Genomic selection empirically appeared valuable for reciprocal recurrent selection in oil palm as it could account for family effects and Mendelian sampling terms, despite small populations and low marker density. Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this is expected mainly through shortened breeding cycles and increased selection intensity, which requires sufficient GS accuracy in selection candidates, despite often small training populations. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), the major world oil crop. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated within-population GS accuracies when predicting breeding values of non-progeny-tested individuals for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that GS could account for family effects and Mendelian sampling terms in Group B but only for family effects in Deli. Presumably, this difference between populations originated from their contrasting breeding history. The GS accuracy ranged from -0.41 to 0.94 and was positively correlated with the relationship between training and test sets. Training sets optimized with the so-called CDmean criterion gave the highest accuracies, ranging from 0.49 (pulp to fruit ratio in Group B) to 0.94 (fruit weight in Group B). The statistical methods did not affect the accuracy. Finally, Group B could be preselected for progeny tests by applying GS to key yield traits, therefore increasing the selection intensity. Our results should be valuable for breeding programs with small populations, long breeding cycles, or reduced effective size.

Sugarcane cultivars are interspecific hybrids with an aneuploid, highly heterozygous polyploid genome. The complexity of the sugarcane genome is the main obstacle to the use of marker-assisted selection in sugarcane breeding. Given the promising results of recent studies of plant genomic selection, we explored the feasibility of genomic selection in this complex polyploid crop. Genetic values were predicted in two independent panels, each composed of 167 accessions representing sugarcane genetic diversity worldwide. Accessions were genotyped with 1,499 DArT markers. One panel was phenotyped in Reunion Island and the other in Guadeloupe. Ten traits concerning sugar and bagasse contents, digestibility and composition of the bagasse, plant morphology, and disease resistance were used. We used four statistical predictive models: bayesian LASSO, ridge regression, reproducing kernel Hilbert space, and partial least square regression. The accuracy of the predictions was assessed through the correlation between observed and predicted genetic values by cross validation within each panel and between the two panels. We observed equivalent accuracy among the four predictive models for a given trait, and marked differences were observed among traits. Depending on the trait concerned, within-panel cross validation yielded median correlations ranging from 0.29 to 0.62 in the Reunion Island panel and from 0.11 to 0.5 in the Guadeloupe panel. Cross validation between panels yielded correlations ranging from 0.13 for smut resistance to 0.55 for brix. This level of correlations is promising for future implementations. Our results provide the first validation of genomic selection in sugarcane.

We identified combinations of metabolites in early pregnancy that are associated with subsequent risk of GDM. Replication of findings may improve understanding of GDM pathogenesis and may have implications for the design of GDM prevention and early diagnosis protocols.

No abstract

While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina’s Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8–12.56) and a 4.46-fold (95% CI: 2.94–6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3∶12313450 and chr3∶12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.

No abstract

Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

Summary Wood production in fast‐growing Eucalyptus grandis trees is highly dependent on both potassium (K) fertilization and water availability but the molecular processes underlying wood formation in response to the combined effects of these two limiting factors remain unknown. E. grandis trees were submitted to four combinations of K‐fertilization and water supply. Weighted gene co‐expression network analysis and MixOmics‐based co‐regulation networks were used to integrate xylem transcriptome, metabolome and complex wood traits. Functional characterization of a candidate gene was performed in transgenic E. grandis hairy roots. This integrated network‐based approach enabled us to identify meaningful biological processes and regulators impacted by K‐fertilization and/or water limitation. It revealed that modules of co‐regulated genes and metabolites strongly correlated to wood complex traits are in the heart of a complex trade‐off between biomass production and stress responses. Nested in these modules, potential new cell‐wall regulators were identified, as further confirmed by the functional characterization of EgMYB137. These findings provide new insights into the regulatory mechanisms of wood formation under stressful conditions, pointing out both known and new regulators co‐opted by K‐fertilization and/or water limitation that may potentially promote adaptive wood traits.