Scald resistance in hybrid rye (Secale cereale): genomic prediction and GWAS

Rye (Secale cereale L.) is an important cereal crop used for food, beverages, and feed, especially in North-Eastern Europe. While rye is generally more tolerant to biotic and abiotic stresses than other cereals, it still can be infected by several diseases, including scald caused by Rhynchosporium secalis. The aims of this study were to investigate the genetic architecture of scald resistance, to identify genetic markers associated with scald resistance, which could be used in breeding of hybrid rye and to develop a model for genomic prediction for scald resistance. Four datasets with records of scald resistance on a population of 251 hybrid winter rye lines grown in 2 years and at 3 locations were used for this study. Four genomic models were used to obtain variance components and heritabilities of scald resistance. All genomic models included additive genetic effects of the parental components of the hybrids and three of the models included additive-by-additive epistasis and/or dominance effects. All models showed moderate to high broad sense heritabilities in the range of 0.31 (SE 0.05) to 0.76 (0.02). The model without non-additive genetic effects and the model with dominance effects had moderate narrow sense heritabilities ranging from 0.24 (0.06) to 0.55 (0.08). None of the models detected significant non-additive genomic variances, likely due to a limited data size. A genome wide association study was conducted to identify markers associated with scald resistance in hybrid winter rye. In three datasets, the study identified a total of twelve markers as being significantly associated with scald resistance. Only one marker was associated with a major quantitative trait locus (QTL) influencing scald resistance. This marker explained 11-12% of the phenotypic variance in two locations. Evidence of genotype-by-environment interactions was found for scald resistance between one location and the other two locations, which suggested that scald resistance was influenced by different QTLs in different environments. Based on the results of the genomic prediction models and GWAS, scald resistance seems to be a quantitative trait controlled by many minor QTL and one major QTL, and to be influenced by genotype-by-environment interactions

A Lotus japonicus cytoplasmic kinase connects Nod factor perception by the NFR5 LysM receptor to nodulation

The establishment of nitrogen-fixing root nodules in legume-rhizobia symbiosis requires an intricate communication between the host plant and its symbiont. We are, however, limited in our understanding of the symbiosis signaling process. In particular, how membrane-localized receptors of legumes activate signal transduction following perception of rhizobial signaling molecules has mostly remained elusive. To address this, we performed a coimmunoprecipitation-based proteomics screen to identify proteins associated with Nod factor receptor 5 (NFR5) in Lotus japonicus. Out of 51 NFR5-associated proteins, we focused on a receptor-like cytoplasmic kinase (RLCK), which we named NFR5-interacting cytoplasmic kinase 4 (NiCK4). NiCK4 associates with heterologously expressed NFR5 in Nicotiana benthamiana, and directly binds and phosphorylates the cytoplasmic domains of NFR5 and NFR1 in vitro. At the cellular level, Nick4 is coexpressed with Nfr5 in root hairs and nodule cells, and the NiCK4 protein relocates to the nucleus in an NFR5/NFR1-dependent manner upon Nod factor treatment. Phenotyping of retrotransposon insertion mutants revealed that NiCK4 promotes nodule organogenesis. Together, these results suggest that the identified RLCK, NiCK4, acts as a component of the Nod factor signaling pathway downstream of NFR5

Characteristics Associated with Serological Covid-19 Vaccine Response and Durability in an Older Population with Significant Comorbidity:The Danish Nationwide ENFORCE Study

OBJECTIVES: To identify individual characteristics associated with serological COVID-19 vaccine responsiveness and durability of vaccine-induced antibodies. METHODS: Adults without history of SARS-CoV-2 infection from the Danish population scheduled for SARS-CoV-2 vaccination were enrolled in this parallel group, phase IV study. SARS-CoV-2 Spike IgG and Spike-ACE2-receptor-blocking antibodies were measured at days 0, 21, 90 and 180. Vaccine responsiveness was categorized according to Spike IgG and Spike-ACE2-receptor-blocking levels at day 90 post-1(st) vaccination. Non-durable vaccine-response was defined as day 90 responders that no longer had significant responses by day 180. RESULTS: Of 6544 participants completing two vaccine doses (median age 64, interquartile range:54–75), 3654 (55.8%) received BTN162b2, 2472 (37.8%) mRNA-1273, and 418 (6.4%) ChAdOx1 followed by a mRNA vaccine. Levels of both types of antibodies increased from baseline to day 90 and then decreased to day 180. The decrease was more pronounced for levels of Spike-ACE2-receptor-blocking antibodies than for Spike IgG. Proportions with vaccine hypo-responsiveness and lack of durable response were 5.0% and 12.1% for Spike IgG; 12.7% and 39.6% for Spike-ACE2-receptor-blocking antibody levels, respectively. Male sex, vaccine type and number of co-morbidities were associated with all four outcomes. Additionally, age >=75y was associated with hypo-responsiveness for Spike-ACE2-receptor-blocking antibodies (adjusted odds-ratio:1.59, 95% confidence interval:1.25–2.01) but not for Spike IgG. CONCLUSIONS: Comorbidity, male sex and vaccine type were risk factors for hypo-responsiveness and non-durable response to COVID-19 vaccination. The functional activity of vaccine-induced antibodies declined with increasing age and had waned to pre-2(nd) vaccination levels for most individuals after 6 months

Genetic influence on environmental sensitivity in livestock breeding - Dataset

Within are data and scripts relating to the genetic influence on environmental sensitivity in livestock breeding research project. This includes scripts for data generation and analysis, and data from simulation studies (relating to Chapters 3 and 5), and data editing scripts from Chapters 4, 6 and 7. 
 Scripts included are in following formats: 
 R scripts for data generation (chapters 3 and 7) 
 ASReml scripts for analysis (chapter 3) 
 DMU scripts for analysis (chapters 4-7

Genetic Influence on Environmental Sensitivity in Livestock Breeding

Variation in the genetic environmental sensitivity (GES) of livestock can cause genotype by environment interactions (G×E). The impacts of G×E differs depending on whether genotypes express different sensitivities across or within environments. Across environments G×E is caused by macro-GES, while within environments G×E is caused by micro-GES. Estimation of GES is challenging especially in unbalanced datasets. The number of animals in each macro-environment and the degree of genetic connection across macro-environments both influence the estimation accuracy of genetic variance due to macro-GES. Meanwhile, it has been suggested that balanced datasets with relatively large sire family sizes are required to accurately estimate micro-GES of single recorded traits. The aim of this thesis was to assess the data structure requirements for estimation of macro and micro-GES in unbalanced data, evaluate the accuracy of modelling micro-GES on one trait in multi-trait models, estimate the relationship between health-related traits and micro-GES of production traits, examine the interaction between macro- and micro-GES, and estimate the magnitude of macro- and micro-GES in livestock. The data structure requirements for estimation of macro- and micro-GES in unbalanced data, was evaluated using a simulation study in Chapter 3. It was shown that the accuracies and bias of estimated variance components for simultaneous estimation of macro- and micro-GES using double hierarchical generalised linear models (DHGLMs) including a linear reaction norm depended primarily on average sire family size. Accurate and unbiased estimates variance components and EBVs of macro- and micro-GES could be obtained with a dataset with 500 sires with 20 offspring per sire on average. The impact of differences in the number of records on the accuracy of variance component estimation when analysing multiple traits of which one exhibit micro-GES was assessed in Chapter 5. The genetic correlations were found to be slightly overestimated when the true genetic correlations were 0.5. However, the models were accurately able to identify the presence of non-zero genetic correlations, showing that these models could provide useful information. The relationship between health-related traits and production traits were examined in Chapter 6 by estimating the genetic correlation between immune competence traits and mean performance and micro-GES of weaning weight, eye muscle area and rib and rump fat depth. It was shown that animals with high immune competence tended to also have high mean performance and micro-GES of rib and rump fat and low mean performance and micro-GES of weaning weight and eye muscle area. The interaction between macro- and micro-GES of body weight in two subpopulations of the same cross reared in Burkina Faso and France was assessed in Chapter 7. Micro-GES of body weight showed considerable macro-GES with both heterogeneity of heritabilities and reranking between the two subpopulations. The existence of macro-GES and micro-GES were found for yearling weight of Australian Angus beef cattle and body weight of purebred and crossbred broiler chicken. Furthermore, micro-GES was found in weaning weight, eye muscle area and rib and rump fat in Australian Angus beef cattle

Micro-genetic environmental sensitivity across macro-environments of chickens reared in Burkina Faso and France

Abstract Background Commercial poultry production systems follow a pyramidal structure with a nucleus of purebred animals under controlled conditions at the top and crossbred animals under commercial production conditions at the bottom. Genetic correlations between the same phenotypes on nucleus and production animals can therefore be influenced by differences both in purebred-crossbred genotypes and in genotype-by-environment interactions across the two environments, known as macro-genetic environmental sensitivity (GES). Within each environment, genotype-by-environment interactions can also occur due to so-called micro-GES. Micro-GES causes heritable variation in phenotypes and decreases uniformity. In this study, genetic variances of body weight (BW) and of micro-GES of BW and the impacts of purebred-crossbred differences and macro-environmental differences on micro-GES of BW were estimated. The dataset contained three subpopulations of slow-growing broiler chickens: purebred chickens (PB) reared in France, and crossbred chickens reared in France (FR) under the same conditions as PB or reared in Burkina Faso (BF) under local conditions. The crossbred chickens were offspring of the same dam line and had PB as their sire line. Results Estimates of heritability of BW and micro-GES of BW were 0.54 (SE of 0.02) and 0.06 (0.01), 0.67 (0.03) and 0.03 (0.01), and 0.68 (0.04) and 0.02 (0.01) for the BF, FR, and PB subpopulations, respectively. Estimates of the genetic correlations for BW between the three subpopulations were moderately positive (0.37 to 0.53) and those for micro-GES were weakly to moderately positive (0.01 to 0.44). Conclusions The results show that the heritability of the micro-GES of BW varies with macro-environment, which indicates that responses to selection are expected to differ between macro-environments. The weak to moderate positive genetic correlations between subpopulations indicate that both macro-environmental differences and purebred-crossbred differences can cause re-ranking of sires based on their estimated breeding values for micro-GES of BW. Thus, the sire that produces the most variable progeny in one macro-environment may not be the one that produces the most variable offspring in another. Similarly, the sire that produces the most variable purebred progeny may not produce the most variable crossbred progeny. The results highlight the need for investigating micro-GES for all subpopulations included in the selection scheme, to ensure optimal genetic gain in all subpopulations