Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees

• 成果类型: Article
• 署名作者: Slavov, Gancho T.; Macaya-Sanz, David; DiFazio, Stephen P.; Howe, Glenn T.
• 署名单位: UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Institute of Biological, Environmental, Rural & Sciences (IBERS); Aberystwyth University; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Rothamsted Research; West Virginia University; Consejo Superior de Investigaciones Cientificas (CSIC); Oregon State University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11945
• DOI: 10.1073/pnas.2425691122
• 发表日期: 2025-07-01
• 关键词: pine pinus-taeda; CLIMATE-CHANGE; populus-trichocarpa; douglas-fir; Linkage Disequilibrium; wide association; black cottonwood; natural-populations; trait variation; architecture

摘要

There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive ability r > 0.9) but almost as well using climate or geography (r > 0.8). In contrast, genomic prediction within populations was poor (r < 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration. Significance Given climate change, there is an urgent need to conserve natural populations of forest trees and associated ecosystems. In contrast to a growing body of literature on genomic offsets and related approaches, we argue that genomic data will play only an ancillary role in the overall management of forest genetic resources, particularly given the immediate need to respond to climate change and the large number of species that will be affected. Instead, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.