random Model ◽  
Monte Carlo Integration ◽  
Ratio Test ◽  
Outbred Population ◽  
Binary Traits ◽  
Trait Loci ◽  
Outbred Populations ◽  
Model Approach

Abstract Mapping quantitative trait loci (QTL) for complex binary traits is more challenging than for normally distributed traits due to the nonlinear relationship between the observed phenotype and unobservable genetic effects, especially when the mapping population contains multiple outbred families. Because the number of alleles of a QTL depends on the number of founders in an outbred population, it is more appropriate to treat the effect of each allele as a random variable so that a single variance rather than individual allelic effects is estimated and tested. Such a method is called the random model approach. In this study, we develop the random model approach of QTL mapping for binary traits in outbred populations. An EM-algorithm with a Fisher-scoring algorithm embedded in each E-step is adopted here to estimate the genetic variances. A simple Monte Carlo integration technique is used here to calculate the likelihood-ratio test statistic. For the first time we show that QTL of complex binary traits in an outbred population can be scanned along a chromosome for their positions, estimated for their explained variances, and tested for their statistical significance. Application of the method is illustrated using a set of simulated data.

scholarly journals Bayesian Mapping of Quantitative Trait Loci for Complex Binary Traits

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1391-1403
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Bayesian Statistics ◽  
Quantitative Trait ◽  
Data Augmentation ◽  
Threshold Model ◽  
Binary Trait ◽  
Monte Carlo Algorithm ◽  
Binary Traits ◽  
Trait Loci ◽  
Bayesian Mapping

Abstract A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

A random model approach to interval mapping of quantitative trait loci.

Genetics ◽  
1995 ◽  
Vol 141 (3) ◽  
pp. 1189-1197 ◽  
Author(s):  
S Xu ◽  
W R Atchley
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Fixed Effects ◽  
Interval Mapping ◽  
Random Model ◽  
Likelihood Method ◽  
Mapping Procedure ◽  
Trait Loci ◽  
Outbred Populations ◽  
Model Approach

Abstract Mapping quantitative trait loci in outbred populations is important because many populations of organisms are noninbred. Unfortunately, information about the genetic architecture of the trait may not be available in outbred populations. Thus, the allelic effects of genes can not be estimated with ease. In addition, under linkage equilibrium, marker genotypes provide no information about the genotype of a QTL (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). To circumvent this problem, an interval mapping procedure based on a random model approach is described. Under a random model, instead of estimating the effects, segregating variances of QTLs are estimated by a maximum likelihood method. Estimation of the variance component of a QTL depends on the proportion of genes identical-by-descent (IBD) shared by relatives at the locus, which is predicted by the IBD of two markers flanking the QTL. The marker IBD shared by two relatives are inferred from the observed marker genotypes. The procedure offers an advantage over the regression interval mapping in terms of high power and small estimation errors and provides flexibility for large sibships, irregular pedigree relationships and incorporation of common environmental and fixed effects.

scholarly journals Detection of Quantitative Trait Loci in Outbred Populations With Incomplete Marker Data

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 409-420 ◽  
Author(s):  
Marco C A M Bink ◽  
Johan A M Van Arendonk
Keyword(s):  
Quantitative Trait Loci ◽  
Bayesian Approach ◽  
Quantitative Trait ◽  
Simulated Data ◽  
Residual Variance ◽  
Monte Carlo Techniques ◽  
Marker Data ◽  
Trait Loci ◽  
Marker Loci ◽  
Outbred Populations

Abstract Augmentation of marker genotypes for ungenotyped individuals is implemented in a Bayesian approach via the use of Markov chain Monte Carlo techniques. Marker data on relatives and phenotypes are combined to compute conditional posterior probabilities for marker genotypes of ungenotyped individuals. The presented procedure allows the analysis of complex pedigrees with ungenotyped individuals to detect segregating quantitative trait loci (QTL). Allelic effects at the QTL were assumed to follow a normal distribution with a covariance matrix based on known QTL position and identity by descent probabilities derived from flanking markers. The Bayesian approach estimates variance due to the single QTL, together with polygenic and residual variance. The method was empirically tested through analyzing simulated data from a complex granddaughter design. Ungenotyped dams were related to one or more sons or grandsires in the design. Heterozygosity of the marker loci and size of QTL were varied. Simulation results indicated a significant increase in power when ungenotyped dams were included in the analysis.

scholarly journals Methodology of Mapping Quantitative Trait Loci for Binary Traits in a Half-sib Design Using Maximum Likelihood

2005 ◽  
Vol 18 (12) ◽  
pp. 1669-1674
Author(s):  
Yin Zongjun ◽  
Zhang Qin ◽  
Zhang Jigang ◽  
Ding Xiangdong ◽  
Wang Chunkao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Quantitative Trait ◽  
Binary Traits ◽  
Trait Loci

scholarly journals Regression-based quantitative trait loci mapping: robust, efficient and effective

2005 ◽  
Vol 360 (1459) ◽  
pp. 1435-1442 ◽  
Author(s):  
Sara A Knott
Keyword(s):  
Molecular Markers ◽  
Quantitative Trait Loci ◽  
Linear Regression ◽  
Quantitative Trait ◽  
Regression Models ◽  
Quantitative Trait Loci Mapping ◽  
Linear Regression Models ◽  
Trait Loci ◽  
The Many ◽  
Outbred Populations

Regression has always been an important tool for quantitative geneticists. The use of maximum likelihood (ML) has been advocated for the detection of quantitative trait loci (QTL) through linkage with molecular markers, and this approach can be very effective. However, linear regression models have also been proposed which perform similarly to ML, while retaining the many beneficial features of regression and, hence, can be more tractable and versatile than ML in some circumstances. Here, the use of linear regression to detect QTL in structured outbred populations is reviewed and its perceived shortfalls are revisited. It is argued that the approach is valuable now and will remain so in the future.

scholarly journals Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations

Genetics ◽  
1997 ◽  
Vol 147 (3) ◽  
pp. 1445-1457 ◽  
Author(s):  
I Hoeschele ◽  
P Uimari ◽  
F E Grignola ◽  
Q Zhang ◽  
K M Gage
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Bayesian Analysis ◽  
Statistical Methods ◽  
Quantitative Trait ◽  
Genetic Parameter ◽  
Bootstrap Sampling ◽  
Test Statistic ◽  
Trait Loci ◽  
Outbred Populations

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLS on a chromosome as an unknown.

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