Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection

RESEARCH ARTICLE Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection Jianbo He1,2,3☯, Jijie Li1☯, Zhongwen Huang4, Tuanjie Zhao1,2,3, Guangnan Xing1,2,3, Junyi Gai1,2,3, Rongzhan Guan1,2,3* a11111 1 National Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China, 2 National Center for Soybean Improvement, Ministry of Agriculture, Nanjing, Jiangsu, China, 3 Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Nanjing, Jiangsu, China, 4 Department of Agronomy, Henan Institute of Science and Technology, Collaborative Innovation Center of Modern Biological Breeding, Xinxiang, Henan, China ☯ These authors contributed equally to this work. * OPEN ACCESS Citation: He J, Li J, Huang Z, Zhao T, Xing G, Gai J, et al. (2015) Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection. PLoS ONE 10(6): e0130125. doi:10.1371/journal.pone.0130125 Academic Editor: Weijun Zhou, Zhejiang University, CHINA Received: January 22, 2015 Accepted: May 18, 2015 Published: June 15, 2015 Copyright: © 2015 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are available via Github (https://github.com/hjbreg/cimld). Funding: This work was supported by the National Basic Research Program of China (973 Program) (No. 2011CB109300), the Fundamental Research Funds for the Central Universities of China (KJQN201423 and KYZ201202-7) in China, the Open Research Fund of State Key Laboratory of Crop Genetics and Germplasm Enhancement (ZW2011006), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The funders had no role in study design, 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. PLOS ONE | DOI:10.1371/journal.pone.0130125 June 15, 2015 1 / 14 Composite Interval Mapping Based on Lattice Design for Error Control data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Introduction A quantitative trait is usually regarded as complex because of its inheritance mechanism [1]. In the past two decades, unraveling the genetic basis of quantitative traits has become an attractive and challenging research field. Great efforts have been made in quantitative trait locus (QTL) mapping, based on molecular markers, to identify the genetic architecture underlying quantitative phenotypic variation [2–6]. Generally, to effectively map the QTLs of a trait, a proper statistical method, a genetic population, and an efficient experimental design are required both for powerful and accurate QTL mapping. Numerous statistical methods have been proposed for QTL mapping [7], among which single marker regression is the simplest method, which identifies QTLs by testing the difference between marker group means on the phenotype, using methods such as analysis of variance (ANOVA). The single marker regression approach can only detect QTLs at marker positions, thus requiring an ultra-high density of markers to obtain accurate estimates of QTL locations [8]. Interval mapping (IM) was proposed to map genome-wide QTLs based on linkage maps [9]. The IM method performs a statistical test for a QTL at each genome position between a pair of markers by conditioning on the genotypes of the two flanking markers. However, two or more linked QTLs may affect the mapping in IM, leading to biased estimates of locations and effects of QTLs [10–12]. Based on IM, composite interval mapping (CIM) was proposed to reduce the impact of linkage on QTL under testing and to improve the precision of QTL mapping [12]. More recently, a further refinement was made to reduce the impact of covariate marker selection on CIM, which was designated as inclusive composite interval mapping (ICIM) [13]. Furthermore, various multi-locus model methods based on Bayesian statistical frameworks have also been developed for simultaneously modeling multiple genome-wide QTLs [14–18]. Although Bayesian methods have a number of advantages for QTL mapping, they are usually computationally intensive and rarely easy to use. With the user-friendly computer program Windows QTL Cartographer [19], CIM is currently the most widely used method for QTL mapping in segregating populations derived from bi-parental crosses. Various types of genetic segregating populations used for QTL mapping may be classified into tentative mapping populations, such as F2 and backcross (BC), and permanent mapping populations, such as recombinant inbred lines (RILs) and doubled haploid lines (DH). Genetic experiments with tentative mapping populations may not repeat between years or locations. On the contrary, genetic experiments with permanent map (...truncated)