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Detection and localization of a single binary trait locus in experimental populations

Published online by Cambridge University Press:  29 August 2001

LAUREN M. McINTYRE ,
CYNTHIA J. COFFMAN  and
R. W. DOERGE
LAUREN M. McINTYRE
Affiliation:
Computational Genomics, Purdue University, West Lafayette, IN 47907, USA Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA Duke University Medical Center, Division of Biometry, Durham, NC 27710, USA
CYNTHIA J. COFFMAN
Affiliation:
Duke University Medical Center, Division of Biometry, Durham, NC 27710, USA Institute for Clinical and Epidemiological Research Biostatistics Unit, Durham VA Medical Center (152), Durham, NC 27705, USA
R. W. DOERGE
Affiliation:
Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA Department of Statistics, 1399 Mathematical Science Building, Purdue University, West Lafayette, IN 47907, USA
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Abstract

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The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this work a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a genetic marker locus and a binary trait locus for backcross and F2 experimental designs. The regression model is reparameterized allowing for tests of detection. Extensive simulations were conducted to assess the performance of estimation and testing in the proposed parameterization. The proposed parameterization was compared with interval regression via simulation. The results indicate that our parameterization shows equivalent estimation capabilities, requires less computational effort and works well with only a single marker.

Type
Research Article
Information
Genetics Research , Volume 78 , Issue 1 , August 2001 , pp. 79 - 92
Copyright
2001 Cambridge University Press