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Anuradha N, Satyavathi CT, Bharadwaj C, Nepolean T, Sankar SM, Singh SP, Meena MC, Singhal T, Srivastava RK. Deciphering Genomic Regions for High Grain Iron and Zinc Content Using Association Mapping in Pearl Millet. FRONTIERS IN PLANT SCIENCE 2017; 8:412. [PMID: 28507551 PMCID: PMC5410614 DOI: 10.3389/fpls.2017.00412] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/10/2017] [Indexed: 05/11/2023]
Abstract
Micronutrient malnutrition, especially deficiency of two mineral elements, iron [Fe] and zinc [Zn] in the developing world needs urgent attention. Pearl millet is one of the best crops with many nutritional properties and is accessible to the poor. We report findings of the first attempt to mine favorable alleles for grain iron and zinc content through association mapping in pearl millet. An association mapping panel of 130 diverse lines was evaluated at Delhi, Jodhpur and Dharwad, representing all the three pearl millet growing agro-climatic zones of India, during 2014 and 2015. Wide range of variation was observed for grain iron (32.3-111.9 ppm) and zinc (26.6-73.7 ppm) content. Genotyping with 114 representative polymorphic SSRs revealed 0.35 mean gene diversity. STRUCTURE analysis revealed presence of three sub-populations which was further supported by Neighbor-Joining method of clustering and principal coordinate analysis (PCoA). Marker-trait associations (MTAs) were analyzed with 267 markers (250 SSRs and 17 genic markers) in both general linear model (GLM) and mixed linear model (MLM), however, MTAs resulting from MLM were considered for more robustness of the associations. After appropriate Bonferroni correction, Xpsmp 2261 (13.34% R2-value), Xipes 0180 (R2-value of 11.40%) and Xipes 0096 (R2-value of 11.38%) were consistently associated with grain iron and zinc content for all the three locations. Favorable alleles and promising lines were identified for across and specific environments. PPMI 1102 had highest number (7) of favorable alleles, followed by four each for PPMFeZMP 199 and PPMI 708 for across the environment performance for both grain Fe and Zn content, while PPMI 1104 had alleles specific to Dharwad for grain Fe and Zn content. When compared with the reference genome Tift 23D2B1-P1-P5, Xpsmp 2261 amplicon was identified in intergenic region on pseudomolecule 5, while the other marker, Xipes 0810 was observed to be overlapping with aspartic proteinase (Asp) gene on pseudomolecule 3. Thus, this study can help in breeding new lines with enhanced micronutrient content using marker-assisted selection (MAS) in pearl millet leading to improved well-being especially for women and children.
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Affiliation(s)
- N. Anuradha
- Division of Genetics, ICAR-Indian Agricultural Research InstituteNew Delhi, India
| | - C. Tara Satyavathi
- Division of Genetics, ICAR-Indian Agricultural Research InstituteNew Delhi, India
- *Correspondence: C. Tara Satyavathi
| | - C. Bharadwaj
- Division of Genetics, ICAR-Indian Agricultural Research InstituteNew Delhi, India
| | - T. Nepolean
- Division of Genetics, ICAR-Indian Agricultural Research InstituteNew Delhi, India
| | - S. Mukesh Sankar
- Division of Genetics, ICAR-Indian Agricultural Research InstituteNew Delhi, India
| | - Sumer P. Singh
- Division of Genetics, ICAR-Indian Agricultural Research InstituteNew Delhi, India
| | - Mahesh C. Meena
- Division of Soil Science and Agricultural Chemistry, ICAR-Indian Agricultural Research InstituteNew Delhi, India
| | - Tripti Singhal
- Division of Genetics, ICAR-Indian Agricultural Research InstituteNew Delhi, India
| | - Rakesh K. Srivastava
- International Crops Research Institute for the Semi-Arid TropicsPatancheru, India
- Rakesh K. Srivastava
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Cui Z, Luo J, Qi C, Ruan Y, Li J, Zhang A, Yang X, He Y. Genome-wide association study (GWAS) reveals the genetic architecture of four husk traits in maize. BMC Genomics 2016; 17:946. [PMID: 27871222 PMCID: PMC5117540 DOI: 10.1186/s12864-016-3229-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2016] [Accepted: 11/01/2016] [Indexed: 12/21/2022] Open
Abstract
Background Maize (Zea mays) husk referring to the leafy outer enclosing the ear, plays an important role in grain production by directly contributing photosynthate and protecting ear from pathogen infection. Although the physiological functions related to husk have been extensively studied, little is known about its morphological variation and genetic basis in natural population. Results Here we utilized a maize association panel including 508 inbred lines with tropical, subtropical and temperate backgrounds to decipher the genetic architecture attributed to four husk traits, i.e. number of layers, length, width and thickness. Evaluating the phenotypic diversity at two different environments showed that four traits exhibit broadly natural variations and moderate levels of heritability with 0.64, 0.74, 0.49 and 0.75 for number, length, width and thickness, respectively. Diversity analysis indicated that different traits have dissimilar responses to subpopulation effects. A series of significantly positive or negative correlations between husk phenotypes and other agronomic traits were identified, indicating that husk growth is coordinated with other developmental processes. Combining husk traits with about half of a million of single nucleotide polymorphisms (SNPs) via genome-wide association study revealed a total of 9 variants significantly associated with traits at P < 1.04 × 10-5, which are implicated in multiple functional categories, such as cellular trafficking, transcriptional regulation and metabolism. Conclusions These results provide instrumental information for understanding the genetic basis of husk development, and further studies on identified candidate genes facilitate to illuminate molecular pathways regulating maize husk growth. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3229-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhenhai Cui
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100094, China.,College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, 110866, China
| | - Jinhong Luo
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100094, China
| | - Chuangye Qi
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100094, China
| | - Yanye Ruan
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, 110866, China
| | - Jing Li
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100094, China
| | - Ao Zhang
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100094, China.,College of Agronomy, Shenyang Agricultural University, Shenyang, 110866, China
| | - Xiaohong Yang
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100094, China.
| | - Yan He
- National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100094, China.
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