1
|
Lu X, Liu P, Tu L, Guo X, Wang A, Zhu Y, Jiang Y, Zhang C, Xu Y, Chen Z, Wu X. Joint-GWAS, Linkage Mapping, and Transcriptome Analysis to Reveal the Genetic Basis of Plant Architecture-Related Traits in Maize. Int J Mol Sci 2024; 25:2694. [PMID: 38473942 DOI: 10.3390/ijms25052694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/04/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Plant architecture is one of the key factors affecting maize yield formation and can be divided into secondary traits, such as plant height (PH), ear height (EH), and leaf number (LN). It is a viable approach for exploiting genetic resources to improve plant density. In this study, one natural panel of 226 inbred lines and 150 family lines derived from the offspring of T32 crossed with Qi319 were genotyped by using the MaizeSNP50 chip and the genotyping by sequence (GBS) method and phenotyped under three different environments. Based on the results, a genome-wide association study (GWAS) and linkage mapping were analyzed by using the MLM and ICIM models, respectively. The results showed that 120 QTNs (quantitative trait nucleotides) and 32 QTL (quantitative trait loci) related to plant architecture were identified, including four QTL and 40 QTNs of PH, eight QTL and 41 QTNs of EH, and 20 QTL and 39 QTNs of LN. One dominant QTL, qLN7-2, was identified in the Zhangye environment. Six QTNs were commonly identified to be related to PH, EH, and LN in different environments. The candidate gene analysis revealed that Zm00001d021574 was involved in regulating plant architecture traits through the autophagy pathway, and Zm00001d044730 was predicted to interact with the male sterility-related gene ms26. These results provide abundant genetic resources for improving maize plant architecture traits by using approaches to biological breeding.
Collapse
Affiliation(s)
- Xuefeng Lu
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
- Ministry of Agriculture and Rural Affairs Key Laboratory of Crop Genetic Resources and Germplasm Innovation in Karst Region, Guiyang 550006, China
| | - Pengfei Liu
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Liang Tu
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Xiangyang Guo
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Angui Wang
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Yunfang Zhu
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Yulin Jiang
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
- Ministry of Agriculture and Rural Affairs Key Laboratory of Crop Genetic Resources and Germplasm Innovation in Karst Region, Guiyang 550006, China
| | - Chunlan Zhang
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Yan Xu
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Zehui Chen
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
| | - Xun Wu
- Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
- Ministry of Agriculture and Rural Affairs Key Laboratory of Crop Genetic Resources and Germplasm Innovation in Karst Region, Guiyang 550006, China
| |
Collapse
|
2
|
Sahito JH, Zhang H, Gishkori ZGN, Ma C, Wang Z, Ding D, Zhang X, Tang J. Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize. Int J Mol Sci 2024; 25:1918. [PMID: 38339196 PMCID: PMC10855973 DOI: 10.3390/ijms25031918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/30/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024] Open
Abstract
Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.
Collapse
Affiliation(s)
- Javed Hussain Sahito
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Hao Zhang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Zeeshan Ghulam Nabi Gishkori
- Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Chenhui Ma
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhihao Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Dong Ding
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Jihua Tang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
- The Shennong Laboratory, Zhengzhou 450002, China
| |
Collapse
|
3
|
Mastrangelo AM, Hartings H, Lanzanova C, Balconi C, Locatelli S, Cassol H, Valoti P, Petruzzino G, Pecchioni N. Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding. PLANTS (BASEL, SWITZERLAND) 2024; 13:336. [PMID: 38337869 PMCID: PMC10857507 DOI: 10.3390/plants13030336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Genetic diversity is fundamental for studying the complex architecture of the traits of agronomic importance, controlled by major and minor loci. Moreover, well-characterized germplasm collections are essential tools for dissecting and analyzing genetic and phenotypic diversity in crops. A panel of 360 entries, a subset of a larger collection maintained within the GenBank at CREA Bergamo, which includes the inbreds derived from traditional Italian maize open-pollinated (OP) varieties and advanced breeding ones (Elite Inbreds), was analyzed to identify SNP markers using the tGBS® genotyping-by-sequencing technology. A total of 797,368 SNPs were found during the initial analysis. Imputation and filtering processes were carried out based on the percentage of missing data, redundant markers, and rarest allele frequencies, resulting in a final dataset of 15,872 SNP markers for which a physical map position was identified. Using this dataset, the inbred panel was characterized for linkage disequilibrium (LD), genetic diversity, population structure, and genetic relationships. LD decay at a genome-wide level indicates that the collection is a suitable resource for association mapping. Population structure analyses, which were carried out with different clustering methods, showed stable grouping statistics for four groups, broadly corresponding to 'Insubria', 'Microsperma', and 'Scagliolino' genotypes, with a fourth group composed prevalently of elite accessions derived from Italian and US breeding programs. Based on these results, the CREA Italian maize collection, genetically characterized in this study, can be considered an important tool for the mapping and characterization of useful traits and associated loci/alleles, to be used in maize breeding programs.
Collapse
Affiliation(s)
- Anna Maria Mastrangelo
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, SS 673 Metri 25200, 71122 Foggia, Italy; (G.P.); (N.P.)
| | - Hans Hartings
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Chiara Lanzanova
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Carlotta Balconi
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Sabrina Locatelli
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Helga Cassol
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Paolo Valoti
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Giuseppe Petruzzino
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, SS 673 Metri 25200, 71122 Foggia, Italy; (G.P.); (N.P.)
| | - Nicola Pecchioni
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, SS 673 Metri 25200, 71122 Foggia, Italy; (G.P.); (N.P.)
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| |
Collapse
|
4
|
Qian F, Jing J, Zhang Z, Chen S, Sang Z, Li W. GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize. PLANTS (BASEL, SWITZERLAND) 2023; 12:3806. [PMID: 38005703 PMCID: PMC10674677 DOI: 10.3390/plants12223806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.
Collapse
Affiliation(s)
- Fu Qian
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
- The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832003, China;
| | - Jianguo Jing
- The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832003, China;
| | - Zhanqin Zhang
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
| | - Shubin Chen
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
| | - Zhiqin Sang
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
| | - Weihua Li
- The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832003, China;
| |
Collapse
|
5
|
Wang J, Zhao S, Zhang Y, Lu X, Du J, Wang C, Wen W, Guo X, Zhao C. Investigating the genetic basis of maize ear characteristics: a comprehensive genome-wide study utilizing high-throughput phenotypic measurement method and system. FRONTIERS IN PLANT SCIENCE 2023; 14:1248446. [PMID: 37701799 PMCID: PMC10493325 DOI: 10.3389/fpls.2023.1248446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/09/2023] [Indexed: 09/14/2023]
Abstract
The morphology of maize ears plays a critical role in the breeding of new varieties and increasing yield. However, the study of traditional ear-related traits alone can no longer meet the requirements of breeding. In this study, 20 ear-related traits, including size, shape, number, and color, were obtained in 407 maize inbred lines at two sites using a high-throughput phenotypic measurement method and system. Significant correlations were found among these traits, particularly the novel trait ear shape (ES), which was correlated with traditional traits: kernel number per row and kernel number per ear. Pairwise comparison tests revealed that the inbred lines of tropical-subtropical were significantly different from other subpopulations in row numbers per ear, kernel numbers per ear, and ear color. A genome-wide association study identified 275, 434, and 362 Single nucleotide polymorphisms (SNPs) for Beijing, Sanya, and best linear unbiased prediction scenarios, respectively, explaining 3.78% to 24.17% of the phenotypic variance. Furthermore, 58 candidate genes with detailed functional descriptions common to more than two scenarios were discovered, with 40 genes being associated with color traits on chromosome 1. After analysis of haplotypes, gene expression, and annotated information, several candidate genes with high reliability were identified, including Zm00001d051328 for ear perimeter and width, zma-MIR159f for ear shape, Zm00001d053080 for kernel width and row number per ear, and Zm00001d048373 for the blue color channel of maize kernels in the red-green-blue color model. This study emphasizes the importance of researching novel phenotypic traits in maize by utilizing high-throughput phenotypic measurements. The identified genetic loci enrich the existing genetic studies related to maize ears.
Collapse
Affiliation(s)
- Jinglu Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shuaihao Zhao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ying Zhang
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xianju Lu
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianjun Du
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chuanyu Wang
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Weiliang Wen
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xinyu Guo
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| |
Collapse
|
6
|
Jiang F, Liu L, Li Z, Bi Y, Yin X, Guo R, Wang J, Zhang Y, Shaw RK, Fan X. Identification of Candidate QTLs and Genes for Ear Diameter by Multi-Parent Population in Maize. Genes (Basel) 2023; 14:1305. [PMID: 37372485 DOI: 10.3390/genes14061305] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/06/2023] [Accepted: 06/17/2023] [Indexed: 06/29/2023] Open
Abstract
Ear diameter (ED) is a critical component of grain yield (GY) in maize (Zea mays L.). Studying the genetic basis of ED in maize is of great significance in enhancing maize GY. Against this backdrop, this study was framed to (1) map the ED-related quantitative trait locus (QTL) and SNPs associated with ED; and (2) identify putative functional genes that may affect ED in maize. To accomplish this, an elite maize inbred line, Ye107, which belongs to the Reid heterotic group, was used as a common parent and crossed with seven elite inbred lines from three different heterotic groups (Suwan1, Reid, and nonReid) that exhibited abundant genetic variation in ED. This led to the construction of a multi-parent population consisting of 1215 F7 recombinant inbred lines (F7RILs). A genome-wide association study (GWAS) and linkage analysis were then conducted for the multi-parent population using 264,694 high-quality SNPs generated via the genotyping-by-sequencing method. Our study identified a total of 11 SNPs that were significantly associated with ED through the GWAS, and three QTLs were revealed by the linkage analysis for ED. The major QTL on chromosome 1 was co-identified in the region by the GWAS at SNP_143985532. SNP_143985532, located upstream of the Zm00001d030559 gene, encodes a callose synthase that is expressed in various tissues, with the highest expression level in the maize ear primordium. Haplotype analysis indicated that the haplotype B (allele AA) of Zm00001d030559 was positively correlated with ED. The candidate genes and SNPs identified in this study provide crucial insights for future studies on the genetic mechanism of maize ED formation, cloning of ED-related genes, and genetic improvement of ED. These results may help develop important genetic resources for enhancing maize yield through marker-assisted breeding.
Collapse
Affiliation(s)
- Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Li Liu
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Ziwei Li
- Yunnan Dehong Dai and Jingpo Nationality Institute of Agricultural Sciences, Mangshi 678400, China
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Xingfu Yin
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Ruijia Guo
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Jing Wang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Ranjan Kumar Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| |
Collapse
|
7
|
Karnatam KS, Chhabra G, Saini DK, Singh R, Kaur G, Praba UP, Kumar P, Goyal S, Sharma P, Ranjan R, Sandhu SK, Kumar R, Vikal Y. Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding. Int J Mol Sci 2023; 24:ijms24076135. [PMID: 37047112 PMCID: PMC10093813 DOI: 10.3390/ijms24076135] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 04/14/2023] Open
Abstract
Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.
Collapse
Affiliation(s)
- Krishna Sai Karnatam
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Gautam Chhabra
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana 141001, India
| | - Rajveer Singh
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Gurwinder Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Umesh Preethi Praba
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Pankaj Kumar
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Simran Goyal
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Priti Sharma
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| | - Rumesh Ranjan
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana 141001, India
| | - Surinder K Sandhu
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana 141001, India
| | - Ramesh Kumar
- Indian Institute of Maize Research, Ludhiana 141001, India
| | - Yogesh Vikal
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141001, India
| |
Collapse
|
8
|
Rahimi Y, Khahani B, Jamali A, Alipour H, Bihamta MR, Ingvarsson PK. Genome-wide association study to identify genomic loci associated with early vigor in bread wheat under simulated water deficit complemented with quantitative trait loci meta-analysis. G3 (BETHESDA, MD.) 2023; 13:jkac320. [PMID: 36458966 PMCID: PMC10248217 DOI: 10.1093/g3journal/jkac320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 12/05/2022]
Abstract
A genome-wide association study (GWAS) was used to identify associated loci with early vigor under simulated water deficit and grain yield under field drought in a diverse collection of Iranian bread wheat landraces. In addition, a meta-quantitative trait loci (MQTL) analysis was used to further expand our approach by retrieving already published quantitative trait loci (QTL) from recombinant inbred lines, double haploids, back-crosses, and F2 mapping populations. In the current study, around 16%, 14%, and 16% of SNPs were in significant linkage disequilibrium (LD) in the A, B, and D genomes, respectively, and varied between 5.44% (4A) and 21.85% (6A). Three main subgroups were identified among the landraces with different degrees of admixture, and population structure was further explored through principal component analysis. Our GWAS identified 54 marker-trait associations (MTAs) that were located across the wheat genome but with the highest number found in the B sub-genome. The gene ontology (GO) analysis of MTAs revealed that around 75% were located within or closed to protein-coding genes. In the MQTL analysis, 23 MQTLs, from a total of 215 QTLs, were identified and successfully projected onto the reference map. MQT-YLD4, MQT-YLD9, MQT-YLD13, MQT-YLD17, MQT-YLD18, MQT-YLD19, and MQTL-RL1 contributed to the highest number of projected QTLs and were therefore regarded as the most reliable and stable QTLs under water deficit conditions. These MQTLs greatly facilitate the identification of putative candidate genes underlying at each MQTL interval due to the reduced confidence of intervals associated with MQTLs. These findings provide important information on the genetic basis of early vigor traits and grain yield under water deficit conditions and set the foundation for future investigations into adaptation to water deficit in bread wheat.
Collapse
Affiliation(s)
- Yousef Rahimi
- Department of Plant Biology, Uppsala BioCenter, Linnean Centre for Plant Biology in Uppsala, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
| | - Bahman Khahani
- Department of Plant Genetics and Production, College of Agriculture, Shiraz University, 71441-65186 Shiraz, Iran
| | - Ali Jamali
- Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Tehran, 31587-77871 Karaj, Iran
| | - Hadi Alipour
- Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Urmia University, 5756151818 Urmia, Iran
| | - Mohammad Reza Bihamta
- Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Tehran, 31587-77871 Karaj, Iran
| | - Pär K Ingvarsson
- Department of Plant Biology, Uppsala BioCenter, Linnean Centre for Plant Biology in Uppsala, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
| |
Collapse
|
9
|
Okunlola G, Badu-Apraku B, Ariyo O, Agre P, Offernedo Q, Ayo-Vaughan M. Genome-wide association studies of Striga resistance in extra-early maturing quality protein maize inbred lines. G3 (BETHESDA, MD.) 2023; 13:jkac237. [PMID: 36073937 PMCID: PMC9911053 DOI: 10.1093/g3journal/jkac237] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/03/2022] [Indexed: 11/14/2022]
Abstract
Identification of genes associated with Striga resistance is invaluable for accelerating genetic gains in breeding for Striga resistance in maize. We conducted a genome-wide association study to identify genomic regions associated with grain yield and other agronomic traits under artificial Striga field infestation. One hundred and forty-one extra-early quality protein maize inbred lines were phenotyped for key agronomic traits. The inbred lines were also genotyped using 49,185 DArTseq markers from which 8,143 were retained for population structure analysis and genome wide-association study. Cluster analysis and population structure revealed the presence of 3 well-defined genetic groups. Using the mixed linear model, 22 SNP markers were identified to be significantly associated with grain yield, Striga damage at 10 weeks after planting, number of emerged Striga plants at 8 and 10 weeks after planting and ear aspect. The identified SNP markers would be useful for breeders for marker-assisted selection to accelerate the genetic enhancement of maize for Striga resistance in sub-Saharan Africa after validation.
Collapse
Affiliation(s)
- Gbemisola Okunlola
- Maize Improvement Programme, International Institute of Tropical Agriculture, IITA, Oyo Road, Ibadan 200001, Oyo ,5320, Nigeria
- Department of Plant Breeding and Seed Technology, Federal University of Agriculture, Abeokuta 110124, Ogun, 2240, Nigeria
| | - Baffour Badu-Apraku
- Maize Improvement Programme, International Institute of Tropical Agriculture, IITA, Oyo Road, Ibadan 200001, Oyo ,5320, Nigeria
| | - Omolayo Ariyo
- Department of Plant Breeding and Seed Technology, Federal University of Agriculture, Abeokuta 110124, Ogun, 2240, Nigeria
| | - Paterne Agre
- Maize Improvement Programme, International Institute of Tropical Agriculture, IITA, Oyo Road, Ibadan 200001, Oyo ,5320, Nigeria
| | - Queen Offernedo
- Maize Improvement Programme, International Institute of Tropical Agriculture, IITA, Oyo Road, Ibadan 200001, Oyo ,5320, Nigeria
| | - Moninuola Ayo-Vaughan
- Department of Plant Breeding and Seed Technology, Federal University of Agriculture, Abeokuta 110124, Ogun, 2240, Nigeria
| |
Collapse
|
10
|
Guo X, Ge Z, Wang M, Zhao M, Pei Y, Song X. Genome-wide association study of quality traits and starch pasting properties of maize kernels. BMC Genomics 2023; 24:59. [PMID: 36732681 PMCID: PMC9893588 DOI: 10.1186/s12864-022-09031-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 11/21/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Starch are the main nutritional components of maize (Zea mays L.), and starch pasting properties are widely used as essential indicators for quality estimation. Based on the previous studies, various genes related to pasting properties have been identified in maize. However, the loci underlying variations in starch pasting properties in maize inbred lines remain to be identified. RESULTS To investigate the genetic architecture of these traits, the starch pasting properties were examined based on 292 maize inbred lines, which were genotyped with the MaizeSNP50 BeadChip composed of 55,126 evenly spaced, random SNPs. A genome-wide association study (GWAS) implemented in the software package FarmCPU was employed to identify genomic loci for the starch pasting properties. 48 SNPs were found to be associated with pasting properties. Moreover, 37 candidate genes were correlated with pasting properties. Among the candidate genes, GRMZM2G143646 and GRMZM2G166407 were associated with breakdown and final viscosity significantly, and both genes encode PPR (Pentatricopeptide repeat) protein. We used GWAS to explore candidate genes of maize starch pasting properties in this study. The identified candidate genes will be useful for further understanding of the genetic architecture of starch pasting properties in maize. CONCLUSION This study showed a complex regulation network about maize quality trait and starch pasting properties. It may provide some useful markers for marker assisted selection and a basis for cloning the genes behind these SNPs.
Collapse
Affiliation(s)
- Xinmei Guo
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Zhaopeng Ge
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Ming Wang
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Meiai Zhao
- grid.412608.90000 0000 9526 6338College of Life Sciences, Qingdao Agricultural University, Qingdao, 266109 China
| | - Yuhe Pei
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Xiyun Song
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| |
Collapse
|
11
|
Khatun M, Monir MM, Lou X, Zhu J, Xu H. Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits. BMC PLANT BIOLOGY 2022; 22:537. [PMID: 36397013 PMCID: PMC9673299 DOI: 10.1186/s12870-022-03913-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Maize (Zea Mays) is one of the world's most important crops. Hybrid maize lines resulted a major improvement in corn production in the previous and current centuries. Understanding the genetic mechanisms of the corn production associated traits greatly facilitate the development of superior hybrid varieties. RESULT In this study, four ear traits associated with corn production of Nested Association Mapping (NAM) population were analyzed using a full genetic model, and further, optimal genotype combinations and total genetic effects of current best lines, superior lines, and superior hybrids were predicted for each of the traits at four different locations. The analysis identified 21-34 highly significant SNPs (-log10P > 5), with an estimated total heritability of 37.31-62.34%, while large contributions to variations was due to dominance, dominance-related epistasis, and environmental interaction effects ([Formula: see text] 14.06% ~ 49.28%), indicating these factors contributed significantly to phenotypic variations of the ear traits. Environment-specific genetic effects were also discovered to be crucial for maize ear traits. There were four SNPs found for three ear traits: two for ear length and weight, and two for ear row number and length. Using the Enumeration method and the stepwise tuning technique, optimum multi-locus genotype combinations for superior lines were identified based on the information obtained from GWAS. CONCLUSIONS Predictions of genetic breeding values showed that different genotype combinations in different geographical regions may be better, and hybrid-line variety breeding with homozygote and heterozygote genotype combinations may have a greater potential to improve ear traits.
Collapse
Affiliation(s)
- Mita Khatun
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China
| | - Md Mamun Monir
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China
| | - Xiangyang Lou
- Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA
| | - Jun Zhu
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China
| | - Haiming Xu
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China.
| |
Collapse
|
12
|
Wang P, Tian T, Ma J, Liu Y, Zhang P, Chen T, Shahinnia F, Yang D. Genome-Wide Association Study of Kernel Traits Using a 35K SNP Array in Bread Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2022; 13:905660. [PMID: 35734257 PMCID: PMC9207461 DOI: 10.3389/fpls.2022.905660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Kernel size and weight are crucial components of grain yield in wheat. Deciphering their genetic basis is essential for improving yield potential in wheat breeding. In this study, five kernel traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), kernel perimeter (KP), and thousand-kernel weight (TKW), were evaluated in a panel consisting of 198 wheat accessions under six environments. Wheat accessions were genotyped using the 35K SNP iSelect chip array, resulting in a set of 13,228 polymorphic SNP markers that were used for genome-wide association study (GWAS). A total of 146 significant marker-trait associations (MTAs) were identified for five kernel traits on 21 chromosomes [-log10(P) ≥ 3], which explained 5.91-15.02% of the phenotypic variation. Of these, 12 stable MTAs were identified in multiple environments, and six superior alleles showed positive effects on KL, KP, and KDR. Four potential candidate genes underlying the associated SNP markers were predicted for encoding ML protein, F-box protein, ethylene-responsive transcription factor, and 1,4-α-glucan branching enzyme. These genes were strongly expressed in grain development at different growth stages. The results will provide new insights into the genetic basis of kernel traits in wheat. The associated SNP markers and predicted candidate genes will facilitate marker-assisted selection in wheat breeding.
Collapse
Affiliation(s)
- Peng Wang
- State Key Laboratory of Aridland Crop Science, Lanzhou, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Tian Tian
- State Key Laboratory of Aridland Crop Science, Lanzhou, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Jingfu Ma
- State Key Laboratory of Aridland Crop Science, Lanzhou, China
- College of Agronomy, Gansu Agricultural University, Lanzhou, China
| | - Yuan Liu
- State Key Laboratory of Aridland Crop Science, Lanzhou, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Peipei Zhang
- State Key Laboratory of Aridland Crop Science, Lanzhou, China
| | - Tao Chen
- State Key Laboratory of Aridland Crop Science, Lanzhou, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Fahimeh Shahinnia
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - Delong Yang
- State Key Laboratory of Aridland Crop Science, Lanzhou, China
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, China
| |
Collapse
|
13
|
Tan B, Ingvarsson PK. Integrating genome-wide association mapping of additive and dominance genetic effects to improve genomic prediction accuracy in Eucalyptus. THE PLANT GENOME 2022; 15:e20208. [PMID: 35441826 DOI: 10.1002/tpg2.20208] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) is a powerful and widely used approach to decipher the genetic control of complex traits. Still, a significant challenge for dissecting quantitative traits in forest trees is statistical power. This study uses a population consisting of 1,123 samples derived from two successive generations of crosses between Eucalyptus grandis (W. Hill) and E. urophylla (S.T. Blake). All samples have been phenotyped for growth and wood property traits and genotyped using the EuChip60K chip, yielding 37,832 informative single nucleotide polymorphisms (SNPs). We use multi-locus GWAS models to assess additive and dominance effects to identify markers associated with growth and wood property traits in the eucalypt hybrids. Additive and dominance association models identified 78 and 82 significant SNPs across all traits, respectively, which captured between 39 and 86% of the genomic-based heritability. We also used SNPs identified from the GWAS and SNPs using less stringent significance thresholds to evaluate predictive abilities in a genomic selection framework. Genomic selection models based on the top 1% SNPs captured a substantially greater proportion of the genetic variance of traits compared with when we used all SNPs for model training. The prediction ability of estimated breeding values improved significantly for all traits when using either the top 1% SNPs or SNPs identified using a relaxed p value threshold (p < 10-3 ). This study also highlights the added value of incorporating dominance effects for identifying genomic regions controlling growth traits in trees. Moreover, integrating GWAS results into genomic selection method provides enhanced power relative to discrete associations for identifying genomic variation potentially valuable for forest tree breeding.
Collapse
Affiliation(s)
- Biyue Tan
- Umeå Plant Science Centre, Dep. of Ecology and Environmental Science, Umeå Univ., Umeå, SE-90187, Sweden
- Stora Enso AB, Nacka, SE-131 04, Sweden
| | - Pär K Ingvarsson
- Linnean Centre for Plant Biology, Dep. of Plant Biology, Uppsala BioCenter, Swedish Univ. of Agricultural Sciences, Uppsala, SE-750 07, Sweden
| |
Collapse
|
14
|
Luo Y, Zhang M, Liu Y, Liu J, Li W, Chen G, Peng Y, Jin M, Wei W, Jian L, Yan J, Fernie AR, Yan J. Genetic variation in YIGE1 contributes to ear length and grain yield in maize. THE NEW PHYTOLOGIST 2022; 234:513-526. [PMID: 34837389 DOI: 10.1111/nph.17882] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 05/12/2023]
Abstract
Ear length (EL), which is controlled by quantitative trait loci (QTLs), is an important component of grain yield and as such is a key target trait in maize breeding. However, very few EL QTLs have been cloned, and their molecular mechanisms are largely unknown. Here, using a genome wide association study (GWAS), we identified a QTL, YIGE1, which encodes an unknown protein that regulates EL by affecting pistillate floret number. Overexpression of YIGE1 increased female inflorescence meristem (IM) size, increased EL and kernel number per row (KNPR), and thus enhanced grain yield. By contrast, CRISPR/Cas9 knockout and Mutator insertion mutant lines of YIGE1 displayed decreased IM size and EL. A single-nucleotide polymorphism (SNP) located in the regulatory region of YIGE1 had a large effect on its promoter strength, which positively affected EL by increasing gene expression. Further analysis shows that YIGE1 may be involved in sugar and auxin signal pathways to regulate maize ear development, thus affecting IM activity and floret production in maize inflorescence morphogenesis. These findings provide new insights into ear development and will ultimately facilitate maize molecular breeding.
Collapse
Affiliation(s)
- Yun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mingliang Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yu Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jie Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Wisconsin Institutes for Discovery, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Wenqiang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Gengshen Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yong Peng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Min Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenjie Wei
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Liumei Jian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jin Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Alisdair R Fernie
- Department of Molecular Physiology, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| |
Collapse
|
15
|
Zhao M, Liu S, Pei Y, Jiang X, Jaqueth JS, Li B, Han J, Jeffers D, Wang J, Song X. Identification of genetic loci associated with rough dwarf disease resistance in maize by integrating GWAS and linkage mapping. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 315:111100. [PMID: 35067294 DOI: 10.1016/j.plantsci.2021.111100] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 10/19/2021] [Accepted: 10/21/2021] [Indexed: 06/14/2023]
Abstract
Maize rough dwarf disease (MRDD) is a viral disease that causes substantial yield loss, especially in China's summer planted maize area. Discovery of resistance genes would help in developing high-yielding resistant maize hybrids. Genome-wide association studies (GWASs) have advanced quickly and are now a powerful tool for dissecting complex genetic architectures. In this study, the disease severity index (DSI) of 292 maize inbred lines and an F6 linkage population were investigated across multiple environments for two years. Using the genotypes obtained from the Maize SNP 50K chip, a GWAS was performed with four analytical models. The results showed that 22 SNPs distributed on chromosomes 1, 3, 4, 6, 7 and 8 were significantly associated with resistance to MRDD (P<0.0001). The SNPs on chromosomes 3, 6 and 8 were consistent with the quantitative trait locus (QTL) regions from linkage mapping in an RIL population. Candidate genes identified by GWAS included an LRR receptor-like serine/threonine-protein kinase (GRMZM2G141288), and a DRE-binding protein (GRMZM2G006745). In addition, we performed an allele variation analysis of the SNP loci selected by GWAS and linkage mapping and found that the main alleles of the two SNP loci PZE_101170408 and PZE_106082685 on chromosome 1 differed in terms of disease-resistant materials and disease-susceptible materials. The identified SNPs and genes provide useful information for MRDD-related gene cloning and insights on the underlying disease resistance mechanisms, and they can be used in marker-assisted breeding to develop MRDD-resistant maize.
Collapse
Affiliation(s)
- Meiai Zhao
- Key Laboratory of Plant Biotechnology in Universities of Shandong Province, College of Life Sciences, Qingdao Agricultural University, Qingdao, 266109, China
| | - Shuangshuang Liu
- Key Laboratory of Plant Biotechnology in Universities of Shandong Province, College of Life Sciences, Qingdao Agricultural University, Qingdao, 266109, China
| | - Yuhe Pei
- Key Laboratory of Qingdao Major Crop Germplasm Resource Innovation and Application, College of Agronomy, Qingdao Agricultural University, Qingdao, 266109, China
| | - Xuwen Jiang
- Key Laboratory of Qingdao Major Crop Germplasm Resource Innovation and Application, College of Agronomy, Qingdao Agricultural University, Qingdao, 266109, China
| | | | - Bailin Li
- Corteva Agriscience, 7300 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Jing Han
- Shandong Denghai Pioneer, Jinan, Shandong, 254000, China
| | - Daniel Jeffers
- Former CIMMYT Breeder, Yunnan Office, Yunnan Academy of Agricultural Sciences, Kunming, 650205, China
| | - Jiabo Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization (Southwest Minzu University), Ministry of Education, Chengdu, Sichuan, 160041, China.
| | - Xiyun Song
- Key Laboratory of Qingdao Major Crop Germplasm Resource Innovation and Application, College of Agronomy, Qingdao Agricultural University, Qingdao, 266109, China.
| |
Collapse
|
16
|
Wang J, Yu J, Lipka AE, Zhang Z. Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:63-80. [PMID: 35641759 DOI: 10.1007/978-1-0716-2237-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.
Collapse
Affiliation(s)
- Jiabo Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, Sichuan, China.
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
| |
Collapse
|
17
|
Siddiqui MN, Teferi TJ, Ambaw AM, Gabi MT, Koua P, Léon J, Ballvora A. New drought-adaptive loci underlying candidate genes on wheat chromosome 4B with improved photosynthesis and yield responses. PHYSIOLOGIA PLANTARUM 2021; 173:2166-2180. [PMID: 34549429 DOI: 10.1111/ppl.13566] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/06/2021] [Indexed: 06/13/2023]
Abstract
Flag leaf serves as an essential source of assimilates during grain filling, thereby contributing to grain yield up to 48%. Thus, high-throughput phenotyping of flag leaves is crucial to determine their physiological and genetic basis of yield formation and drought adaptation. Here, we utilized 200 wheat cultivars to identify drought-adaptive loci underlying candidate genes associated with flag leaf biomass and photosynthesis-related traits using a genome-wide association study (GWAS). GWAS revealed 21 significant marker-trait associations for key photosynthetic traits in response to drought stress. Analysis of linkage disequilibrium (LD) in these SNPs intervals discovered 103 significant SNPs that established distinct LD blocks containing a total of 382 candidate genes putatively involved in physiological processes, including photosynthesis and water responses. Further, in silico transcript analysis identified two candidate genes in locus AX-580365925 on chromosome 4B, those were found to be highly expressed under drought and associated with proton-transporting ATP synthase activity and stress response pathways. Accordingly, we identified significant allelic haplotype differences on this same locus. The tolerant haplotype (higher chlorophyll content under drought) representing major allele was more abundant and stably increased photosynthetic efficiency and yield under drought scenarios. Collectively, this study offers new adaptive loci and beneficial alleles to reshape the flag leaf physiological and associated photosynthetic components for better yield and sustainability to water-deficit stress.
Collapse
Affiliation(s)
- Md Nurealam Siddiqui
- Institute of Crop Science and Resource Conservation (INRES)-Plant Breeding and Biotechnology, University of Bonn, Bonn, Germany
- Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Tesfaye J Teferi
- Institute of Crop Science and Resource Conservation (INRES)-Plant Breeding and Biotechnology, University of Bonn, Bonn, Germany
| | - Abebaw M Ambaw
- Institute of Crop Science and Resource Conservation (INRES)-Plant Breeding and Biotechnology, University of Bonn, Bonn, Germany
| | - Melesech T Gabi
- Institute of Crop Science and Resource Conservation (INRES)-Plant Breeding and Biotechnology, University of Bonn, Bonn, Germany
| | - Patrice Koua
- Institute of Crop Science and Resource Conservation (INRES)-Plant Breeding and Biotechnology, University of Bonn, Bonn, Germany
| | - Jens Léon
- Institute of Crop Science and Resource Conservation (INRES)-Plant Breeding and Biotechnology, University of Bonn, Bonn, Germany
- Field Lab Campus Klein-Altendorf, University of Bonn, Rheinbach, Germany
| | - Agim Ballvora
- Institute of Crop Science and Resource Conservation (INRES)-Plant Breeding and Biotechnology, University of Bonn, Bonn, Germany
| |
Collapse
|
18
|
Zuo Z, Lu Y, Zhu M, Chen R, Zhang E, Hao D, Huang Q, Wang H, Su Y, Wang Z, Xu Y, Li P, Xu C, Yang Z. Nucleotide Diversity of the Maize ZmCNR13 Gene and Association With Ear Traits. Front Genet 2021; 12:773597. [PMID: 34764988 PMCID: PMC8576287 DOI: 10.3389/fgene.2021.773597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022] Open
Abstract
The maize (Zea mays L.) ZmCNR13 gene, encoding a protein of fw2.2-like (FWL) family, has been demonstrated to be involved in cell division, expansion, and differentiation. In the present study, the genomic sequences of the ZmCNR13 locus were re-sequenced in 224 inbred lines, 56 landraces and 30 teosintes, and the nucleotide polymorphism and selection signature were estimated. A total of 501 variants, including 415 SNPs and 86 Indels, were detected. Among them, 51 SNPs and 4 Indels were located in the coding regions. Although neutrality tests revealed that this locus had escaped from artificial selection during the process of maize domestication, the population of inbred lines possesses lower nucleotide diversity and decay of linkage disequilibrium. To estimate the association between sequence variants of ZmCNR13 and maize ear characteristics, a total of ten ear-related traits were obtained from the selected inbred lines. Four variants were found to be significantly associated with six ear-related traits. Among them, SNP2305, a non-synonymous mutation in exon 2, was found to be associated with ear weight, ear grain weight, ear diameter and ear row number, and explained 4.59, 4.61, 4.31, and 8.42% of the phenotypic variations, respectively. These results revealed that natural variations of ZmCNR13 might be involved in ear development and can be used in genetic improvement of maize ear-related traits.
Collapse
Affiliation(s)
- Zhihao Zuo
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Yue Lu
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Minyan Zhu
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Rujia Chen
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Enying Zhang
- College of Agronomy, Qingdao Agricultural University, Qingdao, China
| | - Derong Hao
- Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong, China
| | - Qianfeng Huang
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Hanyao Wang
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China
| | - Yanze Su
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China
| | - Zhichao Wang
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China
| | - Yang Xu
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Pengcheng Li
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Chenwu Xu
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Zefeng Yang
- Jiangsu Key Laboratory of Crop Genetics and Physiology Key Laboratory of Plant Functional Genomics of the Ministry of Education Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou, China.,Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.,Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, China
| |
Collapse
|
19
|
Gahlaut V, Jaiswal V, Balyan HS, Joshi AK, Gupta PK. Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:758631. [PMID: 34745191 PMCID: PMC8568012 DOI: 10.3389/fpls.2021.758631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 09/20/2021] [Indexed: 05/04/2023]
Abstract
In wheat, a multi-locus genome-wide association study (ML-GWAS) was conducted for the four grain weight-related traits (days to anthesis, grain filling duration, grain number per ear, and grain weight per ear) using data recorded under irrigated (IR) and rain-fed (RF) conditions. Seven stress-related indices were estimated for these four traits: (i) drought resistance index (DI), (ii) geometric mean productivity (GMP), (iii) mean productivity index (MPI), (iv) relative drought index (RDI), (v) stress tolerance index (STI), (vi) yield index, and (vii) yield stability index (YSI). The association panel consisted of a core collection of 320 spring wheat accessions representing 28 countries. The panel was genotyped using 9,627 single nucleotide polymorphisms (SNPs). The genome-wide association (GWA) analysis provided 30 significant marker-trait associations (MTAs), distributed as follows: (i) IR (15 MTAs), (ii) RF (14 MTAs), and (iii) IR+RF (1 MTA). In addition, 153 MTAs were available for the seven stress-related indices. Five MTAs co-localized with previously reported QTLs/MTAs. Candidate genes (CGs) associated with different MTAs were also worked out. Gene ontology (GO) analysis and expression analysis together allowed the selection of the two CGs, which may be involved in response to drought stress. These two CGs included: TraesCS1A02G331000 encoding RNA helicase and TraesCS4B02G051200 encoding microtubule-associated protein 65. The results supplemented the current knowledge on genetics for drought tolerance in wheat. The results may also be used for future wheat breeding programs to develop drought-tolerant wheat cultivars.
Collapse
Affiliation(s)
- Vijay Gahlaut
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Council of Scientific & Industrial Research-Institute of Himalayan Bioresource Technology, Palampur, India
| | - Vandana Jaiswal
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Council of Scientific & Industrial Research-Institute of Himalayan Bioresource Technology, Palampur, India
| | - Harindra S. Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Arun Kumar Joshi
- International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
- Borlaug Institute for South Asia (BISA), New Delhi, India
| | - Pushpendra K. Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| |
Collapse
|
20
|
Li H, Hu X, Lovell JT, Grabowski PP, Mamidi S, Chen C, Amirebrahimi M, Kahanda I, Mumey B, Barry K, Kudrna D, Schmutz J, Lachowiec J, Lu C. Genetic dissection of natural variation in oilseed traits of camelina by whole-genome resequencing and QTL mapping. THE PLANT GENOME 2021; 14:e20110. [PMID: 34106529 DOI: 10.1002/tpg2.20110] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 04/29/2021] [Indexed: 06/12/2023]
Abstract
Camelina [Camelina sativa (L.) Crantz] is an oilseed crop in the Brassicaceae family that is currently being developed as a source of bioenergy and healthy fatty acids. To facilitate modern breeding efforts through marker-assisted selection and biotechnology, we evaluated genetic variation among a worldwide collection of 222 camelina accessions. We performed whole-genome resequencing to obtain single nucleotide polymorphism (SNP) markers and to analyze genomic diversity. We also conducted phenotypic field evaluations in two consecutive seasons for variations in key agronomic traits related to oilseed production such as seed size, oil content (OC), fatty acid composition, and flowering time. We determined the population structure of the camelina accessions using 161,301 SNPs. Further, we identified quantitative trait loci (QTL) and candidate genes controlling the above field-evaluated traits by genome-wide association studies (GWAS) complemented with linkage mapping using a recombinant inbred line (RIL) population. Characterization of the natural variation at the genome and phenotypic levels provides valuable resources to camelina genetic studies and crop improvement. The QTL and candidate genes should assist in breeding of advanced camelina varieties that can be integrated into the cropping systems for the production of high yield of oils of desired fatty acid composition.
Collapse
Affiliation(s)
- Huang Li
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, 59717, USA
| | - Xiao Hu
- School of Computing, Montana State University, Bozeman, MT, 59717, USA
| | - John T Lovell
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, 38508, USA
| | - Paul P Grabowski
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, 38508, USA
| | - Sujan Mamidi
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, 38508, USA
| | - Cindy Chen
- United States Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Mojgan Amirebrahimi
- United States Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Indika Kahanda
- School of Computing, Montana State University, Bozeman, MT, 59717, USA
| | - Brendan Mumey
- School of Computing, Montana State University, Bozeman, MT, 59717, USA
| | - Kerrie Barry
- United States Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - David Kudrna
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Jeremy Schmutz
- Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, AL, 38508, USA
- United States Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jennifer Lachowiec
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, 59717, USA
| | - Chaofu Lu
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, 59717, USA
| |
Collapse
|
21
|
Ruanjaichon V, Khammona K, Thunnom B, Suriharn K, Kerdsri C, Aesomnuk W, Yongsuwan A, Chaomueang N, Thammapichai P, Arikit S, Wanchana S, Toojinda T. Identification of Gene Associated with Sweetness in Corn ( Zea mays L.) by Genome-Wide Association Study (GWAS) and Development of a Functional SNP Marker for Predicting Sweet Corn. PLANTS (BASEL, SWITZERLAND) 2021; 10:1239. [PMID: 34207135 PMCID: PMC8235792 DOI: 10.3390/plants10061239] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022]
Abstract
Sweetness is an economically important eating quality trait for sweet-corn breeding. To investigate the genetic control of the sweetness trait, we conducted a genome-wide association study (GWAS) in an association panel consisting of 250 sweet corn and waxy corn inbred and recombinant inbred lines (RILs), together with the genotypes obtained from the high-density 600K maize genotyping single-nucleotide polymorphism (SNP) array. GWAS results identified 12 significantly associated SNPs on chromosomes 3, 4, 5, and 7. The most associated SNP, AX_91849634, was found on chromosome 3 with a highly significant p-value of ≤1.53 × 10-14. The candidate gene identified within the linkage disequilibrium (LD) of this marker was shrunken2 (Zm00001d044129; sh2), which encodes ADP-glucose pyrophosphorylase (AGPase), a 60 kDa subunit enzyme that affects starch metabolism in the maize endosperm. Several SNP markers specific to variants in sh2 were developed and validated. According to the validation in a set of 81 inbred, RIL, and popular corn varieties, marker Sh2_rs844805326, which was developed on the basis of the SNP at the position 154 of exon 1, was highly efficient in classifying sh2-based sweet corn from other types of corn. This functional marker is extremely useful for marker-assisted breeding in sh2-sweet corn improvement and marketable seed production.
Collapse
Affiliation(s)
- Vinitchan Ruanjaichon
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| | - Kanogporn Khammona
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| | - Burin Thunnom
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| | - Khundej Suriharn
- Department of Agronomy, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand;
- Plant Breeding Research Center for Sustainable Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Chalong Kerdsri
- Chai Nat Field Crops Research Center, Chai Nat 17000, Thailand;
| | - Wanchana Aesomnuk
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| | - Arweewut Yongsuwan
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| | - Naraporn Chaomueang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| | - Paradee Thammapichai
- Department of Plant and Soil Sciences, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Siwaret Arikit
- Department of Agronomy, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand;
- Rice Science Center, Kasetsart University Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand
| | - Samart Wanchana
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| | - Theerayut Toojinda
- National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand; (V.R.); (K.K.); (B.T.); (W.A.); (A.Y.); (N.C.)
| |
Collapse
|
22
|
Goyal RK, Mattoo AK, Schmidt MA. Rhizobial-Host Interactions and Symbiotic Nitrogen Fixation in Legume Crops Toward Agriculture Sustainability. Front Microbiol 2021; 12:669404. [PMID: 34177848 PMCID: PMC8226219 DOI: 10.3389/fmicb.2021.669404] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/29/2021] [Indexed: 11/13/2022] Open
Abstract
Symbiotic nitrogen fixation (SNF) process makes legume crops self-sufficient in nitrogen (N) in sharp contrast to cereal crops that require an external input by N-fertilizers. Since the latter process in cereal crops results in a huge quantity of greenhouse gas emission, the legume production systems are considered efficient and important for sustainable agriculture and climate preservation. Despite benefits of SNF, and the fact that chemical N-fertilizers cause N-pollution of the ecosystems, the focus on improving SNF efficiency in legumes did not become a breeder’s priority. The size and stability of heritable effects under different environment conditions weigh significantly on any trait useful in breeding strategies. Here we review the challenges and progress made toward decoding the heritable components of SNF, which is considerably more complex than other crop allelic traits since the process involves genetic elements of both the host and the symbiotic rhizobial species. SNF-efficient rhizobial species designed based on the genetics of the host and its symbiotic partner face the test of a unique microbiome for its success and productivity. The progress made thus far in commercial legume crops with relevance to the dynamics of host–rhizobia interaction, environmental impact on rhizobial performance challenges, and what collectively determines the SNF efficiency under field conditions are also reviewed here.
Collapse
Affiliation(s)
- Ravinder K Goyal
- Agriculture and Agri-Food Canada, Lacombe Research and Development Centre, Lacombe, AB, Canada
| | - Autar K Mattoo
- Sustainable Agricultural Systems Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville Agricultural Research Center, Beltsville, MD, United States
| | - Maria Augusta Schmidt
- Agriculture and Agri-Food Canada, Lacombe Research and Development Centre, Lacombe, AB, Canada
| |
Collapse
|
23
|
Genetic diversity and selection signatures in maize landraces compared across 50 years of in situ and ex situ conservation. Heredity (Edinb) 2021; 126:913-928. [PMID: 33785893 PMCID: PMC8178342 DOI: 10.1038/s41437-021-00423-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 02/28/2021] [Accepted: 02/28/2021] [Indexed: 02/01/2023] Open
Abstract
Genomics-based, longitudinal comparisons between ex situ and in situ agrobiodiversity conservation strategies can contribute to a better understanding of their underlying effects. However, landrace designations, ambiguous common names, and gaps in sampling information complicate the identification of matching ex situ and in situ seed lots. Here we report a 50-year longitudinal comparison of the genetic diversity of a set of 13 accessions from the state of Morelos, Mexico, conserved ex situ since 1967 and retrieved in situ from the same donor families in 2017. We interviewed farmer families who donated in situ landraces to understand their germplasm selection criteria. Samples were genotyped by sequencing, producing 74,739 SNPs. Comparing the two sample groups, we show that ex situ and in situ genome-wide diversity was similar. In situ samples had 3.1% fewer SNPs and lower pairwise genetic distances (Fst 0.008-0.113) than ex situ samples (Fst 0.031-0.128), but displayed the same heterozygosity. Despite genome-wide similarities across samples, we could identify several loci under selection when comparing in situ and ex situ seed lots, suggesting ongoing evolution in farmer fields. Eight loci in chromosomes 3, 5, 6, and 10 showed evidence of selection in situ that could be related with farmers' selection criteria surveyed with focus groups and interviews at the sampling site in 2017, including wider kernels and larger ear size. Our results have implications for ex situ collection resampling strategies and the in situ conservation of threatened landraces.
Collapse
|
24
|
Gaikpa DS, Kessel B, Presterl T, Ouzunova M, Galiano-Carneiro AL, Mayer M, Melchinger AE, Schön CC, Miedaner T. Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:793-805. [PMID: 33274402 PMCID: PMC7925457 DOI: 10.1007/s00122-020-03731-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/13/2020] [Indexed: 06/12/2023]
Abstract
KEY MESSAGE High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, "Kemater Landmais Gelb" (KE) and "Petkuser Ferdinand Rot" (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r = - 0.27 to 0.20; [Formula: see text]= - 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low [Formula: see text] that could be improved by using fixed marker effects in the GS model.
Collapse
Affiliation(s)
| | - Bettina Kessel
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Thomas Presterl
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Milena Ouzunova
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | | | - Manfred Mayer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Population Genetics and Seed Science, University of Hohenheim, Stuttgart, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany.
| |
Collapse
|
25
|
Ma J, Wang L, Cao Y, Wang H, Li H. Association Mapping and Transcriptome Analysis Reveal the Genetic Architecture of Maize Kernel Size. FRONTIERS IN PLANT SCIENCE 2021; 12:632788. [PMID: 33815440 PMCID: PMC8013726 DOI: 10.3389/fpls.2021.632788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/04/2021] [Indexed: 05/05/2023]
Abstract
Kernel length, kernel width, and kernel thickness are important traits affecting grain yield and product quality. Here, the genetic architecture of the three kernel size traits was dissected in an association panel of 309 maize inbred lines using four statistical methods. Forty-two significant single nucleotide polymorphisms (SNPs; p < 1.72E-05) and 70 genes for the three traits were identified under five environments. One and eight SNPs were co-detected in two environments and by at least two methods, respectively, and they explained 5.87-9.59% of the phenotypic variation. Comparing the transcriptomes of two inbred lines with contrasting seed size, three and eight genes identified in the association panel showed significantly differential expression between the two genotypes at 15 and 39 days after pollination, respectively. Ten and 17 genes identified by a genome-wide association study were significantly differentially expressed between the two development stages in the two genotypes. Combining environment-/method-stable SNPs and differential expression analysis, ribosomal protein L7, jasmonate-regulated gene 21, serine/threonine-protein kinase RUNKEL, AP2-EREBP-transcription factor 16, and Zm00001d035222 (cell wall protein IFF6-like) were important candidate genes for maize kernel size and development.
Collapse
|
26
|
Cirilli M, Micali S, Aranzana MJ, Arús P, Babini A, Barreneche T, Bink M, Cantin CM, Ciacciulli A, Cos-Terrer JE, Drogoudi P, Eduardo I, Foschi S, Giovannini D, Guerra W, Liverani A, Pacheco I, Pascal T, Quilot-Turion B, Verde I, Rossini L, Bassi D. The Multisite PeachRefPop Collection: A True Cultural Heritage and International Scientific Tool for Fruit Trees. PLANT PHYSIOLOGY 2020; 184:632-646. [PMID: 32727910 PMCID: PMC7536698 DOI: 10.1104/pp.19.01412] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 07/17/2020] [Indexed: 05/21/2023]
Abstract
Plants have evolved a range of adaptive mechanisms that adjust their development and physiology to variable external conditions, particularly in perennial species subjected to long-term interplay with the environment. Exploiting the allelic diversity within available germplasm and leveraging the knowledge of the mechanisms regulating genotype interaction with the environment are crucial to address climatic challenges and assist the breeding of novel cultivars with improved resilience. The development of multisite collections is of utmost importance for the conservation and utilization of genetic materials and will greatly facilitate the dissection of genotype-by-environment interaction. Such resources are still lacking for perennial trees, especially with the intrinsic difficulties of successful propagation, material exchange, and living collection maintenance. This work describes the concept, design, and realization of the first multisite peach (Prunus persica) reference collection (PeachRefPop) located across different European countries and sharing the same experimental design. Other than an invaluable tool for scientific studies in perennial species, PeachRefPop provides a milestone in an international collaborative project for the conservation and exploitation of European peach germplasm resources and, ultimately, as a true heritage for future generations.
Collapse
Affiliation(s)
- Marco Cirilli
- Department of Agricultural and Environmental Sciences, University of Milan, 20133 Milan, Italy
| | - Sabrina Micali
- Consiglio Per La Ricerca In Agricoltura E L'analisi Dell'Economia Agraria, Research Centre for Olive, Fruit, and Citrus Crops, 00134 Rome, Italy
| | - Maria José Aranzana
- Institut de Recerca i Tecnologia Agroalimentàries, Centre de Recerca en Agrigenòmica Consejo Superior de Investigaciones Científicas, Institut de Recerca i Tecnologia Agroalimentàries, Universitat Autònoma de Barcelona, Universitat de Barcelona, Campus UAB, 08193 Barcelona, Spain
| | - Pere Arús
- Institut de Recerca i Tecnologia Agroalimentàries, Centre de Recerca en Agrigenòmica Consejo Superior de Investigaciones Científicas, Institut de Recerca i Tecnologia Agroalimentàries, Universitat Autònoma de Barcelona, Universitat de Barcelona, Campus UAB, 08193 Barcelona, Spain
| | - Annarosa Babini
- Phytosanitary Service, Regione Emilia-Romagna, 40128 Bologna, Italy
| | - Teresa Barreneche
- Université de Bordeaux, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Biologie du Fruit et Pathologie, F-33140 Villenave d'Ornon, France
| | - Marco Bink
- Hendrix Genetics Research, Technology, and Services, 5830 AC Boxmeer, The Netherlands
| | - Celia M Cantin
- Institut de Recerca i Tecnologia Agroalimentàries, Centre de Recerca en Agrigenòmica Consejo Superior de Investigaciones Científicas, Institut de Recerca i Tecnologia Agroalimentàries, Universitat Autònoma de Barcelona, Universitat de Barcelona, Campus UAB, 08193 Barcelona, Spain
| | - Angelo Ciacciulli
- Department of Agricultural and Environmental Sciences, University of Milan, 20133 Milan, Italy
| | | | - Pavlina Drogoudi
- Hellenic Agricultural Organization 'Demeter', Department of Deciduous Fruit Trees, Institute of Plant Breeding and Genetic Resources, 59200 Naoussa, Greece
| | - Iban Eduardo
- Institut de Recerca i Tecnologia Agroalimentàries, Centre de Recerca en Agrigenòmica Consejo Superior de Investigaciones Científicas, Institut de Recerca i Tecnologia Agroalimentàries, Universitat Autònoma de Barcelona, Universitat de Barcelona, Campus UAB, 08193 Barcelona, Spain
| | - Stefano Foschi
- Centro Ricerche Produzioni Vegetali, 47522 Cesena, Italy
| | - Daniela Giovannini
- Consiglio per la Ricerca in Agricoltura e L'Analisi Del'Economia Agraria, Research Centre for Olive, Fruit, and Citrus Crops, 47121 Forlì, Italy
| | | | - Alessandro Liverani
- Consiglio per la Ricerca in Agricoltura e L'Analisi Del'Economia Agraria, Research Centre for Olive, Fruit, and Citrus Crops, 47121 Forlì, Italy
| | - Igor Pacheco
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, 7830490 Macul, Chile
| | - Thierry Pascal
- Institut National de Recherche pour L'Agriculture, L'Alimentation et L'Environnement, Génétique et Amélioration des Fruits et Légumes, F-84143 Montfavet, France
| | - Benedicte Quilot-Turion
- Institut National de Recherche pour L'Agriculture, L'Alimentation et L'Environnement, Génétique et Amélioration des Fruits et Légumes, F-84143 Montfavet, France
| | - Ignazio Verde
- Consiglio Per La Ricerca In Agricoltura E L'analisi Dell'Economia Agraria, Research Centre for Olive, Fruit, and Citrus Crops, 00134 Rome, Italy
| | - Laura Rossini
- Department of Agricultural and Environmental Sciences, University of Milan, 20133 Milan, Italy
| | - Daniele Bassi
- Department of Agricultural and Environmental Sciences, University of Milan, 20133 Milan, Italy
| |
Collapse
|
27
|
Zhang X, Guan Z, Li Z, Liu P, Ma L, Zhang Y, Pan L, He S, Zhang Y, Li P, Ge F, Zou C, He Y, Gao S, Pan G, Shen Y. A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:2881-2895. [PMID: 32594266 DOI: 10.1007/s00122-020-03639-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 06/18/2020] [Indexed: 05/05/2023]
Abstract
Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E-06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize.
Collapse
Affiliation(s)
- Xiaoxiang Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Zhongrong Guan
- Chongqing Yudongnan Academy of Agricultural Sciences, Chongqing, 408000, China
| | - Zhaoling Li
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Peng Liu
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Langlang Ma
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yinchao Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Lang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Shijiang He
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yanling Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Peng Li
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Fei Ge
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Chaoying Zou
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yongcong He
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Shibin Gao
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China
| | - Guangtang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yaou Shen
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China.
| |
Collapse
|
28
|
Sant’Ana GC, Espolador FG, Granato ÍSC, Mendonça LF, Fritsche-Neto R, Borém A. Population structure analysis and identification of genomic regions under selection associated with low-nitrogen tolerance in tropical maize lines. PLoS One 2020; 15:e0239900. [PMID: 32991596 PMCID: PMC7523979 DOI: 10.1371/journal.pone.0239900] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/15/2020] [Indexed: 11/18/2022] Open
Abstract
Increasing low nitrogen (N) tolerance in maize is an important goal for food security and agricultural sustainability. In order to analyze the population structure of tropical maize lines and identify genomic regions associated with low-N tolerance, a set of 64 inbred lines were evaluated under low-N and optimal-N conditions. The low-N Agronomic Efficiency index (LNAE) of each line was calculated. The maize lines were genotyped using 417,112 SNPs markers. The grouping based on the LNAE values classified the lines into two phenotypic groups, the first comprised by genotypes with high LNAE (named H_LNAE group), while the second one comprised genotypes with low LNAE (named L_LNAE group). The H_LNAE and L_LNAE groups had LNAE mean values of 3,304 and 1,644, respectively. The population structure analysis revealed a weak relationship between genetic and phenotypic diversity. Pairs of lines were identified, having at the same time high LNAE and high genetic distance from each other. A set of 29 SNPs markers exhibited a significant difference in allelic frequencies (Fst > 0.2) between H_LNAE and L_LNAE groups. The Pearson's correlation between LNAE and the favorable alleles in this set of SNPs was 0.69. These SNPs could be useful for marker-assisted selection for low-N tolerance in maize breeding programs. The results of this study could help maize breeders identify accessions to be used in the development of low-N tolerant cultivars.
Collapse
Affiliation(s)
| | - Fernando Garcia Espolador
- Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
| | | | - Leandro Freitas Mendonça
- Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
| | - Roberto Fritsche-Neto
- Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
- * E-mail:
| | - Aluízio Borém
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| |
Collapse
|
29
|
Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization. Genes (Basel) 2020; 11:genes11060689. [PMID: 32599710 PMCID: PMC7349181 DOI: 10.3390/genes11060689] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 01/01/2023] Open
Abstract
Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology's single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.
Collapse
|
30
|
Bollinedi H, Yadav AK, Vinod KK, Gopala Krishnan S, Bhowmick PK, Nagarajan M, Neeraja CN, Ellur RK, Singh AK. Genome-Wide Association Study Reveals Novel Marker-Trait Associations (MTAs) Governing the Localization of Fe and Zn in the Rice Grain. Front Genet 2020; 11:213. [PMID: 32391041 PMCID: PMC7188789 DOI: 10.3389/fgene.2020.00213] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 02/24/2020] [Indexed: 01/16/2023] Open
Abstract
Micronutrient malnutrition due to Fe and Zn, affects around two billion people globally particularly in the developing countries. More than 90% of the Asian population is dependent on rice-based diets, which is low in these micronutrients. In the present study, a set of 192 Indian rice germplasm accessions, grown at two locations, were evaluated for Fe and Zn in brown rice (BR) and milled rice (MR). A significant variation was observed in the rice germplasm for these micronutrients. The grain Fe concentration was in the range of 6.2–23.1 ppm in BR and 0.8–12.3 ppm in MR, while grain Zn concentration was found to be in the range of 11.0–47.0 ppm and 8.2–40.8 ppm in the BR and MR, respectively. Grain Fe exhibited maximum loss upon milling with a mean retention of 24.9% in MR, while Zn showed a greater mean retention of 74.2% in MR. A genome-wide association study (GWAS) was carried out implementing the FarmCPU model to control the population structure and kinship, and resulted in the identification of 29 marker-trait associations (MTAs) with significant associations for traits viz. FeBR (6 MTAs), FeMR (7 MTAs), ZnBR (11 MTAs), and ZnMR (5 MTAs), which could explain the phenotypic variance from 2.1 to as high as 53.3%. The MTAs governing the correlated traits showed co-localization, signifying the possibility of their simultaneous improvement. The robust MTAs identified in the study could be valuable resource for enhancing Fe and Zn concentration in the rice grain and addressing the problem of Fe and Zn malnutrition among rice consumers.
Collapse
Affiliation(s)
- Haritha Bollinedi
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Ashutosh Kumar Yadav
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - K K Vinod
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - S Gopala Krishnan
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | - M Nagarajan
- ICAR-Indian Agricultural Research Institute, Rice Breeding and Genetics Research Centre, Aduthurai, India
| | - C N Neeraja
- ICAR-Indian Institute of Rice Research, Hyderabad, India
| | - Ranjith Kumar Ellur
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Ashok Kumar Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| |
Collapse
|
31
|
Fu W, Wang Y, Ye Y, Zhen S, Zhou B, Wang Y, Hu Y, Zhao Y, Huang Y. Grain Yields and Nitrogen Use Efficiencies in Different Types of Stay-Green Maize in Response to Nitrogen Fertilizer. PLANTS 2020; 9:plants9040474. [PMID: 32283610 PMCID: PMC7238017 DOI: 10.3390/plants9040474] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 03/27/2020] [Accepted: 04/01/2020] [Indexed: 12/02/2022]
Abstract
The stay-green leaf phenotype is typically associated with increased yields and improved stress resistance in maize breeding, due to higher nitrogen (N) nutrient levels that prolong greenness. The application of N fertilizer can regulate the N status of plants, and furthermore, impact the photosynthetic rates of leaves at the productive stage; however, N deficiencies and N excesses will reduce maize yields. Consequently, it is necessary to develop N fertilizer management strategies for different types of stay-green maize. For this study, the senescent cultivar Lianchuang 808 (LC808), moderate-stay-green cultivar Zhengdan 958 (ZD958), and over stay-green cultivar Denghai 685 (DH685) were selected as experimental models. Our results revealed that yields of ZD958 were slightly higher than DH685 and notably improved over than LC808. Compared with a non-stay-green cultivar LC808, ZD958 and DH685 still maintained higher chlorophyll contents and cell activities following the silking stage, while efficiently slowing the senescence rate. The supply of N fertilizer significantly prolonged leaf greenness and delayed senescence for ZD958 and DH685; however, the effect was not obvious for LC808. The stem remobilization efficiency of N was higher in the moderate-stay-green cultivar ZD958, in contrast to LC808, while the transfer of leaf N was lower than LC808, which guaranteed high leaf N levels, and that sufficient N was transferred to grains in ZD958. To obtain the highest yields, the optimal N fertilizer rates were 228.1 kg hm−2 for LC0808, 180 kg hm−2 for ZD958, and 203.8 kg hm−2 for DH685. In future, the selection of stay-green type crops might serve as an important agricultural strategy to reduce the quantity of N fertilizer and increase N efficiency.
Collapse
|
32
|
Identification of quantitative trait loci for kernel-related traits and the heterosis for these traits in maize (Zea mays L.). Mol Genet Genomics 2019; 295:121-133. [PMID: 31511973 DOI: 10.1007/s00438-019-01608-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 08/31/2019] [Indexed: 12/24/2022]
Abstract
Heterosis has been extensively applied for many traits during maize breeding, but there has been relatively little attention paid to the heterosis for kernel size. In this study, we evaluated a population of 301 recombinant inbred lines derived from a cross between 08-641 and YE478, as well as 298 hybrids from an immortalized F2 (IF2) population to detect quantitative trait loci (QTLs) for six kernel-related traits and the mid-parent heterosis (MPH) for these traits. A total of 100 QTLs, six pairs of loci with epistatic interactions, and five significant QTL × environment interactions were identified in both mapping populations. Seven QTLs accounted for over 10% of the phenotypic variation. Only four QTLs affected both the trait means and the MPH, suggesting the genetic mechanisms for kernel-related traits and the heterosis for kernel size are not completely independent. Moreover, more than half of the QTLs for each trait in the IF2 population exhibited dominance, implying that dominance is more important than other genetic effects for the heterosis for kernel-related traits. Additionally, 20 QTL clusters comprising 46 QTLs were detected across ten chromosomes. Specific chromosomal regions (bins 2.03, 6.04-6.05, and 9.01-9.02) exhibited pleiotropy and congruency across diverse heterotic patterns in previous studies. These results may provide additional insights into the genetic basis for the MPH for kernel-related traits.
Collapse
|
33
|
You FM, Xiao J, Li P, Yao Z, Jia G, He L, Kumar S, Soto-Cerda B, Duguid SD, Booker HM, Rashid KY, Cloutier S. Genome-Wide Association Study and Selection Signatures Detect Genomic Regions Associated with Seed Yield and Oil Quality in Flax. Int J Mol Sci 2018; 19:ijms19082303. [PMID: 30082613 PMCID: PMC6121305 DOI: 10.3390/ijms19082303] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/02/2018] [Accepted: 08/03/2018] [Indexed: 12/31/2022] Open
Abstract
A genome-wide association study (GWAS) was performed on a set of 260 lines which belong to three different bi-parental flax mapping populations. These lines were sequenced to an averaged genome coverage of 19× using the Illumina Hi-Seq platform. Phenotypic data for 11 seed yield and oil quality traits were collected in eight year/location environments. A total of 17,288 single nucleotide polymorphisms were identified, which explained more than 80% of the phenotypic variation for days to maturity (DTM), iodine value (IOD), palmitic (PAL), stearic, linoleic (LIO) and linolenic (LIN) acid contents. Twenty-three unique genomic regions associated with 33 quantitative trait loci (QTL) for the studied traits were detected, thereby validating four genomic regions previously identified. The 33 QTL explained 48–73% of the phenotypic variation for oil content, IOD, PAL, LIO and LIN but only 8–14% for plant height, DTM and seed yield. A genome-wide selective sweep scan for selection signatures detected 114 genomic regions that accounted for 7.82% of the flax pseudomolecule and overlapped with the 11 GWAS-detected genomic regions associated with 18 QTL for 11 traits. The results demonstrate the utility of GWAS combined with selection signatures for dissection of the genetic structure of traits and for pinpointing genomic regions for breeding improvement.
Collapse
Affiliation(s)
- Frank M You
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Jin Xiao
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Department of Agronomy, Nanjing Agricultural University, Nanjing 210095, China.
| | - Pingchuan Li
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
| | - Zhen Yao
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Gaofeng Jia
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Crop Development Centre, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada.
| | - Liqiang He
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
| | - Santosh Kumar
- Brandon Research and Development Centre, Agriculture and Agri-Food Canada, Brandon, MB R7A 5Y3, Canada.
| | - Braulio Soto-Cerda
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
- Agriaquaculture Nutritional Genomic Center, CGNA, Temuco 4871158, Chile.
| | - Scott D Duguid
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Helen M Booker
- Crop Development Centre, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada.
| | - Khalid Y Rashid
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Sylvie Cloutier
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
| |
Collapse
|