This document summarizes methods for predicting the functional effects of amino acid substitutions, including SIFT (Sorts Intolerant From Tolerant), which uses sequence conservation to predict whether an amino acid substitution will affect protein function. SIFT has similar prediction accuracy to tools using protein structure. Case studies on disease genes like MSHR, PPARα, and MTHFR show SIFT can distinguish pathogenic from benign variants and detect variants under balancing selection. Focusing genetic association studies on predicted functional variants in genes and conserved non-coding regions improves the direct approach to finding disease loci.

1. Sifting the human genome for functional polymorphisms Pauline C. Ng, PhD

2. From genotype to phenotype humans are ~99.9% identical to each other genetic variation causes different phenotypes

3. Variation around genes are most likely to contribute to phenotype Coding Nonsynonymous SNPs, variation that causes an amino acid substitution 3’UTR Change in protein function? 5’UTR upstream 5’UTR

4. Amino acid substitutions can cause disease gene lesions responsible for disease : aa substitutions ~50% (Human Mutation 15:45-51) Hemoglobin  E6V  sickle-cell anemia

5. 1 SNP / 1000 bp Protein: 1:1 synonymous:nonsynonymous 1:2 expected 1:1 conservative:nonconservative 1:2 expected Nat. Genetics 22:231-238, 22:239-247 Science 293:489-93 nsSNPs in humans are selected against ? some of the observed nsSNPs may be involved in disease

6. Predicting the effect of an amino acid substitution Applications Nonsynonymous SNPs Large-scale random mutagenesis projects Cheap and quick for suggesting experiments

7. Computational Tools for Predicting AA Substitution Effects 1) SIFT ( s orts i ntolerant f rom t olerant) uses sequence Genome Research 11:863-574 12:436-446 2) EMBL uses structure + sequence + annotation Human Mol. Gen. 10:591-597 3) Variagenics uses structure + sequence J. Mol. Biol. 307:683-706

8. Sequence conservation correlated with intolerance to substitutions Conservation  log 2 20 +  f aa log f aa

9. SIFT Choosing sequences a) Database search b) Choose closely related sequences Obtain alignment with related proteins. For each position, calculate scaled probabilities for each amino acid substitution. Query protein < cutoff > cutoff tolerated affects function

10. SIFT: Choosing sequences # of sequences: 1 2 3 4 5 6 7 8 9 10 11 12 13 14

11. SIFT: Calculating probabilities 1 0 2 0 1 0 4 0 1 0 1 0 1 0 1 0 1 0 2 0 3 0 1 0 p x /p max < 0.05 => x affects function 20 12 4 1 20 16 13 9 4 2 12 0 9 7 18 13 19 12 16 11 c 20 14 c 13 9 c 16 10 16 9 c 5 2 13 7 12 8 12 8

12. SIFT output Substitution Probability Prediction Confidence M24S 0.04 Affect Function Low S82T 0.36 Tolerated High V247A 0.03 Affect Function High !!!

13. Confidence is determined by the diversity of sequences in the alignment many highly identical sequences Ideal case: Diverse set of orthologous proteins few sequences available Low confidence examples

14. Case Study: LacI lac operon repressed LacI expressed lactose present normal state 4000 single amino acid substitutions assayed: throughout entire protein both neutral and affected phenotypes TIBS 22:334-339 c c

15. Prediction on LacI substitutions 63% 28% Substitutions that affect protein function Substitutions that give no phenotype Total prediction accuracy 68% (2726/4004) Pr(observe affected phenotype | predicted to be damaging) 63% false - false + 37% 72% predicted to affect function predicted to be tolerated 37%

16. False negative error: Positions not conserved among paralogues dimer & sugar interface not conserved

17. False positive error in LacI: surface with unknown function?

18. SIFTing human variant databases 69% 25% Substitutions involved in disease 7397 subst., 606 proteins from SWISS-PROT Predicted on 76% proteins 71% subst nsSNPs in normal individuals 19% Putative polymorphisms 5780 nsSNPs, 3005 proteins from dbSNP Predicted on 60% prot., 53% subst. 185 nsSNPs, 69 proteins from Whitehead Institute Predicted on 77% prot. 62% subst 31% 81% 75%

19. On functionally neutral substitutions, expected false positive error ~20% dbSNP nsSNPs in normal individuals Whitehead Institute Putative polymorphisms suggests that most nsSNPs are functionally neutral What accounts for the 5% difference? 25% 19%

20. Account for 5% difference in dbSNP 16 genes with a high fraction of dbSNP variants predicted to affect function 1) Substitutions found in patients 2) Substitutions mapped to nonfunctional genes/regions 3) Substitutions detected in error Supports SIFT as a prediction tool

21. Account for 5% difference in dbSNP 16 genes with a high fraction of dbSNP variants predicted to affect function 1) Substitutions found in patients 2) Substitutions mapped to nonfunctional genes/regions 3) Substitutions detected in error Supports SIFT as a prediction tool

22. Mutations in MSHR increase skin cancer Mutations associated with cutaneous malignant melanoma 1 Mutations not associated with CMM 1-3 1 Am. J. Hum. Genet . 66: 176-186, 2 J. Invest. Dermatol . 116 :224-229, 3 J. Invest. Dermatol . 112: 512-513 R151C L60V  R151C  D294H  R160W Tolerated Affect function Prediction Substitution  L60V  R163Q  D84E Tolerated Affect function Prediction Substitution

23. Mutations in PPAR  , a candidate gene for diabetes *** In diabetics and controls, but increases cholesterol levels in diabetics and perhaps nondiabetics 2-4 SIFT will detect what has been selected against in evolution; inappropriate assay may fail to detect 1 Am. J. Hum. Genet . 63:abs997 2 Diabetologia 43:673-680 3 Diabetes Metab . 26:393-401 4 J.Lipid Res. 41: 945-952 5 J. Hum. Genet . 46: 285-288 Mutations in diabetics 1 Mutations in nondiabetics 1-5  R127Q  R409T  D304N Tolerated Affect function Prediction Substitution  V227A  A268V  *** L162V Tolerated Affect function Prediction Substitution

24. Mutations in MTHFR Mutations with diminished enzyme activity 1-5 Unknown effect Common Under balancing selection Increases neural tube defects Reduce risk for some types of leukemia Found by contig comparison 1 Nat. Genet . 10:111-113 2 PNAS 96:12810-12815 3 PNAS 98:4004-4009 4 Cancer Res . 57:1098-1102 5 Mol. Genet. Metab . 64: 169-172  A222V  E429A Tolerated Affect function Prediction Substitution  R68Q

25. dbSNP variants from patients Can distinguish patients from controls Individuals with disease: 18/22 predicted to be damaging Control individuals: 9/10 predicted to be functionally neutral SIFT detects what’s selected against in evolution & is independent of assay Example: PPAR  Detect substitutions that are deleterious in the context of the protein, not the organism Can detect nsSNPs with minor effects on phenotype genes increase risk of skin cancer, diabetes, cholesterol levels The protein need not be essential because SIFT predicts on the substitution. Can detect nsSNPs under balancing selection Example : MTHFR

26. 16 genes with a high fraction of dbSNP variants predicted to affect function 1) Substitutions found in patients 2) Substitutions mapped to nonfunctional genes or regions 3) Substitutions detected in error

27. 16 genes with a high fraction of dbSNP variants predicted to affect function 1) Substitutions found in patients 2) Substitutions mapped to nonfunctional genes or regions 3) Substitutions detected in error

28. 16 genes with a high fraction of dbSNP variants predicted to affect function 1) Substitutions found in patients 2) Substitutions mapped to nonfunctional genes/regions 3) Substitutions detected in error Changes found in patients Confirms SIFT prediction and its sensitivity Unlikely to affect human health Irrelevant to human health

29. Comparison of Prediction Tools 69% 69% 63% 75% 28% 9% 25% 32% 15% 19% Variagenics SIFT SIFT EMBL disease subst. LacI Variagenics SIFT LacI EMBL* 15% Variagenics SIFT SIFT EMBL SNP databases normal individuals Substitutions that affect function Substitutions that do not affect function Polymorphisms 31% 72% 69% 91% 75% 68% 81% 85% SIFT has similar prediction accuracy to tools that use structure

30. http:// blocks.fhcrc.org/sift/SIFT.html SIFT, a prediction tool for the effect of substitutions prediction is based only on sequence Detect damaging nsSNPs on a large scale

31. Association studies for finding disease loci Direct approach SNPs likely to affect gene function Association leads directly to candidate gene Fewer SNPs to genotype Indirect approach using haplotypes tagSNPs to identify common haplotypes in a region Relies on LD with causal variant Genotype 200K-1 million SNPs AATACGAT AATACGAT AATACGAT GATACAAC GATACAAC GATACAAC

32. Feasibility of direct approach Have we identified all the causative variants? common variant, common disease hypothesis 80% of common SNPs in Europeans in dbSNP, 50% of common SNPs in Africans. Nat Genet. 33:518-21 What types of variants are involved in disease? nsSNPs & splicing variants account for a large proportion of Mendelian disease regulatory variation has a role in disease ~50% genes show allele-specific expression Science 297 :1143; Hum. Genet. 113: 149–153 ~1/3 of promoter variants may alter gene expression Hum. Mol. Genet. 12: 2249–2254

33. nsSNPs SNPs near intron/exon boundary UTRs and promoter region 4. synonymous SNPs Possible effect? In LD with causative variant? SNPs in and near genes protein function splicing regulation

34. covers 20,024 genes Double-hit and known-frequency SNPs in genes

35. Non-genic regions could potentially harbor disease variants ~70% of bases in conserved sequences are noncoding (Genome Res. 13:2507-18 ) regulatory elements noncoding RNAs unknown genes 41,193 SNPs in noncoding conserved regions >= 80% identity with mouse

36. Adding SNPs in conserved regions improves SNP density

37. Focusing on variation in functional regions Large # of SNPs makes direct approach possible If causative variant is not in set, may be in LD with another SNP in the functional region Concentrating on functional regions allows interesting experiments genotyping DNA copy number allele-expression differences Complementary to the indirect approach using haplotypes

38. Acknowledgments FHCRC Steve Henikoff Jorja Henikoff Henikoff Lab