Analyzing Medicago spp. seed morphology using GWAS and machine learning.
Medicago sativa
Alfalfa
Area size
GWAS
Machine learning
RGB
Seed color
Seed morphology
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 Jul 2024
30 Jul 2024
Historique:
received:
08
04
2024
accepted:
16
07
2024
medline:
31
7
2024
pubmed:
31
7
2024
entrez:
30
7
2024
Statut:
epublish
Résumé
Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 other Medicago spp., for seed area size, length, width, length-to-width ratio, perimeter, circularity, the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness & red-green-blue (RGB) intensities. The results revealed phenotypic diversity and correlations among the tested accessions. Based on the phenotypic data of M. sativa subsp. sativa, a genome-wide association study (GWAS) was conducted using single nucleotide polymorphisms (SNPs) called against the Medicago truncatula genome. Genes in proximity to associated markers were detected, including CPR1, MON1, a PPR protein, and Wun1(threshold of 1E-04). Machine learning models were utilized to validate GWAS, and identify additional marker-trait associations for potentially complex traits. Marker S7_33375673, upstream of Wun1, was the most important predictor variable for red color intensity and highly important for brightness. Fifty-two markers were identified in coding regions. Along with strong correlations observed between seed morphology traits, these genes will facilitate the process of understanding the genetic basis of seed morphology in Medicago spp.
Identifiants
pubmed: 39080407
doi: 10.1038/s41598-024-67790-4
pii: 10.1038/s41598-024-67790-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17588Informations de copyright
© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
Références
Chastain, T. G., Ward, K. J. & Wysocki, D. J. Stand establishment response of soft white winter wheat to seedbed residue and seed size. Crop Sci. 35, 213–218 (1995).
doi: 10.2135/cropsci1995.0011183X003500010040x
Boukail, S. et al. Genome wide association study of agronomic and seed traits in a world collection of proso millet (Panicum miliaceum L.). BMC Plant Biol. 21, 330 (2021).
doi: 10.1186/s12870-021-03111-5
pubmed: 34243721
pmcid: 8268170
United States Department of Agriculture. USDA https://www.nass.usda.gov/Statistics_by_Subject/result.php?32C485CC-791F-3173-9B46-886391B5222A§or=CROPS&group=FIELD+CROPS&comm=HAY (2022).
Veronesi, F., Brummer, E. C. & Huyghe, C. Alfalfa. In Fodder Crops and Amenity Grasses (eds Boller, B. et al.) 395–437 (Springer, 2010).
doi: 10.1007/978-1-4419-0760-8_17
Teuber, L. R. & Brick, M. A. Morphology and Anatomy. In Alfalfa and Alfalfa Improvement (eds Hanson, A. A. et al.) 125–162 (American Society of Agronomy, 1988).
Attri, P. et al. Impact of seed color and storage time on the radish seed germination and sprout growth in plasma agriculture. Sci. Rep. 11, 2539 (2021).
doi: 10.1038/s41598-021-81175-x
pubmed: 33510231
pmcid: 7844220
Liu, W., Peffley, E. B., Powell, R. J., Auld, D. L. & Hou, A. Association of seedcoat color with seed water uptake, germination, and seed components in guar (Cyamopsis tetragonoloba (L.) Taub). J. Arid Environ. 70, 29–38 (2007).
doi: 10.1016/j.jaridenv.2006.12.011
Mavi, K. The relationship between seed coat color and seed quality in watermelon Crimson sweet. Hortic. Sci. 37, 62–69 (2010).
doi: 10.17221/53/2009-HORTSCI
Xie, J. et al. Seed color represents salt resistance of alfalfa seeds (Medicago sativa L.): Based on the analysis of germination characteristics, seedling growth and seed traits. Front. Plant Sci. 14, 1104948 (2023).
doi: 10.3389/fpls.2023.1104948
pubmed: 36875586
pmcid: 9978207
Cervantes, E., Martín, J. J. & Saadaoui, E. Updated methods for seed shape analysis. Scientifica 2016, 1–10 (2016).
doi: 10.1155/2016/5691825
Hareesh, V. S. & Sabu, M. Significance of seed morphology and anatomy in the systematics of Musaceae. Bot. J. Linn. Soc. 201, 1–35 (2023).
doi: 10.1093/botlinnean/boac017
Prom, L. K., Ahn, E., Isakeit, T. & Magill, C. Correlations among grain mold severity seed weight and germination rate of sorghum association panel lines inoculated with Alternaria Alternata fusarium Thapsinum and Curvularia lunata. JAC https://doi.org/10.32861/jac.81.7.11 (2021).
doi: 10.32861/jac.81.7.11
Ahn, E. et al. Genome-wide association study of seed morphology traits in Senegalese sorghum cultivars. Plants 12, 2344 (2023).
doi: 10.3390/plants12122344
pubmed: 37375969
pmcid: 10302255
Chen, Z. et al. Genome-wide association study identified candidate genes for seed size and seed composition improvement in M. truncatula. Sci. Rep. 11, 4224 (2021).
doi: 10.1038/s41598-021-83581-7
pubmed: 33608604
pmcid: 7895968
Pecrix, Y. et al. Whole-genome landscape of Medicago truncatula symbiotic genes. Nat. Plants 4, 1017–1025 (2018).
doi: 10.1038/s41477-018-0286-7
pubmed: 30397259
Service, USDA Agricultural Research. Germplasm Resources Information Network (GRIN). USDA Agricultural Research Service. Collection. (2023). https://doi.org/10.15482/USDA.ADC/1212393
Tanabata, T., Shibaya, T., Hori, K., Ebana, K. & Yano, M. SmartGrain : high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiol. 160, 1871–1880 (2012).
doi: 10.1104/pp.112.205120
pubmed: 23054566
pmcid: 3510117
Rueden, C. T. et al. Image J2: ImageJ for the next generation of scientific image data. BMC Bioinform. 18, 529 (2017).
doi: 10.1186/s12859-017-1934-z
Zhang, T. et al. Identification of loci associated with drought resistance traits in heterozygous Autotetraploid Alfalfa (Medicago sativa L.) using genome-wide association studies with genotyping by sequencing. PLoS ONE 10, e0138931 (2015).
doi: 10.1371/journal.pone.0138931
pubmed: 26406473
pmcid: 4583413
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
doi: 10.1093/bioinformatics/btp352
pubmed: 19505943
pmcid: 2723002
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at http://arxiv.org/abs/1207.3907 (2012).
Lipka, A. E. et al. GAPIT: Genome association and prediction integrated tool. Bioinformatics 28, 2397–2399 (2012).
doi: 10.1093/bioinformatics/bts444
pubmed: 22796960
Wang, Q., Tian, F., Pan, Y., Buckler, E. S. & Zhang, Z. A SUPER powerful method for genome wide association study. PLoS ONE 9, e107684 (2014).
doi: 10.1371/journal.pone.0107684
pubmed: 25247812
pmcid: 4172578
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Soft. https://doi.org/10.18637/jss.v028.i05 (2008).
doi: 10.18637/jss.v028.i05
Gavazzi, G. & Sangiorgio, S. Seed Size: an Important Yield Component. In More Food: Road to Survival (eds Pilu, R. & Gavazzi, G.) 143–167 (Bentham science publishers, 2017).
Giordani, W., Gama, H. C., Chiorato, A. F., Garcia, A. A. F. & Vieira, M. L. C. Genome-wide association studies dissect the genetic architecture of seed shape and size in common bean. G3 Genes Genom. Genet. 12, 48 (2022).
Dong, R. et al. Estimation of morphological variation in seed traits of Sophora moorcroftiana using digital image analysis. Front. Plant Sci. 14, 1185393 (2023).
doi: 10.3389/fpls.2023.1185393
pubmed: 37313255
pmcid: 10258342
Tehseen, M. M. et al. Exploring the genetic diversity and population structure of wheat landrace population conserved at ICARDA genebank. Front. Genet. 13, 900572 (2022).
doi: 10.3389/fgene.2022.900572
pubmed: 35783289
pmcid: 9240388
Kumar, S. Biotechnological advancements in alfalfa improvement. J Appl Genetics 52, 111–124 (2011).
doi: 10.1007/s13353-011-0028-2
He, F. et al. A genome-wide association study coupled with a transcriptomic analysis reveals the genetic loci and candidate genes governing the flowering time in alfalfa (Medicago sativa L.). Front. Plant Sci. 13, 913947 (2022).
doi: 10.3389/fpls.2022.913947
pubmed: 35898229
pmcid: 9310038
Lin, S. et al. Genome-wide association studies identifying multiple loci associated with alfalfa forage quality. Front. Plant Sci. 12, 648192 (2021).
doi: 10.3389/fpls.2021.648192
pubmed: 34220880
pmcid: 8253570
He, F. et al. Transcriptome and GWAS analyses reveal candidate gene for root traits of alfalfa during germination under salt stress. IJMS 24, 6271 (2023).
doi: 10.3390/ijms24076271
pubmed: 37047244
pmcid: 10094355
Xu, M. et al. Genome-wide association study (GWAS) identifies key candidate genes associated with leaf size in alfalfa (Medicago sativa L.). Agriculture 13, 2237 (2023).
doi: 10.3390/agriculture13122237
Tan, X. et al. A review of plant vacuoles: Formation, located proteins, and functions. Plants 8, 327 (2019).
doi: 10.3390/plants8090327
pubmed: 31491897
pmcid: 6783984
Cui, Y. et al. Activation of the Rab7 GTPase by the MON1-CCZ1 complex is essential for PVC-to-vacuole trafficking and plant growth in Arabidopsis. Plant Cell 26, 2080–2097 (2014).
doi: 10.1105/tpc.114.123141
pubmed: 24824487
pmcid: 4079370
Barkan, A. & Small, I. Pentatricopeptide repeat proteins in plants. Annu. Rev. Plant Biol. 65, 415–442 (2014).
doi: 10.1146/annurev-arplant-050213-040159
pubmed: 24471833
Logemann, J. et al. 5’ Upstream sequences from the wun1 gene are responsible for gene activation by wounding in transgenic plants. Plant cell 1, 151–158 (1989).
pubmed: 2535462
pmcid: 159746
Grinberg, N. F., Orhobor, O. I. & King, R. D. An evaluation of machine-learning for predicting phenotype: Studies in yeast, rice, and wheat. Mach Learn 109, 251–277 (2020).
doi: 10.1007/s10994-019-05848-5
pubmed: 32174648
Branca, A. et al. Whole-genome nucleotide diversity, recombination, and linkage disequilibrium in the model legume Medicago truncatula. Proc. Natl. Acad. Sci. U.S.A. https://doi.org/10.1073/pnas.1104032108 (2011).
doi: 10.1073/pnas.1104032108
pubmed: 21949378
pmcid: 3198318