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Flowering phenology is important in the adaptation of many plants to their local environment, but its adaptive value has not been extensively studied in herbaceous perennials. We used Arabis alpina as a model system to determine the importance of flowering phenology to fitness of a herbaceous perennial with a wide geographical range. Individual plants representative of local genetic diversity (accessions) were collected across Europe, including in Spain, the Alps and Scandinavia. The flowering behaviour of these accessions was documented in controlled conditions, in common-garden experiments at native sites and in situ in natural populations. Accessions from the Alps and Scandinavia varied in whether they required exposure to cold (vernalization) to induce flowering, and in the timing and duration of flowering. By contrast, all Spanish accessions obligately required vernalization and had a short duration of flowering. Using experimental gardens at native sites, we show that an obligate requirement for vernalization increases survival in Spain. Based on our analyses of genetic diversity and flowering behaviour across Europe, we propose that in the model herbaceous perennial A. alpina, an obligate requirement for vernalization, which is correlated with short duration of flowering, is favoured by selection in Spain where the plants experience a long growing season.

BACKGROUND: The domesticated turkey (Meleagris gallopavo) is a species of significant agricultural importance and is the second largest contributor, behind broiler chickens, to world poultry meat production. The previous genome is of draft quality and partly based on the chicken (Gallus gallus) genome. A high-quality reference genome of M. gallopavo is essential for turkey genomics and genetics research and the breeding industry. RESULTS: By adopting the trio-binning approach, we were able to assemble a high-quality chromosome-level F1 assembly and 2 parental haplotype assemblies, leveraging long-read technologies and genome-wide chromatin interaction data (Hi-C). From a total of 40 chromosomes (2n = 80), we captured 35 chromosomes in a single scaffold, showing much improved genome completeness and continuity compared to the old assembly build. The 3 assemblies are of higher quality than the previous draft quality assembly and comparable to the chicken assemblies (GRCg7) shown by the largest contig N50 (26.6 Mb) and comparable BUSCO gene set completeness scores (96-97%). Comparative analyses confirm a previously identified large inversion of around 19 Mbp on the Z chromosome not found in other Galliformes. Structural variation between the parent haplotypes was identified, which poses potential new target genes for breeding. CONCLUSIONS: We contribute a new high-quality turkey genome at the chromosome level, benefiting turkey genetics and other avian genomics research as well as the turkey breeding industry.

The domesticated turkey (Meleagris gallopavo) is a species of significant agricultural importance and is the second largest contributor, behind broiler chickens, to world poultry meat production. The previous genome is of draft quality and partly based on the chicken (Gallus gallus) genome. A high-quality reference genome of Meleagris gallopavo is essential for turkey genomics and genetics research and the breeding industry. By adopting the trio-binning approach, we were able to assemble a high-quality chromosome-level F1 assembly and two parental haplotype assemblies, leveraging long-read technologies and genome-wide chromatin interaction data (Hi-C). From a total of 40 chromosomes (2n=80), we capture 35 chromosomes in a single scaffold, and show much improved genome completeness and continuity compared to the old assembly build. The three assemblies are of higher quality than the previous draft quality assembly and comparable to the chicken assemblies (GRCg7) shown by the largest contig N50 (26.6 Mb) and comparable BUSCO gene set completeness scores (96-97%). Comparative analyses confirms a previously identified large inversion of around 19 Mbp on the Z chromosome not found in other Galliformes. Structural variation between the parent haplotypes were identified which pose potential new target genes for breeding. We contribute a new high-quality turkey genome at chromosome-level, benefiting turkey genetics and other avian genomics research as well as turkey breeding industry., The domesticated turkey (Meleagris gallopavo) is a species of significant agricultural importance and is the second largest contributor, behind broiler chickens, to world poultry meat production. The previous genome is of draft quality and partly based on the chicken (Gallus gallus) genome. A high-quality reference genome of Meleagris gallopavo is essential for turkey genomics and genetics research and the breeding industry. By adopting the trio-binning approach, we were able to assemble a high-quality chromosome-level F1 assembly and two parental haplotype assemblies, leveraging long-read technologies and genome-wide chromatin interaction data (Hi-C). From a total of 40 chromosomes (2n=80), we capture 35 chromosomes in a single scaffold, and show much improved genome completeness and continuity compared to the old assembly build. The three assemblies are of higher quality than the previous draft quality assembly and comparable to the chicken assemblies (GRCg7) shown by the largest contig N50 (26.6 Mb) and comparable BUSCO gene set completeness scores (96-97%). Comparative analyses confirms a previously identified large inversion of around 19 Mbp on the Z chromosome not found in other Galliformes. Structural variation between the parent haplotypes were identified which pose potential new target genes for breeding. We contribute a new high-quality turkey genome at chromosome-level, benefiting turkey genetics and other avian genomics research as well as turkey breeding industry.

Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects.

We performed high-coverage long read PacBio sequencing to produce a high quality assembly (Mgal_WUR_HG_1.0) of the Meleagris gallopavo (Turkey) using the Trio-Binning approach. We used the Trio-binning approach to create two high quality parental haplotype assemblies, Mgal_WUR_HG_1.0_p1 and Mgal_WUR_HG_1.0_p2, for parent 1 and parent 2, respectively.