Cryptic Variation in the Human mutation rate

1. Cryptic Variation in the Human mutation rate Alan Hodgkinson Adam Eyre-Walker, Manolis Ladoukakis

2. Variation in the mutation rate: • Between different chromosomes • Between regions on chromosomes • Neighbouring nucleotides

3. Simple context effects: Hwang and Green (2004) PNAS101: 13994-14001

4. Cryptic Variation: • Remote context: • AGTCGGTTACCGTGACGTTGAACGTGT

5. Cryptic Variation: • Remote context: • AGTCGGTTACCGTGACGTTGAACGTGT • Degenerate context: • AGTCGGTTACCGTGYSRGYGAACGTGT

6. Cryptic Variation: • Remote context: • AGTCGGTTACCGTGACGTTGAACGTGT • Degenerate context: • AGTCGGTTACCGTGYSRGYGAACGTGT • No context / Complex context

7. Human Chimp Our approach to the problem • Search for SNPs in human sequences that also have a SNP in the orthologous position in chimp.

8. Human Chimp Our approach to the problem • Search for SNPs in human sequences that also have a SNP in the orthologous position in chimp. Do we see more coincident SNPs than expected by chance?

9. The method • Extract all human SNPs from dbSNP and construct a BLAST database on a chromosome by chromosome basis.

10. The method • Extract all human SNPs from dbSNP and construct a BLAST database on a chromosome by chromosome basis. • Extract all chimp SNPs from dbSNP with 50bp either side of SNP.

11. The method • Extract all human SNPs from dbSNP and construct a BLAST database on a chromosome by chromosome basis. • Extract all chimp SNPs from dbSNP with 50bp either side of SNP. • BLAST chimp SNPs against human database.

12. The method • Extract all human SNPs from dbSNP and construct a BLAST database on a chromosome by chromosome basis. • Extract all chimp SNPs from dbSNP with 50bp either side of SNP. • BLAST chimp SNPs against human database. • Extract results above a certain level of homology where there is a SNP on both sequences and reduce to 40bp either side of central position.

13. The method • Extract all human SNPs from dbSNP and construct a BLAST database on a chromosome by chromosome basis. • Extract all chimp SNPs from dbSNP with 50bp either side of SNP. • BLAST chimp SNPs against human database. • Extract results above a certain level of homology where there is a SNP on both sequences and reduce to 40bp either side of central position. • Repeating both including and excluding CpG effects.

14. Results • ~1.5 million chimp SNPs. • ~310,000 81bp alignments containing a human and chimp SNP.

15. Results • ~1.5 million chimp SNPs. • ~310,000 81bp alignments containing a human and chimp SNP. • Observe the number of coincident SNPs. • Calculate the expected number, taking into account the effects of neighbouring nucleotides.

16. Results

17. Results

18. Alternative Explanations • Bias in the Method • Selection • Ancestral Polymorphism • Paralogous SNPs

19. Alternative Explanations • Bias in the Method • Selection • Ancestral Polymorphism • Paralogous SNPs

20. Methodological Bias • Simulated data with same density of human and chimp SNPs as dbSNP under different divergence and mutation patterns. • Method worked well under realistic conditions.

21. Methodological Bias All sites (H&G): Non CpG sites (H&G):

22. Methodological Bias All sites (H&G): Non CpG sites (H&G):

23. Alternative Explanations • Bias in the method • Selection • Ancestral Polymorphism • Paralogous SNPs

24. Selection • Areas of low SNP density result in clustering: Human Chimp

25. Selection • Areas of low SNP density result in clustering: Human Chimp Apparent excess of coincident SNPs

26. Selection • No clustering:

27. Alternative Explanations • Bias in the method • Selection • Ancestral Polymorphism • Paralogous SNPs

28. T T Common Ancestor T T T T A A T Human T Chimp A A Ancestral Polymorphism • SNP inherited from common ancestor of chimp and human:

29. T T Common Ancestor T T T T A A T Human T Chimp A A Ancestral Polymorphism • SNP inherited from common ancestor of chimp and human: Increase in coincident SNPs

30. Ancestral Polymorphism • Expect observed/expected ratio to be same for all transitions:

31. Ancestral Polymorphism • Repeated initial analysis with macaque data. • Humans and Macaque split ~23-24 million years ago so we expect there to be no shared polymorphisms.

32. Ancestral Polymorphism • Repeated initial analysis with macaque data. • Humans and Macaque split ~23-24 million years ago so we expect there to be no shared polymorphisms.

33. Alternative Explanations • Bias in the method • Selection • Ancestral Polymorphism • Paralogous SNPs

34. Paralogous SNPs • Excess of coincidentSNPs a consequence of artifactualSNPscalled as a result of substitutions in paralogousregions.

35. Paralogous SNPs • Excess of coincident SNPs a consequence of artifactual SNPs called as a result of substitutions in paralogous regions. • Musumeci et al (2010): 8.32% of human variation in dbSNP may be due to paralogy.

36. Paralogous SNPs • Excess of coincident SNPs a consequence of artifactual SNPs called as a result of substitutions in paralogous regions. • Musumeci et al (2010): 8.32% of human variation in dbSNP may be due to paralogy. AGCTGCACGT Y CGGCATCCAA SNP AGCTGCACGT T CGGCATCCAA Chromosome 1 AGCTGCACGT A CGGCATCCAA Chromosome 7 Artifactual SNP

37. Paralogous SNPs AGCTGCACGT (T/A) CGGCATCCAA AGCTGCACGT T CGGCATCCAA AGCTGCACGT (T/A) CGGCATCCAA AGCTGCACGT T CGGCATCCAA AGCTGCACGT A CGGCATCCAA

38. Paralogous SNPs AGCTGCACGT (T/A) CGGCATCCAA AGCTGCACGT T CGGCATCCAA AGCTGCACGT (T/A) CGGCATCCAA AGCTGCACGT T CGGCATCCAA AGCTGCACGT A CGGCATCCAA 3.6% of coincident SNPs are possibly a consequence of paralogous sequences

39. Alternative Explanations • Bias in the method • Selection • Ancestral Polymorphism • Paralogous SNPs Cryptic variation in the mutation rate

40. Context Analysis • 4517 sequences containing non-CpG coincident SNPs flanked by 200bp. • Tabulate triplet frequencies at each position in surrounding sequences. • Test whether the proportions of triplets we observe at each position significantly different from the proportions in the sequences as a whole.

41. Context Analysis • Coincident SNP in central position:

42. Context Analysis • Coincident SNP in central position: No obvious context surrounding coincident SNPs

43. Genomic Distribution • Tallied the number of coincident SNPs per MB: • 3.91 coincident SNPs per MB. • 1.68 non-CpG coincident SNPs per MB.

44. Genomic Distribution • Tallied the number of coincident SNPs per MB: • 3.91 coincident SNPs per MB. • 1.68 non-CpG coincident SNPs per MB. • If randomly distributed expect Poisson distribution and  = 2 = 3.91

45. Genomic Distribution • Tallied the number of coincident SNPs per MB: • 3.91 coincident SNPs per MB. • 1.68 non-CpG coincident SNPs per MB. • If randomly distributed expect Poisson distribution and  = 2 = 3.91 • 2 = 13.27 (p<0.001) and so sampling variance explains approximately 30% of total variance.

46. Genomic Distribution

47. Genomic Distribution • SNP densities must drive coincident SNP densities to a certain extent as approximately half of coincident SNPs are created by chance alone.

48. Genomic Distribution • SNP densities must drive coincident SNP densities to a certain extent as approximately half of coincident SNPs are created by chance alone. • Recombination rate positively correlated with SNP density (r = 0.242, p<0.001). • Partial correlation controlling for SNP density: r = 0.048, p=0.011**.

49. Genomic Distribution • SNP densities must drive coincident SNP densities to a certain extent as approximately half of coincident SNPs are created by chance alone. • Recombination rate positively correlated with SNP density (r = 0.242, p<0.001). • Partial correlation controlling for SNP density: r = 0.048, p=0.011**. • SNP densities explain 6.5% of the variance, recombination rate explains 0.2% of the variance of coincident SNPs.

50. Genomic Distribution