Joint Analysis Method for Major Genes Controlling Multiple Correlated Quantitative Traits

• Published in: Agricultural sciences in China Vol. 5; no. 3; pp. 179 - 187
• Main Authors: XIAO, Jing, WANG, Xue-feng, HU, Zhi-qiu, TANG, Zai-xiang, SUI, Jiong-ming, LI, Xin, XU, Chen-wu
• Format: Journal Article
• Language: English
• Published: Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Yangzhou University, Yangzhou 225009, P.R. China 01.03.2006
• Subjects: multiple correlated quantitative traits; major gene; maximum likelihood estimation; EM algorithm; joint segregation analysis
• ISSN: 1671-2927
• DOI: 10.1016/S1671-2927(06)60036-9

Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quantitative traits, which include major gene detection and its effect and variation estimation. The effect and variation of major gene are estimated by the maximum likelihood method implemented via expectation-maximization （EM） algorithm. Major gene is tested with the likelihood ratio （LR） test statistic. Extensive simulation studies showed that joint analysis not only increases the statistical power of major gene detection but also improves the precision and accuracy of major gene effect estimates. An example of the plant height and the number of tiller of F2 population in rice cross Duonieai x Zhonghua 11 was used in the illustration. The results indicated that the genetic difference of these two traits in this cross refers to only one pleiotropic major gene. The additive effect and dominance effect of the major gene are estimated as -21.3 and 40.6 cm on plant height, and 22.7 and -25.3 on number of tiller, respectively. The major gene shows overdominance for plant height and close to complete dominance for number of tillers.