Multiple-trait quantitative trait locus mapping with incomplete phenotypic data.
BACKGROUND
Conventional multiple-trait quantitative trait locus (QTL) mapping methods must discard cases (individuals) with incomplete phenotypic data, thereby sacrificing other phenotypic and genotypic information contained in the discarded cases. Under standard assumptions about the missing-data mechanism, it is possible to exploit these cases.


RESULTS
We present an expectation-maximization (EM) algorithm, derived for recombinant inbred and F2 genetic models but extensible to any mating design, that supports conventional hypothesis tests for QTL main effect, pleiotropy, and QTL-by-environment interaction in multiple-trait analyses with missing phenotypic data. We evaluate its performance by simulations and illustrate with a real-data example.


CONCLUSIONS
The EM method affords improved QTL detection power and precision of QTL location and effect estimation in comparison with case deletion or imputation methods. It may be incorporated into any least-squares or likelihood-maximization QTL-mapping approach.
Guo
Zhigang
Z
Department of Plant Pathology, Kansas State University, Manhattan, Kansas 66506, USA. zhigang_guo@syngenta.com
Nelson
James C
JC
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D016014:Linear Models; D008957:Models, Genetic; D015999:Multivariate Analysis; D010641:Phenotype; D040641:Quantitative Trait Loci; D012680:Sensitivity and Specificity