An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2. (18th July 2018)

• Record Type: Journal Article
• Authors: Wen, Yang-Jun; Zhang, Ya-Wen; Zhang, Jin; Feng, Jian-Ying; Dunwell, Jim M; Zhang, Yuan-Ming
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 5(2020)
• Page Start: 1913
• Page End: 1924
• Publication Date: 2018-07-18
• Subjects: genome-wide composite interval mapping -- small-effect QTL -- linked QTLs -- mixed linear model -- multi-locus model -- adaptive lasso; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby058
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA editing levels and DNA methylation, a series of small and linked genes exist. To date, however, little is known about how to design an efficient framework for the detection of these kinds of genes. In this article, we propose a genome-wide composite interval mapping (GCIM) in F2 . First, controlling polygenic background via selecting markers in the genome scanning of linkage analysis was replaced by estimating polygenic variance in a genome-wide association study. This can control large, middle and minor polygenic backgrounds in genome scanning. Then, additive and dominant effects for each putative quantitative trait locus (QTL) were separately scanned so that a negative logarithm P -value curve against genome position could be separately obtained for each kind of effect. In each curve, all the peaks were identified as potential QTLs. Thus, almost all the small-effect and linked QTLs are included in a multi-locus model. Finally, adaptive least absolute shrinkage and selection operator (adaptive lasso) was used to estimate all the effects in the multi-locus model, and all the nonzero effects were further identified by likelihood ratio test for true QTL identification. This method was used to reanalyze four rice traits. Among 25 known genes detected in this study, 16 small-effect genes were identified only by GCIM. To further demonstrate GCIM, a series of Monte Carlo simulation experiments was performed. As a result, GCIM is demonstrated to be more powerful than the widely used methods for the detection of closely linked and small-effect QTLs.