Heterogeneity in convergence behaviour of the single-step SNP-BLUP model across different effects and animal groups.

BACKGROUND: The single-step model is becoming increasingly popular for national genetic evaluations of dairy cattle due to the benefits that it offers such as joint breeding value estimation for genotyped and ungenotyped animals. However, the complexity of the model due to a large number of correlated effects can lead to significant computational challenges, especially in terms of accuracy and efficiency of the preconditioned conjugate gradient method used for the estimation. The aim of this study was to investigate the effect of pedigree depth on the model's overall convergence rate as well as on the convergence of different components of the model, in the context of the single-step single nucleotide polymorphism best linear unbiased prediction (SNP-BLUP) model. RESULTS: The results demonstrate that the dataset with a truncated pedigree converged twice as fast as the full dataset. Still, both datasets showed very high Pearson correlations between predicted breeding values. In addition, by comparing the top 50 bulls between the two datasets we found a high correlation between their rankings. We also analysed the specific convergence patterns underlying different animal groups and model effects, which revealed heterogeneity in convergence behaviour. Effects of SNPs converged the fastest while those of genetic groups converged the slowest, which reflects the difference in information content available in the dataset for those effects. Pre-selection criteria for the SNP set based on minor allele frequency had no impact on either the rate or pattern of their convergence. Among different groups of individuals, genotyped animals with phenotype data converged the fastest, while non-genotyped animals without own records required the largest number of iterations. CONCLUSIONS: We conclude that pedigree structure markedly impacts the convergence rate of the optimisation which is more efficient for the truncated than for the full dataset.

Better safe than sorry-Whole-genome sequencing indicates that missense variants are significant in susceptibility to COVID-19.

Undoubtedly, genetic factors play an important role in susceptibility and resistance to COVID-19. In this study, we conducted the GWAS analysis. Out of 15,489,173 SNPs, we identified 18,191 significant SNPs for severe and 11,799 SNPs for resistant phenotype, showing that a great number of loci were significant in different COVID-19 representations. The majority of variants were synonymous (60.56% for severe, 58.46% for resistant phenotype) or located in introns (55.77% for severe, 59.83% for resistant phenotype). We identified the most significant SNPs for a severe outcome (in AJAP1 intron) and for COVID resistance (in FIG4 intron). We found no missense variants with a potential causal function on resistance to COVID-19; however, two missense variants were determined as significant a severe phenotype (in PM20D1 and LRP4 exons). None of the aforementioned SNPs and missense variants found in this study have been previously associated with COVID-19.

Beyond GWAS-Could Genetic Differentiation within the Allograft Rejection Pathway Shape Natural Immunity to COVID-19?

COVID-19 infections pose a serious global health concern so it is crucial to identify the biomarkers for the susceptibility to and resistance against this disease that could help in a rapid risk assessment and reliable decisions being made on patients' treatment and their potential hospitalisation. Several studies investigated the factors associated with severe COVID-19 outcomes that can be either environmental, population based, or genetic. It was demonstrated that the genetics of the host plays an important role in the various immune responses and, therefore, there are different clinical presentations of COVID-19 infection. In this study, we aimed to use variant descriptive statistics from GWAS (Genome-Wide Association Study) and variant genomic annotations to identify metabolic pathways that are associated with a severe COVID-19 infection as well as pathways related to resistance to COVID-19. For this purpose, we applied a custom-designed mixed linear model implemented into custom-written software. Our analysis of more than 12.5 million SNPs did not indicate any pathway that was significant for a severe COVID-19 infection. However, the Allograft rejection pathway (hsa05330) was significant (p = 0.01087) for resistance to the infection. The majority of the 27 SNP marking genes constituting the Allograft rejection pathway were located on chromosome 6 (19 SNPs) and the remainder were mapped to chromosomes 2, 3, 10, 12, 20, and X. This pathway comprises several immune system components crucial for the self versus non-self recognition, but also the components of antiviral immunity. Our study demonstrated that not only single variants are important for resistance to COVID-19, but also the cumulative impact of several SNPs within the same pathway matters.