Revealing stable SNPs and genomic prediction insights across environments enhance breeding strategies of productivity, defense, and climate-adaptability traits in white spruce. - Cappa, Eduardo Pablo; Chen, Charles; Klutsch, Jennifer; Sebastian - Azcona, Jaime; Ratcliffe, Blaise; Wei, Xiaojing; Da Ros, Letitia; Liu, Yang; Bhumireddy, Sudarshana; Benowicz, Andy; Mansfieid, Shawn; Erbilgin, Nadir; et al. (Springer, 2025)
Exploring the relationship between phenotype, genotype, and environment is essential in quantitative genetics. Considering the complex genetic architecture of economically important traits, integrating genotype-by-environment interactions in a genome wide association (GWAS) and genomic prediction (GP) framework is imperative. This integration is crucial for identifying robust markers with stability across diverse environments and improving the predictive accuracy of individuals’ performance within specific target environments. We conducted a multi-environment GWAS and GP analysis for 30 productivity, defense, and climate adaptability traits on 1540 white spruce trees from Alberta, Canada, genotyped for 467,224 SNPs and growing across three environments. We identified 563 significant associations (p-value < 1.07 ×10−05) across the studied traits and environments, with 105 SNPs showing overlapping associations in two or three environments. Wood density, myrcene, total monoterpenes, α-pinene, and catechin exhibited the highest overlap (>50%) across environments. Gas exchange traits, including intercellular CO2 concentration and intrinsic water use efficiency, showed the highest number of significant associations (>38%) but less stability (<1.2%) across environments. Predictive ability (PA) varied significantly (0.03–0.41) across environments for 20 traits, with stable carbon isotope ratio having the highest average PA (0.36) and gas exchange traits the lowest (0.07). Only two traits showed differences in prediction bias (PB) across environments, with 80% of site-trait PB falling within a narrow range (0.90 to 1.10). Integrating multi-environment GWAS and GP analyses proved useful in identifying site-specific markers, understanding environmental impacts on PA and PB, and ultimately providing indirect insights into the environmental factors that influenced this white spruce breeding program.