Estimation of direct and maternal genetic parameters for weaning weight in Hungarian Simmental cattle

The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maximum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.

Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials

Key message New forms of the coefficient of determination can help to forecast the accuracy of genomic prediction and optimize experimental designs in multi-environment trials with genotype-by-environment interactions. Abstract In multi-environment trials, the relative performance of genotypes may vary depending on the environmental conditions, and this phenomenon is commonly referred to as genotype-by-environment interaction (G$$\times$$ × E). With genomic prediction, G$$\times$$ × E can be accounted for by modeling the genetic covariance between trials, even when the overall experimental design is highly unbalanced between trials, thanks to the genomic relationship between genotypes. In this study, we propose new forms of the coefficient of determination (CD, i.e., the expected model-based square correlation between a genetic value and its corresponding prediction) that can be used to forecast the genomic prediction reliability of genotypes, both for their trial-specific performance and their mean performance. As the expected prediction reliability based on these new CD criteria is generally a good approximation of the observed reliability, we demonstrate that they can be used to optimize multi-environment trials in the presence of G$$\times$$ × E. In addition, this reliability may be highly variable between genotypes, especially in unbalanced designs with complex pedigree relationships between genotypes. Therefore, it can be useful for breeders to assess it before selecting genotypes based on their predicted genetic values. Using a wheat population evaluated both for simulated and phenology traits, and two maize populations evaluated for grain yield, we illustrate this approach and confirm the value of our new CD criteria.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Abstract Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

PSI-17 Genetic analysis of weaning weight in Hungarian Simmental cattle

The aim of the study was to partition the total phenotypic variation in the weaning weight of Hungarian Simmental calves into their various causal components. The data used was provided by the Association of Hungarian Simmental Breeders. The dataset comprised of the weaning weight records of 44,278 calves (sire = 879, dam = 14,811) born from 1975 to 2020. A total of six models were fitted to the weaning weight data. Herd, birth year, calving order and sex were included as fixed effects in the models. Model 1 had direct genetic effect as the only random effect. Model 2 had a permanent maternal environment as an additional random effect. Model 3 had both direct and maternal genetic effects, with their covariance is being zero. Model 4 was similar to Model 3 but with non-zero direct-maternal genetic covariance. Model 5 had direct, maternal genetic and permanent environmental effects and a zero direct-maternal genetic covariance. Model 6 was similar to model 5 but the direct-maternal genetic effect was assumed to be correlated. Variance components and genetic parameters were estimated using restricted maximum likelihood method with the Wombat software. The best fit model was determined using the Log likelihood ratio test. Inclusion of direct maternal genetic covariance increased the variance components estimates dramatically which resulted in a corresponding increase in the direct and maternal heritability estimates. The best fitted model (Model 4) had direct and maternal genetic effects as the only random effects with a non-zero direct-maternal genetic covariance. The direct heritability, maternal heritability and direct-maternal genetic correlation estimate of the best model was 0.57, 0.16 and -0.78, respectively. Our result suggests the problem of (co)sampling variation in the partitioning of additive genetic effect into direct and maternal components.

Gene-by-Crisis Interaction for Optimism and Meaning in Life: The Effects of the COVID-19 Pandemic

The corona virus disease 2019 (COVID-19) pandemic and the restrictions to reduce the spread of the virus has had a large impact on daily life. We investigated the individual differences in the effect of the COVID-19 pandemic and first lockdown on optimism and meaning in life in a sample from the Netherlands Twin Register. Participants completed surveys before (N = 9964, Mean age: 48.2, SD = 14.4) and during the first months of the pandemic (i.e. April–May 2020, N = 17,464, Mean age: 44.6 SD = 14.8), with a subsample completing both surveys (N = 6461, Mean age T1: 48.8, SD = 14.5). We applied genetic covariance structure models to twin data to investigate changes in the genetic architecture of the outcome traits due to the pandemic and the interaction of genes with the environmental exposure. Although 56% and 35% of the sample was negatively affected by the pandemic in their optimism and meaning in life, many participants were stable (32% and 43%) or even showed increased optimism and meaning in life (11% and 22%). Subgroups, specifically women, higher educated people, and people with poorer health, experienced larger negative effects. During the first months of the pandemic, slightly lower heritability estimates for optimism and meaning in life (respectively 20% and 25%) were obtained compared to pre-pandemic (respectively 26% and 32%), although confidence intervals overlap. The lower than unity genetic correlations across time (.75 and .63) suggest gene-environment interactions, where the expression of genes that influence optimism and meaning in life differs before and during the pandemic. The COVID-19 pandemic is a strong exposure that leads to imbalanced effects on the well-being of individuals. Some people decrease in well-being, while others get more optimistic and consider their lives as more meaningful during the pandemic. These differences are partly explained by individual differences in genetic sensitivity to extreme environmental change. More knowledge on the person-specific response to specific environmental variables underlying these individual differences is urgently needed to prevent further inequality.

Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. Most variance (89%) and covariance (70%) prediction accuracy estimates were greater than zero. The usefulness of crosses were accurately predicted with good correspondence between the predicted and the actual mean performance of family members breeders selected for advancement as new parents and candidate varieties. We also used a directional dominance model to quantify significant inbreeding depression for most traits. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with top breeding values) and varieties (progeny with top own performance).

Discovery Of 42 Genome-Wide Significant Loci Associated With Dyslexia

Reading and writing are crucial for many aspects of modern life but up to 1 in 10 children are affected by dyslexia, which can persist into adulthood. Family studies of dyslexia suggest heritability up to 70%, yet no convincing genetic markers have been found due to limited study power. Here, we present a genome-wide association study representing a 20-fold increase in sample size from prior work, with 51,800 adults self-reporting a dyslexia diagnosis and 1,087,070 controls. We identified 42 independent genome-wide significant loci: 17 are in genes linked to or pleiotropic with cognitive ability/educational attainment; 25 are novel and may be more specifically associated with dyslexia. Twenty-three loci (12 novel) were validated in independent cohorts of Chinese and European ancestry. We confirmed a similar genetic aetiology of dyslexia between sexes, and found genetic covariance with many traits, including ambidexterity, but not neuroanatomical measures of language-related circuitry. Causal analyses revealed a directional effect of dyslexia on attention deficit hyperactivity disorder and bidirectional effects on socio-educational traits but these relationships require further investigation. Dyslexia polygenic scores explained up to 6% of variance in reading traits in independent cohorts, and might in future enable earlier identification and remediation of dyslexia.

Quantitative Inheritance of Sclerotinia Stem Rot Resistance in Brassica napus and Relationship to Cotyledon and Leaf Resistances

Sclerotinia sclerotiorum is a necrotrophic fungus causing devastating stem rot and associated yield losses of canola/rapeseed (Brassica napus) worldwide, including in Australia. Developing host resistance against Sclerotinia stem rot is critical if this disease in canola/rapeseed is to be successfully managed, as cultural or chemical control options provide only partial or sporadic control. Three B. napus breeding populations, C2, C5 and C6, including the parents, F1, F2, BC1P1 and BC2P2, were utilised in a field study with an objective of exploring the inheritance pattern of disease resistance (based on stem lesion length, SLL), the genetic relationships of disease with stem diameter (SD) or days to flowering (DTF), and to compare these new adult plant stem resistances against S. sclerotiorum with those of seedling (cotyledon and leaf) resistances in earlier studies. Heritability (broad-sense) of SLL, was 0.57 and 0.73 for populations C2 at 3 and 5 weeks post-inoculation, and was 0.21 for C5 at 5 weeks post-inoculation. Additive genetic variance was evident within all three populations for DTF but not for SD. Narrow sense heritability for DTF was 0.48 (C2), 0.42 (C5) and 0.32 (C6). SD, DTF and SLL were all inherited independently with no significant genetic covariance between traits in bivariate analysis. Genetic variance for SLL in populations C2 and C5 was entirely non-additive, and there was significant non-additive genetic covariance of SLL at 3 and 5 weeks post-inoculation. Generation means analysis in population C2 supported the conclusion that complex epistatic interactions controlled SLL. Several C2 and C5 progeny showed high adult plant stem resistance which may be critical in developing enhanced stem resistance in canola/rapeseed. While population C6 showed no genetic variation for SLL resistance in this study, it showed significant non-additive genetic variance at the cotyledon and leaf stages in earlier studies. We conclude that host resistance varies across different plant growth stages and breeding must be targeted for resistance at each growth stage. In populations C2, C5 and C6, resistance to S. sclerotiorum in stem, leaf and cotyledon is always controlled by non-additive effects such as complex epistasis or dominance. Overall, our findings in relation to the quantitative inheritance of Sclerotinia stem rot resistance, together with the new, high-level resistances identified, will enable breeders to select/develop genotypes with enhanced resistances to S. sclerotiorum.