Walsh, M. | Search bioRxiv

bioRxiv2018

The wildlife-livestock interface modulates anthrax suitability in India

Anthrax is a potentially life-threatening bacterial disease that can circulate in wild and domestic animals and subsequently spillover to human contacts with devastating consequences for human and animal health, as well as livestock economies and ecosystem conservation. India has a high annual occurrence of anthrax in some regions, but a country-wide delineation of risk has not yet been undertaken. The current study modeled the geographic suitability of anthrax across India and its associated environmental features using a biogeographical application of machine learning. Both biotic and abiotic features contributed to risk across multiple scales of influence and the wildlife-livestock interface, using elephants as a wildlife sentinel species, was the dominant feature in delineating anthrax suitability. In addition, water-soil balance, soil chemistry, and historical forest loss were also influential. These findings suggest that the wildlife-livestock interface plays an important role in the cycling of anthrax in India. Prevention efforts targeted toward this interface, particularly within anthropogenic ecotones, may yield successes in reducing ongoing transmission between animal hosts and subsequent zoonotic transmission to humans.

epidemiology↗

bioRxiv2018

Towards automation of germline variant curation inclinical cancer genetics

Cancer care professionals are confronted with interpreting results from multiplexed gene sequencing of patients at hereditary risk for cancer. Assessments for variant classification now require orthogonal data searches, requiring aggregation of multiple lines of evidence from diverse resources. The burden of evidence for each variant to meet thresholds for pathogenicity or actionability now poses a growing challenge for those seeking to counsel patients and families following germline genetic testing. A computational algorithm that automates, provides uniformity and significantly accelerates this interpretive process is needed. The tool described here, Pathogenicity of Mutation Analyzer (PathoMAN) automates germline genomic variant curation from clinical sequencing based on ACMG guidelines. PathoMAN aggregates multiple tracks of genomic, protein and disease specific information from public sources. We compared expert manually curated variant data from studies on (i) prostate cancer (ii) breast cancer and (iii) ClinVar to assess performance. PathoMAN achieves high concordance (83.1% pathogenic, 75.5% benign) and negligible discordance (0.04% pathogenic, 0.9% benign) when contrasted against expert curation. Some loss of resolution (8.6% pathogenic, 23.64% benign) and gain of resolution (6.6% pathogenic, 1.6% benign) was also observed. We highlight the advantages and weaknesses related to the programmable automation of variant classification. We also propose a new nosology for the five ACMG classes to facilitate more accurate reporting to ClinVar. The proposed refinements will enhance utility of ClinVar to allow further automation in cancer genetics. PathoMAN will reduce the manual workload of domain level experts. It provides a substantial advance in rapid classification of genetic variants by generating robust models using a knowledge-base of diverse genetic data https://pathoman.mskcc.org.

genetics↗