The role of viral genomics in understanding COVID-19 outbreaks in long-term care facilities

We reviewed all genomic epidemiology studies on COVID-19 in long-term care facilities (LTCFs) that had been published to date. We found that staff and residents were usually infected with identical, or near identical, SARS-CoV-2 genomes. Outbreaks usually involved one predominant cluster, and the same lineages persisted in LTCFs despite infection control measures. Outbreaks were most commonly due to single or few introductions followed by a spread rather than a series of seeding events from the community into LTCFs. The sequencing of samples taken consecutively from the same individuals at the same facilities showed the persistence of the same genome sequence, indicating that the sequencing technique was robust over time. When combined with local epidemiology, genomics allowed probable transmission sources to be better characterised. The transmission between LTCFs was detected in multiple studies. The mortality rate among residents was high in all facilities, regardless of the lineage. Bioinformatics methods were inadequate in a third of the studies reviewed, and reproducing the analyses was difficult because sequencing data were not available in many facilities

Are Differences in Genomic Data Sets due to True Biological Variants or Errors in Genome Assembly: An Example from Two Chloroplast Genomes

DNA sequencing has been revolutionized by the development of high-throughput sequencing technologies. Plummeting costs and the massive throughput capacities of second and third generation sequencing platforms have transformed many fields of biological research. Concurrently, new data processing pipelines made rapid de novo genome assemblies possible. However, high quality data are critically important for all investigations in the genomic era. We used chloroplast genomes of one Oryza species (O. australiensis) to compare differences in sequence quality: one genome (GU592209) was obtained through Illumina sequencing and reference-guided assembly and the other genome (KJ830774) was obtained via target enrichment libraries and shotgun sequencing. Based on the whole genome alignment, GU592209 was more similar to the reference genome (O. sativa: AY522330) with 99.2% sequence identity (SI value) compared with the 98.8% SI values in the KJ830774 genome; whereas the opposite result was obtained when the SI values in coding and noncoding regions of GU592209 and KJ830774 were compared. Additionally, the junctions of two single copies and repeat copies in the chloroplast genome exhibited differences. Phylogenetic analyses were conducted using these sequences, and the different data sets yielded dissimilar topologies: phylogenetic replacements of the two individuals were remarkably different based on whole genome sequencing or SNP data and insertions and deletions (indels) data. Thus, we concluded that the genomic composition of GU592209 was heterogeneous in coding and non-coding regions. These findings should impel biologists to carefully consider the quality of sequencing and assembly when working with next-generation data

The Cost Effectiveness of Pharmacological Treatments for Generalized Anxiety Disorder

Background: Generalized anxiety disorder (GAD) is one of the most prevalent anxiety disorders, with important implications for patients and healthcare resources. However, few economic evaluations of pharmacological treatments for GAD have been published to date, and those available have assessed only a limited number of drugs. / Objective: To assess the cost effectiveness of pharmacological interventions for patients with GAD in the UK. / Methods: A decision-analytic model in the form of a decision tree was constructed to compare the costs and QALYs of six drugs used as first-line pharmacological treatments in people with GAD (duloxetine, escitalopram, paroxetine, pregabalin, sertraline and venlafaxine extended release [XL]) and ‘no pharmacological treatment’. The analysis adopted the perspective of the NHS and Personal Social Services (PSS) in the UK. Efficacy data were derived from a systematic literature review of double-blind, randomized controlled trials and were synthesized using network meta-analytic techniques. Two network meta-analyses were undertaken to assess the comparative efficacy (expressed by response rates) and tolerability (expressed by rates of discontinuation due to intolerable side effects) of the six drugs and no treatment in the study population. Cost data were derived from published literature and national sources, supplemented by expert opinion. The price year was 2011. Probabilistic sensitivity analysis was conducted to evaluate the underlying uncertainty of the model input parameters. / Results: Sertraline was the best drug in limiting discontinuation due to side effects and the second best drug in achieving response in patients not discontinuing treatment due to side effects. It also resulted in the lowest costs and highest number of QALYs among all treatment options assessed. Its probability of being the most cost-effective drug reached 75 % at a willingness-to-pay threshold of £20,000 per extra QALY gained. / Conclusion: Sertraline appears to be the most cost-effective drug in the treatment of patients with GAD. However, this finding is based on limited evidence for sertraline (two published trials). Sertraline is not licensed for the treatment of GAD in the UK, but is commonly used by primary care practitioners for the treatment of depression and mixed depression and anxiety

Interpreting noncoding genetic variation in complex traits and human disease

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment