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Chapter 10: Mining genome-wide genetic markers.

ABSTRACT: Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3) The limitations of current approaches and future directions.

SUBMITTER: Zhang X

PROVIDER: S-EPMC3531292 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Chapter 10: Mining genome-wide genetic markers.

Zhang Xiang X Huang Shunping S Zhang Zhaojun Z Wang Wei W

PLoS computational biology 20121227 12

Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used i ...[more]

PMID: 23300418

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Project description:The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.

Project description:Mycoplasma bovis causes many health and welfare problems in cattle. Due to the absence of clear insights regarding transmission dynamics and the lack of a registered vaccine in Europe, control of an outbreak depends mainly on antimicrobial therapy. Unfortunately, antimicrobial susceptibility testing (AST) is usually not performed, because it is time-consuming and no standard protocol or clinical breakpoints are available. Fast identification of genetic markers associated with acquired resistance may at least partly resolve former issues. Therefore, the aims of this study were to implement a first genome-wide association study (GWAS) approach to identify genetic markers linked to antimicrobial resistance (AMR) in M. bovis using rapid long-read sequencing and to evaluate different epidemiological cutoff (ECOFF) thresholds. High-quality genomes of 100 M. bovis isolates were generated by Nanopore sequencing, and isolates were categorized as wild-type or non-wild-type isolates based on MIC testing results. Subsequently, a k-mer-based GWAS analysis was performed to link genotypes with phenotypes based on different ECOFF thresholds. This resulted in potential genetic markers for macrolides (gamithromycin and tylosin) (23S rRNA gene and 50S ribosomal unit) and enrofloxacin (GyrA and ParC). Also, for tilmicosin and the tetracyclines, previously described mutations in both 23S rRNA alleles and in one or both 16S rRNA alleles were observed. In addition, two new 16S rRNA mutations were possibly associated with gentamicin resistance. In conclusion, this study shows the potential of quick high-quality Nanopore sequencing and GWAS analysis in the evaluation of phenotypic ECOFF thresholds and the rapid identification of M. bovis strains with acquired resistance. IMPORTANCE Mycoplasma bovis is a leading cause of pneumonia but also causes other clinical signs in cattle. Since no effective vaccine is available, current M. bovis outbreak treatment relies primarily on the use of antimicrobials. However, M. bovis is naturally resistant to different antimicrobials, and acquired resistance against macrolides and fluoroquinolones is frequently described. Therefore, AST is important to provide appropriate and rapid antimicrobial treatment in the framework of AMR and to prevent the disease from spreading and/or becoming chronic. Unfortunately, phenotypic AST is time-consuming and, due to the lack of clinical breakpoints, the interpretation of AST in M. bovis is limited to the use of ECOFF values. Therefore, the objective of this study was to identify known and potentially new genetic markers linked to AMR phenotypes of M. bovis isolates, exploiting the power of a GWAS approach. For this, we used high-quality and complete Nanopore-sequenced M. bovis genomes of 100 isolates.

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Chapter 10: Mining genome-wide genetic markers.

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Chapter 10: Mining genome-wide genetic markers.

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