Mazen A. Abuzayed, Asena A. Baytar, Ertuğrul G. Yanar, Sami Doğanlar, Anne Frary
{"title":"蚕豆植株结构与产量性状的关联图谱","authors":"Mazen A. Abuzayed, Asena A. Baytar, Ertuğrul G. Yanar, Sami Doğanlar, Anne Frary","doi":"10.1002/jsf2.154","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Faba bean (<i>Vicia faba</i> L.) is an important crop with high protein content. Tens of thousands of faba bean accessions are available in germplasm collections throughout the world. Morphological characterization of these materials can enrich these collections and aid in the selection of genotypes for use in breeding programs.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>In this study, 26 morphological characters were analyzed for 61 faba bean landraces and 53 cultivars over two seasons in Izmir, Turkey. The genotypes had high diversity for several yield traits including number of pods per plant, dry seed yield, and 100-seed weight. Association mapping was conducted for the morphological characters using 651 alleles from 100 simple sequence repeat (SSR) markers and a general linear model based on the Q matrix. A false discovery rate of 0.20 was used to test the significance of marker–trait associations resulting in 75 loci detected for 20 of the morphological characters (<i>p</i> ≤ 0.001).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Overall, 44% of the quantitative trait loci (QTLs) were for seed traits, with 24%, 15%, and 17% of QTL identified for vegetative, inflorescence, and pod traits, respectively. The phenotypic data and marker–trait associations generated by this work can help breeding programs in the selection and improvement of faba bean.</p>\n </section>\n </div>","PeriodicalId":93795,"journal":{"name":"JSFA reports","volume":"3 11","pages":"536-548"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Association mapping of plant structure and yield traits in faba bean (Vicia faba L.)\",\"authors\":\"Mazen A. Abuzayed, Asena A. Baytar, Ertuğrul G. Yanar, Sami Doğanlar, Anne Frary\",\"doi\":\"10.1002/jsf2.154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Faba bean (<i>Vicia faba</i> L.) is an important crop with high protein content. Tens of thousands of faba bean accessions are available in germplasm collections throughout the world. Morphological characterization of these materials can enrich these collections and aid in the selection of genotypes for use in breeding programs.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>In this study, 26 morphological characters were analyzed for 61 faba bean landraces and 53 cultivars over two seasons in Izmir, Turkey. The genotypes had high diversity for several yield traits including number of pods per plant, dry seed yield, and 100-seed weight. Association mapping was conducted for the morphological characters using 651 alleles from 100 simple sequence repeat (SSR) markers and a general linear model based on the Q matrix. A false discovery rate of 0.20 was used to test the significance of marker–trait associations resulting in 75 loci detected for 20 of the morphological characters (<i>p</i> ≤ 0.001).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>Overall, 44% of the quantitative trait loci (QTLs) were for seed traits, with 24%, 15%, and 17% of QTL identified for vegetative, inflorescence, and pod traits, respectively. The phenotypic data and marker–trait associations generated by this work can help breeding programs in the selection and improvement of faba bean.</p>\\n </section>\\n </div>\",\"PeriodicalId\":93795,\"journal\":{\"name\":\"JSFA reports\",\"volume\":\"3 11\",\"pages\":\"536-548\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JSFA reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jsf2.154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JSFA reports","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jsf2.154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Association mapping of plant structure and yield traits in faba bean (Vicia faba L.)
Background
Faba bean (Vicia faba L.) is an important crop with high protein content. Tens of thousands of faba bean accessions are available in germplasm collections throughout the world. Morphological characterization of these materials can enrich these collections and aid in the selection of genotypes for use in breeding programs.
Results
In this study, 26 morphological characters were analyzed for 61 faba bean landraces and 53 cultivars over two seasons in Izmir, Turkey. The genotypes had high diversity for several yield traits including number of pods per plant, dry seed yield, and 100-seed weight. Association mapping was conducted for the morphological characters using 651 alleles from 100 simple sequence repeat (SSR) markers and a general linear model based on the Q matrix. A false discovery rate of 0.20 was used to test the significance of marker–trait associations resulting in 75 loci detected for 20 of the morphological characters (p ≤ 0.001).
Conclusion
Overall, 44% of the quantitative trait loci (QTLs) were for seed traits, with 24%, 15%, and 17% of QTL identified for vegetative, inflorescence, and pod traits, respectively. The phenotypic data and marker–trait associations generated by this work can help breeding programs in the selection and improvement of faba bean.