Abstract
Many important phenotypic traits in plants are ordinal. However, relatively little is known about the methodologies for ordinal trait association studies. In this study, we proposed a hierarchical generalized linear mixed model for mapping quantitative trait locus (QTL) of ordinal traits in crop cultivars. In this model, all the main-effect QTL and QTL-by-environment interaction were treated as random, while population mean, environmental effect and population structure were fixed. In the estimation of parameters, the pseudo data normal approximation of likelihood function and empirical Bayes approach were adopted. A series of Monte Carlo simulation experiments were performed to confirm the reliability of new method. The result showed that new method works well with satisfactory statistical power and precision. The new method was also adopted to dissect the genetic basis of soybean alkaline-salt tolerance in 257 soybean cultivars obtained, by stratified random sampling, from 6 geographic ecotypes in China. As a result, 6 main-effect QTL and 3 QTL-by-environment interactions were identified.
Highlights
Many characters of biological interest and economic importance vary in an ordinal form, i.e. disease and tolerance, but are not inherited in a simple Mendelian fashion
Phenotypic variation for soybean alkaline-salt tolerance We measured lengths of main root (LR) of 257 soybean cultivars under the cases of control (CK), 100 mM NaCl and 10 mM Na2CO3
These original trait observations might be transferred into alkaline tolerance index (ATI) and salt tolerance index (STI)
Summary
Many characters of biological interest and economic importance vary in an ordinal form, i.e. disease and tolerance, but are not inherited in a simple Mendelian fashion. Almost all the approaches are based on single QTL genetic model [1,2,3,4,5,6,7]. Bayesian methodology has been used to map multi-QTL and epistatic QTL for binary and ordinal traits [10,11,12,13,14]. All the above approaches are based on bi-parental segregating populations
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