Development of an SNP set for marker-assisted breeding based on the genotyping-by-sequencing of elite inbred lines in watermelon
2018
Lee, J., Pusan National University, Miryang, Republic of Korea | Son, B., Pusan National University, Miryang, Republic of Korea | Choi, Y., Pusan National University, Miryang, Republic of Korea | Kang, J., Pusan National University, Miryang, Republic of Korea | Lee, Y., Pusan National University, Miryang, Republic of Korea | Je, B.I., Pusan National University, Miryang, Republic of Korea | Park, Y., Pusan National University, Miryang, Republic of Korea
This study was conducted to develop an SNP set that can be useful for marker-assisted breeding (MAB) in watermelon (Citrullus. lanatus L) using Genotyping-bysequencing (GBS) analysis of 20 commercial elite watermelon inbreds. The result of GBS showed that 77% of approximately 1.1 billion raw reads were mapped on the watermelon genome with an average mapping region of about 4,000 Kb, which indicated genome coverage of 2.3%. After the filtering process, a total of 2,670 SNPs with an average depth of 31.57 and the PIC (Polymorphic Information Content) value of 0.1~0.38 for 20 elite inbreds were obtained. Among those SNPs, 55 SNPs (5 SNPs per chromosome that are equally distributed on each chromosome) were selected. For the understanding genetic relationship of 20 elite inbreds, PCA (Principal Component Analysis) was carried out with 55 SNPs, which resulted in the classification of inbreds into 4 groups based on PC1 (52%) and PC2 (11%), thus causing differentiation between the inbreds. A similar classification pattern for PCA was observed from hierarchical clustering analysis. The SNP set developed in this study has the potential for application to cultivar identification, F1 seed purity test, and marker-assisted backcross (MABC) not only for 20 elite inbreds but also for diverse resources for watermelon breeding.
اظهر المزيد [+] اقل [-]الكلمات المفتاحية الخاصة بالمكنز الزراعي (أجروفوك)
المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل Korea Agricultural Science Digital Library