Genomic selection of juvenile height across a single-generational gap in Douglas-fir.

Here, we perform cross-generational GS analysis on coastal Douglas-fir (Pseudotsuga menziesii), reflecting trans-generational selective breeding application. A total of 1321 trees, representing 37 full-sib F1 families from 3 environments in British Columbia, Canada, were used as the training population for (1) EBVs (estimated breeding values) of juvenile height (HTJ) in the F1 generation predicting genomic EBVs of HTJ of 136 individuals in the F2 generation, (2) deregressed EBVs of F1 HTJ predicting deregressed genomic EBVs of F2 HTJ, (3) F1 mature height (HT35) predicting HTJ EBVs in F2, and (4) deregressed F1 HT35 predicting genomic deregressed HTJ EBVs in F2. Ridge regression best linear unbiased predictor (RR-BLUP), generalized ridge regression (GRR), and Bayes-B GS methods were used and compared to pedigree-based (ABLUP) predictions. GS accuracies for scenarios 1 (0.92, 0.91, and 0.91) and 3 (0.57, 0.56, and 0.58) were similar to their ABLUP counterparts (0.92 and 0.60, respectively) (using RR-BLUP, GRR, and Bayes-B). Results using deregressed values fell dramatically for both scenarios 2 and 4 which approached zero in many cases. Cross-generational GS validation of juvenile height in Douglas-fir produced predictive accuracies almost as high as that of ABLUP. Without capturing LD, GS cannot surpass the prediction of ABLUP. Here we tracked pedigree relatedness between training and validation sets. More markers or improved distribution of markers are required to capture LD in Douglas-fir. This is essential for accurate forward selection among siblings as markers that track pedigree are of little use for forward selection of individuals within controlled pollinated families.

Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform.

BACKGROUND: Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding; especially for late expressing and low heritability traits. Here, we used: 1) exome capture as a genotyping platform for 1372 Douglas-fir trees representing 37 full-sib families growing on three sites in British Columbia, Canada and 2) height growth and wood density (EBVs), and deregressed estimated breeding values (DEBVs) as phenotypes. Representing models with (EBVs) and without (DEBVs) pedigree structure. Ridge regression best linear unbiased predictor (RR-BLUP) and generalized ridge regression (GRR) were used to assess their predictive accuracies over space (within site, cross-sites, multi-site, and multi-site to single site) and time (age-age/ trait-trait). RESULTS: The RR-BLUP and GRR models produced similar predictive accuracies across the studied traits. Within-site GS prediction accuracies with models trained on EBVs were high (RR-BLUP: 0.79-0.91 and GRR: 0.80-0.91), and were generally similar to the multi-site (RR-BLUP: 0.83-0.91, GRR: 0.83-0.91) and multi-site to single-site predictive accuracies (RR-BLUP: 0.79-0.92, GRR: 0.79-0.92). Cross-site predictions were surprisingly high, with predictive accuracies within a similar range (RR-BLUP: 0.79-0.92, GRR: 0.78-0.91). Height at 12 years was deemed the earliest acceptable age at which accurate predictions can be made concerning future height (age-age) and wood density (trait-trait). Using DEBVs reduced the accuracies of all cross-validation procedures dramatically, indicating that the models were tracking pedigree (family means), rather than marker-QTL LD. CONCLUSIONS: While GS models' prediction accuracies were high, the main driving force was the pedigree tracking rather than LD. It is likely that many more markers are needed to increase the chance of capturing the LD between causal genes and markers.

Transmission dynamics of SARS-CoV-2 in British Columbia's largest school district during the second half of the 2020-2021 school year.

OBJECTIVES: To determine the extent and characteristics of in-school transmission of SARS-CoV-2 and determine risk factors for in-school acquisition of COVID-19 in one of Canada's largest school districts. METHODS: We conducted a retrospective chart review of all reportable cases of COVID-19 who attended a kindergarten-Grade 12 (K-12) school within the study area between January and June of the 2020-2021 school year. The acquisition source was inferred based on epidemiological data and, when available, whole genome sequencing results. Mixed effects logistic regression was performed to identify risk factors independently associated with in-school acquisition of COVID-19. RESULTS: Overall, 2877 cases of COVID-19 among staff and students were included in the analysis; of those, 9.1% had evidence of in-school acquisition. The median cluster size was two cases (interquartile range: 1). Risk factors for in-school acquisition included being male (adjusted odds ratio [aOR]: 1.59, 95% confidence interval [CI]: 1.17-2.17), being a staff member (aOR: 2.62, 95% CI: 1.64-4.21) and attending or working in an independent school (aOR: 2.28, 95% CI: 1.13-4.62). CONCLUSION: In-school acquisition of COVID-19 was uncommon during the study period. Risk factors were identified in order to support the implementation of mitigation strategies that can reduce transmission further.

RéSUMé: OBJECTIFS: Déterminer l'étendue et les caractéristiques de la transmission de la SRAS-CoV-2 en milieu scolaire, et déterminer les facteurs de risque de l'acquisition de la COVID-19 dans l'un des plus larges arrondissements scolaires du Canada. MéTHODES: Nous avons mené un examen rétrospectif des dossiers de tous les cas signalés de COVID-19 ayant fréquenté une école de niveau élémentaire, primaire ou secondaire dans la zone à l'étude entre janvier et juin de l'année scolaire 2020-2021. La source d'acquisition était inférée sur la base des données épidémiologiques et, lorsque disponibles, les résultats de séquençage du génome entier. Nous avons eu recours à des régressions logistiques multiniveaux pour identifier les facteurs indépendamment associés avec l'acquisition de la COVID-19 en milieu scolaire. RéSULTATS: Au total, 2 877 cas de COVID-19 parmi les employés et les élèves ont été inclus dans l'analyse; de ceux-ci, 9,1 % avaient acquis l'infection en milieu scolaire. La grosseur médiane des agrégats était de deux cas (écart interquartile : 1). Les risques facteurs de l'acquisition en milieu scolaire incluaient le fait d'être de sexe masculin (rapport de cotes ajusté [RCa] : 1,59, intervalle de confiance [IC] de 95% : 1,17-2,17), être un membre du personnel (RCa : 2,62, IC de 95% : 1,64-4,21) et fréquenter ou travailler dans une école indépendante (RCa : 2,28, IC de 95% : 1,13-4,62). CONCLUSION: Nos résultats suggèrent que l'acquisition de la COVID-19 en milieu scolaire était peu commune pendant la période d'étude. Des facteurs de risque ont été identifiés afin de supporter l'implémentation de mesures de contrôle pouvant réduire davantage la transmission.

Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species.

BACKGROUND: The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ~ 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200-50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F1 families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. RESULTS: As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000-15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. CONCLUSIONS: Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits.