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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  W s = PAR window size  N x = No. distinct annotation elements  W(i) = PAR window function  A f (x,j) = annotation function for jth annotation at xth position  A s (x,j) = annotation score for jth annotation at xth position  A o (x,j) = annotation scalar offset  A m (j) = annotation multiplier for jth annotation feature

10 Materials and Methods

11  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.  (2) "Single Strand Conformation Polymorphism." Wikipedia. 28 May 2008. 21 Sept. 2008.


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