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Translation of continuous artificial selection on phenotype into genotype during rice breeding programs.
Fujino, Kenji; Kawahara, Yoshihiro; Koyanagi, Kanako O; Shirasawa, Kenta.
Afiliação
  • Fujino K; Hokkaido Agricultural Research Center, National Agricultural Research Organization, Sapporo, Hokkaido 062-8555, Japan.
  • Kawahara Y; Institute of Crop Science, National Agricultural Research Organization, Tsukuba, Ibaraki 305-8518, Japan.
  • Koyanagi KO; Advanced Analysis Center, National Agricultural Research Organization, Tsukuba, Ibaraki 305-8602, Japan.
  • Shirasawa K; Faculty of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido 060-0814, Japan.
Breed Sci ; 71(2): 125-133, 2021 Apr.
Article em En | MEDLINE | ID: mdl-34377060
ABSTRACT
Understanding genetic diversity among local populations is a primary goal of modern crop breeding programs. Here, we demonstrated the genetic relationships of rice varieties in Hokkaido, Japan, one of the northern limits of rice cultivation around the world. Furthermore, artificial selection during rice breeding programs has been characterized using genome sequences. We utilized 8,565 single nucleotide polymorphisms and insertion/deletion markers distributed across the genome in genotype-by-sequencing for genetic diversity analyses. Phylogenetics, genetic population structure, and principal component analysis showed that a total of 110 varieties were classified into four distinct clusters according to different populations geographically and historically. Furthermore, the genome sequences of 19 rice varieties along with historic representations in Hokkaido, nucleotide diversity and FST values in each cluster revealed that artificial selection of elite phenotypes focused on chromosomal regions. These results clearly demonstrated the history of the selections on agronomic traits as genome sequences among current rice varieties from Hokkaido.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article