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State College, United States

Xu F.,Beijing Forestry University | Tong C.,Nanjing Forestry University | Lyu Y.,The Pennsylvanis State University | Bo W.,Beijing Forestry University | And 3 more authors.
Briefings in Bioinformatics | Year: 2013

As a group of important plant species in agriculture and biology, polyploids have been increasingly studied in terms of their genome structure and organization. There are two types of polyploids, allopolyploids and autopolyploids, each resulting from a different genetic origin, which undergo meiotic divisions of a distinct complexity. A set of statistical models has been developed for linkage analysis, respectively for each type, by taking into account their unique meiotic behavior, i.e. preferential pairing for allopolyploids and double reduction for autopolyploids. We synthesized these models and modified them to accommodate the linkage analysis of less informative dominant markers. By reanalysing a published data set of varying ploidy in Arabidopsis, we corrected the estimates of the meiotic recombination frequency aimed to study the significance of polyploidization. © The Author 2013. Published by Oxford University Press. Source


Xu F.,Beijing Forestry University | Lyu Y.,The Pennsylvanis State University | Tong C.,Nanjing Forestry University | Wu W.,Beijing Forestry University | And 8 more authors.
Briefings in Bioinformatics | Year: 2013

As a group of economically important species, linkage mapping of polysomic autotetraploids, including potato, sugarcane and rose, is difficult to conduct due to their unique meiotic property of double reduction that allows sister chromatids to enter into the same gamete. We describe and assess a statisticalmodel for mapping quantitative trait loci (QTLs) in polysomic autotetraploids. The model incorporates double reduction, built in the mixture model-based framework and implemented with the expectation-maximization algorithm. It allows the simultaneous estimation of QTL positions, QTL effects and the degree of double reduction as well as the assessment of the estimation precision of these parameters. We performed computer simulation to examine the statistical properties of the method and validate its use through analyzing real data in tetraploid switchgrass. © The Author 2013. Published by Oxford University Press. Source


Zhou T.,Beijing Forestry University | Lyu Y.,The Pennsylvanis State University | Xu F.,Beijing Forestry University | Bo W.,Beijing Forestry University | And 6 more authors.
Briefings in Bioinformatics | Year: 2013

As an important mechanism for adaptation to heterogeneous environment, plastic responses of correlated traits to environmental alteration may also be genetically correlated, but less is known about the underlying genetic basis. We describe a statistical model for mapping specific quantitative trait loci (QTLs) that control the interrelationship of phenotypic plasticity between different traits. The model is constructed by a bivariate mixture setting, implemented with the EM algorithm to estimate the genetic effects of QTLs on correlative plastic response.We provide a series of procedure that test (1) how a QTL controls the phenotypic plasticity of a single trait; and (2) how the QTL determines the correlation of environment-induced changes of different traits. The model is readily extended to test how epistatic interactions among QTLs play a part in the correlations of different plastic traits. The model was validated through computer simulation and used to analyse multi-environment data of genetic mapping in winter wheat, showing its utilization in practice. © The Author 2013. Published by Oxford University Press. Source

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