Dataset Information

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions.

ABSTRACT: Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27?bits compared with 0.37?bits for the SNP-threshold method and 0.84?bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.

SUBMITTER: Stimson J

PROVIDER: S-EPMC6389316 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions.

Stimson James J Gardy Jennifer J Mathema Barun B Crudu Valeriu V Cohen Ted T Colijn Caroline C

Molecular biology and evolution 20190301 3

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propos ...[more]

PMID: 30690464

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Project description:For facilitating tuberculosis (TB) control, we used a whole-genome sequencing (WGS)-based approach to delineate transmission networks in a country with an intermediate burden of TB. A cluster was defined as Mycobacterium tuberculosis isolates with identical genotypes, and an outbreak was defined as clustered cases with epidemiological links (epi-links). To refine a cluster predefined using space oligonucleotide typing and mycobacterial interspersed repetitive unit variable tandem repeat typing, we analyzed one pansusceptible TB (C1) and three multidrug-resistant (MDR)-TB (C2-C4) clusters from different scenarios. Pansusceptible TB cluster (C1) consisting of 28 cases had ≤5 single nucleotide polymorphisms (SNPs) difference between their isolates. C1 was a definite outbreak, with cases attending the same junior high school in 2012. Three MDR-TB clusters (C2-C4) with distinct genotypes were identified, each consisting of 12-22 cases. Some of the cases had either ≤5 or ≤15 SNPs difference with clear or probable epi-links. Of note, even though WGS could effectively assist TB contact tracing, we still observed missing epi-links in some cases within the same cluster. Our results showed that thresholds of ≤5 and ≤15 SNPs difference between isolates were used to categorize definite and probable TB transmission, respectively. Furthermore, a higher SNP threshold might be required to define an MDR-TB outbreak. WGS still needs to be combined with classical epidemiological methods for improving outbreak investigations. Importantly, different SNP thresholds have to be applied to define outbreaks.ImportanceTB is a chronic disease. Depending on host factors and TB burden, clusters of cases may continue to increase for several years. Conventional genotyping methods overestimate TB transmission, hampering precise detection of outbreaks and comprehensive surveillance. WGS can be used to obtain SNP information of M. tuberculosis to improve discriminative limitations of conventional methods and to strengthen delineation of transmission networks. It is important to define the country-specific SNP thresholds for investigation of transmission. This study demonstrated the use of thresholds of ≤5 and ≤15 SNPs difference between isolates to categorize definite and probable transmission, respectively. Different SNP thresholds should be applied while a higher cutoff was required to define an MDR-TB outbreak. The utilization of SNP thresholds proves to be crucial for guiding public health interventions, eliminating the need for unnecessary public health actions, and potentially uncovering undisclosed TB transmissions.

Project description:Purpose of reviewNeurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer's disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several 'omics' approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits.Recent findingsSeveral common 'omics' approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants' effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits.SummaryGWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.

Dataset Information

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions.

Publications

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions.

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