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Zheng Y, Han X, Zhao Y, Zhu L, Huang Y, Jia X, Zhang Z, Chen J, Guo J. Association mapping for general combining ability with yield, plant height and ear height using F1 population in maize. PLoS One 2021; 16:e0258327. [PMID: 34653186 PMCID: PMC8519473 DOI: 10.1371/journal.pone.0258327] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 09/24/2021] [Indexed: 11/19/2022] Open
Abstract
General combining ability (GCA) is an important index for inbred lines breeding of maize. To identify the genetic loci of GCA and associated agronomic traits, an association analysis with 195 SSRs was made in phenotypic traits of 240 F1 derived from 120 elite inbred lines containing current breeding resources of maize crossed with 2 testers (Zheng58 and Chang7-2) in two places in 2018. All of the 20 association loci detected for grain yield (GY), plant height (PH), ear height (EH) and GCA for the three traits in two places could explain a phenotypic variation range of 7.31%-9.29%. Among the 20 association loci, 9 (7.31%-9.04%) were associated with GY, 4 (7.22%-8.91%) were related to GCA of GY, 1 (7.56%) was associated with PH, and 3 (7.53%-8.96%) were related to EH. In addition, 3 loci (9.14%-9.29%) were associated with GCA of PH whereas no locus was identified for GCA of EH. In the comparison of the association loci detected in Baoding and Handan, interestingly, one locus (7.69% and 8.11%) was identified in both environments and one locus (7.52% and 7.82%) was identified for yield and GCA of yield. Therefore, the identification of GY-, PH-, EH- and GCA-related association loci could not only provide references for high yield breeding of maize, but also help us comprehend the relationships among GY, agricultural traits and GCA.
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Affiliation(s)
- Yunxiao Zheng
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xintong Han
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Yongfeng Zhao
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Liying Zhu
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Yaqun Huang
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xiaoyan Jia
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Zhongqin Zhang
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jingtang Chen
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
- College of Agronomy, Qingdao Agricultural University, Qingdao, China
| | - Jinjie Guo
- Hebei Sub-Center of National Maize Improvement Center, Key Laboratory Jointly Constructed by the Ministry of Education and Hebei Province, Key Laboratory for Crop Germplasm Resources of Hebei, North China Key Laboratory for Crop Germplasm Resources under the Ministry of Education, College of Agronomy, Hebei Agricultural University, Baoding, China
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2
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Genetic dissection of heterosis of indica-japonica by introgression line, recombinant inbred line and their testcross populations. Sci Rep 2021; 11:10265. [PMID: 33986411 PMCID: PMC8119717 DOI: 10.1038/s41598-021-89691-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 04/27/2021] [Indexed: 12/03/2022] Open
Abstract
The successful implementation of heterosis in rice has significantly enhanced rice productivity, but the genetic basis of heterosis in rice remains unclear. To understand the genetic basis of heterosis in rice, main-effect and epistatic quantitative trait loci (QTLs) associated with heterosis for grain yield-related traits in the four related rice mapping populations derived from Xiushui09 (XS09) (japonica) and IR2061 (indica), were dissected using single nucleotide polymorphism bin maps and replicated phenotyping experiments under two locations. Most mid-parent heterosis of testcross F1s (TCF1s) of XS09 background introgression lines (XSILs) with Peiai64S were significantly higher than those of TCF1s of recombinant inbred lines (RILs) with PA64S at two locations, suggesting that the effects of heterosis was influenced by the proportion of introgression of IR2061’s genome into XS09 background. A total of 81 main-effect QTLs (M-QTLs) and 41 epistatic QTLs were identified for the phenotypic variations of four traits of RILs and XSILs, TCF1s and absolute mid-parent heterosis in two locations. Furthermore, overdominance and underdominance were detected to play predominant effects on most traits in this study, suggesting overdominance and underdominance as well as epistasis are the main genetic bases of heterosis in rice. Some M-QTLs exhibiting positive overdominance effects such as qPN1.2, qPN1.5 and qPN4.3 for increased panicle number per plant, qGYP9 and qGYP12.1 for increased grain yield per plant, and qTGW3.4 and qTGW8.2 for enhanced 1000-grain weight would be highly valuable for breeding to enhance grain yield of hybrid rice by marker-assisted selection.
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Geng X, Sun G, Qu Y, Sarfraz Z, Jia Y, He S, Pan Z, Sun J, Iqbal MS, Wang Q, Qin H, Liu J, Liu H, Yang J, Ma Z, Xu D, Yang J, Zhang J, Li Z, Cai Z, Zhang X, Zhang X, Zhou G, Li L, Zhu H, Wang L, Pang B, Du X. Genome-wide dissection of hybridization for fiber quality- and yield-related traits in upland cotton. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 104:1285-1300. [PMID: 32996179 PMCID: PMC7756405 DOI: 10.1111/tpj.14999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 07/14/2020] [Accepted: 09/03/2020] [Indexed: 06/11/2023]
Abstract
An evaluation of combining ability can facilitate the selection of suitable parents and superior F1 hybrids for hybrid cotton breeding, although the molecular genetic basis of combining ability has not been fully characterized. In the present study, 282 female parents were crossed with four male parents in accordance with the North Carolina II mating scheme to generate 1128 hybrids. The parental lines were genotyped based on restriction site-associated DNA sequencing and 306 814 filtered single nucleotide polymorphisms were used for genome-wide association analysis involving the phenotypes, general combining ability (GCA) values, and specific combining ability values of eight fiber quality- and yield-related traits. The main results were: (i) all parents could be clustered into five subgroups based on population structure analyses and the GCA performance of the female parents had significant differences between subgroups; (ii) 20 accessions with a top 5% GCA value for more than one trait were identified as elite parents for hybrid cotton breeding; (iii) 120 significant single nucleotide polymorphisms, clustered into 66 quantitative trait loci, such as the previously reported Gh_A07G1769 and GhHOX3 genes, were found to be significantly associated with GCA; and (iv) identified quantitative trait loci for GCA had a cumulative effect on GCA of the accessions. Overall, our results suggest that pyramiding the favorable loci for GCA may improve the efficiency of hybrid cotton breeding.
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Affiliation(s)
- Xiaoli Geng
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
- Zhengzhou Research BaseState Key Laboratory of Cotton BiologyZhengzhou UniversityZhengzhou455001China
| | - Gaofei Sun
- Anyang Institute of TechnologyAnyang455000China
| | - Yujie Qu
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
| | - Zareen Sarfraz
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
| | - Yinhua Jia
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
- Zhengzhou Research BaseState Key Laboratory of Cotton BiologyZhengzhou UniversityZhengzhou455001China
| | - Shoupu He
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
- Zhengzhou Research BaseState Key Laboratory of Cotton BiologyZhengzhou UniversityZhengzhou455001China
| | - Zhaoe Pan
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
| | - Junling Sun
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
| | - Muhammad S. Iqbal
- Cotton Research StationAyub Agricultural Research InstituteFaisalabad38000Pakistan
| | - Qinglian Wang
- Henan Institute of Science and TechnologyXinxiang453003China
| | - Hongde Qin
- Cash Crop InstituteHubei Academy of Agricultural SciencesWuhan430000China
| | - Jinhai Liu
- Zhongmian Cotton Seed Industry Technology Co., LtdZhengzhou455001China
| | - Hui Liu
- Jing Hua Seed Industry Technologies IncJingzhou434000China
| | - Jun Yang
- Cotton Research Institute of Jiangxi ProvinceJiujiang332000China
| | - Zhiying Ma
- Key Laboratory of Crop Germplasm Resources of HebeiHebei Agricultural UniversityBaoding071000China
| | - Dongyong Xu
- Guoxin Rural Technical Service AssociationHejian062450China
| | - Jinlong Yang
- Zhongmian Cotton Seed Industry Technology Co., LtdZhengzhou455001China
| | | | - Zhikun Li
- Key Laboratory of Crop Germplasm Resources of HebeiHebei Agricultural UniversityBaoding071000China
| | - Zhongmin Cai
- Zhongmian Cotton Seed Industry Technology Co., LtdZhengzhou455001China
| | - Xuelin Zhang
- Hunan Cotton Research InstituteChangde415000China
| | - Xin Zhang
- Henan Institute of Science and TechnologyXinxiang453003China
| | - Guanyin Zhou
- Zhongmian Cotton Seed Industry Technology Co., LtdZhengzhou455001China
| | - Lin Li
- Zhongli Company of ShandongDongying257000China
| | - Haiyong Zhu
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
| | - Liru Wang
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
| | - Baoyin Pang
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
| | - Xiongming Du
- State Key Laboratory of Cotton BiologyInstitute of Cotton ResearchChinese Academy of Agricultural SciencesAnyang455000China
- Zhengzhou Research BaseState Key Laboratory of Cotton BiologyZhengzhou UniversityZhengzhou455001China
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Su J, Wang C, Hao F, Ma Q, Wang J, Li J, Ning X. Genetic Detection of Lint Percentage Applying Single-Locus and Multi-Locus Genome-Wide Association Studies in Chinese Early-Maturity Upland Cotton. FRONTIERS IN PLANT SCIENCE 2019; 10:964. [PMID: 31428110 PMCID: PMC6688134 DOI: 10.3389/fpls.2019.00964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 07/10/2019] [Indexed: 05/28/2023]
Abstract
Upland cotton (Gossypium hirsutum L.) is the most important source of natural fiber in the world. Early-maturity upland cotton varieties are commonly planted in China. Nevertheless, lint yield of early-maturity upland cotton varieties is strikingly lower than that of middle- and late-maturity ones. How to effectively improve lint yield of early maturing cotton, becomes a focus of cotton research. Here, based on 72,792 high-quality single nucleotide polymorphisms of 160 early-maturing upland cotton accessions, we performed genome-wide association studies (GWASs) for lint percentage (LP), one of the most lint-yield component traits, applying one single-locus method and six multi-locus methods. A total of 4 and 45 significant quantitative trait nucleotides (QTNs) were respectively identified to be associated with LP. Interestingly, in two of four planting environments, two of these QTNs (A02_74713290 and A02_75551547) were simultaneously detected via both one single-locus and three or more multi-locus GWAS methods. Among the 42 genes within a genomic region (A02: 74.31-75.95 Mbp) containing the above two peak QTNs, Gh_A02G1269, Gh_A02G1280, and Gh_A02G1295 had the highest expression levels in ovules during seed development from 20 to 25 days post anthesis, whereas Gh_A02G1278 was preferentially expressed in the fibers rather than other organs. These results imply that the four potential candidate genes might be closely related to cotton LP by regulating the proportion of seed weight and fiber yield. The QTNs and potential candidate genes for LP, identified in this study, provide valuable resource for cultivating novel cotton varieties with earliness and high lint yield in the future.
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Affiliation(s)
- Junji Su
- Gansu Provincial Key Laboratory of Aridland Crop Science, College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Caixiang Wang
- Gansu Provincial Key Laboratory of Aridland Crop Science, College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Fushun Hao
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Science, Henan University, Kaifeng, China
| | - Qi Ma
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Ji Wang
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Science, Henan University, Kaifeng, China
| | - Jilian Li
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Xinzhu Ning
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
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5
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Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief Bioinform 2019; 19:700-712. [PMID: 28158525 PMCID: PMC6054291 DOI: 10.1093/bib/bbw145] [Citation(s) in RCA: 190] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Indexed: 12/01/2022] Open
Abstract
The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with ≤ 0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.
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Affiliation(s)
- Yang-Jun Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Hanwen Zhang
- Applied Science, University of British Columbia, Columbia, Canada
| | - Yuan-Li Ni
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Bo Huang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jin Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jian-Ying Feng
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shi-Bo Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jim M Dunwell
- School of Agriculture, Policy and Development, University of Reading, Berkshire, UK
| | - Yuan-Ming Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.,College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Rongling Wu
- Public Health Sciences and Statistics and Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA.,Center for Computational Biology, Beijing Forestry University, Beijing, China
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Sarfraz Z, Iqbal MS, Pan Z, Jia Y, He S, Wang Q, Qin H, Liu J, Liu H, Yang J, Ma Z, Xu D, Yang J, Zhang J, Gong W, Geng X, Li Z, Cai Z, Zhang X, Zhang X, Huang A, Yi X, Zhou G, Li L, Zhu H, Qu Y, Pang B, Wang L, Iqbal MS, Jamshed M, Sun J, Du X. Integration of conventional and advanced molecular tools to track footprints of heterosis in cotton. BMC Genomics 2018; 19:776. [PMID: 30373509 PMCID: PMC6206862 DOI: 10.1186/s12864-018-5129-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 09/27/2018] [Indexed: 12/26/2022] Open
Abstract
Background Heterosis, a multigenic complex trait extrapolated as sum total of many phenotypic features, is widely utilized phenomenon in agricultural crops for about a century. It is mainly focused on establishing vigorous cultivars with the fact that its deployment in crops necessitates the perspective of genomic impressions on prior selection for metric traits. In spite of extensive investigations, the actual mysterious genetic basis of heterosis is yet to unravel. Contemporary crop breeding is aimed at enhanced crop production overcoming former achievements. Leading cotton improvement programs remained handicapped to attain significant accomplishments. Results In mentioned context, a comprehensive project was designed involving a large collection of cotton accessions including 284 lines, 5 testers along with their respective F1 hybrids derived from Line × Tester mating design were evaluated under 10 diverse environments. Heterosis, GCA and SCA were estimated from morphological and fiber quality traits by L × T analysis. For the exploration of elite marker alleles related to heterosis and to provide the material carrying such multiple alleles the mentioned three dependent variables along with trait phenotype values were executed for association study aided by microsatellites in mixed linear model based on population structure and linkage disequilibrium analysis. Highly significant 46 microsatellites were discovered in association with the fiber and yield related traits under study. It was observed that two-thirds of the highly significant associated microsatellites related to fiber quality were distributed on D sub-genome, including some with pleiotropic effect. Newly discovered 32 hQTLs related to fiber quality traits are one of prominent findings from current study. A set of 96 exclusively favorable alleles were discovered and C tester (A971Bt) posited a major contributor of these alleles primarily associated with fiber quality. Conclusions Hence, to uncover hidden facts lying within heterosis phenomenon, discovery of additional hQTLs is required to improve fibre quality. To grab prominent improvement in influenced fiber quality and yield traits, we suggest the A971 Bt cotton cultivar as fundamental element in advance breeding programs as a parent of choice. Electronic supplementary material The online version of this article (10.1186/s12864-018-5129-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zareen Sarfraz
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Muhammad Shahid Iqbal
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China.,Cotton Research Station, Ayub Agricultural Research Institute, Faisalabad, Pakistan
| | - Zhaoe Pan
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Yinhua Jia
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Shoupu He
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Qinglian Wang
- Henan Institute of Science and Technology, Xinxiang, China
| | - Hongde Qin
- Cash Crop Institute, Hubei Academy of Agricultural Sciences, Wuhan, China
| | - Jinhai Liu
- Zhongmian Cotton Seed Industry Technology CO., LTD, Zhengzhou, China
| | - Hui Liu
- Jing Hua Seed Industry Technologies Inc, Jingzhou, China
| | - Jun Yang
- Cotton Research Institute of Jiangxi Province, Jiujiang, China
| | - Zhiying Ma
- Key Laboratory of Crop Germplasm Resources of Hebei, Agricultural University of Hebei, Baoding, China
| | - Dongyong Xu
- Guoxin Rural Technical Service Association, Hebei, China
| | - Jinlong Yang
- Zhongmian Cotton Seed Industry Technology CO., LTD, Zhengzhou, China
| | | | - Wenfang Gong
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Xiaoli Geng
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Zhikun Li
- Key Laboratory of Crop Germplasm Resources of Hebei, Agricultural University of Hebei, Baoding, China
| | - Zhongmin Cai
- Zhongmian Cotton Seed Industry Technology CO., LTD, Zhengzhou, China
| | | | - Xin Zhang
- Henan Institute of Science and Technology, Xinxiang, China
| | - Aifen Huang
- Sanyi Seed Industry of Changde in Hunan Inc, Changde, China
| | - Xianda Yi
- Cash Crop Institute, Hubei Academy of Agricultural Sciences, Wuhan, China
| | - Guanyin Zhou
- Zhongmian Cotton Seed Industry Technology CO., LTD, Zhengzhou, China
| | - Lin Li
- Zhongli Company of Shandong, Shandong, China
| | - Haiyong Zhu
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Yujie Qu
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Baoyin Pang
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Liru Wang
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Muhammad Sajid Iqbal
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Muhammad Jamshed
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China
| | - Junling Sun
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China.
| | - Xiongming Du
- State Key Laboratory of Cotton Biology/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), P. O. Box 455000, Anyang, Henan, China.
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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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8
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Genetic dissection of main and epistatic effects of QTL based on augmented triple test cross design. PLoS One 2017; 12:e0189054. [PMID: 29240818 PMCID: PMC5730204 DOI: 10.1371/journal.pone.0189054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 11/17/2017] [Indexed: 02/06/2023] Open
Abstract
The use of heterosis has considerably increased the productivity of many crops; however, the biological mechanism underpinning the technique remains elusive. The North Carolina design III (NCIII) and the triple test cross (TTC) are powerful and popular genetic mating design that can be used to decipher the genetic basis of heterosis. However, when using the NCIII design with the present quantitative trait locus (QTL) mapping method, if epistasis exists, the estimated additive or dominant effects are confounded with epistatic effects. Here, we propose a two-step approach to dissect all genetic effects of QTL and digenic interactions on a whole genome without sacrificing statistical power based on an augmented TTC (aTTC) design. Because the aTTC design has more transformation combinations than do the NCIII and TTC designs, it greatly enriches the QTL mapping for studying heterosis. When the basic population comprises recombinant inbred lines (RIL), we can use the same materials in the NCIII design for aTTC-design QTL mapping with transformation combination Z1, Z2, and Z4 to obtain genetic effect of QTL and digenic interactions. Compared with RIL-based TTC design, RIL-based aTTC design saves time, money, and labor for basic population crossed with F1. Several Monte Carlo simulation studies were carried out to confirm the proposed approach; the present genetic parameters could be identified with high statistical power, precision, and calculation speed, even at small sample size or low heritability. Additionally, two elite rice hybrid datasets for nine agronomic traits were estimated for real data analysis. We dissected the genetic effects and calculated the dominance degree of each QTL and digenic interaction. Real mapping results suggested that the dominance degree in Z2 that mainly characterize heterosis showed overdominance and dominance for QTL and digenic interactions. Dominance and overdominance were the major genetic foundations of heterosis in rice.
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Tamba CL, Ni YL, Zhang YM. Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies. PLoS Comput Biol 2017; 13:e1005357. [PMID: 28141824 PMCID: PMC5308866 DOI: 10.1371/journal.pcbi.1005357] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 02/14/2017] [Accepted: 01/09/2017] [Indexed: 12/18/2022] Open
Abstract
Genome-wide association study (GWAS) entails examining a large number of single nucleotide polymorphisms (SNPs) in a limited sample with hundreds of individuals, implying a variable selection problem in the high dimensional dataset. Although many single-locus GWAS approaches under polygenic background and population structure controls have been widely used, some significant loci fail to be detected. In this study, we used an iterative modified-sure independence screening (ISIS) approach in reducing the number of SNPs to a moderate size. Expectation-Maximization (EM)-Bayesian least absolute shrinkage and selection operator (BLASSO) was used to estimate all the selected SNP effects for true quantitative trait nucleotide (QTN) detection. This method is referred to as ISIS EM-BLASSO algorithm. Monte Carlo simulation studies validated the new method, which has the highest empirical power in QTN detection and the highest accuracy in QTN effect estimation, and it is the fastest, as compared with efficient mixed-model association (EMMA), smoothly clipped absolute deviation (SCAD), fixed and random model circulating probability unification (FarmCPU), and multi-locus random-SNP-effect mixed linear model (mrMLM). To further demonstrate the new method, six flowering time traits in Arabidopsis thaliana were re-analyzed by four methods (New method, EMMA, FarmCPU, and mrMLM). As a result, the new method identified most previously reported genes. Therefore, the new method is a good alternative for multi-locus GWAS. Genome-wide association study is concerned with the associations between markers and traits of interest so as to identify all the significantly associated markers. In genome-wide association studies, hundreds of thousands of markers are genotyped for several hundreds of individuals. Usually, only a very minor subset of these markers is associated with the trait. Most penalization methods fail when the number of markers is much larger than the sample size. Based on this fact, we have developed an algorithm that proceeds in two stages. In the first stage (screening), we reduced the number of markers via correlation learning to a moderate size. We then used a moderate-scale variable selection method to select variables in the reduced model. Conditional on the selected variables, we repeated the screening procedure and chose another set of variables. In the second stage (estimation), all the above-selected variables are accurately estimated in a multi-locus model. Our approach is simple, accurate in estimation, fast and shows high statistical power of detecting relevant markers on simulated data. We have also used this method to identify relevant genes in real data analysis. We recommend our approach for conducting a multi-locus genome-wide association study.
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Affiliation(s)
- Cox Lwaka Tamba
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- Department of Mathematics, Egerton University, Egerton, Kenya
| | - Yuan-Li Ni
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Yuan-Ming Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- Statistical Genomics Lab, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- * E-mail: ,
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Development of a multiple-hybrid population for genome-wide association studies: theoretical consideration and genetic mapping of flowering traits in maize. Sci Rep 2017; 7:40239. [PMID: 28071695 PMCID: PMC5223130 DOI: 10.1038/srep40239] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 12/05/2016] [Indexed: 12/18/2022] Open
Abstract
Various types of populations have been used in genetics, genomics and crop improvement, including bi- and multi-parental populations and natural ones. The latter has been widely used in genome-wide association study (GWAS). However, inbred-based GWAS cannot be used to reveal the mechanisms involved in hybrid performance. We developed a novel maize population, multiple-hybrid population (MHP), consisting of 724 hybrids produced using 28 temperate and 23 tropical inbreds. The hybrids can be divided into three subpopulations, two diallels and NC (North Carolina Design) II. Significant genetic differences were identified among parents, hybrids and heterotic groups. A cluster analysis revealed heterotic groups existing in the parental lines and the results showed that MHPs are well suitable for GWAS in hybrid crops. MHP-based GWAS was performed using 55 K SNP array for flowering time traits, days to tassel, days to silk, days to anthesis and anthesis-silking interval. Two independent methods, PEPIS developed for hybrids and TASSEL software designed for inbred line populations, revealed highly consistent results with five overlapping chromosomal regions identified and used for discovery of candidate genes and quantitative trait nucleotides. Our results indicate that MHPs are powerful in GWAS for hybrid-related traits with great potential applications in the molecular breeding era.
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Liu P, Zhao Y, Liu G, Wang M, Hu D, Hu J, Meng J, Reif JC, Zou J. Hybrid Performance of an Immortalized F 2 Rapeseed Population Is Driven by Additive, Dominance, and Epistatic Effects. FRONTIERS IN PLANT SCIENCE 2017; 8:815. [PMID: 28572809 PMCID: PMC5435766 DOI: 10.3389/fpls.2017.00815] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Accepted: 05/01/2017] [Indexed: 05/19/2023]
Abstract
Genomics-based prediction of hybrid performance promises to boost selection gain. The main goal of our study was to investigate the relevance of additive, dominance, and epistatic effects for determining hybrid seed yield in a biparental rapeseed population. We re-analyzed 60,000 SNP array and seed yield data points from an immortalized F2 population comprised of 318 hybrids and 180 parental lines by performing genome-wide QTL mapping and predictions in combination with five-fold cross-validation. Moreover, an additional set of 37 hybrids were genotyped and phenotyped in an independent environment. The decomposition of the phenotypic variance components and the cross-validated results of the QTL mapping and genome-wide predictions revealed that the hybrid performance in rapeseed was driven by a mix of additive, dominance, and epistatic effects. Interestingly, the genome-wide prediction accuracy in the additional 37 hybrids remained high when modeling exclusively additive effects but was severely reduced when dominance or epistatic effects were also included. This loss in accuracy was most likely caused by more pronounced interactions of environments with dominance and epistatic effects than with additive effects. Consequently, the development of robust hybrid prediction models, including dominance and epistatic effects, required much deeper phenotyping in multi-environmental trials.
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Affiliation(s)
- Peifa Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Yusheng Zhao
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)Stadt Seeland, Germany
| | - Guozheng Liu
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)Stadt Seeland, Germany
| | - Meng Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Dandan Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Jun Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Jinling Meng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Jochen C. Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)Stadt Seeland, Germany
- *Correspondence: Jochen C. Reif
| | - Jun Zou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
- Jun Zou
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Exploitation of heterosis loci for yield and yield components in rice using chromosome segment substitution lines. Sci Rep 2016; 6:36802. [PMID: 27833097 PMCID: PMC5105071 DOI: 10.1038/srep36802] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 10/21/2016] [Indexed: 01/11/2023] Open
Abstract
We constructed 128 chromosome segment substitution lines (CSSLs), derived from a cross between indica rice (Oryza sativa L.) 9311 and japonica rice Nipponbare, to investigate the genetic mechanism of heterosis. Three photo-thermo-sensitive-genic male sterile lines (Guangzhan63-4s, 036s, and Lian99s) were selected to cross with each CSSL to produce testcross populations (TCs). Field experiments were carried out in 2009, 2011, and 2015 to evaluate yield and yield-related traits in the CSSLs and TCs. Four traits (plant height, spikelet per panicle, thousand-grain weight, and grain yield per plant) were significantly related between CSSLs and TCs. In the TCs, plant height, panicle length, seed setting rate, thousand-grain weight, and grain yield per plant showed partial dominance, indicating that dominance largely contributes to heterosis of these five traits. While overdominance may be more important for heterosis of panicles per plant and spikelet per panicle. Based on the bin-maps of CSSLs and TCs, we detected 62 quantitative trait loci (QTLs) and 97 heterotic loci (HLs) using multiple linear regression analyses. Some of these loci were clustered together. The identification of QTLs and HLs for yield and yield-related traits provide useful information for hybrid rice breeding, and help to uncover the genetic basis of rice heterosis.
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