Prioritizing Regions of Candidate genes for efficient mutation screening

1. Prioritizing Regions of Candidate genes for efficient mutation screening

2. Outline • Abstract • Background • Materials and Methods • Results • Discussion • Conclusion

3. Abstract • Complete sequence of human genome has altered search process for disease-causing mutations • Previously, mostly rare diseases studied. Took years to analyze data • Now, rate-limiting step is screening patients and interpreting results • Tests hypothesis that disease-causing mutations are not uniformly distributed and can be predicted bioinformatically • Developed prioritization of annotated regions (PAR) technique

4. Abstract • Tested by analyzing 710 genes with 4,498 previously identified mutations • Nearly 50% of disease-associated genes found after analyzing only 9% of complete coding sequence • PAR found 90% of genes as containing at least one mutation using less than 40% of screening resources

5. Background • When screening for mutations, researchers usually focus on coding sequence • Not enough to show relationship between mutation and disease • Ex. Age-related macular degeneration • Today’s techniques: • Single strand conformational polymorphism analysis (SSCP) • Denaturing high-performance liquid chromatography • Automated DNA sequencing

6. Background • SSCP • Compares conformational differences in strands of DNA of the same length (1) • Denaturing high-performance liquid chromatography • Compares two or more chromosomes as a mixture of denatured and reannealed PCR amplicons, revealing the presence of a mutation by the differential retention of homo- and heteroduplex DNA on reversed-phase chromatography supports under partial denaturation (2)

7. Background • Through own work, found disease-causing variations are not uniformly distributed throughout sequence • Ex. Bardet-Biedl: Restrict to patients with retinitis pigmentosa with ulnar polydactyl • Disease-causing mutations more likely lie in structural and functional regions

8. Materials and Methods • List of 710 genes obtained via OMIM • Cross-referenced with transcripts in Ensembl Release NCBI31 • Gene structure and annotated protein domains obtained from Ensembl • Information on mutation locations obtained from OMIM • Secondary structure prediction performed by nnPredict

9. Materials and Methods • x = nucleotide position • Ws = PAR window size • Nx= No. distinct annotation elements • W(i) = PAR window function • Af(x,j) = annotation function for jth annotation at xth position • As(x,j) = annotation score for jth annotation at xth position • Ao(x,j) = annotation scalar offset • Am(j) = annotation multiplier for jth annotation feature

10. Materials and Methods

11. Materials and Methods • Impractical to perform manually for every gene in candidate set • Graphic representation of gene structure of EFEMP1 gene and corresponding PAR values

12. Materials and Methods • Regions in each gene were identified that maximized PAR function • Primer pair positions selected consistent with default parameters of Primer3 until at least one mutation flanked

13. Materials and Methods • Other methods used for comparison • Serial • Generates minimally overlapping primer pair positions for each exon with same PCR product size requirements • Models traditional screening approach • Examines complete coding sequence • Random • Selects region from any transcript without replacement • Continues to select with minimal overlap • Complete screening with laboratory information management system (LIMS)

14. Results - Efficiency • PAR • Found 90% of mutations with 60% coverage • Serial • Linear: 90% at 90%, 100% at 100% • Random: • Fell short of identifying 100% of mutations

15. Results

16. Results – Figure 2 • PAR • 819 mutations identified in 350 distinct genes using a single best PAR-selected region per gene • Corresponds to 18% of mutations in approximately half the transcripts • Of 1,908,911 nucleotides, PAR selected only 168,980 • One mutation was identified in 50% of genes with only 9% of total transcript screened

17. Results

18. Results – Figure 3 • Serial • Linear relationship between screening resource utilization and number of genes • PAR • Identified 90% of genes with 60% reduction in screening resources • Only one primer pair in each transcript was evaluated and nearly 40% of transcripts found to contain at least one mutation

19. Discussion • History of genetic screening • PCR • Lengthy clinical work • Therefore, always evaluated entire coding sequence in all patients • Explains current use of serial screening

20. Discussion • Changes • More common diseases being analyzed • More available patients • Availability of genomic sequence • Develop PCR-based assay in less than a day with algorithms • More involvement from other professions (engineers, statisticians) • Supply tools to keep track of experiments • Realization that many disease-causing mutations do not affect coding sequences

21. Discussion • Advantages of PAR • Effective use of gene annotation • Prioritizes gene segments for screening • Conservation of protein structure • Focus on gene segments vs. entire gene • Evident that likelihood of finding disease-causing variation in a gene falls with each exon screened with no positive result • Serial approach screens all no matter what • PAR screens a section with an average chance of finding mutation

22. Conclusion • Consideration of parameters resulted in significantly higher discoveries per unit of effort • Algorithm can be easily modified and expanded • Most useful for large number of candidate genes in large number of patients • Select best two or four regions in each candidate gene • Screen all as initial screening strategy • Additional screening based on findings from first round and PAR algorithm • Clear PAR approach is preferable to serial screening

23. References • (1) "Single Strand Conformation Polymorphism." Wikipedia. 28 May 2008. 21 Sept. 2008 <http://en.wikipedia.org/wiki/single_strand_conformation_polymorphism>. • (2) "Single Strand Conformation Polymorphism." Wikipedia. 28 May 2008. 21 Sept. 2008 <http://en.wikipedia.org/wiki/single_strand_conformation_polymorphism>.