Cappa Eduardo P, Chen Charles, Klutsch Jennifer G, Sebastian-Azcona Jaime, Ratcliffe Blaise, Wei Xiaojing, Da Ros Letitia, Liu Yang, Bhumireddy Sudarshana Reddy, Benowicz Andy, Mansfield Shawn D, Erbilgin Nadir, Thomas Barb R, El-Kassaby Yousry A
Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina.
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
Heredity (Edinb). 2025 Apr;134(3-4):186-199. doi: 10.1038/s41437-025-00747-z. Epub 2025 Feb 12.
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) 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 CO 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.
在数量遗传学中，探索表型、基因型和环境之间的关系至关重要。考虑到经济重要性状的复杂遗传结构，在全基因组关联研究（GWAS）和基因组预测（GP）框架中整合基因型与环境的相互作用势在必行。这种整合对于识别在不同环境中具有稳定性的稳健标记以及提高个体在特定目标环境中的表现预测准确性至关重要。我们对来自加拿大艾伯塔省的1540株白云杉进行了多环境GWAS和GP分析，这些树木针对467,224个单核苷酸多态性（SNP）进行了基因分型，并在三种环境中生长，研究了30个生产力、防御和气候适应性性状。我们在研究的性状和环境中鉴定出563个显著关联（p值 < 1.07×10），其中105个SNP在两个或三个环境中显示出重叠关联。木材密度、月桂烯、总单萜、α-蒎烯和儿茶素在不同环境中的重叠率最高（>50%）。气体交换性状，包括细胞间CO浓度和内在水分利用效率，显示出最高数量的显著关联（>38%），但在不同环境中的稳定性较低（<1.2%）。20个性状的预测能力（PA）在不同环境中差异显著（0.03 - 0.41），稳定碳同位素比率的平均PA最高（0.36），气体交换性状最低（0.07）。只有两个性状在不同环境中的预测偏差（PB）存在差异，80%的地点 - 性状PB落在狭窄范围内（0.90至1.10）。整合多环境GWAS和GP分析被证明有助于识别特定地点的标记，了解环境对PA和PB的影响，并最终间接深入了解影响该白云杉育种计划的环境因素。
