@article{kao_zeng_2002, title={Modeling epistasis of quantitative trait loci using Cockerham's model}, volume={160}, number={3}, journal={Genetics}, author={Kao, C. H. and Zeng, Z. B.}, year={2002}, pages={1243–1261} }
 @article{zeng_liu_stam_kao_mercer_laurie_2000, title={Genetic architecture of a morphological shape difference between two Drosophila species}, volume={154}, number={1}, journal={Genetics}, author={Zeng, Z. B. and Liu, J. J. and Stam, L. F. and Kao, C. H. and Mercer, J. M. and Laurie, C. C.}, year={2000}, pages={299–310} }
 @article{zeng_kao_basten_1999, title={Estimating the genetic architecture of quantitative traits}, volume={74}, ISSN={["0016-6723"]}, DOI={10.1017/S0016672399004255}, abstractNote={Understanding and estimating the structure and parameters associated with the genetic architecture 
of quantitative traits is a major research focus in quantitative genetics. With the availability of a 
well-saturated genetic map of molecular markers, it is possible to identify a major part of the 
structure of the genetic architecture of quantitative traits and to estimate the associated 
parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping 
multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the 
genetic architecture parameters, including the number, genomic positions, effects and interactions 
of significant QTL and their contribution to the genetic variance. With multiple traits and multiple 
environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment 
interactions can also be estimated. We review the method and discuss issues associated with 
multiple interval mapping, such as likelihood analysis, model selection, stopping rules and 
parameter estimation. The potential power and advantages of the method for mapping multiple 
QTL and estimating the genetic architecture are discussed. We also point out potential problems 
and difficulties in resolving the details of the genetic architecture as well as other areas that require 
further investigation. One application of the analysis is to improve genome-wide marker-assisted 
selection, particularly when the information about epistasis is used for selection with mating.}, number={3}, journal={GENETICAL RESEARCH}, author={Zeng, ZB and Kao, CH and Basten, CJ}, year={1999}, month={Dec}, pages={279–289} }
 @article{kao_zeng_teasdale_1999, title={Multiple interval mapping for quantitative trait loci}, volume={152}, number={3}, journal={Genetics}, author={Kao, C. H. and Zeng, Z. B. and Teasdale, R. D.}, year={1999}, pages={1203–1216} }
 @article{kao_zeng_1997, title={General formulas for obtaining the MLEs and the asymptotic variance-covariance matrix in mapping quantitative trait loci when using the EM algorithm}, volume={53}, ISSN={["0006-341X"]}, DOI={10.2307/2533965}, abstractNote={We present in this paper general formulas for deriving the maximum likelihood estimates and the asymptotic variance-covariance matrix of the positions and effects of quantitative trait loci (QTLs) in a finite normal mixture model when the EM algorithm is used for mapping QTLs. The general formulas are based on two matrices D and Q, where D is the genetic design matrix, characterizing the genetic effects of the QTLs, and Q is the conditional probability matrix of QTL genotypes given flanking marker genotypes, containing the information on QTL positions. With the general formulas, it is relatively easy to extend QTL mapping analysis to using multiple marker intervals simultaneously for mapping multiple QTLs, for analyzing QTL epistasis, and for estimating the heritability of quantitative traits. Simulations were performed to evaluate the performance of the estimates of the asymptotic variances of QTL positions and effects.}, number={2}, journal={BIOMETRICS}, author={Kao, CH and Zeng, ZB}, year={1997}, month={Jun}, pages={653–665} }