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.

完善了用于绘制双亲隔离群体中的数量性状基因座（QTL）的方法和软件包。但是，仍然很难检测到效果不佳的QTL。为了解决这个问题，我们提出了双亲分离群体中的全基因组复合间隔图谱（GCIM）。为了推广这种方法，我们开发了一个R包。该程序具有两个版本（图形用户界面：QTL.gCIMapping.GUI v2.0和代码：QTL.gCIMapping v3.2），可用于识别重组自交系，双单倍体系，回交和F _2中的数量性状的QTL。人口。为了节省运行时间，使用fread函数读取数据集，使用并行运算进行参数估计，并使用C ++实现条件概率计算。一旦将一个具有* .csv或* .txt格式的输入文件上载到程序包中，就可以获得一个或两个输出文件和一个图形。也提供带有ICIM和Win QTL制图员格式的输入文件。对稻米中1000粒重的真实数据分析表明，GCIM检测到了以前报道的最大QTL和基因，并且在所有已鉴定的QTL QTL逐步回归中具有最小的AIC值。使用逐步回归和经验贝叶斯分析，在先前报道的QTL和CIM方法的基因周围存在一些错误的QTL。Windows上的上述软件包，