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Sahu TK, Verma SK, Gayacharan, Singh NP, Joshi DC, Wankhede DP, Singh M, Bhardwaj R, Singh B, Parida SK, Chattopadhyay D, Singh GP, Singh AK. Transcriptome-wide association mapping provides insights into the genetic basis and candidate genes governing flowering, maturity and seed weight in rice bean (Vigna umbellata). BMC PLANT BIOLOGY 2024; 24:379. [PMID: 38720284 PMCID: PMC11077894 DOI: 10.1186/s12870-024-04976-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 04/02/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Rice bean (Vigna umbellata), an underrated legume, adapts to diverse climatic conditions with the potential to support food and nutritional security worldwide. It is used as a vegetable, minor food crop and a fodder crop, being a rich source of proteins, minerals, and essential fatty acids. However, little effort has been made to decipher the genetic and molecular basis of various useful traits in this crop. Therefore, we considered three economically important traits i.e., flowering, maturity and seed weight of rice bean and identified the associated candidate genes employing an associative transcriptomics approach on 100 diverse genotypes out of 1800 evaluated rice bean accessions from the Indian National Genebank. RESULTS The transcriptomics-based genotyping of one-hundred diverse rice bean cultivars followed by pre-processing of genotypic data resulted in 49,271 filtered markers. The STRUCTURE, PCA and Neighbor-Joining clustering of 100 genotypes revealed three putative sub-populations. The marker-trait association analysis involving various genome-wide association study (GWAS) models revealed significant association of 82 markers on 48 transcripts for flowering, 26 markers on 22 transcripts for maturity and 22 markers on 21 transcripts for seed weight. The transcript annotation provided information on the putative candidate genes for the considered traits. The candidate genes identified for flowering include HSC80, P-II PsbX, phospholipid-transporting-ATPase-9, pectin-acetylesterase-8 and E3-ubiquitin-protein-ligase-RHG1A. Further, the WRKY1 and DEAD-box-RH27 were found to be associated with seed weight. Furthermore, the associations of PIF3 and pentatricopeptide-repeat-containing-gene with maturity and seed weight, and aldo-keto-reductase with flowering and maturity were revealed. CONCLUSION This study offers insights into the genetic basis of key agronomic traits in rice bean, including flowering, maturity, and seed weight. The identified markers and associated candidate genes provide valuable resources for future exploration and targeted breeding, aiming to enhance the agronomic performance of rice bean cultivars. Notably, this research represents the first transcriptome-wide association study in pulse crop, uncovering the candidate genes for agronomically useful traits.
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Affiliation(s)
- Tanmaya Kumar Sahu
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India
- ICAR-Indian Grassland and Fodder Research Institute, Jhansi, Uttar Pradesh, India
| | - Sachin Kumar Verma
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India
| | - Gayacharan
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India
| | | | - Dinesh Chandra Joshi
- ICAR-Vivekananda Parvatiya Krishi Anusandhan Sansthan, Almora, Uttarakhand, India
| | - D P Wankhede
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India
| | - Mohar Singh
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India
| | - Rakesh Bhardwaj
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India
| | - Badal Singh
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India
| | - Swarup Kumar Parida
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, India
| | | | | | - Amit Kumar Singh
- ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110012, India.
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Wojcik MH, Reuter CM, Marwaha S, Mahmoud M, Duyzend MH, Barseghyan H, Yuan B, Boone PM, Groopman EE, Délot EC, Jain D, Sanchis-Juan A, Starita LM, Talkowski M, Montgomery SB, Bamshad MJ, Chong JX, Wheeler MT, Berger SI, O'Donnell-Luria A, Sedlazeck FJ, Miller DE. Beyond the exome: What's next in diagnostic testing for Mendelian conditions. Am J Hum Genet 2023; 110:1229-1248. [PMID: 37541186 PMCID: PMC10432150 DOI: 10.1016/j.ajhg.2023.06.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 08/06/2023] Open
Abstract
Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.
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Affiliation(s)
- Monica H Wojcik
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Chloe M Reuter
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shruti Marwaha
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Michael H Duyzend
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hayk Barseghyan
- Center for Genetics Medicine Research, Children's National Research Institute, Children's National Hospital, Washington, DC 20010, USA; Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA
| | - Bo Yuan
- Department of Molecular and Human Genetics and Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Philip M Boone
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Emily E Groopman
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Emmanuèle C Délot
- Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA; Center for Genetics Medicine Research, Children's National Research and Innovation Campus, Washington, DC, USA; Department of Pediatrics, George Washington University, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA
| | - Deepti Jain
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Alba Sanchis-Juan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lea M Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Michael Talkowski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephen B Montgomery
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael J Bamshad
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Pediatrics, Division of Genetic Medicine, University of Washington, Seattle, WA 98195, USA
| | - Jessica X Chong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA; Department of Pediatrics, Division of Genetic Medicine, University of Washington, Seattle, WA 98195, USA
| | - Matthew T Wheeler
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Seth I Berger
- Center for Genetics Medicine Research and Rare Disease Institute, Children's National Hospital, Washington, DC 20010, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA; Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Danny E Miller
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA; Department of Pediatrics, Division of Genetic Medicine, University of Washington, Seattle, WA 98195, USA; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA.
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