Book chapter
Semiparametric methods for mapping quantitative trait loci
Development Of Modern Statistics And Related Topics: In Celebration Of Prof Yaoting Zhang's 70th Birthday : In Celebration of Prof. Yaoting Zhang's 70th Birthday, pp.262-271
Series In Biostatistics, World Scientific Publishing
2003
Abstract
We propose a semiparametric normal copula model for mapping quantitative trait loci when the normality assumption of the phenotypic values may not be satisfied. This model is constructed by first making an appropriate transformation (the normal-quantile distribution transformation) of the original data so that it is marginally normally distributed, then the joint distribution of the transformed data is assumed to be multivariate normal. Since we do not know a priori what form of the transformation will result in the marginally normal distribution for the data, we estimate it nonparametrically by use of the empirical distribution function. We then propose using a pseudo-likelihood ratio (LR) statistic for the detection of linkage of quantitative traits.
Details
- Title: Subtitle
- Semiparametric methods for mapping quantitative trait loci
- Creators
- Kai Wang - University of Iowa, BiostatisticsJian Huang - University of Iowa, Statistics and Actuarial Science
- Contributors
- Heping Zhang (Editor) - Yale UniversityJian Huang (Editor) - University of Iowa, Statistics and Actuarial Science
- Resource Type
- Book chapter
- Publication Details
- Development Of Modern Statistics And Related Topics: In Celebration Of Prof Yaoting Zhang's 70th Birthday : In Celebration of Prof. Yaoting Zhang's 70th Birthday, pp.262-271
- Publisher
- World Scientific Publishing; Singapore
- Series
- Series In Biostatistics
- Language
- English
- Date published
- 2003
- Academic Unit
- Biostatistics; Statistics and Actuarial Science
- Record Identifier
- 9984229855602771
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