title: Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection.
abstract: Experimental error control is very important in quantitative trait locus (QTL) mapping. Although numerous statistical methods have been developed for QTL mapping, a QTL detection model based on an appropriate experimental design that emphasizes error control has not been developed. Lattice design is very suitable for experiments with large sample sizes, which is usually required for accurate mapping of quantitative traits. However, the lack of a QTL mapping method based on lattice design dictates that the arithmetic mean or adjusted mean of each line of observations in the lattice design had to be used as a response variable, resulting in low QTL detection power. As an improvement, we developed a QTL mapping method termed composite interval mapping based on lattice design (CIMLD). In the lattice design, experimental errors are decomposed into random errors and block-within-replication errors. Four levels of block-within-replication errors were simulated to show the power of QTL detection under different error controls. The simulation results showed that the arithmetic mean method, which is equivalent to a method under random complete block design (RCBD), was very sensitive to the size of the block variance and with the increase of block variance, the power of QTL detection decreased from 51.3% to 9.4%. In contrast to the RCBD method, the power of CIMLD and the adjusted mean method did not change for different block variances. The CIMLD method showed 1.2- to 7.6-fold higher power of QTL detection than the arithmetic or adjusted mean methods. Our proposed method was applied to real soybean (Glycine max) data as an example and 10 QTLs for biomass were identified that explained 65.87% of the phenotypic variation, while only three and two QTLs were identified by arithmetic and adjusted mean methods, respectively.
authors.affiliation: National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China; National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, Jiangsu, China; Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Nanjing, Jiangsu, China.
authors.forename: Jianbo
authors.initials: J
authors.lastname: He
authors.affiliation: National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China.
authors.forename: Jijie
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Agronomy, Henan Institute of Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Xinxiang, Henan, China.
authors.forename: Zhongwen
authors.initials: Z
authors.lastname: Huang
authors.affiliation: National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China; National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, Jiangsu, China; Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Nanjing, Jiangsu, China.
authors.forename: Tuanjie
authors.initials: T
authors.lastname: Zhao
authors.affiliation: National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China; National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, Jiangsu, China; Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Nanjing, Jiangsu, China.
authors.forename: Guangnan
authors.initials: G
authors.lastname: Xing
authors.affiliation: National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China; National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, Jiangsu, China; Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Nanjing, Jiangsu, China.
authors.forename: Junyi
authors.initials: J
authors.lastname: Gai
authors.affiliation: National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China; National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, Jiangsu, China; Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Nanjing, Jiangsu, China.
authors.forename: Rongzhan
authors.initials: R
authors.lastname: Guan
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D032461:Chromosomes, Plant / Q000235:genetics; D003198:Computer Simulation; D005819:Genetic Markers; D005820:Genetic Testing; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D040641:Quantitative Trait Loci; D013025:Glycine max / Q000235:genetics*
chemical_list: D005819:Genetic Markers