One of the seminal events since 2019 has been the outbreak of the SARS-CoV-2 pandemic. Countries have adopted various policies to deal with it, but they also differ in their socio-geographical ...characteristics and public health care facilities. Our study aimed to investigate differences between epidemiological parameters across countries.
The analysed data represents SARS-CoV-2 repository provided by the Johns Hopkins University. Separately for each country, we estimated recovery and mortality rates using the SIRD model applied to the first 30, 60, 150, and 300 days of the pandemic. Moreover, a mixture of normal distributions was fitted to the number of confirmed cases and deaths during the first 300 days. The estimates of peaks' means and variances were used to identify countries with outlying parameters.
For 300 days Belgium, Cyprus, France, the Netherlands, Serbia, and the UK were classified as outliers by all three outlier detection methods. Yemen was classified as an outlier for each of the four considered timeframes, due to high mortality rates. During the first 300 days of the pandemic, the majority of countries underwent three peaks in the number of confirmed cases, except Australia and Kazakhstan with two peaks.
Considering recovery and mortality rates we observed heterogeneity between countries. Liechtenstein was the "positive" outlier with low mortality rates and high recovery rates, at the opposite, Yemen represented a "negative" outlier with high mortality for all four considered periods and low recovery for 30 and 60 days.
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.
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 (
= 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.
The number of cases of pancreatic cancers in 2019 in Poland was 3852 (approx. 2% of all cancers). The course of the disease is very fast, and the average survival time from the diagnosis is 6 months. ...Only <2% of patients live for 5 years from the diagnosis, 8% live for 2 years, and almost half live for only about 3 months. A family predisposition to pancreatic cancer occurs in about 10% of cases. Several oncogenes in which somatic changes lead to the development of tumours, including genes BRCA1/2 and PALB2, TP53, CDKN2A, SMAD4, MLL3, TGFBR2, ARID1A and SF3B1, are involved in pancreatic cancer. Between 4% and 10% of individuals with pancreatic cancer will have a mutation in one of these genes. Six percent of patients with pancreatic cancer have NTRK pathogenic fusion. The pathogenesis of pancreatic cancer can in many cases be characterised by homologous recombination deficiency (HRD)-cell inability to effectively repair DNA. It is estimated that from 24% to as many as 44% of pancreatic cancers show HRD. The most common cause of HRD are inactivating mutations in the genes regulating this DNA repair system, mainly BRCA1 and BRCA2, but also PALB2, RAD51C and several dozen others.
Population-based cancer screening has raised many controversies in recent years, not only regarding the costs but also regarding the ethical nature and issues related to variant interpretation. ...Nowadays, genetic cancer screening standards are different in every country and usually encompass only individuals with a personal or family history of relevant cancer.
Here we performed a broad genetic screening for cancer-related rare germline variants on population data from the Thousand Polish Genomes database based on 1076 Polish unrelated individuals that underwent whole genome sequencing (WGS).
We identified 19 551 rare variants in 806 genes related to oncological diseases, among them 89% have been located in non-coding regions. The combined BRCA1/BRCA2 pathogenic/likely pathogenic according to ClinVar allele frequency in the unselected population of 1076 Poles was 0.42%, corresponding to nine carriers.
Altogether, on the population level, we found especially problematic the assessment of the pathogenicity of variants and the relation of ACMG guidelines to the population frequency. Some of the variants may be overinterpreted as disease-causing due to their rarity or lack of annotation in the databases. On the other hand, some relevant variants may have been overseen given that there is little pooled population whole genome data on oncology. Before population WGS screening will become a standard, further studies are needed to assess the frequency of the variants suspected to be pathogenic on the population level and with reporting of likely benign variants.
•CPU times of genomic evaluation depend on the compiler and compilation parameters.•In general, the Intel Fortran performs faster than the Absoft Fortran compiler.•Programs compiled with Intel ...Fortran are less dependent on compilation parameters.•Absoft can better tune programs performing many arithmetic evaluations in memory.
The aim of this study was to compare the CPU time of two most widely used Fortran compilers – the Absoft and the Intel, in the context of two programs used in the genomic evaluation system of dairy cattle - a program for re-coding of the raw genotyping data of single nucleotide polymorphisms and a program for the estimation of additive genetic effects of single nucleotide polymorphisms. The data set used for the analysis corresponded to the Polish national routine genomic evaluation for non-return rate of heifers and comprised genotypes of 46,267 single nucleotide polymorphisms for each of 2904 evaluated bulls, which resulted in 130,393,459 non-missing genotype records. Programs were compiled under several different compilation options using the Absoft (release 2015) and the Intel (release 2019) compilers. The CPU times of both programmes significantly differed between compilers. In the program for genotype re-coding the Intel compiler always resulted in shorter running times than the Absoft compiler. More variation was observed in CPU times of the program for effect estimation, for which the Absoft compiler resulted in both, the shortest and the longest computing times. A more detailed run-time memory consumption visualisation of single runs of the fastest Absoft and Intel compiled program executions demonstrated that the former compiler allows for the utilisation of more of the available memory, which in the case of a memory intensive program for effect estimation resulted in a faster execution time. In conclusion, an overall performance of the Intel compiler was better than this of the Absoft compiler. Even though significant differences in CPU time were observed, the Intel compiled execution times were less dependent on the pre-imposed compilation parameters. However, it is important to note that for programs performing a large number of arithmetic evaluations it is possible to tune the Absoft compiler to outperform the Intel compiler. The key compilation parameters are those, which allow for lower floating point accuracy and dedicated optimization of mathematical operations. Although blind use of such options is not recommended, in the case of genomic evaluation the math and floating point based optimisations did not affect the convergence of the solver nor the estimates of single nucleotide polymorphisms’ effects.
This study compared computational approaches to parallelization of an SNP calling workflow. The data comprised DNA from five Holstein-Friesian cows sequenced with the Illumina platform. The pipeline ...consisted of quality control, alignment to the reference genome, post-alignment, and SNP calling. Three approaches to parallelization were compared: (i) a plain Bash script in which a pipeline for each cow was executed as separate processes invoked at the same time, (ii) a Bash script wrapped in a single Nextflow process and (iii) a Nextflow script with each component of the pipeline defined as a separate process. The results demonstrated that on average, the multi-process Nextflow script performed 15-27% faster depending on the number of assigned threads, with the biggest execution time advantage over the plain Bash approach observed with 10 threads. In terms of RAM usage, the most substantial variation was observed for the multi-process Nextflow, for which it increased with the number of assigned threads, while RAM consumption of the other setups did not depend much on the number of threads assigned for computations. Due to intermediate and log files generated, disk usage was markedly higher for the multi-process Nextflow than for the plain Bash and for the single-process Nextflow.
We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We consider the problem ...of minimizing a strongly convex function represented as the sum of an average of a large number of smooth convex functions, and a simple nonsmooth convex regularizer. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate becoming the new starting point. The novelty of our method is in introduction of mini-batching into the computation of stochastic steps. In each step, instead of choosing a single function, we sample b functions, compute their gradients, and compute the direction based on this. We analyze the complexity of the method and show that it benefits from two speedup effects. First, we prove that as long as b is below a certain threshold, we can reach any predefined accuracy with less overall work than without mini-batching. Second, our mini-batching scheme admits a simple parallel implementation, and hence is suitable for further acceleration by parallelization.
Spatial genome organization and its effect on transcription remains a fundamental question. We applied an advanced chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) strategy to ...comprehensively map higher-order chromosome folding and specific chromatin interactions mediated by CCCTC-binding factor (CTCF) and RNA polymerase II (RNAPII) with haplotype specificity and nucleotide resolution in different human cell lineages. We find that CTCF/cohesin-mediated interaction anchors serve as structural foci for spatial organization of constitutive genes concordant with CTCF-motif orientation, whereas RNAPII interacts within these structures by selectively drawing cell-type-specific genes toward CTCF foci for coordinated transcription. Furthermore, we show that haplotype variants and allelic interactions have differential effects on chromosome configuration, influencing gene expression, and may provide mechanistic insights into functions associated with disease susceptibility. 3D genome simulation suggests a model of chromatin folding around chromosomal axes, where CTCF is involved in defining the interface between condensed and open compartments for structural regulation. Our 3D genome strategy thus provides unique insights in the topological mechanism of human variations and diseases.
Display omitted
•ChIA-PET is inclusive in mapping 3D genome at multi-scale and nucleotide resolution•CTCF foci spatially arrange RNAPII transcription concordant in CTCF-motif direction•SNPs alter haplotype chromatin topology and function that link to disease risks•3D genome models elucidate topological framework for transcriptional regulation
Advanced ChIA-PET shows that CTCF/cohesin and RNA polymerase II arrange spatial organization for coordinated transcription. Haplotype variants exhibit allelic effects on chromatin topology and transcription that link disease susceptibility.
PDF
