Identifying and testing marker–trait associations for growth and phenology in three pine species: Implications for genomic prediction. (10th February 2022)

• Record Type: Journal Article
• Authors: Perry, Annika; Wachowiak, Witold; Beaton, Joan; Iason, Glenn; Cottrell, Joan; Cavers, Stephen
• Is Part Of: Evolutionary applications. Volume 15:Number 2(2022)
• Page Start: 330
• Page End: 348
• Publication Date: 2022-02-10
• Subjects: common garden trial -- genetic variation -- local adaptation -- marker–trait association -- predictive model -- quantitative trait -- Scots pine -- SNP array; Evolution (Biology) -- Periodicals; Genetics -- Periodicals; Natural selection -- Periodicals; Ecology -- Periodicals; 576.8
• DOI: 10.1111/eva.13345
• Languages: English
• ISSNs: 1752-4571

Abstract

Abstract: In tree species, genomic prediction offers the potential to forecast mature trait values in early growth stages, if robust marker–trait associations can be identified. Here we apply a novel multispecies approach using genotypes from a new genotyping array, based on 20, 795 single nucleotide polymorphisms (SNPs) from three closely related pine species ( Pinus sylvestris, Pinus uncinata and Pinus mugo ), to test for associations with growth and phenology data from a common garden study. Predictive models constructed using significantly associated SNPs were then tested and applied to an independent multisite field trial of P . sylvestris and the capability to predict trait values was evaluated. One hundred and eighteen SNPs showed significant associations with the traits in the pine species. Common SNPs (MAF > 0.05) associated with bud set were only found in genes putatively involved in growth and development, whereas those associated with growth and budburst were also located in genes putatively involved in response to environment and, to a lesser extent, reproduction. At one of the two independent sites, the model we developed produced highly significant correlations between predicted values and observed height data (YA, height 2020: r = 0.376, p < 0.001). Predicted values estimated with our budburst model were weakly but positively correlated with duration of budburst at one of the sites (GS, 2015: r = 0.204, p = 0.034; 2018: r = 0.205, p = 0.034–0.037) and negatively associated with budburst timing at the other (YA: r = −0.202, p = 0.046). Genomic prediction resulted in the selection of sets of trees whose mean height was taller than the average for each site. Our results provide tentative support for the capability of prediction models to forecast trait values in trees, while highlighting the need for caution in applying them to trees grown in different environments.