Home   >   CSC-OpenAccess Library   >    Manuscript Information
Detection of Quantitative Trait Loci in Presence of Phenotypic Contamination
Md. Nurul Haque Mollah
Pages - 13 - 21     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
Quantitative trait loci, Gaussian mixture distribution, LOD scores, Likelihood ratio test, Method of maximum B-likelihood, Robustness.
Genes controlling a certain trait of organism is known as quantitative trait loci (QTL). The standard Interval mapping (Lander and Botstein, 1989) is a popular way to scan the whole genome for the evidence of QTLs. It searches a QTL within each interval between two adjacent markers by performing likelihood ratio test (LRT). However, the standard Interval mapping (SIM) approach is not robust against outliers. An attempt is made to robustify SIM for QTL analysis by maximizing $eta$-likelihood function using the EM like algorithm. We investigate the robustness performance of the proposed method in a comparison of SIM algorithm using synthetic datasets. Experimental results show that the proposed method significantly improves the performance over the SIM approach for QTL mapping in presence of outliers; otherwise, it keeps equal performance.
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 Libsearch 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
14 PdfSR 
A. H. Paterson, S. Damon, J. D. Hewitt, D. Zamir, H. D. Rabinowitch, S.E. Lincoln, E. S. Lander, S.D.Tanksley. “Mendelian factors underlying quantitative traits in tomato: comparison across species, generations and environments”. Genetics, 127, pp. 181-197, 1991.
A. P. Dempster, Laird, and Rubin, D. B.: “Maximum likelihood from incomplete data via the EM algorithm”. J. Roy. Statist. Soc. B, 39, pp. 1-38, 1977.
C. H. Kao. “On the differences between maximum likelihood and regression interval mapping in the analysis of quantitative trait loci”. Genetics, 156, pp.855-865, 2000.
C. S. Haley and S. A. Knott. “A simple regression method for mapping quantitative trait in line crosses using flanking markers”. Heredity 69, pp.315-324, 1992.
E. S. Lander and D. Botstein. “Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps”. Genetics, 121, pp. 185-199, 1989.
Elston, R. C., Stewart, J. “The Analysis of Quantitative Traits for Simple Genetic Models from Parental, F1 and Backcross Data”. Genetics, 73, pp. 695-711, 1973.
G. A. Churchill and R. W. Doerge,. “Empirical Threshold Values for Quantitative Triat Mapping”. Genetics, Vol 138, pp. 963-971, 1994.
J. M. Thoday. “Effects of disruptive selection. III. Coupling and repulsion”. Heredity, 14, pp. 35-49, 1960.
K. W. Broman, H. Wu, S. Sen and G. A. Churchill. “R/qtl: QTL mapping in experimental crosses”. Bioinformatics, Vol. 19, pp. 889-890, 2003.
M. N. H. Mollah and S. Eguchi. “Robust Composite interval Mapping for QTL Analysis by Minimum ?-Divergence Method”. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM08) , pp. 115-120, Philadelphia, USA, 2008.
M. N. H. Mollah, N. Sultana, M. Minami and S. Eguchi. “Robust extraction of local structures by the minimum ?-divergence method”. Neural Network, 23, pp. 226-238, 2010.
R. C. Jansen, “ A general mixture model for mapping quantitative trait loci by using molecular markers”. Theor Appl Genet., 85, 252-260, 1992.
Dr. Md. Nurul Haque Mollah
Department of Statistics - Bangladesh

View all special issues >>