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He L, Sui Y, Che Y, Wang H, Rashid KY, Cloutier S, You FM. Genome-wide association studies using multi-models and multi-SNP datasets provide new insights into pasmo resistance in flax. FRONTIERS IN PLANT SCIENCE 2023; 14:1229457. [PMID: 37954993 PMCID: PMC10634603 DOI: 10.3389/fpls.2023.1229457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 07/24/2023] [Indexed: 11/14/2023]
Abstract
Introduction Flax (Linum usitatissimum L.) is an economically important crop due to its oil and fiber. However, it is prone to various diseases, including pasmo caused by the fungus Septoria linicola. Methods In this study, we conducted field evaluations of 445 flax accessions over a five-year period (2012-2016) to assess their resistance to pasmo A total of 246,035 single nucleotide polymorphisms (SNPs) were used for genetic analysis. Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance. Results We identified 372 significant QTNs or 132 tag QTNs associated with pasmo resistance from five pasmo resistance datasets (PAS2012-PAS2016 and the 5-year average, namely PASmean) and three genotypic datasets (the all SNPs/ALL, the gene-based SNPs/GB and the RGA-based SNPs/RGAB). The tag QTNs had R2 values of 0.66-16.98% from the ALL SNP dataset, 0.68-20.54%from the GB SNP dataset, and 0.52-22.42% from the RGAB SNP dataset. Of these tag QTNs, 93 were novel. Additionally, 37 resistance gene analogs (RGAs)co-localizing with 39 tag QTNs were considered as potential candidates for controlling pasmo resistance in flax and 50 QTN-by-environment interactions(QEIs) were identified to account for genes by environmental interactions. Nine RGAs were predicted as candidate genes for ten QEIs. Discussion Our results suggest that pasmo resistance in flax is polygenic and potentially influenced by environmental factors. The identified QTNs provide potential targets for improving pasmo resistance in flax breeding programs. This study sheds light on the genetic basis of pasmo resistance and highlights the importance of considering both genetic and environmental factors in breeding programs for flax.
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Affiliation(s)
- Liqiang He
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Yao Sui
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Yanru Che
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Huixian Wang
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Khalid Y. Rashid
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Sylvie Cloutier
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Frank M. You
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
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Ortiz M, Jauset-Rubio M, Trummer O, Foessl I, Kodr D, Acero JL, Botero ML, Biggs P, Lenartowicz D, Trajanoska K, Rivadeneira F, Hocek M, Obermayer-Pietsch B, O’Sullivan CK. Generic Platform for the Multiplexed Targeted Electrochemical Detection of Osteoporosis-Associated Single Nucleotide Polymorphisms Using Recombinase Polymerase Solid-Phase Primer Elongation and Ferrocene-Modified Nucleoside Triphosphates. ACS CENTRAL SCIENCE 2023; 9:1591-1602. [PMID: 37637735 PMCID: PMC10450878 DOI: 10.1021/acscentsci.3c00243] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Indexed: 08/29/2023]
Abstract
Osteoporosis is a multifactorial disease influenced by genetic and environmental factors, which contributes to an increased risk of bone fracture, but early diagnosis of this disease cannot be achieved using current techniques. We describe a generic platform for the targeted electrochemical genotyping of SNPs identified by genome-wide association studies to be associated with a genetic predisposition to osteoporosis. The platform exploits isothermal solid-phase primer elongation with ferrocene-labeled nucleoside triphosphates. Thiolated reverse primers designed for each SNP were immobilized on individual gold electrodes of an array. These primers are designed to hybridize to the SNP site at their 3'OH terminal, and primer elongation occurs only where there is 100% complementarity, facilitating the identification and heterozygosity of each SNP under interrogation. The platform was applied to real blood samples, which were thermally lysed and directly used without the need for DNA extraction or purification. The results were validated using Taqman SNP genotyping assays and Sanger sequencing. The assay is complete in just 15 min with a total cost of 0.3€ per electrode. The platform is completely generic and has immense potential for deployment at the point of need in an automated device for targeted SNP genotyping with the only required end-user intervention being sample addition.
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Affiliation(s)
- Mayreli Ortiz
- INTERFIBIO
Research Group, Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Miriam Jauset-Rubio
- INTERFIBIO
Research Group, Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Olivia Trummer
- Division
of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria
| | - Ines Foessl
- Division
of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria
| | - David Kodr
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, Flemingovo namesti 2, CZ 16610 Prague 6, Czech Republic
| | - Josep Lluís Acero
- INTERFIBIO
Research Group, Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Mary Luz Botero
- INTERFIBIO
Research Group, Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Phil Biggs
- Labman
Automation
Ltd., Seamer Hill, Stokesley, North Yorkshire, TS9 5NQ U.K.
| | - Daniel Lenartowicz
- Labman
Automation
Ltd., Seamer Hill, Stokesley, North Yorkshire, TS9 5NQ U.K.
| | - Katerina Trajanoska
- Department
of Internal Medicine, Erasmus MC, 40 3015 Rotterdam, The Netherlands
| | | | - Michal Hocek
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, Flemingovo namesti 2, CZ 16610 Prague 6, Czech Republic
- Department
of Organic Chemistry, Faculty of Science, Charles University, Hlavova 8, CZ-12843 Prague 2, Czech Republic
| | - Barbara Obermayer-Pietsch
- Division
of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8036 Graz, Austria
| | - Ciara K. O’Sullivan
- INTERFIBIO
Research Group, Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Institució
Catalana de Recerca i Estudis Avancats (ICREA), 08010 Barcelona, Spain
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Multi-omics research in sarcopenia: Current progress and future prospects. Ageing Res Rev 2022; 76:101576. [PMID: 35104630 DOI: 10.1016/j.arr.2022.101576] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 12/13/2021] [Accepted: 01/26/2022] [Indexed: 12/17/2022]
Abstract
Sarcopenia is a systemic disease with progressive and generalized skeletal muscle dysfunction defined by age-related low muscle mass, high content of muscle slow fibers, and low muscle function. Muscle phenotypes and sarcopenia risk are heritable; however, the genetic architecture and molecular mechanisms underlying sarcopenia remain largely unclear. In recent years, significant progress has been made in determining susceptibility loci using genome-wide association studies. In addition, recent advances in omics techniques, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, offer new opportunities to identify novel targets to help us understand the pathophysiology of sarcopenia. However, each individual technology cannot capture the entire view of the biological complexity of this disorder, while integrative multi-omics analyses may be able to reveal new insights. Here, we review the latest findings of multi-omics studies for sarcopenia and provide an in-depth summary of our current understanding of sarcopenia pathogenesis. Leveraging multi-omics data could give us a holistic understanding of sarcopenia etiology that may lead to new clinical applications. This review offers guidance and recommendations for fundamental research, innovative perspectives, and preventative and therapeutic interventions for sarcopenia.
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Lu X, Lü P, Liu H, Chen H, Pan X, Liu P, Feng L, Zhong S, Zhou B. Identification of Chilling Accumulation-Associated Genes for Litchi Flowering by Transcriptome-Based Genome-Wide Association Studies. FRONTIERS IN PLANT SCIENCE 2022; 13:819188. [PMID: 35283888 PMCID: PMC8905319 DOI: 10.3389/fpls.2022.819188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Litchi is an important Sapindaceae fruit tree. Flowering in litchi is triggered by low temperatures in autumn and winter. It can be divided into early-, medium-, and late-flowering phenotypes according to the time for floral induction. Early-flowering varieties need low chilling accumulation level for floral induction, whereas the late-flowering varieties require high chilling accumulation level. In the present study, RNA-Seq of 87 accessions was performed and transcriptome-based genome-wide association studies (GWAS) was used to identify candidate genes involved in chilling accumulation underlying the time for floral induction. A total of 98,155 high-quality single-nucleotide polymorphism (SNP) sites were obtained. A total of 1,411 significantly associated SNPs and 1,115 associated genes (AGs) were identified, of which 31 were flowering-related, 23 were hormone synthesis-related, and 27 were hormone signal transduction-related. Association analysis between the gene expression of the AGs and the flowering phenotypic data was carried out, and differentially expressed genes (DEGs) in a temperature-controlled experiment were obtained. As a result, 15 flowering-related candidate AGs (CAGs), 13 hormone synthesis-related CAGs, and 11 hormone signal transduction-related CAGs were further screened. The expression levels of the CAGs in the early-flowering accessions were different from those in the late-flowering ones, and also between the flowering trees and non-flowering trees. In a gradient chilling treatment, flowering rates of the trees and the CAGs expression were affected by the treatment. Our present work for the first time provided candidate genes for genetic regulation of flowering in litchi using transcriptome-based GWAS.
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Affiliation(s)
- Xingyu Lu
- Guangdong Litchi Engineering Research Center, College of Horticulture, South China Agricultural University, Guangzhou, China
- College of Life and Health Science, Kaili University, Kaili, China
| | - Peitao Lü
- College of Horticulture, Fujian Agriculture and Forestry University-University of California Riverside (FAFU-UCR) Joint Center for Horticultural Biology and Metabolomics, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Hao Liu
- Guangdong Litchi Engineering Research Center, College of Horticulture, South China Agricultural University, Guangzhou, China
| | - Houbin Chen
- Guangdong Litchi Engineering Research Center, College of Horticulture, South China Agricultural University, Guangzhou, China
| | - Xifen Pan
- Guangdong Litchi Engineering Research Center, College of Horticulture, South China Agricultural University, Guangzhou, China
| | - Pengxu Liu
- Guangdong Litchi Engineering Research Center, College of Horticulture, South China Agricultural University, Guangzhou, China
| | - Lei Feng
- Guangdong Litchi Engineering Research Center, College of Horticulture, South China Agricultural University, Guangzhou, China
| | - Silin Zhong
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Biyan Zhou
- Guangdong Litchi Engineering Research Center, College of Horticulture, South China Agricultural University, Guangzhou, China
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Yu GE, Shin Y, Subramaniyam S, Kang SH, Lee SM, Cho C, Lee SS, Kim CK. Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus. Sci Rep 2021; 11:8019. [PMID: 33850210 PMCID: PMC8044237 DOI: 10.1038/s41598-021-87281-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 03/24/2021] [Indexed: 11/27/2022] Open
Abstract
Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.
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Affiliation(s)
- Go-Eun Yu
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea
| | - Younhee Shin
- Research and Development Center, Insilicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of Korea
| | | | - Sang-Ho Kang
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea
| | - Si-Myung Lee
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea
| | - Chuloh Cho
- Crop Foundation Research Division, National Institute of Crop Science, RDA, Wanju, 55365, Korea
| | - Seung-Sik Lee
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, 29 Geumgu-gil, Jeongeup, 56212, Korea
- Department of Radiation Science and Technology, University of Science and Technology, Daejeon, 34113, Korea
| | - Chang-Kug Kim
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea.
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Yang TL, Shen H, Liu A, Dong SS, Zhang L, Deng FY, Zhao Q, Deng HW. A road map for understanding molecular and genetic determinants of osteoporosis. Nat Rev Endocrinol 2020; 16:91-103. [PMID: 31792439 PMCID: PMC6980376 DOI: 10.1038/s41574-019-0282-7] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/18/2019] [Indexed: 12/16/2022]
Abstract
Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.
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Affiliation(s)
- Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Hui Shen
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lei Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, China
| | - Fei-Yan Deng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, China
| | - Qi Zhao
- Department of Preventive Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.
- School of Basic Medical Science, Central South University, Changsha, China.
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Kang MJ, Shin AY, Shin Y, Lee SA, Lee HR, Kim TD, Choi M, Koo N, Kim YM, Kyeong D, Subramaniyam S, Park EJ. Identification of transcriptome-wide, nut weight-associated SNPs in Castanea crenata. Sci Rep 2019; 9:13161. [PMID: 31511588 PMCID: PMC6739505 DOI: 10.1038/s41598-019-49618-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/28/2019] [Indexed: 01/27/2023] Open
Abstract
Nut weight is one of the most important traits that can affect a chestnut grower's returns. Due to the long juvenile phase of chestnut trees, the selection of desired characteristics at early developmental stages represents a major challenge for chestnut breeding. In this study, we identified single nucleotide polymorphisms (SNPs) in transcriptomic regions, which were significantly associated with nut weight in chestnuts (Castanea crenata), using a genome-wide association study (GWAS). RNA-sequencing (RNA-seq) data were generated from large and small nut-bearing trees, using an Illumina HiSeq. 2000 system, and 3,271,142 SNPs were identified. A total of 21 putative SNPs were significantly associated with chestnut weight (false discovery rate [FDR] < 10-5), based on further analyses. We also applied five machine learning (ML) algorithms, support vector machine (SVM), C5.0, k-nearest neighbour (k-NN), partial least squares (PLS), and random forest (RF), using the 21 SNPs to predict the nut weights of a second population. The average accuracy of the ML algorithms for the prediction of chestnut weights was greater than 68%. Taken together, we suggest that these SNPs have the potential to be used during marker-assisted selection to facilitate the breeding of large chestnut-bearing varieties.
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Affiliation(s)
- Min-Jeong Kang
- Forest Biotechnology Division, National Institute of Forest Science, Suwon, 16631, Republic of Korea
| | - Ah-Young Shin
- Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea
| | - Younhee Shin
- Research and Development Center, Insillicogen Inc, Yongin, 16954, Republic of Korea
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Sang-A Lee
- Forest Biotechnology Division, National Institute of Forest Science, Suwon, 16631, Republic of Korea
| | - Hyo-Ryeon Lee
- Forest Biotechnology Division, National Institute of Forest Science, Suwon, 16631, Republic of Korea
| | - Tae-Dong Kim
- Forest Biotechnology Division, National Institute of Forest Science, Suwon, 16631, Republic of Korea
| | - Mina Choi
- Plant Resources Industry Division, Baekdudaegan National Arboretum, Bonghwa, 36209, Republic of Korea
| | - Namjin Koo
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea
| | - Yong-Min Kim
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea
| | - Dongsoo Kyeong
- Research and Development Center, Insillicogen Inc, Yongin, 16954, Republic of Korea
- Laboratory of Developmental Biology and Genomics, Seoul National University, Seoul, 08826, Republic of Korea
| | | | - Eung-Jun Park
- Forest Biotechnology Division, National Institute of Forest Science, Suwon, 16631, Republic of Korea.
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