A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations

• Published in: Computational and structural biotechnology journal Vol. 21; pp. 2241 - 2252
• Main Authors: Li, Guo, Zhou, Ya-Hui, Li, Hong-Fu, Zhang, Yuan-Ming
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Linear mixed model; Method; Multi-locus model; Multiparent advanced generation intercross (MAGIC); Nested association mapping (NAM); Random-open-parent association mapping (ROAM); GCIM; MPPs; GWS; MVN; CIM; EM; FPR; Multi-locus model; cM; QTLs; ROAM; Linear mixed model; MAD; Nested association mapping (NAM); RIL; ANOVA; MAGIC; BLUP; Random-open-parent association mapping (ROAM); NAM; ICIM; Multiparent advanced generation intercross (MAGIC); CUBIC; MSE
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.03.022

Although multi-parent populations (MPPs) integrate the advantages of linkage and association mapping populations in the genetic dissection of complex traits and especially combine genetic analysis with crop breeding, it is difficult to detect small-effect quantitative trait loci (QTL) for complex traits in multiparent advanced generation intercross (MAGIC), nested association mapping (NAM), and random-open-parent association mapping (ROAM) populations. To address this issue, here we proposed a multi-locus linear mixed model method, namely mppQTL, to detect QTLs, especially small-effect QTLs, in these MPPs. The new method includes two steps. The first is genome-wide scanning based on a single-locus linear mixed model; the P-values are obtained from likelihood-ratio test, the peaks of negative logarithm P-value curve are selected by group-lasso, and all the selected peaks are regarded as potential QTLs. In the second step, all the potential QTLs are placed on a multi-locus linear mixed model, all the effects are estimated using expectation-maximization empirical Bayes algorithm, and all the non-zero effect vectors are further evaluated via likelihood-ratio test for significant QTLs. In Monte Carlo simulation studies, the new method has higher power in QTL detection, lower false positive rate, lower mean absolute deviation for QTL position estimate, and lower mean squared error for the estimate of QTL size (r2) than existing methods because the new method increases the power of detecting small-effect QTLs. In real dataset analysis, the new method (19) identified five more known genes than the existing three methods (14). This study provides an effective method for detecting small-effect QTLs in any MPPs. •Although MPPs integrate the advantages of linkage and association mapping populations, it is difficult to detect small QTLs.•The mppQTL (new) method with two steps was proposed to detect small QTLs and validated by simulated and real data analysis.•The mppQTL software is available at GitHub (https://github.com/YuanmingZhang65/IIIVmrMLM).