Development of a composite indicator to prioritize districts for implementation of human immunodeficiency virus programmes in Maharashtra,India

A key recommendation of the National AIDS Control Programme‐IV of India was to develop new strategies for geo‐prioritization of the human immunodeficiency virus (HIV) epidemic. We conducted this study to categorize the districts in Maharashtra (India) based on a multidimensional framework for geo‐prioritization of services. Programmatic data on trends of HIV prevalence, coverage of marginalized populations and vulnerability factors were included. A composite indicator based on these was developed, and the cumulative score was calculated for each district. HIV prevalence among general population has declined steadily from 0.60% in 2007 to 0.33% in 2017. The programme coverage was stable but inadequate for men who have sex with men (MSM). The coverage for female sex workers (FSWs) was inadequate and reduced over time. Nine districts were categorized as high priority, 13 as moderate priority and 11 were classified as low‐priority districts based on burden and vulnerability for HIV. The high‐priority districts were Pune, Solapur and Yavatmal for FSW interventions and Pune, Thane and Latur for MSM interventions. This multidimensional indicator is based on existing programmatic data, dynamic and can be made state‐specific. It is useful to categorize and prioritize districts for allocation of resources and geo‐prioritization of services in resource limited settings.     

eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease

PurposeDisease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.MethodsWe developed an eye disease–specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure.ResultseyeVarP outperformed pan disease–specific tools in identifying eye disease–specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders.ConclusioneyeVarP performs better in identifying eye disease–specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.     

Fine‐Mapping Additive and Dominant SNP Effects Using Group‐LASSO and Fractional Resample Model Averaging

Genomewide association studies (GWAS) sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple single‐nucleotide polymorphisms (SNPs) simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow‐up studies. Current multi‐SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA‐dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights. It estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing an SNP prioritization that best identifies underlying true signals, we show the following: our method easily outperforms a single‐marker analysis; when additive‐only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive‐only effects; and when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation.     

Whole exome sequencing identifies a novel mutation in the transglutaminase 6 gene for spinocerebellar ataxia in a Chinese family

Li M, Pang SYY, Song Y, Kung MHW, Ho S‐L, Sham P‐C. Whole exome sequencing identifies a novel mutation in the transglutaminase 6 gene for spinocerebellar ataxia in a Chinese family. Autosomal dominant spinocerebellar ataxias (SCA) constitute a heterogeneous group of inherited disorders. The transglutaminase 6 (TGM6) gene was recently suggested as a SCA causative gene in Chinese SCA families. In this study, two affected members of a three‐generation Chinese family with SCA characterized by progressive cerebellar ataxia and lower limb pyramidal signs were subjected to whole exome sequencing. Through bioinformatics analysis of the sequence variants in these two individuals, we identified a novel mutation in the TGM6 gene (c.1528G>C) which showed perfect co‐segregation with disease phenotype in all nine members of this family. This finding confirms that mutations in TGM6 gene represent an important cause of SCA in Chinese. This study also shows that whole exome sequencing of a small number of affected individuals, leveraged on bioinformatics analysis, can be an efficient strategy for identifying causative mutations in rare Mendelian disorders.     

The Impact of Decision Makers’ Constraints on the Outcome of Value of Information Analysis

Background

When proven effective, decision making regarding reimbursement of new health technology typically involves ethical, social, legal, and health economic aspects and constraints. Nevertheless, when applying standard value of information (VOI) analysis, the value of collecting additional evidence is typically estimated assuming that only cost-effectiveness outcomes guide such decisions.

Prioritization of livestock transboundary diseases in Belgium using a multicriteria decision analysis tool based on drivers of emergence

During the past decade, livestock diseases have (re‐)emerged in areas where they had been previously eradicated or never been recorded before. Drivers (i.e. factors of (re‐)emergence) have been identified. Livestock diseases spread irrespective of borders, and therefore, reliable methods are required to help decision‐makers to identify potential threats and try stopping their (re‐)emergence. Ranking methods and multicriteria approaches are cost‐effective tools for such purpose and were applied to prioritize a list of selected diseases (N = 29 including 6 zoonoses) based on the opinion of 62 experts in accordance with 50 drivers‐related criteria. Diseases appearing in the upper ranking were porcine epidemic diarrhoea, foot‐and‐mouth disease, low pathogenic avian influenza, African horse sickness and highly pathogenic avian influenza. The tool proposed uses a multicriteria decision analysis approach to prioritize pathogens according to drivers and can be applied to other countries or diseases.     

Agricultural Pesticide Usage and Prioritization in South Korea

This study aims to review agricultural pesticide usage and trends and to identify hazardous pesticides for regulation, in terms of public health, in South Korea. The authors collected data on usage and trends of agricultural pesticides through agriculture-related databases. Criteria from the US Environmental Protection Agency classification for carcinogenicity, World Health Organization classification for acute toxicity, and European Union prioritization list for endocrine-disrupting chemicals were used for the hazard categorization of identified individual active ingredients. Pesticides to be prioritized among all pesticides used in South Korea between 2007 and 2011 were selected by taking into account the volume of usage, toxicity, and epidemiological evidence. Annual agricultural use of pesticides has increased rapidly from the 1970s to 1990s in South Korea, but has declined since 2001. The quantity of pesticides used in 2011 was reported as 19,131 tons, and was comprised of 34.7% insecticides, 28.0% fungicides, and 27.1% herbicides. The 50 pesticides with the greatest volume of usage accounted for 82.6% of the total volume of pesticides used between 2007 and 2011, with the most-used active ingredient being machine oil, followed by mancozeb and then paraquat. Organophosphates were the most used among the top 50 pesticides. A total of 24 pesticides were selected for recommendation of intensive regulation in South Korea. In conclusion, the authors described the usage and trends of overall agricultural pesticides, which would serve as a fundamental step forward in managing pesticide in terms of public health. Intensive efforts are required for the prevention of potential health effects from the 24 identified pesticides.     

The roles of the LpSTS and DLPFC in self‐prioritization: A transcranial magnetic stimulation study

The Self‐Attention Network (SAN) has been proposed to describe the underlying neural mechanism of the self‐prioritization effect, yet the roles of the key nodes in the SAN—the left posterior superior temporal sulcus (LpSTS) and the dorsolateral prefrontal cortex (DLPFC)—still need to be clarified. One hundred and nine participants were randomly assigned into the LpSTS group, the DLPFC group, or the sham group. We used the transcranial magnetic stimulation (TMS) technique to selectively disrupt the functions of the corresponding targeted region, and observed its impacts on self‐prioritization effect based on the difference between the performance of the self‐matching task before and after the targeted stimulation. We analyzed both model‐free performance measures and HDDM‐based performance measures for the self‐matching task. The results showed that the inhibition of LpSTS could lead to reduced performance in processing self‐related stimuli, which establishes a causal role for the LpSTS in self‐related processing and provide direct evidence to support the SAN framework. However, the results of the DLPFC group from HDDM analysis were distinct from the results based on response efficiency. Our investigation further the understanding of the differentiated roles of key nodes in the SAN in supporting the self‐salience in information processing.