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Kaur R, Vasistha NK, Ravat VK, Mishra VK, Sharma S, Joshi AK, Dhariwal R. Genome-Wide Association Study Reveals Novel Powdery Mildew Resistance Loci in Bread Wheat. Plants (Basel) 2023; 12:3864. [PMID: 38005757 PMCID: PMC10675159 DOI: 10.3390/plants12223864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023]
Abstract
Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020-2021, 2021-2022, and 2022-2023), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K Infinium iSelect assay to obtain genome-wide single-nucleotide polymorphism (SNP) marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 marker-trait associations (MTAs) located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p ≤ 0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020-2021 and 2021-2022. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host-pathogen interaction-related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.
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Affiliation(s)
- Ramandeep Kaur
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Sigh Gill Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour 173101, India
| | - Neeraj Kumar Vasistha
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Sigh Gill Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour 173101, India
- Department of Genetics and Plant Breeding, Rajiv Gandhi University, Rono Hills, Itanagar 791112, India
| | - Vikas Kumar Ravat
- Department of Plant Pathology, Rajiv Gandhi University, Rono Hills, Itanagar 791112, India
| | - Vinod Kumar Mishra
- Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Sandeep Sharma
- Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Arun Kumar Joshi
- Borlaug Institute for South Asia (BISA), NASC Complex, DPS Marg, New Delhi 110012, India
- International Maize and Wheat Improvement Center (CIMMYT) Regional Office, NASC Complex, DPS Marg, New Delhi 110012, India
| | - Raman Dhariwal
- Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, 5403 1 Avenue South, Lethbridge, AB T1J 4B1, Canada
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Deng S, Qiu Y, Zhuang Z, Wu J, Li X, Ruan D, Xu C, Zheng E, Yang M, Cai G, Yang J, Wu Z, Huang S. Genome-Wide Association Study of Body Conformation Traits in a Three-Way Crossbred Commercial Pig Population. Animals (Basel) 2023; 13:2414. [PMID: 37570223 PMCID: PMC10417164 DOI: 10.3390/ani13152414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/28/2023] [Accepted: 07/05/2023] [Indexed: 08/13/2023] Open
Abstract
Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include INTS10, KIRREL3, SOX21, BMP2, MAP4K3, SOD3, FAM160B1, ATL2, SPRED2, SEC16B, and RASAL2. Furthermore, our analysis revealed a novel significant quantitative trait locus (QTL) on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.
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Affiliation(s)
- Shaoxiong Deng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Xuehua Li
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Enqing Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu 527400, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu 527400, China
| | - Sixiu Huang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits. Plants (Basel) 2023; 12:2659. [PMID: 37514272 PMCID: PMC10383196 DOI: 10.3390/plants12142659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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Pradhan AK, Budhlakoti N, Chandra Mishra D, Prasad P, Bhardwaj SC, Sareen S, Sivasamy M, Jayaprakash P, Geetha M, Nisha R, Shajitha P, Peter J, Kaur A, Kaur S, Vikas VK, Singh K, Kumar S. Identification of Novel QTLs/Defense Genes in Spring Wheat Germplasm Panel for Seedling and Adult Plant Resistance to Stem Rust and Their Validation Through KASP Marker Assays. Plant Dis 2023:PDIS09222242RE. [PMID: 37311158 DOI: 10.1094/pdis-09-22-2242-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Stem rust is one of the major diseases threatening wheat production globally. To identify novel resistance quantitative trait loci (QTLs), we performed 35K Axiom Array SNP genotyping assays on an association mapping panel of 400 germplasm accessions, including Indian landraces, in conjunction with phenotyping for stem rust at seedling and adult plant stages. Association analyses using three genome wide association study (GWAS) models (CMLM, MLMM, and FarmCPU) revealed 20 reliable QTLs for seedling and adult plant resistance. Among these 20 QTLs, five QTLs were found consistent with three models, i.e., four QTLs on chromosome 2AL, 2BL, 2DL, and 3BL for seedling resistance and one QTL on chromosome 7DS for adult plant resistance. Further, we identified a total of 21 potential candidate genes underlying QTLs using gene ontology analysis, including a leucine rich repeat receptor (LRR) and P-loop nucleoside triphosphate hydrolase, which have a role in pathogen recognition and disease resistance. Furthermore, four QTLs (Qsr.nbpgr-3B_11, QSr.nbpgr-6AS_11, QSr.nbpgr-2AL_117-6, and QSr.nbpgr-7BS_APR) were validated through KASP located on chromosomes 3B, 6A, 2A, and 7B. Out of these QTLs, QSr.nbpgr-7BS_APR was identified as a novel QTL for stem rust resistance which has been found effective in both seedling as well as the adult plant stages. Identified novel genomic regions and validated QTLs have the potential to be deployed in wheat improvement programs to develop disease resistant varieties for stem rust and can diversify the genetic basis of resistance.
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Affiliation(s)
| | - Neeraj Budhlakoti
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
| | | | - Pramod Prasad
- ICAR-Indian Institute of Wheat and Barley Research, Flowerdale, Shimla, Himachal Pradesh 171002, India
| | - S C Bhardwaj
- ICAR-Indian Institute of Wheat and Barley Research, Flowerdale, Shimla, Himachal Pradesh 171002, India
| | - Sindhu Sareen
- ICAR-Indian Institute of Wheat and Barley Research, Karnal 132001, India
| | - M Sivasamy
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - P Jayaprakash
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - M Geetha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - R Nisha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - P Shajitha
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - John Peter
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - Amandeep Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141004, India
| | - Satinder Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana 141004, India
| | - V K Vikas
- ICAR-Indian Agricultural Research Institute, Regional Station, Wellington 643 231, India
| | - Kuldeep Singh
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana 502324, India
| | - Sundeep Kumar
- ICAR-National Bureau of Plant Genetic Resources, New Delhi 110012, India
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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) 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Schierenbeck M, Alqudah AM, Thabet SG, Lohwasser U, Simón MR, Börner A. Association mapping unravels the genetics controlling seedling drought stress tolerance in winter wheat. Front Plant Sci 2023; 14:1061845. [PMID: 36818842 PMCID: PMC9933780 DOI: 10.3389/fpls.2023.1061845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
Drought is a major constraint in wheat (Triticum aestivum L.) grain yield. The present work aimed to identify quantitative trait nucleotides (QTNs)/ candidate genes influencing drought tolerance-related traits at the seedling stage in 261 accessions of a diverse winter wheat panel. Seeds from three consecutive years were exposed to polyethylene glycol 12% (PEG-6000) and a control treatment (distilled water). The Farm-CPU method was used for the association analysis with 17,093 polymorphic SNPs. PEG treatment reduced shoot length (SL) (-36.3%) and root length (RL) (-11.3%) compared with control treatments, while the coleoptile length (CL) was increased by 11% under drought conditions, suggesting that it might be considered as an indicator of stress-tolerance. Interestingly, we revealed 70 stable QTN across 17 chromosomes. Eight QTNs related to more than one trait were detected on chromosomes 1B, 2A (2), 2B, 2D, 4B, 7A, and 7B and located nearby or inside candidate genes within the linkage disequilibrium (LD) interval. For instance, the QTN on chromosome 2D is located inside the gene TraesCS2D02G133900 that controls the variation of CL_S and SL_C. The allelic variation at the candidate genes showed significant influence on the associated traits, demonstrating their role in controlling the natural variation of multi-traits of drought stress tolerance. The gene expression of these candidate genes under different stress conditions validates their biological role in stress tolerance. Our findings offer insight into understanding the genetic factors and diverse mechanisms in response to water shortage conditions that are important for wheat improvement and adaptation at early developmental stages.
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Affiliation(s)
- Matías Schierenbeck
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Seeland, Germany
- Cereals, Faculty of Agricultural Sciences and Forestry, National University of La Plata, La Plata, Argentina
- CONICET CCT La Plata, La Plata, Argentina
| | - Ahmad M. Alqudah
- Biological Science Program, Department of Biological and Environmental Sciences, College of Art and Science, Qatar University, Doha, Qatar
| | - Samar G. Thabet
- Department of Botany, Faculty of Science, Fayoum University, Fayoum, Egypt
| | - Ulrike Lohwasser
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Seeland, Germany
| | - María Rosa Simón
- Cereals, Faculty of Agricultural Sciences and Forestry, National University of La Plata, La Plata, Argentina
- CONICET CCT La Plata, La Plata, Argentina
| | - Andreas Börner
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Seeland, Germany
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Arkwazee HA, Wallace LT, Hart JP, Griffiths PD, Myers JR. Genome-Wide Association Study (GWAS) of White Mold Resistance in Snap Bean. Genes (Basel) 2022; 13. [PMID: 36553566 DOI: 10.3390/genes13122297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
White mold can result in snap bean yield losses of 90 to 100% when field conditions favor the pathogen. A genome-wide association study (GWAS) was conducted to detect loci significantly associated with white mold resistance in a panel of snap bean (Phaseolus vulgaris L.) cultivars. Two populations of snap bean were used in this study. The first population was the BeanCAP (Coordinated Agriculture Project) Snap Bean Diversity Panel (SBDP) (n = 136), and the second population was the Snap Bean Association Panel (SnAP) (n = 378). SBDP was evaluated for white mold reaction in the field in 2012 and 2013, and SnAP was screened in a greenhouse only using the seedling straw test in 2016. Two reference genomes representing the Andean and Middle American centers of domestication were utilized to align the genotyping-by-sequencing (GBS) data. A GWAS was performed using FarmCPU with one principal component after comparing five models. Thirty-four single-nucleotide polymorphisms (SNPs) significantly associated with white mold resistance were detected. Eleven significant SNPs were identified by the seedling straw test, and 23 significant SNPs were identified by field data. Fifteen SNPs were identified within a 100 kb window containing pentatricopeptide repeat (PPR)-encoding genes, and eleven were close to leucine-rich repeat (LRR)-encoding genes, suggesting that these two classes are of outsized importance for snap bean resistance to white mold.
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Nazar M, Abdalla IM, Chen Z, Ullah N, Liang Y, Chu S, Xu T, Mao Y, Yang Z, Lu X. Genome-Wide Association Study for Udder Conformation Traits in Chinese Holstein Cattle. Animals (Basel) 2022; 12:2542. [PMID: 36230283 PMCID: PMC9559277 DOI: 10.3390/ani12192542] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/14/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Udder conformation traits are one of the most economic traits in dairy cows, greatly affecting animal health, milk production, and producer profitability in the dairy industry. Genetic analysis of udder structure and scores have been developed in Holstein cattle. In our research, we conducted a genome-wide association study for five udder traits, including anterior udder attachment (AUA), central suspensory ligament (CSL), posterior udder attachment height (PUAH), posterior udder attachment width (PUAW), and udder depth (UD), in which the fixed and random model circulating probability unification (FarmCPU) model was applied for the association analysis. The heritability and the standard errors of these five udder traits ranged from 0.04 ± 0.00 to 0.49 ± 0.03. Phenotype data were measured from 1000 Holstein cows, and the GeneSeek Genomic Profiler (GGP) Bovine 100 K SNP chip was used to analyze genotypic data in Holstein cattle. For GWAS analysis, 984 individual cows and 84,407 single-nucleotide polymorphisms (SNPs) remained after quality control; a total of 18 SNPs were found at the GW significant threshold (p < 5.90 × 10−7). Many candidate genes were identified within 200kb upstream or downstream of the significant SNPs, which include MGST1, MGST2, MTUS1, PRKN, STXBP6, GRID2, E2F8, CDH11, FOXP1, SLF1, TMEM117, SBF2, GC, ADGRB3, and GCLC. Pathway analysis revealed that 58 Gene Ontology (GO) terms and 18 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched with adjusted p values, and these GO terms and the KEGG pathway analysis were associated with biological information, metabolism, hormonal growth, and development processes. These results could give valuable biological information for the genetic architecture of udder conformation traits in dairy Holstein cattle.
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Affiliation(s)
- Mudasir Nazar
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | | | - Zhi Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Numan Ullah
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Yan Liang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Shuangfeng Chu
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Tianle Xu
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Yangzhou University, Yangzhou 225009, China
| | - Yongjiang Mao
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Zhangping Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Xubin Lu
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
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Srungarapu R, Mahendrakar MD, Mohammad LA, Chand U, Jagarlamudi VR, Kondamudi KP, Kudapa H, Samineni S. Genome-Wide Association Analysis Reveals Trait-Linked Markers for Grain Nutrient and Agronomic Traits in Diverse Set of Chickpea Germplasm. Cells 2022; 11:cells11152457. [PMID: 35954301 PMCID: PMC9367858 DOI: 10.3390/cells11152457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/22/2022] [Accepted: 07/29/2022] [Indexed: 11/16/2022] Open
Abstract
Chickpea is an inexpensive source of protein, minerals, and vitamins to the poor people living in arid and semi-arid regions of Southern Asia and Sub-Saharan Africa. New chickpea cultivars with enhanced levels of protein, Fe and Zn content are a medium-term strategy for supplying essential nutrients for human health and reducing malnutrition. In the current study, a chickpea reference set of 280 accessions, including landraces, breeding lines, and advanced cultivars, was evaluated for grain protein, Fe, Zn content and agronomic traits over two seasons. Using a mid-density 5k SNP array, 4603 highly informative SNPs distributed across the chickpea genome were used for GWAS analysis. Population structure analysis revealed three subpopulations (K = 3). Linkage disequilibrium (LD) was extensive, and LD decay was relatively low. A total of 20 and 46 marker-trait associations (MTAs) were identified for grain nutrient and agronomic traits, respectively, using FarmCPU and BLINK models. Of which seven SNPs for grain protein, twelve for Fe, and one for Zn content were distributed on chromosomes 1, 4, 6, and 7. The marker S4_4477846 on chr4 was found to be co-associated with grain protein over seasons. The markers S1_11613376 and S1_2772537 co-associated with grain Fe content under NSII and pooled seasons and S7_9379786 marker under NSI and pooled seasons. The markers S4_31996956 co-associated with grain Fe and days to maturity. SNP annotation of associated markers were found to be related to gene functions of metal ion binding, transporters, protein kinases, transcription factors, and many more functions involved in plant metabolism along with Fe and protein homeostasis. The identified significant MTAs has potential use in marker-assisted selection for developing nutrient-rich chickpea cultivars after validation in the breeding populations.
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Affiliation(s)
- Rajasekhar Srungarapu
- Accelerated Crop Improvement, Chickpea Breeding, International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502324, India
- Department of Molecular Biology and Biotechnology, Acharya N.G. Ranga Agricultural University, Guntur 522034, India
| | - Mahesh Damodhar Mahendrakar
- Accelerated Crop Improvement, Chickpea Breeding, International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502324, India
| | - Lal Ahamed Mohammad
- Department of Genetics and Plant Breeding, Acharya N.G. Ranga Agricultural University, Guntur 522034, India
| | - Uttam Chand
- Accelerated Crop Improvement, Chickpea Breeding, International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502324, India
| | - Venkata Ramana Jagarlamudi
- Department of Genetics and Plant Breeding, Acharya N.G. Ranga Agricultural University, Guntur 522034, India
| | - Kiran Prakash Kondamudi
- Department of Statistics and Computer Applications, Acharya N.G. Ranga Agricultural University, Guntur 522034, India
| | - Himabindu Kudapa
- Genomics, Pre-Breeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502324, India
| | - Srinivasan Samineni
- Accelerated Crop Improvement, Chickpea Breeding, International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502324, India
- Correspondence:
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Yoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I. Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. Int J Mol Sci 2022; 23:5538. [PMID: 35628351 PMCID: PMC9141736 DOI: 10.3390/ijms23105538] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 12/14/2022] Open
Abstract
A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.
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Affiliation(s)
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC G1V 0A6, Canada;
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.-N.); (S.T.); (I.R.)
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11
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da Silva Linge C, Cai L, Fu W, Clark J, Worthington M, Rawandoozi Z, Byrne DH, Gasic K. Corrigendum: Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome. Front Plant Sci 2022; 13:879112. [PMID: 35371176 PMCID: PMC8970174 DOI: 10.3389/fpls.2022.879112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
[This corrects the article DOI: 10.3389/fpls.2021.644799.].
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Affiliation(s)
- Cassia da Silva Linge
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Lichun Cai
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Department of Horticulture, Michigan State University, East Lansing, MI, United States
| | - Wanfang Fu
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - John Clark
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Margaret Worthington
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Zena Rawandoozi
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, United States
| | - David H. Byrne
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, United States
| | - Ksenija Gasic
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
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Liu X, Yin L, Zhang H, Li X, Zhao S. Performing Genome-Wide Association Studies Using rMVP. Methods Mol Biol 2022; 2481:219-245. [PMID: 35641768 DOI: 10.1007/978-1-0716-2237-7_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Genome wide association study (GWAS), which is a powerful tool to detect the relationship between the traits of interest and high-density markers, has provided unprecedented insights into the genetic basis of quantitative variation for complex traits. Along with the development of high-throughput sequencing technology, both sample sizes and marker sizes are increasing rapidly, which make computations more challenging than ever. Therefore, to efficiently process big data with limited computing resources in a reasonable time and to use state-of-the-art statistical models to reduce false positive and false negative rates have always been hot topics in the domain of GWAS. In this chapter, we describe how to perform GWAS using an R package, rMVP, which includes data preparation, evaluation of population structure, association tests by different models, and high-quality visualization of GWAS results.
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Affiliation(s)
- Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Haohao Zhang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
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Hautsalo J, Novakazi F, Jalli M, Göransson M, Manninen O, Isolahti M, Reitan L, Bergersen S, Krusell L, Damsgård Robertsen C, Orabi J, Due Jensen J, Jahoor A, Bengtsson T. Pyramiding of scald resistance genes in four spring barley MAGIC populations. Theor Appl Genet 2021; 134:3829-3843. [PMID: 34350474 PMCID: PMC8580920 DOI: 10.1007/s00122-021-03930-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
Genome-Wide Association Studies (GWAS) of four Multi-parent Advanced Generation Inter-Cross (MAGIC) populations identified nine regions on chromosomes 1H, 3H, 4H, 5H, 6H and 7H associated with resistance against barley scald disease. Three of these regions are putatively novel resistance Quantitative Trait Loci (QTL). Barley scald is caused by Rhynchosporium commune, one of the most important barley leaf diseases that are prevalent in most barley-growing regions. Up to 40% yield losses can occur in susceptible barley cultivars. Four MAGIC populations were generated in a Nordic Public-Private Pre-breeding of spring barley project (PPP Barley) to introduce resistance to several important diseases. Here, these MAGIC populations consisting of six to eight founders each were tested for scald resistance in field trials in Finland and Iceland. Eight different model covariate combinations were compared for GWAS studies, and the models that deviated the least from the expected p-values were selected. For all QTL, candidate genes were identified that are predicted to be involved in pathogen defence. The MAGIC progenies contained new haplotypes of significant SNP-markers with high resistance levels. The lines with successfully pyramided resistance against scald and mildew and the significant markers are now distributed among Nordic plant breeders and will benefit development of disease-resistant cultivars.
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Affiliation(s)
- Juho Hautsalo
- Natural Resources Institute Finland (Luke), Survontie 9, 40500, Jyväskylä, Finland
| | - Fluturë Novakazi
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden
| | - Marja Jalli
- Natural Resources Institute Finland (Luke), Tietotie 4, 31600, Jokioinen, Finland
| | - Magnus Göransson
- Faculty of Land and Animal Resources, The Agricultural University of Iceland, Hvanneyri, 311, Borgarnes, Iceland
| | - Outi Manninen
- Boreal Plant Breeding Ltd., Myllytie 10, 31600, Jokioinen, Norway
| | - Mika Isolahti
- Boreal Plant Breeding Ltd., Myllytie 10, 31600, Jokioinen, Norway
| | - Lars Reitan
- Graminor Ltd. Hommelstadvegen 60, 2322, Ridabu, Norway
| | | | - Lene Krusell
- Sejet Plant Breeding, Nørremarksvej 67, 8700, Horsens, Norway
| | | | - Jihad Orabi
- Nordic Seed A/S, Kornmarken 1, 8464, Galten, Denmark
| | | | - Ahmed Jahoor
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden
- Nordic Seed A/S, Kornmarken 1, 8464, Galten, Denmark
| | - Therése Bengtsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 190, 234 22, Lomma, Sweden.
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Zhao H, Zhu S, Guo T, Han M, Chen B, Qiao G, Wu Y, Yuan C, Liu J, Lu Z, Sun W, Wang T, Li F, Zhang Y, Hou F, Yue Y, Yang B. Whole-genome re-sequencing association study on yearling wool traits in Chinese fine-wool sheep. J Anim Sci 2021; 99:6319907. [PMID: 34255028 PMCID: PMC8418636 DOI: 10.1093/jas/skab210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 07/10/2021] [Indexed: 12/11/2022] Open
Abstract
To investigate single nucleotide polymorphism (SNP) loci associated with yearling wool traits of fine-wool sheep for optimizing marker-assisted selection and dissection of the genetic architecture of wool traits, we conducted a genome-wide association study (GWAS) based on the fixed and random model circulating probability unification (FarmCPU) for yearling staple length (YSL), yearling mean fiber diameter (YFD), yearling greasy fleece weight (YGFW), and yearling clean fleece rate (YCFR) by using the whole-genome re-sequenced data (totaling 577 sheep) from the following four fine-wool sheep breeds in China: Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Qinghai fine-wool sheep (QHS), and Aohan fine-wool sheep (AHS). A total of 16 SNPs were detected above the genome-wise significant threshold (P = 5.45E-09), and 79 SNPs were located above the suggestive significance threshold (P = 5.00E-07) from the GWAS results. For YFD and YGFW traits, 7 and 9 SNPs reached the genome-wise significance thresholds, whereas 10 and 12 SNPs reached the suggestive significance threshold, respectively. For YSL and YCFR traits, none of the SNPs reached the genome-wise significance thresholds, whereas 57 SNPs exceeded the suggestive significance threshold. We recorded 14 genes located at the region of ±50-kb near the genome-wise significant SNPs and 59 genes located at the region of ±50-kb near the suggestive significant SNPs. Meanwhile, we used the Average Information Restricted Maximum likelihood algorithm (AI-REML) in the “HIBLUP” package to estimate the heritability and variance components of the four desired yearling wool traits. The estimated heritability values (h2) of YSL, YFD, YGFW, and YCFR were 0.6208, 0.7460, 0.6758, and 0.5559, respectively. We noted that the genetic parameters in this study can be used for fine-wool sheep breeding. The newly detected significant SNPs and the newly identified candidate genes in this study would enhance our understanding of yearling wool formation, and significant SNPs can be applied to genome selection in fine-wool sheep breeding.
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Affiliation(s)
- Hongchang Zhao
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Shaohua Zhu
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Tingting Guo
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Mei Han
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Bowen Chen
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Guoyan Qiao
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Yi Wu
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Chao Yuan
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Jianbin Liu
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Zengkui Lu
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Weibo Sun
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Tianxiang Wang
- Gansu Provincial Sheep Breeding Technology Extension Station, Sunan, 734031, China
| | - Fanwen Li
- Gansu Provincial Sheep Breeding Technology Extension Station, Sunan, 734031, China
| | - Yajun Zhang
- Xinjiang Gongnaisi Breeding Sheep Farm, Xinyuan, 835808, China
| | - Fujun Hou
- Aohan Banner Breeding Sheep Farm, Chifeng, 024300, China
| | - Yaojing Yue
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
| | - Bohui Yang
- Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Sheep Breeding Engineering Technology Research Center, Lanzhou, 730050, China
- Corresponding author:
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Abdalla IM, Lu X, Nazar M, Arbab AAI, Xu T, Yousif MH, Mao Y, Yang Z. Genome-Wide Association Study Identifies Candidate Genes Associated with Feet and Leg Conformation Traits in Chinese Holstein Cattle. Animals (Basel) 2021; 11:2259. [PMID: 34438715 DOI: 10.3390/ani11082259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/24/2021] [Accepted: 07/28/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Feet and leg problems are among the major reasons for dairy cows leaving the herd, as well as having direct association with production and reproduction efficiency, health (e.g., claw disorders and lameness) and welfare. Hence, understanding the genetic architecture underlying feet and conformation traits in dairy cattle offers new opportunities toward the genetic improvement and long-term selection. Through a genome-wide association study on Chinese Holstein cattle, we identified several candidate genes associated with feet and leg conformation traits. These results could provide useful information about the molecular breeding basis of feet and leg traits, thus improving the longevity and productivity of dairy cattle. Abstract Feet and leg conformation traits are considered one of the most important economical traits in dairy cattle and have a great impact on the profitability of milk production. Therefore, identifying the single nucleotide polymorphisms (SNPs), genes and pathways analysis associated with these traits might contribute to the genomic selection and long-term plan selection for dairy cattle. We conducted genome-wide association studies (GWASs) using the fixed and random model circulating probability unification (FarmCPU) method to identify SNPs associated with bone quality, heel depth, rear leg side view and rear leg rear view of Chinese Holstein cows. Phenotypic measurements were collected from 1000 individuals of Chinese Holstein cattle and the GeneSeek Genomic Profiler Bovine 100 K SNP chip was utilized for individual genotyping. After quality control, 984 individual cows and 84,906 SNPs remained for GWAS work; as a result, we identified 20 significant SNPs after Bonferroni correction. Several candidate genes were identified within distances of 200 kb upstream or downstream to the significant SNPs, including ADIPOR2, INPP4A, DNMT3A, ALDH1A2, PCDH7, XKR4 and CADPS. Further bioinformatics analyses showed 34 gene ontology terms and two signaling pathways were significantly enriched (p ≤ 0.05). Many terms and pathways are related to biological quality, metabolism and development processes; these identified SNPs and genes could provide useful information about the genetic architecture of feet and leg traits, thus improving the longevity and productivity of Chinese Holstein dairy cattle.
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da Silva Linge C, Cai L, Fu W, Clark J, Worthington M, Rawandoozi Z, Byrne DH, Gasic K. Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome. Front Plant Sci 2021; 12:644799. [PMID: 33732279 PMCID: PMC7959719 DOI: 10.3389/fpls.2021.644799] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/04/2021] [Indexed: 05/23/2023]
Abstract
Peach is one of the most important fruit crops in the world, with the global annual production about 24.6 million tons. The United States is the fourth-largest producer after China, Spain, and Italy. Peach consumption has decreased over the last decade, most likely due to inconsistent quality of the fruit on the market. Thus, marker-assisted selection for fruit quality traits is highly desired in fresh market peach breeding programs and one of the major goals of the RosBREED project. The ability to use DNA information to select for desirable traits would enable peach breeders to efficiently plan crosses and select seedlings with desired quality traits early in the selection process before fruiting. Therefore, we assembled a multi-locus genome wide association study (GWAS) of 620 individuals from three public fresh market peach breeding programs (Arkansas, Texas, and South Carolina). The material was genotyped using 9K SNP array and the traits were phenotyped for three phenological (bloom date, ripening date, and days after bloom) and 11 fruit quality-related traits (blush, fruit diameter, fruit weight, adherence, fruit firmness, redness around pit, fruit texture, pit weight, soluble solid concentration, titratable acidity, and pH) over three seasons (2010, 2011, and 2012). Multi-locus association analyses, carried out using mrMLM 4.0 and FarmCPU R packages, revealed a total of 967 and 180 quantitative trait nucleotides (QTNs), respectively. Among the 88 consistently reliable QTNs detected using multiple multi-locus GWAS methods and/or at least two seasons, 44 were detected for the first time. Fruit quality hotspots were identified on chromosomes 1, 3, 4, 5, 6, and 8. Out of 566 candidate genes detected in the genomic regions harboring the QTN clusters, 435 were functionally annotated. Gene enrichment analyses revealed 68 different gene ontology (GO) terms associated with fruit quality traits. Data reported here advance our understanding of genetic mechanisms underlying important fruit quality traits and further support the development of DNA tools for breeding.
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Affiliation(s)
- Cassia da Silva Linge
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Lichun Cai
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Department of Horticulture, Michigan State University, East Lansing, MI, United States
| | - Wanfang Fu
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - John Clark
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Margaret Worthington
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Zena Rawandoozi
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, United States
| | - David H. Byrne
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, United States
| | - Ksenija Gasic
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
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Liu L, Zhou J, Chen CJ, Zhang J, Wen W, Tian J, Zhang Z, Gu Y. GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle. Animals (Basel) 2020; 10:ani10112048. [PMID: 33167458 PMCID: PMC7694478 DOI: 10.3390/ani10112048] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/01/2020] [Accepted: 11/03/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Understanding the genetic architecture underlying milk production traits in cattle is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we performed a genome-wide association study for milk production and quality traits in Holstein cattle. In the total of ten significant single-nucleotide polymorphisms (SNPs) associated with milk fat and protein, six are located in previously reported quantitative traits locus (QTL) regions. The study not only identified the effect of DGAT1 gene on milk fat and protein but also found several novel candidate genes. In addition, some pleiotropic SNPs and QTLs were identified that associated with more than two traits, these results could provide some basis for molecular breeding in dairy cattle. Abstract High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10−7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.
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Affiliation(s)
- Liyuan Liu
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Jinghang Zhou
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Chunpeng James Chen
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Juan Zhang
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
| | - Wan Wen
- Animal Husbandry Workstation, Yinchuan 750001, Ningxia, China; (W.W.); (J.T.)
| | - Jia Tian
- Animal Husbandry Workstation, Yinchuan 750001, Ningxia, China; (W.W.); (J.T.)
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
- Correspondence: (Z.Z.); (Y.G.)
| | - Yaling Gu
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Correspondence: (Z.Z.); (Y.G.)
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Gan Q, Li Y, Liu Q, Lund M, Su G, Liang X. Genome-wide association studies for the concentrations of insulin, triiodothyronine, and thyroxine in Chinese Holstein cattle. Trop Anim Health Prod 2019; 52:1655-1660. [PMID: 31853785 DOI: 10.1007/s11250-019-02170-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 12/01/2019] [Indexed: 10/25/2022]
Abstract
To further understand the genetic structure that is associated with insulin (INS) and thyroid hormones (TH), including triiodothyronine (T3) and thyroxine (T4), in Chinese Holstein cows, we conducted a genome-wide association study (GWAS) of thyroid hormones and insulin in cows. We conducted GWAS analysis on 1217 Chinese Holstein cows raised in southern China and found 19 significant single nucleotide polymorphisms (SNPs) in this study: 10 SNPs were associated with INS, 5 SNPs were associated with T3, and 4 SNPs were associated with T4. In our study, the GWAS method was used for preliminary screening on related genes of traits, and due to insufficient relevant literature, a functional analysis of genes could only be based on human studies. We observed that DGKB from Bos taurus chromosome (BTA)4 is strongly associated with insulin secretion. We found that EXOC4 gene was significantly correlated with T3 and T4 traits. Another significant SNP was located in the CYP7A1 gene, which has been confirmed to be affected by thyroid hormones.
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Affiliation(s)
- QianFu Gan
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - YiRan Li
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - QingHua Liu
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - M Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - GuoSheng Su
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - XueWu Liang
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
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López-Hernández F, Cortés AJ. Last-Generation Genome-Environment Associations Reveal the Genetic Basis of Heat Tolerance in Common Bean ( Phaseolus vulgaris L.). Front Genet 2019; 10:954. [PMID: 31824551 PMCID: PMC6883007 DOI: 10.3389/fgene.2019.00954] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/06/2019] [Indexed: 01/10/2023] Open
Abstract
Genome-environment associations (GEAs) are a powerful strategy for the study of adaptive traits in wild plant populations, yet they still lack behind in the use of modern statistical methods as the ones suggested for genome-wide association studies (GWASs). In order to bridge this gap, we couple GEA with last-generation GWAS algorithms in common bean to identify novel sources of heat tolerance across naturally heterogeneous ecosystems. Common bean (Phaseolus vulgaris L.) is the most important legume for human consumption, and breeding it for resistance to heat stress is key because annual increases in atmospheric temperature are causing decreases in yield of up to 9% for every 1°C. A total of 78 geo-referenced wild accessions, spanning the two gene pools of common bean, were genotyped by sequencing (GBS), leading to the discovery of 23,373 single-nucleotide polymorphism (SNP) markers. Three indices of heat stress were developed for each accession and inputted in last-generation algorithms (i.e. SUPER, FarmCPU, and BLINK) to identify putative associated loci with the environmental heterogeneity in heat stress. Best-fit models revealed 120 significantly associated alleles distributed in all 11 common bean chromosomes. Flanking candidate genes were identified using 1-kb genomic windows centered in each associated SNP marker. Some of these genes were directly linked to heat-responsive pathways, such as the activation of heat shock proteins (MED23, MED25, HSFB1, HSP40, and HSP20). We also found protein domains related to thermostability in plants such as S1 and Zinc finger A20 and AN1. Other genes were related to biological processes that may correlate with plant tolerance to high temperature, such as time to flowering (MED25, MBD9, and PAP), germination and seedling development (Pkinase_Tyr, Ankyrin-B, and Family Glicosil-hydrolase), cell wall stability (GAE6), and signaling pathway of abiotic stress via abscisic acid (histone-like transcription factors NFYB and phospholipase C) and auxin (Auxin response factor and AUX_IAA). This work offers putative associated loci for marker-assisted and genomic selection for heat tolerance in common bean. It also demonstrates that it is feasible to identify genome-wide environmental associations with modest sample sizes by using a combination of various carefully chosen environmental indices and last-generation GWAS algorithms.
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Affiliation(s)
- Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria (Agrosavia) - CI La Selva, Rionegro, Colombia
- Facultad de Ciencias – Grupo de Investigación en Sistemática Molecular, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia
| | - Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria (Agrosavia) - CI La Selva, Rionegro, Colombia
- Facultad de Ciencias Agrarias - Departamento de Ciencias Forestales, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia
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Gyawali A, Shrestha V, Guill KE, Flint-Garcia S, Beissinger TM. Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs. BMC Plant Biol 2019; 19:412. [PMID: 31590656 PMCID: PMC6781408 DOI: 10.1186/s12870-019-2000-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 08/30/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs. RESULTS In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960's, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA. CONCLUSION Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.
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Affiliation(s)
- Abiskar Gyawali
- Division of Biological Sciences, University of Missouri, Columbia, USA
| | - Vivek Shrestha
- Division of Biological Sciences, University of Missouri, Columbia, USA
| | | | - Sherry Flint-Garcia
- USDA-ARS, Columbia, MO USA
- Division of Plant Sciences, University of Missouri, Columbia, USA
| | - Timothy M. Beissinger
- Department of Crop Sciences, Georg-August Universität Göttingen, Göttingen, Germany
- Center for Integrated Breeding Research, Georg August Universität Göttingen, Göttingen, Germany
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Wang YY, Li YQ, Wu HY, Hu B, Zheng JJ, Zhai H, Lv SX, Liu XL, Chen X, Qiu HM, Yang J, Zong CM, Han DZ, Wen ZX, Wang DC, Xia ZJ. Genotyping of Soybean Cultivars With Medium-Density Array Reveals the Population Structure and QTNs Underlying Maturity and Seed Traits. Front Plant Sci 2018; 9:610. [PMID: 29868067 PMCID: PMC5954420 DOI: 10.3389/fpls.2018.00610] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 04/17/2018] [Indexed: 05/08/2023]
Abstract
Soybean was domesticated about 5,000 to 6,000 years ago in China. Although genotyping technologies such as genotyping by sequencing (GBS) and high-density array are available, it is convenient and economical to genotype cultivars or populations using medium-density SNP array in genetic study as well as in molecular breeding. In this study, 235 cultivars, collected from China, Japan, USA, Canada and some other countries, were genotyped using SoySNP8k iSelect BeadChip with 7,189 single nucleotide polymorphisms (SNPs). In total, 4,471 polymorphic SNP markers were used to analyze population structure and perform genome-wide association study (GWAS). The most likely K value was 7, indicating this population can be divided into 7 subpopulations, which is well in accordance with the geographic origins of cultivars or accession studied. The LD decay rate was estimated at 184 kb, where r2 dropped to half of its maximum value (0.205). GWAS using FarmCPU detected a stable quantitative trait nucleotide (QTN) for hilum color and seed color, which is consistent with the known loci or genes. Although no universal QTNs for flowering time and maturity were identified across all environments, a total of 30 consistent QTNs were detected for flowering time (R1) or maturity (R7 and R8) on 16 chromosomes, most of them were corresponding to known E1 to E4 genes or QTL region reported in SoyBase (soybase.org). Of 16 consistent QTNs for protein and oil contents, 11 QTNs were detected having antagonistic effects on protein and oil content, while 4 QTNs soly for oil content, and one QTN soly for protein content. The information gained in this study demonstrated that the usefulness of the medium-density SNP array in genotyping for genetic study and molecular breeding.
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Affiliation(s)
- Ya-ying Wang
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yu-qiu Li
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
- University of Chinese Academy of Sciences, Beijing, China
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun, China
| | - Hong-yan Wu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
| | - Bo Hu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jia-jia Zheng
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hong Zhai
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
| | - Shi-xiang Lv
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
- Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Xin-lei Liu
- Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Xin Chen
- Jiangsu Academy of Agricultural Sciences, Nanjing, China
| | - Hong-mei Qiu
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun, China
| | - Jiayin Yang
- Huaiyin Institute of Agricultural Sciences in Xuhuai Region of Jiangsu Province, Huaian, China
| | - Chun-mei Zong
- Mudanjiang Branch of Heilongjiang Academy of Agricultural Sciences, Mudanjiang, China
| | - De-zhi Han
- Heihe Branch of Heilongjiang Academy of Agricultural Sciences, Heihe, China
| | - Zi-xiang Wen
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States
| | - De-chun Wang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States
| | - Zheng-jun Xia
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
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Wei W, Mesquita ACO, Figueiró ADA, Wu X, Manjunatha S, Wickland DP, Hudson ME, Juliatti FC, Clough SJ. Genome-wide association mapping of resistance to a Brazilian isolate of Sclerotinia sclerotiorum in soybean genotypes mostly from Brazil. BMC Genomics 2017; 18:849. [PMID: 29115920 PMCID: PMC5674791 DOI: 10.1186/s12864-017-4160-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 10/05/2017] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Sclerotinia Stem Rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is ubiquitous in cooler climates where soybean crops are grown. Breeding for resistance to SSR remains challenging in crops like soybean, where no single gene provides strong resistance, but instead, multiple genes work together to provide partial resistance. In this study, a genome-wide association study (GWAS) was performed to dissect the complex genetic architecture of soybean quantitative resistance to SSR and to provide effective molecular markers that could be used in breeding programs. A collection of 420 soybean genotypes were selected based on either reports of resistance, or from one of three different breeding programs in Brazil, two commercial, one public. Plant genotype sensitivity to SSR was evaluated by the cut stem inoculation method, and lesion lengths were measured at 4 days post inoculation. RESULTS Genotyping-by-sequencing was conducted to genotype the 420 soybean lines. The TASSEL 5 GBSv2 pipeline was used to call SNPs under optimized parameters, and with the extra step of trimming adapter sequences. After filtering missing data, heterozygosity, and minor allele frequency, a total of 11,811 SNPs and 275 soybean genotypes were obtained for association analyses. Using a threshold of FDR-adjusted p-values <0.1, the Compressed Mixed Linear Model (CMLM) with Genome Association and Prediction Integrated Tool (GAPIT), and the Fixed and Random Model Circulating Probability Unification (FarmCPU) methods, both approaches identified SNPs with significant association to disease response on chromosomes 1, 11, and 18. The CMLM also found significance on chromosome 19, whereas FarmCPU also identified significance on chromosomes 4, 9, and 16. CONCLUSIONS These similar and yet different results show that the computational methods used can impact SNP associations in soybean, a plant with a high degree of linkage disequilibrium, and in SSR resistance, a trait that has a complex genetic basis. A total of 125 genes were located within linkage disequilibrium of the three loci shared between the two models. Their annotations and gene expressions in previous studies of soybean infected with S. sclerotiorum were examined to narrow down the candidates.
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Affiliation(s)
- Wei Wei
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801 USA
| | | | | | - Xing Wu
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801 USA
| | - Shilpa Manjunatha
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801 USA
| | - Daniel P. Wickland
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801 USA
| | - Matthew E. Hudson
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801 USA
| | | | - Steven J. Clough
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801 USA
- United States Department of Agriculture, Agricultural Research Service, Urbana, IL 61801 USA
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