Dataset Information

A conceptual framework for mapping quantitative trait Loci regulating ontogenetic allometry.

ABSTRACT: Although ontogenetic changes in body shape and its associated allometry has been studied for over a century, essentially nothing is known about their underlying genetic and developmental mechanisms. One of the reasons for this ignorance is the unavailability of a conceptual framework to formulate the experimental design for data collection and statistical models for data analyses. We developed a framework model for unraveling the genetic machinery for ontogenetic changes of allometry. The model incorporates the mathematical aspects of ontogenetic growth and allometry into a maximum likelihood framework for quantitative trait locus (QTL) mapping. As a quantitative platform, the model allows for the testing of a number of biologically meaningful hypotheses to explore the pleiotropic basis of the QTL that regulate ontogeny and allometry. Simulation studies and real data analysis of a live example in soybean have been performed to investigate the statistical behavior of the model and validate its practical utilization. The statistical model proposed will help to study the genetic architecture of complex phenotypes and, therefore, gain better insights into the mechanistic regulation for developmental patterns and processes in organisms.

SUBMITTER: Li H

PROVIDER: S-EPMC2080758 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A conceptual framework for mapping quantitative trait Loci regulating ontogenetic allometry.

Li Hongying H Huang Zhongwen Z Gai Junyi J Wu Song S Zeng Yanru Y Li Qin Q Wu Rongling R

PloS one 20071128 11

Although ontogenetic changes in body shape and its associated allometry has been studied for over a century, essentially nothing is known about their underlying genetic and developmental mechanisms. One of the reasons for this ignorance is the unavailability of a conceptual framework to formulate the experimental design for data collection and statistical models for data analyses. We developed a framework model for unraveling the genetic machinery for ontogenetic changes of allometry. The model ...[more]

PMID: 18043752

Similar Datasets

Project description:The apetalous trait of rapeseed (Brassica napus, AACC, 2n = 38) is important for breeding an ideal high-yield rapeseed with superior klendusity to Sclerotinia sclerotiorum. Currently, the molecular mechanism underlying the apetalous trait of rapeseed is unclear. In this study, 14 petal regulators genes were chosen as target genes (TGs), and the expression patterns of the 14 TGs in the AH population, containing 189 recombinant inbred lines derived from a cross between apetalous "APL01" and normal "Holly," were analyzed in two environments using qRT-PCR. Phenotypic data of petalous degree (PDgr) in the AH population were obtained from the two environments. Both quantitative trait transcript (QTT)-association mapping and expression QTL (eQTL) analyses of TGs expression levels were performed to reveal regulatory relationships among TGs and PDgr. QTT mapping for PDgr determined that PLURIPETALA (PLP) was the major negative QTT associated with PDgr in both environments, suggesting that PLP negatively regulates the petal development of line "APL01." The QTT mapping of PLP expression levels showed that CHROMATIN-REMODELING PROTEIN 11 (CHR11) was positively associated with PLP expression, indicating that CHR11 acts as a positive regulator of PLP expression. Similarly, QTT mapping for the remaining TGs identified 38 QTTs, associated with 13 TGs, and 31 QTTs, associated with 10 TGs, respectively, in the first and second environments. Additionally, eQTL analyses of TG expression levels showed that 12 and 11 unconditional eQTLs were detected in the first and second environment, respectively. Based on the QTTs and unconditional eQTLs detected, we presented a hypothetical molecular regulatory network in which 14 petal regulators potentially regulated the apetalous trait in "APL01" through the CHR11-PLP pathway. PLP acts directly as the terminal signal integrator negatively regulating petal development in the CHR11-PLP pathway. These findings will aid in the understanding the molecular mechanism underlying the apetalous trait of rapeseed.

Project description:A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive × additive, dominance × dominance, additive × dominance, and dominance × additive variances. The six different kinship matrices along with the six estimated polygenic variances were used to control the genetic background of a QTL mapping model. Simulation studies showed that incorporating epistatic polygenic covariance structure can improve QTL mapping resolution. The method was applied to yield component traits of rice. We analyzed four traits (yield, tiller number, grain number, and grain weight) using 278 immortal F2 crosses (crosses between recombinant inbred lines) and 1619 markers. We found that the relative importance of each type of genetic variance varies across different traits. The total genetic variance of yield is contributed by additive × additive (18%), dominance × dominance (14%), additive × dominance (48%), and dominance × additive (15%) variances. Tiller number is contributed by additive (17%), additive × additive (22%), and dominance × additive (43%) variances. Grain number is mainly contributed by additive (42%), additive × additive (19%), and additive × dominance (31%) variances. Grain weight is almost exclusively contributed by the additive (73%) variance plus a small contribution from the additive × additive (10%) variance. Using the estimated genetic variance components to capture the polygenic covariance structure, we detected 39 effects for yield, 39 effects for tiller number, 24 for grain number, and 15 for grain weight. The new method can be directly applied to polygenic-effect-adjusted genome-wide association studies (GWAS) in human and other species.

Dataset Information

A conceptual framework for mapping quantitative trait Loci regulating ontogenetic allometry.

Publications

A conceptual framework for mapping quantitative trait Loci regulating ontogenetic allometry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets