Low-input breeding potential in stone pine, a multipurpose forest tree with low genome diversity

Area of Science:

• * Genetics and Plant Breeding
• * Forestry and Agroforestry

Background:

• * Stone pine (Pinus pinea L.) is ecologically and economically vital in the Mediterranean, primarily for its edible nuts.
• * Past breeding efforts were limited by low genetic diversity and a lack of genomic tools.
• * Developing new genomic resources is essential for advancing stone pine breeding programs.

Purpose of the Study:

• * To assess novel stone pine genomic resources for breeding and sustainable use.
• * To utilize a commercial SNP-array (5,671 SNPs) for clonal identification, relationship estimation, and genomic prediction.
• * To evaluate the effectiveness of genomic prediction for cone production traits.

Main Methods:

• * Utilized a 5,671 SNP-array for clonal identification and genetic relationship analysis in 99 stone pine clones.
• * Applied genomic prediction models to estimate traits related to cone production (number and weight).
• * Validated predictions using data from three Spanish clonal tests.

Main Results:

• * Successfully confirmed clonal identity and accurately estimated genomic relationships among stone pine clones.
• * Genomic prediction showed significant predictive ability for mean cone weight across tests.
• * Predictive ability for the number of cones was significant in one of the three tests.

Conclusions:

• * The developed SNP-array and genomic prediction models are highly promising for identifying superior stone pine clones, particularly for cone weight.
• * This approach facilitates early selection and opens new avenues for stone pine breeding.
• * Integration of genomic data with phenotyping accelerates the development of improved stone pine varieties for sustainable production.