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Huang Z.,Henan Institute of Science and Technology | Tong C.,Nanjing Forestry University | Bo W.,Beijing Forestry University | Pang X.,Beijing Forestry University | And 5 more authors.
Briefings in Bioinformatics | Year: 2014

Despite a tremendous effort to map quantitative trait loci (QTLs) responsible for agriculturally and biologically important traits in plants, our understanding of how a QTL governs the developmental process of plant seeds remains elusive. In this article, we address this issue by describing a model for functional mapping of seed development through the incorporation of the relationship between vegetative and reproductive growth. The time difference of reproductive from vegetative growth is described by Reeve and Huxley's allometric equation. Thus, the implementation of this equation into the framework of functional mapping allows dynamic QTLs for seed development to be identified more precisely. By estimating and testing mathematical parameters that define Reeve and Huxley's allometric equations of seed growth, the dynamic pattern of the genetic effects of the QTLs identified can be analyzed. We used the model to analyze a soybean data, leading to the detection of QTLs that control the growth of seed dry weight. Three dynamic QTLs, located in two different linkage groups, were detected to affect growth curves of seed dry weight. The QTLs detected may be used to improve seed yield with marker-assisted selection by altering the pattern of seed development in a hope to achieve a maximum size of seeds at a harvest time. © The Author 2013. Published by Oxford University Press.


Bo W.,Beijing Forestry University | Fu G.,Utah State University | Wang Z.,Pennsylvania State University | Xu F.,Beijing Forestry University | And 8 more authors.
Briefings in Bioinformatics | Year: 2014

The recent availability of high-throughput genetic and genomic data allows the genetic architecture of complex traits to be systematically mapped. The application of these genetic results to design and breed new crop types can be made possible through systems mapping. Systems mapping is a computational model that dissects a complex phenotype into its underlying components, coordinates different components in terms of biological laws through mathematical equations and maps specific genes that mediate each component and its connection with other components. Here, we present a new direction of systems mapping by integrating this tool with carbon economy. With an optimal spatial distribution of carbon fluxes between sources and sinks, plants tend to maximize whole-plant growth and competitive ability under limited availability of resources. We argue that such an economical strategy for plant growth and development, once integrated with systems mapping, will not only provide mechanistic insights into plant biology, but also help to spark a renaissance of interest in ideotype breeding in crops and trees. © The Author 2013. Published by Oxford University Press.


Korir P.C.,Nanjing Agricultural University | Korir P.C.,National Center for Soybean Improvement | Korir P.C.,National Key Laboratory for Crop Genetics and Germplasm Enhancement | Zhao T.,Nanjing Agricultural University | And 5 more authors.
Frontiers of Agriculture in China | Year: 2010

To determine an appropriate indicator and a suitable stage for evaluating tolerance of soybeans to aluminum (Al) toxin is one of the keys to effective breeding for the trait. Seventeen accessions selected as tolerant from a previous test program by using average membership index (FAi) as indicator, plus one tolerant (PI.416937) and one sensitive (NN1138-2) check, were assayed in sand culture pot experiments, totaling four experiments, each for evaluation at V3, V5, V7 and V9 stage, respectively, each in a randomized complete block design with three replications, and each genotype exposed to two Al levels (0 and 480 μM). The relative values of shoot dry weight (RSDW), root dry weight (RRDW), total plant dry weight (RTDW), total root length (RTRL) and total root surface area (RRSA) as the tolerance indicators as well as FAi were compared. All the indicators showed significant variation in Al tolerance among genotypes over and across the leaf stages, but Genotype × Stage interactions were significant only for RTRL and RRSA, indicating that they were less stable among stages than RTDW, RSDW and RRDW. Among the latter three, RTDW was chosen as the major indicator of Al tolerance due to its relatively better stability, higher correlation with other indicators and easier measuring procedure than the others. The seedling age applicable for screening was not definitive, but V5 appeared to compromise between time spent resulting from screening the relatively older seedlings at later stages and low variation among genotypes at a younger stage. The differences of Al tolerance among the tested accessions were further detected by using RTDW, and superior Al tolerant accessions identified were PI.509080 (South Korea), N23533 and N24282 (Northeast China) and PI.159322 (USA), comparable to the putative tolerant check PI.416937 (Japan) at all vegetative stages. © 2010 Higher Education Press and Springer-Verlag Berlin Heidelberg.


He J.,Nanjing Agricultural University | He J.,National Center for Soybean Improvement | He J.,Key Laboratory of Biology and Genetic Improvement of Soybean | Li J.,Nanjing Agricultural University | And 13 more authors.
PLoS ONE | Year: 2015

Experimental error control is very important in quantitative trait locus (QTL) mapping. Although numerous statistical methods have been developed for QTL mapping, a QTL detection model based on an appropriate experimental design that emphasizes error control has not been developed. Lattice design is very suitable for experiments with large sample sizes, which is usually required for accurate mapping of quantitative traits. However, the lack of a QTL mapping method based on lattice design dictates that the arithmetic mean or adjusted mean of each line of observations in the lattice design had to be used as a response variable, resulting in low QTL detection power. As an improvement, we developed a QTL mapping method termed composite interval mapping based on lattice design (CIMLD). In the lattice design, experimental errors are decomposed into random errors and block-within-replication errors. Four levels of block-within-replication errors were simulated to show the power of QTL detection under different error controls. The simulation results showed that the arithmetic mean method, which is equivalent to a method under random complete block design (RCBD), was very sensitive to the size of the block variance and with the increase of block variance, the power of QTL detection decreased from 51.3% to 9.4%. In contrast to the RCBD method, the power of CIMLD and the adjusted mean method did not change for different block variances. The CIMLD method showed 1.2- to 7.6-fold higher power of QTL detection than the arithmetic or adjusted mean methods. Our proposed method was applied to real soybean (Glycine max) data as an example and 10 QTLs for biomass were identified that explained 65.87% of the phenotypic variation, while only three and two QTLs were identified by arithmetic and adjusted mean methods, respectively. © 2015 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Li H.,Nanjing Agricultural University | Li H.,National Center for Soybean Improvement | Li H.,National Key Laboratory for Crop Genetics and Germplasm Enhancement | Zhao T.,Nanjing Agricultural University | And 13 more authors.
Euphytica | Year: 2011

The relative importance of various types of quantitative trait locus (QTL) conferring oil content and its fatty acid components in soybean seeds was assessed through testing a recombinant inbred line (RIL) population (derived from KF1 × NN1138-2) in randomized blocks experiments in 2004-2006. The contents of oil and oleic, linoleic, linolenic, palmitic and stearic acids were determined with automatic Soxhlet extraction system and gas chromatography, respectively. Based on the established genetic linkage map with 834 markers, QTLNetwork2. 0 was used to detect QTL under the genetic model composed of additive, additive × additive (epistasis), additive × year and epistasis × year effects. The contributions to the phenotypic variances of additive QTL and epistatic QTL pairs were 15.7% (3 QTL) and 10.8% (2 pairs) for oil content, 10.4% (3 QTL) and 10.3% (3 pairs) for oleic acid, 11.6% (3 QTL) and 8.5% (2 pairs) for linoleic acid, 28.5% (7 QTL) and 7.6% (3 pairs) for linolenic acid, 27.0% (6 QTL) and 16.6% (7 pairs) for palmitic acid and 29.7% (5 QTL) and 4.3% (1 pair) for stearic acid, respectively. Those of additive QTL by year interaction were small and no epistatic QTL pair by year interaction was found. Among the 27 additive QTL and 36 epistatic QTL (18 pairs), three are duplicated between the two QTL types. A large difference was found between the genotypic variance among RILs and the total variance of mapped QTL, which accounted for 52.9-74.8% of the genotypic variation, much larger than those of additive QTL and epistatic QTL pairs. This part of variance was recognized as that due to a collection of unmapped minor QTL, like polygenes in biometrical genetics, and was designated as collective unmapped minor QTL. The results challenge the breeders for how to pyramid different types of QTL. In addition, the present study supports the mapping strategy of a full model scanning followed by verification with other procedures corresponding to the first results. © 2011 Springer Science+Business Media B.V.

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