Mapping Quantitative Trait Loci in Complex Pedigrees: A Two-Step Variance Component Approach

Genetics ◽  
2000 ◽  
Vol 156 (4) ◽  
pp. 2081-2092 ◽  
Author(s):  
Andrew W George ◽  
Peter M Visscher ◽  
Chris S Haley
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Variance Component ◽  
Simulated Data ◽  
Statistical Techniques ◽  
Animal Populations ◽  
Trait Loci ◽  
Component Approach ◽  
Complex Relationships ◽  
Variance Component Approach

Abstract There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

scholarly journals How to deal with genotype uncertainty in variance component quantitative trait loci analyses

2011 ◽  
Vol 93 (5) ◽  
pp. 333-342 ◽  
Author(s):  
XIA SHEN ◽  
LARS RÖNNEGÅRD ◽  
ÖRJAN CARLBORG
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Variance Component ◽  
Large Animal ◽  
Trait Loci ◽  
Sib Families ◽  
Variance Component Estimates ◽  
Variance Component Models ◽  
Component Models

SummaryDealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

scholarly journals Genome-wide evaluation for quantitative trait loci under the variance component model

Genetica ◽  
2010 ◽  
Vol 138 (9-10) ◽  
pp. 1099-1109 ◽  
Author(s):  
Lide Han ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Variance Component ◽  
Component Model ◽  
Variance Component Model ◽  
Genome Wide ◽  
Trait Loci

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 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 A comparison between methods for linkage disequilibrium fine mapping of quantitative trait loci

2004 ◽  
Vol 83 (1) ◽  
pp. 41-47 ◽  
Author(s):  
JIHAD M. ABDALLAH ◽  
BRIGITTE MANGIN ◽  
BRUNO GOFFINET ◽  
CHRISTINE CIERCO-AYROLLES ◽  
MIGUEL PÉREZ-ENCISO
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Simulated Data ◽  
Likelihood Method ◽  
Trait Loci ◽  
Single Marker ◽  
Linkage Disequilibrium Information ◽  
Marker Regression ◽  
Trait Locus

We present a maximum likelihood method for mapping quantitative trait loci that uses linkage disequilibrium information from single and multiple markers. We made paired comparisons between analyses using a single marker, two markers and six markers. We also compared the method to single marker regression analysis under several scenarios using simulated data. In general, our method outperformed regression (smaller mean square error and confidence intervals of location estimate) for quantitative trait loci with dominance effects. In addition, the method provides estimates of the frequency and additive and dominance effects of the quantitative trait locus.

scholarly journals Estimation of Effects of Quantitative Trait Loci in Large Complex Pedigrees

Genetics ◽  
1997 ◽  
Vol 146 (1) ◽  
pp. 409-416 ◽  
Author(s):  
T H E Meuwissen ◽  
M E Goddard
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Simulated Data ◽  
Data Set ◽  
Genotype Information ◽  
Trait Loci ◽  
Basic Equations ◽  
Marker Information

A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was <2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.

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