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Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5–3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs. [ABSTRACT FROM AUTHOR]

The methodologies and software packages for mapping quantitative trait loci (QTLs) in bi-parental segregation populations are well established. However, it is still difficult to detect small-effect and linked QTLs. To address this issue, we proposed a genome-wide composite interval mapping (GCIM) in bi-parental segregation populations. To popularize this method, we developed an R package. This program with two versions (Graphical User Interface: QTL.gCIMapping.GUI v2.0 and code: QTL.gCIMapping v3.2) can be used to identify QTLs for quantitative traits in recombinant inbred lines, doubled haploid lines, backcross and F2 populations. To save running time, fread function was used to read the dataset, parallel operation was used in parameter estimation, and conditional probability calculation was implemented by C++. Once one input file with *.csv or *.txt formats is uploaded into the package, one or two output files and one figure can be obtained. The input file with the ICIM and win QTL cartographer formats is available as well. Real data analysis for 1000-grain weight in rice showed that the GCIM detects the maximum previously reported QTLs and genes, and has the minimum AIC value in the stepwise regression of all the identified QTLs for this trait; using stepwise regression and empirical Bayesian analyses, there are some false QTLs around the previously reported QTLs and genes from the CIM method. The above software packages on Windows, Mac and Linux can be downloaded from https://cran.r-project.org/web/packages/ or https://bigd.big.ac.cn/biocode/tools/7078/releases/27 in order to identify all kinds of omics QTLs.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]