Predicting effect of SNPs and de novo mutations on splicing

1. Predicting effect of SNPs and de novo mutations on splicing presented by Alexander Tchourbanov Biology Department New Mexico State University

2. Motivation • Recently, high throughput genotyping methods became available • High-density 500K chips are available for genotyping (Illumina Hap550, Affymetrix 5.0) • Genome resequencing (SOLID Applied Biosystems, Solexa/Illumina genome analyzer, Roche 454 FLX) • Researchers, interested to understand genetic risk factors contributing to a disorder, routinely genotype patients

3. Motivation • Many SNPs have been associated with predisposition to various diseases (Breast cancer, Alzheimer's, Multiple sclerosis, etc.) • Only fraction of actual SNPs are genotyped with chips • Some SNPs with significantly low P-values have been associated through LD with affected haplotypes • Fraction of associated SNPs are causal variants • There is a growing evidence that Autism Spectrum Disorder (ASD) could be triggered by de novo mutations absent in both parents

4. Types of SNPs • Several classes of variants to consider: • Single Nucleotide Polymorphisms (SNPs) • Deletion/Insertion polymorphisms (DIPs) • Simple Tandem Repeat polymorphisms (STRs) • Named polymorphisms (e.g., Alu/ dimorphisms) • Multinucleotide polymorphisms (MNPs)

5. SNPs distribution • ~ 6 million SNPs are located in human gene loci (dbSNP build 129) • 63% intronic • 11% untranslated region • 1% nonsynonymous • 1% synonymous • 24% 2 kBp from a gene • <1% splice site • <1% unknown coding variant

6. What are the common disease causing variants? • SNPs are defined as former mutations with >1% of population penetrance • According to Human Gene Mutation Database HGMD (http://www.hgmd.cf.ac.uk) • 49,806 mutations are missence/nonsense • 8,548 mutations have consequences in mRNA splicing • Many missence/nonsence mutations are eliminated by purifying selection and never make it to SNPs…

7. Splicing components Image credit: Understanding alternative splicing: towards a cellular code: Arianne J. Matlin, Francis Clark and Christopher W. J. Smith, Nature Reviews Molecular Cell Biology 6, 386-398 (May 2005)

8. Existing elements

9. Orthologos blocks from UCSC GB • 2,333,379 extended exons from 23 Tetrapoda organisms were obtained • A number of experimental reports showed that genes from distantly related Tetrapoda organisms were correctly expressed and post-transcriptionally modified in transgenic animals (Capetanaki Y et al.: Proc Natl Acad Sci USA 1989, Jacobs GH et al.: Science 2007) • The genes encoding well-known RNA binding proteins involved in splicing regulation are enriched with ultraconserved elements (Bejerano G. et al.:Science 2004)

10. Counting oligos

11. Comparing oligo counts

12. Elements found • Using the orthologous exons available for 23 Tetrapoda organisms we have identified 2,546 unique splicing regulatory elements. • Among these elements 203 (7.97%) 3’SS and 177 (6.95%) 5’SS supporting motifs are novel and have not been previously reported in systematic screens detecting such elements. • Among our predicted elements, 41.08% of sequences were heptamers and 51.81% were octamers and only 6.76% hexamers and 0.35% pentamers

13. Example of 5’SS ISEs found

14. Example of LOD profiles (5’SS ISE)

15. Optimal exon length • Depends on flanking 5’SS and 3’SS strengths

16. 5’GC SS Bayesian sensor

17. Exon scoring method • LOD scores associated with 5’SS,3’SS, exonic length, competing SSs and Enhancer/Silencer signals are combined towards an exon strength

18. IVS2+2delC mutation >IVS2+2delC ttcggataagacaaagattttatataatattttgaaaacattaaataatt tgtcattcctttatttcctttattttagCTTCGCAGAATCAAGAACGGCTATGTGCGTTTAAAGATCCGTATCAGCAAGACCTTGGGATAG/GTGAGAGTAGAATCTCTCATGAAAATGGGACAATATTATGCTCGAAAG/GTAGCACCTGCTATGGCCTTTGGGAGAAATCAAAAGGGGACATAAATCTTGTAAAACAAGg(c)aagtgatactttccttacctgaaatgactgtgttttatacaattgatatttatctaaaaaggacatgggagtatgttaaaatcctgttcagaaaaacagtgaatttaaaagtgtatatataaagccaggtgtggtggctcatgcctgtaattccagcacttttcgaggctgaggtgggcggatcacttgaggccaggagtttgagaccagcctgggtaataacatggtgaaaccccgt

19. Example of SpliceScan II predicting effects of mutations • An example of successfully predicted effect of mutation IVS2+2delC causing familial pulmonary arterial hypertension (Cogan JD et al: Am J Respir Crit Care Med 2006) • Another example of SpliceScan II correctly predicting the effect of IVS10-6del34 micro deletion causing gastrointestinal stromal tumors (Chen LL et al:Oncogene 2005 )

20. SpliceScan II performance on mutations

21. Effect of rs849563 (Autism associated SNP) • There is a change in annotated exon potential here: • rs849563 changes the exon sharing one boundary with annotated exon gi|41872561|ref|NM_201266.1| 2433-2577 where the exon score changes 0.60->0.19

22. Effect of rs885747 (Autism associated SNP) • There is a change in annotated exon potential here: • rs885747 changes the exon sharing one boundary with annotated exon gi|194097340|ref|NM_002616.2| 1627-1735 where the exon score changes 0.30->0.49

23. SNPs performance

24. SpliceScan II tool • SpliceScan II tool http://splicescan2.lumc.edu/ • Is more sensitive than existing splicing simulators (NetUTR, ExonScan) • Uses novel 5’ GC SS Bayesian sensor • Method allows predicting aberrant splicing events associated with genomic variants • ACGMAP companion database http://www.stritch.luc.edu/node/375

25. Thanks!