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| 1 | QTL IciMapping:Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations显示文摘QTL Ici Mapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci(QTL) in biparental populations. Eight functionalities are integrated in this software package:(1) BIN: binning of redundant markers;(2) MAP: construction of linkage maps in biparental populations;(3) CMP: consensus map construction from multiple linkage maps sharing common markers;(4) SDL: mapping of segregation distortion loci;(5) BIP: mapping of additive, dominant, and digenic epistasis genes;(6) MET: QTL-by-environment interaction analysis;(7) CSL: mapping of additive and digenic epistasis genes with chromosome segment substitution lines; and(8) NAM: QTL mapping in NAM populations. Input files can be arranged in plain text, MS Excel 2003, or MS Excel 2007 formats. Output files have the same prefix name as the input but with different extensions. As examples, there are two output files in BIN, one for summarizing the identified bin groups and deleted markers in each bin, and the other for using the MAP functionality. Eight output files are generated by MAP, including summary of the completed linkage maps, Mendelian ratio test of individual markers, estimates of recombination frequencies, LOD scores, and genetic distances, and the input files for using the BIP, SDL,and MET functionalities. More than 30 output files are generated by BIP, including results at all scanning positions, identified QTL, permutation tests, and detection powers for up to six mapping methods. Three supplementary tools have also been developed to display completed genetic linkage maps, to estimate recombination frequency between two loci,and to perform analysis of variance for multi-environmental trials. | Lei Meng Huihui Li Luyan Zhang Jiankang Wang | 2015 | The Crop Journal2015，3，3： | 174 |
| 2 | Quantitative trait locus mapping with background control in genetic populations of clonal F_1 and double cross显示文摘In this study, we considered five categories of molecular markers in clonal F_1 and double cross populations,based on the number of distinguishable alleles and the number of distinguishable genotypes at the marker locus. Using the completed linkage maps, incomplete and missing markers were imputed as fully informative markers in order to simplify the linkage mapping approaches of quantitative trait genes. Under the condition of fully informative markers, we demonstrated that dominance effect between the female and male parents in clonal F_1 and double cross populations can cause the interactions between markers. We then developed an inclusive linear model that includes marker variables and marker interactions so as to completely control additive effects of the female and male parents, as well as the dominance effect between the female and male parents. The linear model was finally used for background control in inclusive composite interval mapping(ICIM) of quantitative trait locus(QTL). The ef ficiency of ICIM was demonstrated by extensive simulations and by comparisons with simple interval mapping, multiple-QTL models and composite interval mapping. Finally, ICIM was applied in one actual double cross population to identify QTL on days to silking in maize. | Luyan Zhang Huihui Li Junqiang Ding Jianyu Wu Jiankang Wang | 2015 | Journal of Integrative Plant Biology2015，57，12： | 4 |
| 3 | Modern quantitative genetics:Dissecting complex polygenic systems into individual genetic factors显示文摘Modern quantitative genetics began in the late 1980s with the advent and application of molecular marker technology.Molecular markers made it possible to genotype the entire genome of an individual.Thus,for the first time,quantitative genetic analysis could be performed based on phenotypic and genotypic information.As a result,quantitative genetic studies were no longer limited to the estimation of genetic parameters by treating the polygenic system as a whole,but were able to identify the locations and effects of individual quantitative trait loci (QTLs) as well as to examine the interactions between QTLs. | WU WeiRen | 2012 | Chinese Science Bulletin2012，57，21： | 0 |
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