Dataset Information

Informing Prevention and Intervention Policy Using Genetic Studies of Resistance.

ABSTRACT: The common paradigm for conceptualizing the influence of genetic and environmental factors on a particular disease relies on the concept of risk. Consequently, the bulk of etiologic, including genetic, work focuses on "risk" factors. These factors are aggregated at the high end of the distribution of liability to disease, the latent variable underlying the distribution of probability and severity of a disorder. However, liability has a symmetric but distinct aspect to risk, resistance to disorder. Resistance factors, aggregated at the low end of the liability distribution and supporting health and recovery, appear to be more promising for effective prevention and intervention. Herein, we discuss existing work on resistance factors, highlighting those with known genetic influences. We examine the utility of incorporating resistance genetics in prevention and intervention trials and compare the statistical power of a series of ascertainment schemes to develop a general framework for examining resistance outcomes in genetically informative designs. We find that an approach that samples individuals discordant on measured liability, a low-risk design, is the most feasible design and yields power equivalent to or higher than commonly used designs for detecting resistance genetic and environmental effects.

SUBMITTER: Maher BS

PROVIDER: S-EPMC5466512 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Informing Prevention and Intervention Policy Using Genetic Studies of Resistance.

Maher Brion S BS Latendresse Shawn S Vanyukov Michael M MM

Prevention science : the official journal of the Society for Prevention Research 20180101 1

The common paradigm for conceptualizing the influence of genetic and environmental factors on a particular disease relies on the concept of risk. Consequently, the bulk of etiologic, including genetic, work focuses on "risk" factors. These factors are aggregated at the high end of the distribution of liability to disease, the latent variable underlying the distribution of probability and severity of a disorder. However, liability has a symmetric but distinct aspect to risk, resistance to disorde ...[more]

PMID: 27943075

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Project description:Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci.

Project description:Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data.

Dataset Information

Informing Prevention and Intervention Policy Using Genetic Studies of Resistance.

Publications

Informing Prevention and Intervention Policy Using Genetic Studies of Resistance.

Similar Datasets

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