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Dive into the research topics where Charles Y. Chen is active.

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Featured researches published by Charles Y. Chen.


Theoretical and Applied Genetics | 2011

Population structure and marker–trait association analysis of the US peanut (Arachis hypogaea L.) mini-core collection

Ming Li Wang; Sivakumar Sukumaran; Noelle A. Barkley; Zhenbang Chen; Charles Y. Chen; Baozhu Guo; Roy N. Pittman; H. Thomas Stalker; C. Corley Holbrook; Gary A. Pederson; Jianming Yu

Peanut (Arachis hypogaea L.) is one of the most important oilseed and nutritional crops in the world. To efficiently utilize the germplasm collection, a peanut mini-core containing 112 accessions was established in the United States. To determine the population structure and its impact on marker–trait association, this mini-core collection was assessed by genotyping 94 accessions with 81 SSR markers and two functional SNP markers from fatty acid desaturase 2 (FAD2). Seed quality traits (including oil content, fatty acid composition, flavonoids, and resveratrol) were obtained through nuclear magnetic resonance (NMR), gas chromatography (GC), and high-performance liquid chromatography (HPLC) analysis. Genetic diversity and population structure analysis identified four major subpopulations that are related to four botanical varieties. Model comparison with different levels of population structure and kinship control was conducted for each trait and association analyses with the selected models verified that the functional SNP from the FAD2A gene is significantly associated with oleic acid (C18:1), linoleic acid (C18:2), and oleic-to-linoleic (O/L) ratio across this diverse collection. Even though the allele distribution of FAD2A was structured among the four subpopulations, the effect of FAD2A gene remained significant after controlling population structure and had a likelihood-ratio-based R2 (RLR2) value of 0.05 (oleic acid), 0.09 (linoleic acid), and 0.07 (O/L ratio) because the FAD2A alleles were not completely fixed within subpopulations. Our genetic analysis demonstrated that this peanut mini-core panel is suitable for association mapping. Phenotypic characterization for seed quality traits and association testing of the functional SNP from FAD2A gene provided information for further breeding and genetic research.


Plant Genetic Resources | 2010

Assessment of oil content and fatty acid composition variability in different peanut subspecies and botanical varieties

M.L. Wang; Charles Y. Chen; J. Davis; Baozhu Guo; H. T. Stalker; R. N. Pittman

Within the cultivated peanut species (Arachis hypogaea L.), there are two subspecies compris- ing six botanical varieties, and the effect of botanical taxon on oil content and fatty acid com- position variability is unclear. To gauge the variability, 83 peanut accessions were analyzed for oil content (expressed at 0% moisture) and fatty acid composition. We found that within the subsp. hypogaea, var. hypogaea contained a much higher amount of oil in seeds than did the var. hirsuta Kohler (520 vs. 473 g/kg, P , 0.05); within the subsp. fastigiata Waldron, the vars. aequatoriana Krapov. & W.C. Gregory and vulgaris Harz contained a similar amount of oil in seeds (491 g/kg), not significantly different from other botanical varieties, but var. fastigiata contained a higher amount of oil (500 g/kg) than the var. peruviana Krapov. & W.C. Gregory (483 g/kg). In terms of the fatty acid composition, oil from seeds of var. hypogaea contained much more oleic acid than did var. hirsuta (491 vs. 377 g/kg, P , 0.05), but much less palmitic acid (97 vs. 138 g/kg, P , 0.05%) and linoleic acid (308 vs. 402 g/kg, P , 0.05). Oil from seeds of var. vulgaris contained much more oleic acid than did var. aequatoriana (437 vs. 402 g/kg, P , 0.05), but much less linoleic acid (346 vs. 380 g/kg, P , 0.05). Significant negative correlations of oleic with palmitic and linoleic acids were detected. The information on the oil content and fatty acid composition variability among botanical varieties would be useful for peanut breeders seeking germplasm containing both high oil content and proper fatty acid composition.


International Journal of Agronomy | 2011

Peanut Seed Vigor Evaluation Using a Thermal Gradient

Timothy L. Grey; J. P. Beasley; Theodore M. Webster; Charles Y. Chen

Experiments conducted from 2007 to 2009 evaluated germination of 11 peanut runner-type cultivars. Germination was evaluated in Petridishes incubated over a thermal gradient ranging from 14 to 30°C at 1.0 C increments. Beginning 24 hr after seeding, peanut was counted as germinated when radicles were greater than 5 mm long, with removal each day. Germination was counted daily for seven days after seeding. Growing-degree day (GDD) accumulation for each temperature increment was calculated based on daily mean temperature for that Petri dish. Two indices were obtained from a logistic growth curve used to elucidate seed germination by cultivar: (1) maximum indices of germination and (2) GDD value at 80% germination (Germ80), an indication of seed vigor the lower the Germ80 value, the greater the seed lot vigor. Based on the two indices, seed lots “AT 3081R”, “AP-3”, “GA-06G”, and “Carver” had the strongest seed vigor (Germ80 26 to 47 GDD) and a high maximum incidence of germination rate (80 to 94%). Seed lots of “C99-R”, “Georgia-01R”, “Georgia-02C”, and “Georgia-03L” had inconsistent seed performance, failing to achieve 80% germination in at least two of three years.


Journal of Crop Improvement | 2012

Vigor Rating and Brix for First Clonal Selection Stage of the Canal Point Sugarcane Cultivar Development Program

Duli Zhao; Jack C. Comstock; Barry Glaz; Serge J. Edmé; Neil C. Glynn; I. A. Del Blanco; Robert A. Gilbert; R. Wayne Davidson; Charles Y. Chen

A better understanding of sugarcane (Saccharum spp.) genetic variability in agronomic performance will help optimize breeding and selection strategies. Vigor ratings and Brix data were collected from the 2009 and 2010 clones in the first clonal selection stage (stage I) of the Canal Point (CP) sugarcane cultivar development program. Stage I individual selection was based on disease resistance and on the product of vigor and Brix. Vigor ratings (from 1 to 9) from all clones and Brix of any clones with a vigor rating ≥6 were collected in the stage I fields and analyzed for relationships between vigor and Brix, for selection rate in each family (i.e., cross), and for their coefficients of variation (CV) within and among families. There was no correlation between vigor and Brix, suggesting that it would be feasible in stage I to select sugarcane clones with both high vigor and high Brix. Variability was high (CV = 59%) for both the number of planted clones and selection rates among families, and vigor (7.2%) had greater CV than Brix (5.4%). Averaged across years, the within-family CVs (9.3% for vigor and 6.3% for Brix) were greater than the among-family CVs (6.3% for vigor and 4.7% for Brix). Results indicated that greater emphasis on family-based than on individual selection in stage I should be avoided, as it would result in the loss of potentially productive clones. However, use of individual selection data on vigor and Brix for analyzing family performance should improve parental selection and optimize crosses.


Genome | 2007

SSR marker diversity of soybean aphid resistance sources in North America

Charles Y. Chen; Cuihua GuC. Gu; Clarice Mensah; Randall L. Nelson; Dechun WangD. Wang

The soybean aphid (Aphis glycines Matsumura) has become a major pest of soybean in North America since 2000. Seven aphid resistance sources, PI 71506, Dowling, Jackson, PI 567541B, PI 567598B, PI 567543C, and PI 567597C, have been identified. Knowledge of genetic relationships among these sources and their ancestral parents will help breeders develop new cultivars with different resistance genes. The objective of this research was to examine the genetic relationships among these resistance sources. Sixty-one lines were tested with 86 simple sequence repeat (SSR) markers from 20 linkage groups. Non-hierarchical (VARCLUS) and hierarchical (Wards) clustering and multidimensional scaling (MDS) were used to determine relationships among the 61 lines. Two hundred and sixty-two alleles of the 86 SSR loci were detected with a mean polymorphism information content of 0.36. The 61 lines were grouped into 4 clusters by both clustering methods and the MDS results consistently corresponded to the assigned clusters. The 7 resistance sources were clustered into 3 different groups corresponding to their geographical origins and known pedigree information, indicating genetic differences among these sources. The largest variation was found among individuals within different clusters by analysis of molecular variance.


Peanut Science | 2011

Nondestructive NIR Reflectance Spectroscopic Method for Rapid Fatty Acid Analysis of Peanut Seeds

Jaya Sundaram; Chari V. Kandala; Christopher L. Butts; Charles Y. Chen; Victor Sobolev

Near Infrared Reflectance Spectroscopy (NIRS) was used to rapidly and nondestructively analyze the fatty acid concentration present in peanut seeds samples. Absorbance spectra were collected in the wavelength range from 400 nm to 2500 nm using NIRS. The oleic, linoleic and palmitic fatty acids were converted to their corresponding methyl esters and their concentrations were measured using a gas chromatograph (GC). Partial least square (PLS) analysis was performed on a calibration set, and models were developed for prediction of fatty acid concentrations. The best model was selected based on the coefficient of determination (R 2 ), Root Mean Square Error of Prediction, residual percent deviation (RPD) and correlation coefficient percentage between the gas chromatography measured values and the NIR predicted values. The NIR reflectance model developed yielded RPD values of three and above for prediction of the three fatty acids, indicating that this nondestructive method would be suitable for fatty acid predictions in peanut seeds.


Peanut Science | 2012

Variability in Field Response of Peanut Genotypes from the U.S. and China to Tomato Spotted Wilt Virus and Leaf Spots

Yan Li; A. K. Culbreath; Charles Y. Chen; Steve J. Knapp; C. Corley Holbrook; Baozhu Guo

Abstract Tomato spotted wilt, caused by Tomato spotted wilt virus (TSWV) and transmitted by thrips, and early leaf spot and late leaf spot are among the most important diseases of peanut in the southeastern United States. The objective of this study was to compare field susceptibility of diverse peanut lines to TSWV and leaf spot pathogens for selection of lines for mapping population development. In field trials in 2007 and 2008, 22 genotypes were evaluated for reactions to TSWV and leaf spots. Early leaf spot was the predominate pathogen in both years. There was a near-continuous range of spotted wilt from 18% to 79% for the total incidence rating with any symptoms caused by TSWV. In general, NC94022, ‘Georganic’, C689-6-2, ‘Georgia-01R’, C724-19-25, TifGP-1, C11-154-61, C12-3-114-58, and ‘Tifguard’ were among the most resistant genotypes to TSWV, whereas GT-C20, GT-C9 and PE-2 were the most susceptible. Final percentage of defoliation by leaf spots ranged from 10% to 97% for both years. Genotypes C689-...


Theoretical and Applied Genetics | 2012

An integrated genetic linkage map of cultivated peanut (Arachis hypogaea L.) constructed from two RIL populations

Hongde Qin; Suping Feng; Charles Y. Chen; Yufang Guo; Steven J. Knapp; A. K. Culbreath; Guohao He; Ming Li Wang; Xinyou Zhang; C. Corley Holbrook; Peggy Ozias-Akins; Baozhu Guo


Genetica | 2008

Mapping QTLs of root morphological traits at different growth stages in rice.

Yanying Qu; Ping Mu; Hongliang Zhang; Charles Y. Chen; Yongming Gao; Yuxiu Tian; Feng Wen; Zichao Li


Crop Science | 2008

Quantitative Trait Loci Mapping of Seed Hardness in Soybean

Bo Zhang; Pengyin Chen; Charles Y. Chen; Dechun Wang; Ainong Shi; A. Hou; Tetsuaki Ishibashi

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Baozhu Guo

Agricultural Research Service

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C. Corley Holbrook

Agricultural Research Service

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M.L. Wang

United States Department of Agriculture

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Marshall C. Lamb

Agricultural Research Service

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Ming Li Wang

United States Department of Agriculture

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A. Hou

University of Arkansas

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Ainong Shi

University of Arkansas

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