Population structure limits inferences from genomic prediction and genome-wide association studies in a forest tree
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, assess risks of 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 a replicated common- garden experiment 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 practice assisted migration. We analyzed hierarchical population structure by partitioning phenotypic and genomic (SNP) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60-81%) occurred at the population level 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). Similarly, 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. Our results highlight the difficulty of using GWAS to identify causal loci when there is population structure, and the limitations of using genomic information alone to guide assisted migration.