Abstract
Although methodologies and software packages for bulked segregant analysis (BSA) are well established, it is difficult to detect extremely over-dominant and small-effect genes for quantitative traits in F(2) population. To address this issue, we proposed a combinatorial strategy to identify all types of quantitative trait loci (QTLs) using extreme phenotype individuals in F(2). To popularize this strategy, we developed an R software package dQTG.seq v1.0.1. It has some features not found in other BSA software packages: 1) new (dQTG-seq1 and dQTG-seq2) and existing (G', deltaSNP, Euclidean distance (ED), and SmoothLOD) methods are available to identify all types of QTLs in bi-parental segregation populations, one data file with two BSA and three QTL-mapping data formats was inputted, and two *.csv files and one figure were outputted; 2) main smoothing methods (AIC, Window size, and Block) have been incorporated into each of the above-mentioned methods; 3) the threshold value of LOD score for significant QTLs is determined by permutation experiments. To save running time, vroom function was used to read the dataset, and parallel operation was used to estimate parameters. In real data analyses, users should select a suitable initial value of window size, depending on the species, and appropriate smoothing methods to obtain the best result. dQTG-seq2 detects more known loci and genes for rice grain number per panicle than composite interval mapping (CIM) and inclusive CIM, especially extremely over-dominant and small-effect genes. A handbook for our software package (https://cran.r-project.org/web/packages/dQTG.seq/index.html) has been provided in the supplemental materials for the users' convenience.