Parallelization of logic regression analysis on SNP-SNP interactions of a Crohn’s disease dataset model

SNP-SNP interactions have been recognized to be basically important for understanding genetic causes of complex disease traits. Logic regression is an effective methods for identifying SNP-SNP interactions associated with risk of complex disease. However, identifying SNP-SNP interactions are computationally challenging and may take hours, weeks and months to complete. Although parallel computing is a powerful method to accelerate computing time, it is arduous for users to apply this method to logic regression analyses of SNP-SNP interactions because it requires advanced programming skills to correctly partition and distribute data, control and monitor tasks across multi-core CPUs or several computers, and merge output files. In this paper, we present a novel R-library called SNPInt to automatically speed up analyses of SNP-SNP interactions of genome-wide association (GWA) studies using parallel computing without the advanced programming skills. The Crohn’s disease GWA studies dataset from the Wellcome Trust Case Control Consortium (WTCCC) that includes 4,680 individuals with 500,000 SNPs’ genotypes was analyzed using logic regression on a computer cluster to evaluate SNPInt performance. The results from SNPInt with any number of CPUs are the same as the results from non-parallel approach, and SNPInt library quite accelerated the logic regression analysis. For instance, with two hundred genes and twenty permutation rounds, the computing time was continuously decreased from 7.3 days to only 0.9 day when SNPInt applied eight CPUs. Executing analyses of SNP-SNP interactions using the SNPInt library is an effective way to boost performance, and simplify the parallelization of analyses of SNP-SNP interactions

Early treatment of Favipiravir in COVID-19 patients without pneumonia: a multicentre, open-labelled, randomized control study

We investigated Favipiravir (FPV) efficacy in mild cases of COVID-19 without pneumonia and its effects towards viral clearance, clinical condition, and risk of COVID-19 pneumonia development. PCR-confirmed SARS-CoV-2-infected patients without pneumonia were enrolled (2:1) within 10 days of symptomatic onset into FPV and control arms. The former received 1800 mg FPV twice-daily (BID) on Day 1 and 800 mg BID 5-14 days thereafter until negative viral detection, while the latter received only supportive care. The primary endpoint was time to clinical improvement, defined by a National Early Warning Score (NEWS) of ≤1. 62 patients (41 female) comprised the FPV arm (median age: 32 years, median BMI: 22 kg/m²) and 31 patients (19 female) comprised the control arm (median age: 28 years, median BMI: 22 kg/m²). The median time to sustained clinical improvement, by NEWS, was 2 and 14 days for FPV and control arms, respectively (adjusted hazard ratio (aHR) of 2.77, 95% CI 1.57-4.88, P P P = .316). All recovered well without complications. We can conclude that early treatment of FPV in symptomatic COVID-19 patients without pneumonia was associated with faster clinical improvement.Trial registration: Thai Clinical Trials Registry identifier: TCTR20200514001

ParallABEL: an R library for generalized parallelization of genome-wide association studies

Background: Genome-Wide Association (GWA) analysis is a powerful method for identifying loci associated with complex traits and drug response. Parts of GWA analyses, especially those involving thousands of individuals and consuming hours to months, will benefit from parallel computation. It is arduous acquiring the necessary programming skills to correctly partition and distribute data, control and monitor tasks on clustered computers, and merge output files.Results: Most components of GWA analysis can be divided into four groups based on the types of input data and statistical outputs. The first group contains statistics computed for a particular Single Nucleotide Polymorphism (SNP), or trait, such as SNP characterization statistics or association test statistics. The input data of this group includes the SNPs/traits. The second group concerns statistics characterizing an individual in a study, for example, the summary statistics of genotype quality for each sample. The input data of this group includes individuals. The third group consists of pair-wise statistics derived from analyses between each pair of individuals in the study, for example genome-wide identity-by-state or genomic kinship analyses. The input data of this group includes pairs of SNPs/traits. The final group concerns pair-wise statistics derived for pairs of SNPs, such as the linkage disequilibrium characterisation. The input data of this group includes pairs of individuals. We developed the ParallABEL library, which utilizes the Rmpi library, to parallelize these four types of computations. ParallABEL library is not only aimed at GenABEL, but may also be employed to parallelize various GWA packages in R. The data set from the North American Rheumatoid Arthritis Consortium (NARAC) includes 2,062 individuals with 545,080, SNPs' genotyping, was used to measure ParallABEL performance. Almost perfect speed-up was achieved for many types of analyses. For example, the computing time for the identity-by-state matrix was linearly reduced from approximately eight hours to one hour when ParallABEL employed eight processors.Conclusions: Executing genome-wide association analysis using the ParallABEL library on a computer cluster is an effective way to boost performance, and simplify the parallelization of GWA studies. ParallABEL is a user-friendly parallelization of GenABEL

Clusters of Drug-Resistant Mycobacterium tuberculosis Detected by Whole-Genome Sequence Analysis of Nationwide Sample, Thailand, 2014-2017.

Multidrug-resistant tuberculosis (MDR TB), pre-extensively drug-resistant tuberculosis (pre-XDR TB), and extensively drug-resistant tuberculosis (XDR TB) complicate disease control. We analyzed whole-genome sequence data for 579 phenotypically drug-resistant M. tuberculosis isolates (28% of available MDR/pre-XDR and all culturable XDR TB isolates collected in Thailand during 2014-2017). Most isolates were from lineage 2 (n = 482; 83.2%). Cluster analysis revealed that 281/579 isolates (48.5%) formed 89 clusters, including 205 MDR TB, 46 pre-XDR TB, 19 XDR TB, and 11 poly-drug-resistant TB isolates based on genotypic drug resistance. Members of most clusters had the same subset of drug resistance-associated mutations, supporting potential primary resistance in MDR TB (n = 176/205; 85.9%), pre-XDR TB (n = 29/46; 63.0%), and XDR TB (n = 14/19; 73.7%). Thirteen major clades were significantly associated with geography (p<0.001). Clusters of clonal origin contribute greatly to the high prevalence of drug-resistant TB in Thailand

Evidence for Host-Bacterial Co-evolution via Genome Sequence Analysis of 480 Thai Mycobacterium tuberculosis Lineage 1 Isolates.

Tuberculosis presents a global health challenge. Mycobacterium tuberculosis is divided into several lineages, each with a different geographical distribution. M. tuberculosis lineage 1 (L1) is common in the high-burden areas in East Africa and Southeast Asia. Although the founder effect contributes significantly to the phylogeographic profile, co-evolution between the host and M. tuberculosis may also play a role. Here, we reported the genomic analysis of 480 L1 isolates from patients in northern Thailand. The studied bacterial population was genetically diverse, allowing the identification of a total of 18 sublineages distributed into three major clades. The majority of isolates belonged to L1.1 followed by L1.2.1 and L1.2.2. Comparison of the single nucleotide variant (SNV) phylogenetic tree and the clades defined by spoligotyping revealed some monophyletic clades representing EAI2_MNL, EAI2_NTM and EAI6_BGD1 spoligotypes. Our work demonstrates that ambiguity in spoligotype assignment could be partially resolved if the entire DR region is investigated. Using the information to map L1 diversity across Southeast Asia highlighted differences in the dominant strain-types in each individual country, despite extensive interactions between populations over time. This finding supported the hypothesis that there is co-evolution between the bacteria and the host, and have implications for tuberculosis disease control

Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis Standard Reporting and Evaluation Guidelines Results of a National Institutes of Health Working Group

Importance: Toxic epidermal necrolysis (TEN) and Stevens-Johnson Syndrome (SJS) are rare, acute, life-threatening dermatologic disorders involving the skin and mucous membranes. Research into these conditions is hampered by a lack of standardization of case reporting and data collection. Objective: To establish a standardized case report form to facilitate comparisons and maintain data quality based on an international panel of SJS/TEN experts who performed a Delphi consensus-building exercise. Evidence Review: The elements presented for committee scrutiny were adapted from previous case report forms and from PubMed literature searches of highly cited manuscripts pertaining to SJS/TEN. The expert opinions and experience of the members of the consensus group were included in the discussion. Findings: Overall, 21 out of 29 experts who were invited to participate in the online Delphi exercise agreed to participate. Surveys at each stage were administered via an online survery software tool. For the first 2 Delphi rounds, results were analyzed using the Interpercentile Range Adjusted for Symmetry method and statements that passed consensus formulated a new case report form. For the third Delphi round, the case report form was presented to the committee, who agreed that it was "appropriate and useful" for documenting cases of SJS/TEN, making it more reliable and valuable for future research endeavors. Conclusions and Relevance: With the consensus of international experts, a case report form for SJS/TEN has been created to help standardize the collection of patient information in future studies and the documentation of individual cases

A novel Ancestral Beijing sublineage of Mycobacterium tuberculosis suggests the transition site to Modern Beijing sublineages.

Global Mycobacterium tuberculosis population comprises 7 major lineages. The Beijing strains, particularly the ones classified as Modern groups, have been found worldwide, frequently associated with drug resistance, younger ages, outbreaks and appear to be expanding. Here, we report analysis of whole genome sequences of 1170 M. tuberculosis isolates together with their patient profiles. Our samples belonged to Lineage 1-4 (L1-L4) with those of L1 and L2 being equally dominant. Phylogenetic analysis revealed several new or rare sublineages. Differential associations between sublineages of M. tuberculosis and patient profiles, including ages, ethnicity, HIV (human immunodeficiency virus) infection and drug resistance were demonstrated. The Ancestral Beijing strains and some sublineages of L4 were associated with ethnic minorities while L1 was more common in Thais. L2.2.1.Ancestral 4 surprisingly had a mutation that is typical of the Modern Beijing sublineages and was common in Akha and Lahu tribes who have migrated from Southern China in the last century. This may indicate that the evolutionary transition from the Ancestral to Modern Beijing sublineages might be gradual and occur in Southern China, where the presence of multiple ethnic groups might have allowed for the circulations of various co-evolving sublineages which ultimately lead to the emergence of the Modern Beijing strains

Molecular Epidemiological Information System to Support Management of Multidrug-Resistant Tuberculosis in Thailand

Objective: To support the End TB strategy with an informatics system that integrates genomic data and the geographic information system (GIS) of Mycobacterium tuberculosis (MTB) clinical isolates. The priority of the information system is to control multiple drug-resistant tuberculosis (MDR-TB).Methods: System requirements were clarified using an exploratory approach. A data value chain was applied for system prototyping. Role-based access control was adopted for system permission management. MDR-TB isolates were collected from Kanchanburi Province, Thailand, from 2013–2017. Genotyping information of the isolated MDR-TB strains was obtained from whole genome sequencing analysis. Spatiotemporal analysis using SaTScan™ version 9.6 was performed to identify significant high rate spatial MDR-TB clusters or hotspots of MDR-TB transmission.Results: The iMoji system architecture was established. The data entry modules consisted of (1) patient registration, (2) sample registration, (3) laboratory data entry and data analysis, and (4) verification and approval of the analyzed data. An integrative analysis of the MDR-TB genotype and geospatial data provided information for the MDR-TB cluster analysis. An MDR-TB transmission hotspot was identified with the log-likelihood ratio of 14.44 (P value &lt; 0.001). Temporal analysis suggested that transmission occurred more frequently between 12/1/2014 to 2/28/2017.Conclusion: Our findings provide a proof of concept for integrating genomic data from MDR-TB and corresponding spatiotemporal information to guide public health interventions for tuberculosis control