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Genetic Gain and Inbreeding in Different Simulated Genomic Selection Schemes for Grain Yield and Oil Content in Safflower.
Zhao, Huanhuan; Khansefid, Majid; Lin, Zibei; Hayden, Matthew J.
Afiliação
  • Zhao H; School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia.
  • Khansefid M; Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia.
  • Lin Z; School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia.
  • Hayden MJ; Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia.
Plants (Basel) ; 13(11)2024 Jun 06.
Article em En | MEDLINE | ID: mdl-38891385
ABSTRACT
Safflower (Carthamus tinctorius L.) is a multipurpose minor crop consumed by developed and developing nations around the world with limited research funding and genetic resources. Genomic selection (GS) is an effective modern breeding tool that can help to fast-track the genetic diversity preserved in genebank collections to facilitate rapid and efficient germplasm improvement and variety development. In the present study, we simulated four GS strategies to compare genetic gains and inbreeding during breeding cycles in a safflower recurrent selection breeding program targeting grain yield (GY) and seed oil content (OL). We observed positive genetic gains over cycles in all four GS strategies, where the first cycle delivered the largest genetic gain. Single-trait GS strategies had the greatest gain for the target trait but had very limited genetic improvement for the other trait. Simultaneous selection for GY and OL via indices indicated higher gains for both traits than crossing between the two single-trait independent culling strategies. The multi-trait GS strategy with mating relationship control (GS_GY + OL + Rel) resulted in a lower inbreeding coefficeint but a similar gain compared to that of the GS_GY + OL (without inbreeding control) strategy after a few cycles. Our findings lay the foundation for future safflower GS breeding.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article