an informatics approach to analyzing the incidentalome j.berg et al. genetics in medicine presented...

An informatics approach to analyzing the incidentalome

J.Berg et al. Genetics in MedicinePresented by Li Changjian

Concept

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

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

Binning System

• Categories the genetic data

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

Screening Process of OMIM genes Provisional

Binning

Allele Frequency cut-off (AF<5%)

Protein-altering variants

Further Screening

• Presence in a binned gene• <5% AF (Low Probability Mendelian Disorder) • 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

Summarized Results

Screening processes of the informatics algorithm

Significant reduction the number of binned genes

Example Results

High specificity for bin 1 and bin 2c variants

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.

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

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)

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

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.