Kim Kyu-Won, Nawade Bhagwat, Nam Jungrye, Chu Sang-Ho, Ha Jungmin, Park Yong-Jin
Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea.
Department of Plant Science, Gangneung-Wonju National University, Gangneung, South Korea.
Front Plant Sci. 2022 Oct 24;13:1036177. doi: 10.3389/fpls.2022.1036177. eCollection 2022.
Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world's population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700 K high-density rice array, Affymetrix 44 K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder's interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, -log -value >7.0, were detected in GWAS, and the GS predictabilities for the three traits were more than 0.5, in both rrBLUP and convolutional neural network (CNN) models. The Axiom 580 K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.
水稻是一种全球广泛种植的作物,是世界上一半以上人口的主要主食来源。各种单核苷酸多态性(SNP)阵列已被开发并用作水稻育种研究的标准基因分型方法。考虑到具有更全面遗传信息的SNP阵列对全基因组关联研究(GWAS)和基因组选择的重要性,我们整合了来自八个不同数据资源的SNP:韩国世界水稻种质库(KRICE)475份材料的重测序数据、3000份水稻基因组计划(3K-RGP)数据、700K高密度水稻阵列、Affymetrix 44K SNP阵列、QTARO、Reactome以及质体和转基因信息。根据育种者的兴趣对收集到的SNP进行筛选和选择,涵盖所有关键性状或研究领域,以开发一个代表全面基因组多态性的整合阵列系统。共合成了581,006个高质量SNP,相邻SNP之间的平均距离为200 bp,生成了一个580K Axiom水稻基因分型芯片(580K_KNU芯片)。在4720个基因型上对该阵列进行的进一步验证显示出强大且高效的基因分型能力。这也在三个性状(茎长、抽穗期和穗长)的全基因组关联研究(GWAS)和基因组选择(GS)中得到了证明。在GWAS中检测到了几个与截止值显著相关的SNP,-log值>7.0,并且在rrBLUP和卷积神经网络(CNN)模型中,这三个性状的GS预测能力均超过0.5。Axiom 580K基因分型阵列将提供一个具有成本效益的基因分型平台,并加速水稻GWAS和GS研究。