{"title":"Frontiers of soybean pan-genome studies.","authors":"Yu-Cheng Liu, Yan-Ting Shen, Zhi-Xi Tian","doi":"10.16288/j.yczz.23-321","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial domestication provided the original motivation to the blooming of agriculture, following with the dramatic change of the genetic background of crops and livestock. According to theory and technology upgradation that contributing to the omics, we appreciate using the pan-genome instead of single reference genome for crop study. By comparison and integration of multiple genomes under the guidance of pan-genome theory, we can estimate the genomic information range of a species, leading to a global understanding of its genetic diversity. Combining pan-genome with large size chromosomal structural variations, high throughput population resequencing, and multi-omics data, we can profoundly study the genetic basis behind species traits we focus on. Soybean is one of the most important commercial crops over the world. It is also essential to our food security. Dissecting the formation of genetic diversity and the causal loci of key agricultural traits of soybean will make the modern soybean breeding more efficiently. In this review, we summarize the core idea of pan-genome and clarified the characteristics of construction strategies of pan-genome such as de novo/mapping assembly, iterative assembly and graph-based genome. Then we used the soybean pan-genome work as a case study to introduce the general way to study pan-genome. We highlighted the contribution of structural variation (SV) to the evolution/domestication of soybean and its value in understanding the genetic bases of agronomy traits. By those, we approved the value of graph-based pan-genome for data integration and SV calculation. Future research directions are also discussed for crop genomics and data science.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"46 3","pages":"183-198"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"遗传","FirstCategoryId":"1091","ListUrlMain":"https://doi.org/10.16288/j.yczz.23-321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial domestication provided the original motivation to the blooming of agriculture, following with the dramatic change of the genetic background of crops and livestock. According to theory and technology upgradation that contributing to the omics, we appreciate using the pan-genome instead of single reference genome for crop study. By comparison and integration of multiple genomes under the guidance of pan-genome theory, we can estimate the genomic information range of a species, leading to a global understanding of its genetic diversity. Combining pan-genome with large size chromosomal structural variations, high throughput population resequencing, and multi-omics data, we can profoundly study the genetic basis behind species traits we focus on. Soybean is one of the most important commercial crops over the world. It is also essential to our food security. Dissecting the formation of genetic diversity and the causal loci of key agricultural traits of soybean will make the modern soybean breeding more efficiently. In this review, we summarize the core idea of pan-genome and clarified the characteristics of construction strategies of pan-genome such as de novo/mapping assembly, iterative assembly and graph-based genome. Then we used the soybean pan-genome work as a case study to introduce the general way to study pan-genome. We highlighted the contribution of structural variation (SV) to the evolution/domestication of soybean and its value in understanding the genetic bases of agronomy traits. By those, we approved the value of graph-based pan-genome for data integration and SV calculation. Future research directions are also discussed for crop genomics and data science.