Accounting for population structure in genomic predictions of Eucalyptus globulus

Abstract

Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP (‘HBLUP’) analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders (MF). Full-sib families from two separate breeding populations were evaluated across 13 sites in the ‘Green Triangle’ region of Australia. Gamma matrices (Γ) describing similarities among MF reflected the geographic distribution of populations and the origins of two land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 to 0.56. Genetic group solutions were strongly correlated with MF solutions across models and MF effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of MF did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of MF in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.

Optimizing Multi-Environment Trials in The US Rice Belt via Smart-Climate-Soil Prediction Based-Models and Economic Importance

“…The advantage of this is that environmental information is broad and free (Costa-Neto et al, 2021), while the traditional GxE matrix is highly dependent on the genotypes and environment combinations used. Therefore, with historical weather data and genotypes reaction norms, it is possible to recommend the best and most stable varieties for each target region and even work to discover potential new producing regions (Araújo et al, 2024;Callister et al, 2024;Cooper & Messina, 2021;Costa-Neto et al, 2023).…”

“…To avoid the yield gaps between the expected yield potential in the TPE and the real on-farm yield that farmers achieve, it is important to characterize better and select the locations used for testing/selection and to understand how they are related (mega-environments) by a process called enviromic or environtyping. Specifically, in this process, the environmental covariates are collected and processed in appropriate MET groupings, which are analyzed concerning the alignment with TPE and, thus, used to capture genotype reaction norms models (Callister et al, 2024;Cooper & Messina, 2021;Costa-Neto et al, 2023).…”

Tendencias en investigación de marcadores moleculares en especies forestales, con énfasis en Eucalyptus: análisis bibliométrico

“…Considerando a Australia como el mayor proponente de investigación sobre la temática una de las investigaciones con mayor relevancia es la desarrollada por Callister et al (2022) sobre "Contabilización de la estructura de la población en las predicciones genómicas de Eucalyptus globulus". Esta investigación se destaca por abordar la estructura de la población en las predicciones genómicas de Eucalyptus globulus, lo cual es fundamental para comprender la diversidad genética y su influencia en los rasgos de interés en esta especie.…”

Confidence intervals for validation statistics with data truncation in genomic prediction

“…These validation statistics focus on the performance of the model to predict breeding values. Validation using these methods was done in dairy [ 5 ] and beef [ 6 ] cattle, pigs [ 7 ], chickens [ 8 ], sheep [ 9 ], goats [ 10 ], fish [ 11 ], wheat [ 12 ], and trees [ 13 ], among others. For validation in dairy cattle, using weighted averages and deregressed evaluations could be more robust than the LR method or predictivity [ 14 ].…”