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Lynn KS, Cheng ML, Yang HC, Liang YJ, Kang MJ, Chen FL, Shiao MS, Pan WH. Vegetable Signatures Derived from Human Urinary Metabolomic Data in Controlled Feeding Studies. J Proteome Res 2019; 18:159-168. [PMID: 30517004 DOI: 10.1021/acs.jproteome.8b00470] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Examination of changes in urinary metabolomic profiles after vegetable ingestion may lead to new methods of assessing plant food intake. To this regard, we developed a proof-of-principle methodology to identify urinary metabolomic signatures for spinach, celery, and onion. Three feeding studies were conducted. In the first study, healthy individuals were fed with spinach, celery, onion, and no vegetables in four separate experiments with pooled urinary samples for metabolite discovery. The same protocol was used to validate the finding at the individual level in the second study and when feeding all three vegetables simultaneously in the third study. An LC-MS-based metabolomics approach was adopted to search for indicative metabolites from urine samples collected during multiple time periods before and after the meal. Consequently, a total of 1, 9, and 3 nonoverlapping urinary metabolites were associated with the intake of spinach, celery, and onion, respectively. The PCA signature of these metabolites followed a similar "time cycle" pattern, which maximized at approximately 2-4 h after intake. In addition, the metabolite profiles for the same vegetable were consistent across samples, regardless of whether it was consumed individually or in combination. The developed methodology along with the identified urinary metabolomic signatures were potential tools for assessing plant food intake.
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Affiliation(s)
- Ke-Shiuan Lynn
- Department of Mathematics , Fu Jen Catholic University , New Taipei City 24205 , Taiwan
| | - Mei-Ling Cheng
- Department of Biomedical Sciences, College of Medicine , Chang Gung University , Taoyuan 33302 , Taiwan.,Metabolomics Core Laboratory, Healthy Aging Research Center , Chang Gung University , Taoyuan 33302 , Taiwan.,Clinical Metabolomics Core Laboratory , Chang Gung Memorial Hospital , Taoyuan 33305 , Taiwan
| | - Hsin-Chou Yang
- Institute of Statistical Science , Academia Sinica , Taipei 11529 , Taiwan
| | - Yu-Jen Liang
- Institute of Statistical Science , Academia Sinica , Taipei 11529 , Taiwan
| | - Mei-Jyh Kang
- Institute of Biomedical Sciences , Academia Sinica , Taipei 11529 , Taiwan
| | - Fong-Ling Chen
- Institute of Biomedical Sciences , Academia Sinica , Taipei 11529 , Taiwan
| | - Ming-Shi Shiao
- Department of Biomedical Sciences, College of Medicine , Chang Gung University , Taoyuan 33302 , Taiwan
| | - Wen-Harn Pan
- Institute of Biomedical Sciences , Academia Sinica , Taipei 11529 , Taiwan.,Institute of Population Health Sciences , National Health Research Institutes , Miaoli 35053 , Taiwan
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Lyra DH, Galli G, Alves FC, Granato ÍSC, Vidotti MS, Bandeira E Sousa M, Morosini JS, Crossa J, Fritsche-Neto R. Modeling copy number variation in the genomic prediction of maize hybrids. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:273-288. [PMID: 30382311 DOI: 10.1007/s00122-018-3215-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 10/20/2018] [Indexed: 06/08/2023]
Abstract
Our study indicates that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids. Moreover, predicting hybrid phenotypes by combining additive-dominance effects with copy variants has the potential to be a viable predictive model. Non-additive effects resulting from the actions of multiple loci may influence trait variation in single-cross hybrids. In addition, complementation of allelic variation could be a valuable contributor to hybrid genetic variation, especially when crossing inbred lines with higher contents of copy gains. With this in mind, we aimed (1) to study the association between copy number variation (CNV) and hybrid phenotype, and (2) to compare the predictive ability (PA) of additive and additive-dominance genomic best linear unbiased prediction model when combined with the effects of CNV in two datasets of maize hybrids (USP and HELIX). In the USP dataset, we observed a significant negative phenotypic correlation of low magnitude between copy number loss and plant height, revealing a tendency that more copy losses lead to lower plants. In the same set, when CNV was combined with the additive plus dominance effects, the PA significantly increased only for plant height under low nitrogen. In this case, CNV effects explicitly capture relatedness between individuals and add extra information to the model. In the HELIX dataset, we observed a pronounced difference in PA between additive (0.50) and additive-dominance (0.71) models for predicting grain yield, suggesting a significant contribution of dominance. We conclude that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids, although the inclusion of CNVs into datasets does not return significant gains concerning PA.
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Affiliation(s)
- Danilo Hottis Lyra
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil.
- Department of Computational and Analytical Sciences, Rothamsted Research, West Common, Harpenden, AL52JQ, UK.
| | - Giovanni Galli
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Filipe Couto Alves
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Ítalo Stefanine Correia Granato
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Miriam Suzane Vidotti
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Massaine Bandeira E Sousa
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Júlia Silva Morosini
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - José Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600, Texcoco, D.F, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
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53
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Xia W, Xiao Z, Cao P, Zhang Y, Du K, Wang N. Construction of a high-density genetic map and its application for leaf shape QTL mapping in poplar. PLANTA 2018; 248:1173-1185. [PMID: 30088086 DOI: 10.1007/s00425-018-2958-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 07/17/2018] [Indexed: 05/12/2023]
Abstract
High-quality and dense genetic maps were constructed, and leaf shape variation was dissected by QTL mapping in poplar. Species in the genus Populus, also known as poplars, are important woody species and considered model plants for perennial trees. High-density genetic maps are valuable genomic resources for population genetics. Here, we generated a high-quality and dense genetic map for an F1 poplar population using high-throughput NGS-based genotyping. A total of 92,097 high-quality SNP markers were developed by stringent filtering and identification. In total, 889 and 1650 SNPs formed the female and male genetic maps, respectively. To test the application of the genetic maps, QTL mapping of leaf shape was conducted for this F1 population. A total of nine parameters were scored for leaf shape variation in three different environments. Combining genetic maps and measurements of the nine leaf shape parameters, we mapped a total of 42 significant QTLs. The highest LOD score of all QTLs was 9.2, and that QTL explained the most (15.13%) trait variation. A total of nine QTLs could be detected in at least two environments, and they were located in two genomic regions. Within these two QTL regions, some candidate genes for regulating leaf shape were predicted through functional annotation. The successful mapping of leaf shape QTLs demonstrated the utility of our genetic maps. According to the performance of this study, we were able to provide high-quality and dense genetic maps and dissect the leaf shape variation in poplar.
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Affiliation(s)
- Wenxiu Xia
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zheng'ang Xiao
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Pei Cao
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yan Zhang
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Kebing Du
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Nian Wang
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Engineering Technology Research Center for Forestry Information, Huazhong Agricultural University, Wuhan, 430070, China.
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54
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Wang X, Xu Y, Hu Z, Xu C. Genomic selection methods for crop improvement: Current status and prospects. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.cj.2018.03.001] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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55
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Du C, Wei J, Wang S, Jia Z. Genomic selection using principal component regression. Heredity (Edinb) 2018; 121:12-23. [PMID: 29713089 DOI: 10.1038/s41437-018-0078-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 03/17/2018] [Accepted: 03/21/2018] [Indexed: 01/02/2023] Open
Abstract
Many statistical methods are available for genomic selection (GS) through which genetic values of quantitative traits are predicted for plants and animals using whole-genome SNP data. A large number of predictors with much fewer subjects become a major computational challenge in GS. Principal components regression (PCR) and its derivative, i.e., partial least squares regression (PLSR), provide a solution through dimensionality reduction. In this study, we show that PCR can perform better than PLSR in cross validation. PCR often requires extracting more components to achieve the maximum predictive ability than PLSR and thus may be associated with a higher computational cost. However, application of the HAT method (a strategy of describing the relationship between the fitted and observed response variables with a hat matrix) to PCR circumvents conventional cross validation in testing predictive ability, resulting in substantially improved computational efficiency over PLSR where cross validation is mandatory. Advantages of PCR over PLSR are illustrated with a simulated trait of a hypothetical population and four agronomical traits of a rice population. The benefit of using PCR in genomic selection is further demonstrated in an effort to predict 1000 metabolomic traits and 24,973 transcriptomic traits in the same rice population.
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Affiliation(s)
- Caroline Du
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Julong Wei
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA.,College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Shibo Wang
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Zhenyu Jia
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA.
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56
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Li C, Fu Y, Sun R, Wang Y, Wang Q. Single-Locus and Multi-Locus Genome-Wide Association Studies in the Genetic Dissection of Fiber Quality Traits in Upland Cotton ( Gossypium hirsutum L.). FRONTIERS IN PLANT SCIENCE 2018; 9:1083. [PMID: 30177935 PMCID: PMC6109694 DOI: 10.3389/fpls.2018.01083] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 07/04/2018] [Indexed: 05/04/2023]
Abstract
A major breeding target in Upland cotton (Gossypium hirsutum L.) is to improve the fiber quality. To address this issue, 169 diverse accessions, genotyped by 53,848 high-quality single-nucleotide polymorphisms (SNPs) and phenotyped in four environments, were used to conduct genome-wide association studies (GWASs) for fiber quality traits using three single-locus and three multi-locus models. As a result, 342 quantitative trait nucleotides (QTNs) controlling fiber quality traits were detected. Of the 342 QTNs, 84 were simultaneously detected in at least two environments or by at least two models, which include 29 for fiber length, 22 for fiber strength, 11 for fiber micronaire, 12 for fiber uniformity, and 10 for fiber elongation. Meanwhile, nine QTNs with 10% greater sizes (R2) were simultaneously detected in at least two environments and between single- and multi-locus models, which include TM80185 (D13) for fiber length, TM1386 (A1) and TM14462 (A6) for fiber strength, TM18616 (A7), TM54735 (D3), and TM79518 (D12) for fiber micronaire, TM77489 (D12) and TM81448 (D13) for fiber uniformity, and TM47772 (D1) for fiber elongation. This indicates the possibility of marker-assisted selection in future breeding programs. Among 455 genes within the linkage disequilibrium regions of the nine QTNs, 113 are potential candidate genes and four are promising candidate genes. These findings reveal the genetic control underlying fiber quality traits and provide insights into possible genetic improvements in Upland cotton fiber quality.
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Affiliation(s)
- Chengqi Li
- Collaborative Innovation Center of Modern Biological Breeding, School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
| | - Yuanzhi Fu
- Collaborative Innovation Center of Modern Biological Breeding, School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
| | - Runrun Sun
- Collaborative Innovation Center of Modern Biological Breeding, School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
| | - Yuanyuan Wang
- Collaborative Innovation Center of Modern Biological Breeding, School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
| | - Qinglian Wang
- Collaborative Innovation Center of Modern Biological Breeding, School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
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57
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Xu Y, Yang T, Zhou Y, Yin S, Li P, Liu J, Xu S, Yang Z, Xu C. Genome-Wide Association Mapping of Starch Pasting Properties in Maize Using Single-Locus and Multi-Locus Models. FRONTIERS IN PLANT SCIENCE 2018; 9:1311. [PMID: 30233634 PMCID: PMC6134291 DOI: 10.3389/fpls.2018.01311] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 08/20/2018] [Indexed: 05/05/2023]
Abstract
Maize starch plays a critical role in food processing and industrial application. The pasting properties, the most important starch characteristics, have enormous influence on fabrication property, flavor characteristics, storage, cooking, and baking. Understanding the genetic basis of starch pasting properties will be beneficial for manipulation of starch properties for a given purpose. Genome-wide association studies (GWAS) are becoming a powerful tool for dissecting the complex traits. Here, we carried out GWAS for seven pasting properties of maize starch with a panel of 230 inbred lines and 145,232 SNPs using one single-locus method, genome-wide efficient mixed model association (GEMMA), and three multi-locus methods, FASTmrEMMA, FarmCPU, and LASSO. We totally identified 60 quantitative trait nucleotides (QTNs) for starch pasting properties with these four GWAS methods. FASTmrEMMA detected the most QTNs (29), followed by FarmCPU (19) and LASSO (12), GEMMA detected the least QTNs (7). Of these QTNs, seven QTNs were identified by more than one method simultaneously. We further investigated locations of these significantly associated QTNs for possible candidate genes. These candidate genes and significant QTNs provide the guidance for further understanding of molecular mechanisms of starch pasting properties. We also compared the statistical powers and Type I errors of the four GWAS methods using Monte Carlo simulations. The results suggest that the multi-locus method is more powerful than the single-locus method and a combination of these multi-locus methods could help improve the detection power of GWAS.
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Affiliation(s)
- Yang Xu
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Tiantian Yang
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Yao Zhou
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Shuangyi Yin
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Pengcheng Li
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Jun Liu
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Shuhui Xu
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Zefeng Yang
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Chenwu Xu
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
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58
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Metabolome-wide association studies for agronomic traits of rice. Heredity (Edinb) 2017; 120:342-355. [PMID: 29225351 DOI: 10.1038/s41437-017-0032-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 11/06/2017] [Accepted: 11/07/2017] [Indexed: 11/09/2022] Open
Abstract
Identification of trait-associated metabolites will advance the knowledge and understanding of the biosynthetic and catabolic pathways that are relevant to the complex traits of interest. In the past, the association between metabolites (treated as quantitative traits) and genetic variants (e.g., SNPs) has been extensively studied using metabolomic quantitative trait locus (mQTL) mapping. Nevertheless, the research on the association between metabolites with agronomic traits has been inadequate. In practice, the regular approaches for QTL mapping analysis may be adopted for metabolites-phenotypes association analysis due to the similarity in data structure of these two types of researches. In the study, we compared four regular QTL mapping approaches, i.e., simple linear regression (LR), linear mixed model (LMM), Bayesian analysis with spike-slab priors (Bayes B) and least absolute shrinkage and selection operator (LASSO), by testing their performances on the analysis of metabolome-phenotype associations. Simulation studies showed that LASSO had the higher power and lower false positive rate than the other three methods. We investigated the associations of 839 metobolites with five agronomic traits in a collection of 533 rice varieties. The results implied that a total of 25 metabolites were significantly associated with five agronomic traits. Literature search and bioinformatics analysis indicated that the identified 25 metabolites are significantly involved in some growth and development processes potentially related to agronomic traits. We also explored the predictability of agronomic traits based on the 839 metabolites through cross-validation, which showed that metabolomic prediction was efficient and its application in plant breeding has been justified.
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