1. Human non-synonymous SNP: molecular function, evolution and disease Shamil Sunyaev Genetics Division, Brigham & Women’s Hospital Harvard Medical School Harvard-M.I.T. Division of HST

2. Effect on molecular function Phenotype Natural selection Medical Genetics Structural Biology Biochemistry Evolutionary Genetics

3. Predicting the effect of mutations in proteins

4. Why is this useful? n Understanding variation in molecular function and structure n Evolutionary genetics: comparison of polymorphism and divergence rates between different functional categories is a robust way to detect selection

5. Linkage analysis Rare

6. Classical association studies ControlDisease Common

7. Why is this useful? n Rare human developmental disorders / mouse mutagenesis screens: linkage studies are impossible n Genetics of complex disease: SNP prioritization n Genetics of complex disease: Rare variants

8. Technically, polymorphism should not exist!

9. Quantitative trait Mendelists Biometricians Forces to maintain variation: Selection Mutation

10. Common disease / Common variant Trade off (antagonistic pleiotropy) Balancing selection Recent positive selection Reverse in direction of selection Examples APOEAlzheimer’s disease AGTHypertension CYP3AHypertension CAPN10Type 2 diabetes

11. Individual human genome is a target for deleterious mutations ! ~40% of human Mendelian diseases are due to hypermutable sites Frequency of deleterious variants is directly proportional to mutation rate (q=  /s)

12. Multiple mostly rare variants Many deleterious alleles in mutation-selection balance Examples Plasma level of HDL-C Plasma level of LDL-C Colorectal adenomas

13. What about late onset phenotypes?

14. Harmful mutations n Function: damaging n Evolution: deleterious n Phenotype: detrimental n Advantageous pseudogenization (Zhang et al. 2006) n Gain of function disease mutations n Sickle Cell Anemia

16. protein multiple alignment profile

17. PolyPhen

18. Prediction rate of damaging substitutions possibly probably Disease mutations Divergence 82%57% 9% 3% Polymorphism 27% 15%

19. 10% of PolyPhen false-positives are due to compensatory substitutions

20. Polyphen Phylogenetic measures PAM-120-5.32 -8.35 * -12.76 * BLOSUM-45-8.41 * -3.96-13.39 * BLOSUM-62-8.41 * -4.09-12.75 * BLOSUM-80-8.46 * -4.49-13.52 * Site-specific structural/phylogenetic measures -23.602 * -6.072*-11.732* Estimate of selection coefficient Williamson et al., PNAS 2005

21. de novo mutation effect spectrum NO DELETERIOUS POLYMORPHISM LOTS OF DELETERIOUS POLYMORPHISM Effect of new mutation may range from lethal, to neutral, to slightly beneficial

22. Mutation effect spectrum NO DELETERIOUS POLYMORPHISM LOTS OF DELETERIOUS POLYMORPHISM ?

23. Neutral mutation model Human ACCTTGCAAAT Chimpanzee ACCTTACAAAT Baboon ACCTTACAAAT Prob(TAC->TGC)  Prob(TGC->TAC) Prob(XY 1 Z->XY 2 Z) 64x3 matrix

24. Strongly detrimental mutations

25. Effectively neutral mutations

26. Mildly deleterious mutations

27. 54 genes, 757 individuals inflammatory response 236 genes, 46-47 individuals DNA repair and cell cycle pathways 518 genes, 90-95 individuals

28. The majority of missense mutations observed at frequency below 1% are deleterious Frequency itself is a reliable predictor of function!

29. Fitness and selection coefficient Wild typeNew mutation N 1 = 4 N 2 = 3 Fitness 1 N1N1 N2N2 = 1 – s Selection coefficient

30. Mildly deleterious mutations 54 genes, 757 individuals inflammatory response 236 genes, 46-47 individuals DNA repair and cell cycle pathways 518 genes, 90-95 individuals

31. Fraction of detectable polymorphism

32. Human effective population size present past 10010011001111010100100101110101000 01111001100011100010111001 Estimation of selection coefficient - simulation

33. Human effective population size present past -log(s) F singl (s)F MAF>25% (s) Selection coefficient SNP probability to be observed

34. Classical association studies ControlDisease Common

35. “Mutation enrichment” association studies ControlDisease Rare

36. ControlDisease “Mutation enrichment” association studies

37. Rare missense variants in NPC1L1 gene contributes to variability in cholesterol absorption and plasma levels of low-density lipoproteins (LDLs) Cohen J et al., PNAS 2006 in press Nonsynonymous sequence variants in ABCA1 gene were significantly more common in individuals with low HDL-C ( 95th percentile). Cohen J et al., Science 2004 Multiple rare variants in different genes account for multifactorial inherited susceptibility to colorectal adenomas Fearnhead NS et al., PNAS 2004 “Mutation enrichment” association studies

38. Cholesterol

39. Adopted from Brewer et al., 2003

40. Effect of rare nsSNPs on HDL-C

41. What about common alleles of smaller effect? n Population of 3500 individuals with known plasma levels of HDL-C n Population includes both genders and three ethnic groups n 839 SNPs genotyped n Independent population of 800 individuals for validation

42. What about common alleles of smaller effect? n Introduce a linear model (ANCOVA) n Subsequently add SNPs to the linear model n Include SNPs based on the likelihood ratio test n Prioritizing SNPs based on conservation did not help

43. Effect of common SNPs on HDL-C HDL

44. And a different population … HDL

45. Acknowledgements The lab: Gregory Kryukov, Steffen Schmidt, Saurabh Asthana, Victor Spirin, Ivan Adzhubey Bioinformatics:Human genetics: Vasily RamenskyJonathan Cohen