Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP.

Modeling environmental spatial heterogeneity can improve the efficiency of forest tree genomic evaluation. Furthermore, genotyping costs can be lowered by reducing the number of markers needed. We investigated the impact on variance components, breeding value accuracy, and bias of two phenotypic data adjustments (experimental design and autoregressive spatial models), and a relationship matrix calculated from a subset of markers selected for their ability to infer ancestry. Using a multiple-trait multiple-site single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, four scenarios (2 phenotype adjustments × 2 marker sets) were applied to diameter at breast height (DBH), height (HT), and resistance to western gall rust (WGR) in four open-pollinated progeny trials of lodgepole pine, with 1490 (out of 11,188) trees genotyped with 25,099 SNPs. As a control, we fitted the conventional ABLUP model using pedigree information. The highest heritability estimates were achieved for the ABLUP followed closely by the ssGBLUP with the full marker set and using the spatial phenotype adjustments. The highest predictive ability was obtained by using a reduced marker subset (8000 SNPs) when either the spatial (DBH: 0.429, and WGR: 0.513) or design (HT: 0.467) phenotype corrections were used. No significant difference was detected in prediction bias among the six fitted models, and all values were close to 1 (0.918-1.014). Results demonstrated that selecting informative markers, such as those capturing ancestry, can improve the predictive ability. The use of spatial correlation structure increased traits' heritability and reduced prediction bias, while increases in predictive ability were trait-dependent.

Decoupling of height growth and drought or pest resistance tradeoffs is revealed through multiple common-garden experiments of lodgepole pine.

The environment could alter growth and resistance tradeoffs in plants by affecting the ratio of resource allocation to various competing traits. Yet, how and why functional tradeoffs change over time and space is poorly understood particularly in long-lived conifer species. By establishing four common-garden test sites for five lodgepole pine populations in western Canada, combined with genomic sequencing, we revealed the decoupling pattern and genetic underpinnings of tradeoffs between height growth, drought resistance based on δ13C and dendrochronology, and metrics of pest resistance based on pest suitability ratings. Height and δ13C correlation displayed a gradient change in magnitude and/or direction along warm-to-cold test sites. All cold test sites across populations showed a positive height and δ13C relationship. However, we did not observe such a clinal correlation pattern between height or δ13C and pest suitability. Further, we found that the study populations exhibiting functional tradeoffs or synergies to various degrees in test sites were driven by non-adaptive evolutionary processes rather than adaptive evolution or plasticity. Finally, we found positive genetic relationships between height and drought or pest resistance metrics and probed five loci showing potential genetic tradeoffs between northernmost and the other populations. Our findings have implications for deciphering the ecological, evolutionary, and genetic bases of the decoupling of functional tradeoffs due to environmental change.