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An informatics approach to analyzing the incidentalome

An informatics approach to analyzing the incidentalome. J.Berg et al. Genetics in Medicine Presented by Li Changjian. Concept. Incidentalome : Incidental findings of genetic variants unrelated to presenting symptoms during the genetic diagnosis using whole genome sequencing (WGS).

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An informatics approach to analyzing the incidentalome

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  1. An informatics approach to analyzing the incidentalome J.Berg et al. Genetics in Medicine Presented by Li Changjian

  2. Concept • Incidentalome: Incidental findings of genetic variants unrelated to presenting symptoms during the genetic diagnosis using whole genome sequencing (WGS)

  3. Challenge on Incidentalome • Reducing cost in WGS makes it available for genetic diagnosis • Vast volume of genomic findings with dubious clinical value is generated, overwhelming of information to physicians and patients • A good screening/sifting method for the genetic data is needed

  4. BinningSystem • Categories the genetic data

  5. Subjects&Methods • Focus: Monogenic Disorders • OMIM genes for provisional binning (12786 genes) • 80 genome sequences used as test sequences, 19 from paints and 61 from presumably healthy individuals • Database: PostgreSQL 8.4.3, Human Gene Mutation Database (HGMD) and NCBI build 37 • Python script used to determine the zygosity

  6. Screening Process of OMIM genes

  7. Further Screening • Presence in a binned gene • <5% AF (Low Probability MendelianDisorder) • Either being annotated as diseasing-causing mutation (DM) in HGMD or predicted to be truncating • Analyze zygosity to assign heterozygous variants in recessive genes to determine carrier status • Finally, manual review to assess evidence of pathogenicity, reclassify the binning

  8. Summarized Results Screening processes of the informatics algorithm Significant reduction the number of binned genes

  9. Example Results High specificity for bin 1 and bin 2c variants

  10. Sensitivity and Specificity • Excluding synonymous variants, noncoding variants scarifies the sensitivity for higher specificity • No gold standard to definitively estimate the specificity and sensitivity • The sensitivity and specificity ties to quality of clinical database due to the data querying and predictive algorithms.

  11. Comparison with other reports • Substantial difference resulted by different assumptions (ignoring SNPs variants) • Stringent requirements on genes having clinical utility raise the thresholds results four orders less (0-2 variants versus 2000 variants by Cassa et al.) returned variants in bin 1. • The specificity of current binning system is higher

  12. Limitations • Only monogenic diseases is studied in this paper • Specificity and Sensitivity needs quantitative estimation • Number of variants in manual review process in the last step is still large (~100s)

  13. Future directions • Extend the method the multifactorial diesease • Subcategorize Bin 2b into disease groups • Establish more granular criteria to determine the novel variants selected for review • To better understand the penetrance of a certain variants • To improve and maintain clinical-grade database of known variants

  14. Conclusion • Proof of concept of an framework to organizing the incidental findings during WGS to reduce the number of variants to be hand curated to a manageable number.

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