Полиморфизм генома человека Алма-Ата, 15.04.06

1. Полиморфизм генома человекаАлма-Ата, 15.04.06 Василий Раменский,Институт молекулярной биологии им. Энгельгардта РАН , Москва

2. People are different…

3. …caccagctcctgtgGggggaggccctgct… …caccagctcctgtgGggggaggccctgct… …caccagctcctgtgGggggaggccctgct… …caccagctcctgtgCggggaggccctgct… …caccagctcctgtgCggggaggccctgct… …and so are their genomes

4. 5’---------------A---------------3’ ||||||||||||||||||||||||||||||| 3’---------------T---------------5’ 5’---------------G---------------3’ ||||||||||||||||||||||||||||||| 3’---------------C---------------5’ Na Ng Определение SNP (single nucleotide polymorphism): существование в популяции на одной и той же позиции геномной ДНК двух нуклеотидных вариантов с частотой более редкого варианта (аллеля) ≥1% Na+Ng = N, Na/N ≥0.01, Ng/N ≥0.01

5. Комментарии к определению • речь идет о сравнении последовательностей одного биол. вида • слово «полиморфизм» не имеет в русском языке множественного числа (Н.Ляпунова, личное сообщение) • в обыденной речи под «полиморфизмом» чаще всего подразумевают именно нуклеотид (т.е. используют его как синоним слова «мутация») • определение подразумевает достоверное измерение частот в популяции(-ях), что в текущей практике пока редкость

6. Типы полиморфизма в геноме * однонуклеотидный (SNP) * короткая вставка/делеция * микросателлитный повтор различной длины (VNTR, variable number tandem repeat) * вставка объекта * множественный нуклеотидный (MNP)

7. Некоторые свойства SNPs • Comprise the ~90% of human genetic variation • Occur with an average density ~1/600bp • Transition C↔T(G↔A) occurs at ~2/3 of all cases, three transversions C↔A (G↔T), C↔G(G↔C), T↔A(A↔T) in ~1/6 of all cases each • Most of them (~85%) are common to all populations (with differing allele frequencies)

8. Why SNPs are important? • Convenient genetic markers • Responsible for existence of various phenotypes, with primary interest in disease ones • Pharmacogenomics: individual response to drugs • Clues to understand human evolution

9. SNP в геноме человека

10. Классификация SNP по положению в геноме 1. гены 1.1 UTR 1.2 экзоны (cSNP) 1.2.1 синонимичные(sSNP) 1.2.2 несинонимичные (nsSNP) 1.3 интроны 1.4 сайты сплайсинга 2. регуляторные участки генов (rSNP) 3. межгенные участки

11. … H Q L LW G E A L … … H Q L LC G E A L … Synonymous vs. non-synonymous SNPs: Example: Lysosomal alpha-glucosidase precursor (SwissProt P10253) Hypothetical SNP: C  T HGVBase ID: SNP000003023 G  C …CAC CAG CTC CTG TGG GGG GAG GCC CTG CT… …CAC CAG CTC CTG TGC GGG GAG GCT CTG CT… nsSNPTrp746Cys sSNPAla749Ala

13. Жизненный цикл SNP (по Miller&Kwok, 2001) • Появление нового аллельного варианта путем мутации (~100 мутаций на индивидуум) • «Выживание» до момента появления гомозигот по этому аллелю • Медленное увеличение частоты в популяции • Фиксация нового аллеля (0 vs. 100%), превращение в between-species difference

14. Замечание Описанный выше жизненный цикл SNP занимает ~0.3млн лет. Предполагая, что разделение человека и шимпанзе произошло ~5 млн лет назад, а выход H.sapiensиз Африки и разделение различных популяций ~0.1-0.2 млн лет назад, понятно отсутствие (а) одинаковых SNPs у человека и других видов, (б) «private» SNP,т.е. локализованных в пределах одной человеческой популяции

15. Why polymorphisms are maintained in the population? • Selectionists: because heterozygotes have higher fitness • Neutralists: because all observed polymoprhisms are selectively neutral - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - Reality: is always somewhat more complicated

16. Why SNPs are important? • Convenient genetic markers • Responsible for existence of various phenotypes, with primary interest in disease ones • Pharmacogenomics: individual response to drugs • Clues to understand human evolution

17. nsSNPs vs. disease mutations • Disease mutations are rare (<<1%) and usually cause monogenic diseases (e.g., cystic fibrosis) • nsSNPs are frequent (>1%) and can modify risks of major common (multigenic, complex) diseases (e.g., cancer, cardiovascular disease, mental illness, autoimmune states, diabetes) In some cases, however, it is difficult to make adistinction

18. Some common nsSNPs are known to affect critical structure features Frequency of the haemochromatosis allelic variant of HLA-H protein Cys260Tyr (with destroyed disulphide bond) is up to 6% in Northern Europe

19. Application area for prediction methods • Genetics of complex diseases • Analysis of human birth defects • Genetics of rare developmental phenotypes (analysis of de novo mutations that cannot be mapped by genetic techniques) • Genetics of model organisms (identification of genes involved in diverse processes by mutagenesis screens) • Genomics and evolutionary genetics (e.g., quantifying selective pressure)

20. Identifying SNPs responsible for complex diseases: general strategies • whole genome scan – hypothesis free approach; extraordinary number of candidate SNPs • candidate gene studies – requires a priori models; nevertheless, large numbers of candidate SNPs must be tested

21. Identifying SNPs responsible for complex diseases: application 1. A SNP with established association need not be functional; therefore, in silico expertise is required for selection of potentially functional SNPs 2. Detection of enrichment of rare potentially functional alleles in the disease population (plasma levels of HDL-cholesterol, hypertension, colorectal cancer)

22. Methods for prediction of effect of nsSNPs * Sequence-based methods: analysis of multiple alignment with homologs Ng-Henikoff [2002] * Structure-based methods: analysis of various structural parameters Wang, Moult [2001]; Chasman, Adams [2001] * Combined methods: sequence and structure analysis Sunyaev,Ramensky,Bork [2000, 2001, 2002]

23. PolyPhen: prediction of amino acid substitution effect on protein function Prediction: benign (neutral), damaging (deleterious)

24. PolyPhen: prediction of amino acid substitution effect on protein function • Data sources: • Sequence annotation of the query protein • PSIC profile matrix values derived from multiple alignment with homologous proteins • Structural parameters and contacts of query protein structure or its >50% homolog Prediction: benign (neutral), damaging (deleterious)

25. I. Sequence annotation Hereditary hemochromatosis protein precursor (HLA-H, Q30201) Features checked: * bond: DISULFID, THIOLEST, THIOETH * site: BINDING, ACT_SITE, LIPID, METAL, SITE, MOD_RES,SE_CYS * region: TRANSMEM, SIGNAL, PROPEP

26. II. PSIC: profile analysis of homologous sequences • Align with homologous proteins with seq. ide. 30..94%

27. SAsn,4 SCys,4 II. PSIC: profile analysis of homologous sequences 2. Calculate the profile matrix with PSIC algorithm Profile matrix:Sa,j = ln[ pa,j / qa ], a = {1,..20}, j = {1,..N}, N = alignment length

28. SAsn,4 SCys,4 II. PSIC: profile analysis of homologous sequences 3. Analyse difference between profile scores for two a.a. variants: AsnCys:  = | SAsn,4 –SCys,4 | = 1.591

29. III. 3D structure analysis 1. Residues that are in spatial contact with a ligand or other “critical” residues Zen 999 Bos Taurus trypsin [PDB ID :1ql7] residues in 5Å contact with Zen 999

30. III. 3D structure analysis 2. Residues that form the hydrophobic core of the protein (buried residues) Surface residues Buried residues Bos Taurus trypsin [PDB ID :1ql7]

31. Structural parameters and contacts • Secondary structure • Phi-psi dihedral angles • Solvent accessible surface area, normed s.a.s.a • Change in accessible surface propensity • Change in residue side chain volume • Contacts with heteroatoms • Interchain contacts • Contacts with functional sites (BINDING, ACT_SITE, LIPID, and METAL) • Region of the phi-psi map (Ramachandran map) • Normalised B-factor (temperature factor)

32. RULES (connected with logical AND) PREDICTION PSIC score difference : Substitution site properties: Substitution type properties: arbitrary annotated as a functional* or bond formation** site arbitrary probably damaging not considered in a region annotated or predicted as transmembrane PHAT matrix difference resulting from substitution is negative possibly damaging 0.5 arbitrary arbitrary benign >1.0 atoms are closer than 3.0Å to atoms of a ligand or residue annotated as BINDING, ACT_SITE, LIPID, METAL arbitrary probably damaging 0.5<1.5 normed accessibility ACC15% absolute change of accessible surface propensity is 0.75 or absolute change of side chain volume is 60 possibly damaging normed accessibility ACC5% absolute change of accessible surface propensity is 1.0 or absolute change of side chain volume is 80 probably damaging 1.5<2.0 arbitrary arbitrary possibly damaging >2.0 arbitrary arbitrary probably damaging

33. all dam unknown dam/(dam+ben) ––––––––––––––––––––––––––––––––––––––––––––– Disease mutations Strict set 444 366 3 82.9% Total 2,782 2,047 70 75.4% Between species substitutions Total 671 58 5 8.7% Validation: control sets

34. Validation: case studies • APEX1 protein: 24 out of 26 substitutions predicted correctly (Xi et al.) • Plasminogen activator inhibitor-2: 18 out of 20 (Di Guisto et al.) • 3 HapMap populations and 10 primate species: analysis of ~27,000 nsSNPs with frequencies (Victoria Carlton, AFFYMETRIX, private communication)

35. Validation: allele frequency

36. Validation: nsSNPs vs. human-mouse interspecies variation

37. PolyPhen predictions for dbSNP b.121 [ Ivan Adzhubei, 2004 ] All: 9,502 unknown 27,991 benign...............67.6% 7,905 possibly damaging....19.1% 5,521 probably damaging....13.3% 50,919 total (44,005 unique rs’s) With structure: 42 unknown 2,142 benign...............57.1% 531 possibly damaging....14.2% 1,076 probably damaging....28.7% 3,791 total (,167 uniqe rs’s)

38. PolyPhen predictions for dbSNP b.121 [ Ivan Adzhubei, 2004 ] All: Filtered: 5 seq. in multiple alignment 16,813 benign...............64.2% 5,195 possibly damaging....19.8% 4,168 probably damaging....15.9% 26,176 total (21,677 unique rs’s) With structure: Filtered: 5 seq. in multiple alignment 2,021 benign...............56.6% 499 possibly damaging....14.0% 1,050 probably damaging....29.4% 3,570 total (2,983 unique rs’s)

39. Hydrophobic core stability parameters are the best predictors Ramensky et al., Nucleic Acids Res. (2002) 30:3894-90

40. PolyPhen http://www.bork.embl.de/PolyPhen • PolyPhen input : • Protein identifier OR sequence • Substitution position • Substitution type

41. PolyPhen http://www.bork.embl.de/PolyPhen

42. PolyPhen:nsSNPs data collection

43. Transphyretin (PDB: 1tyr, SNP000012365) Thr118  Asnoccurs at the ligand (REA)binding site Thr 118 REA 130 DAMAGING nsSNPs

44. Trypsin (PDB: 1trn, SNP000012965) Ser142Pheresults in the strong side chain volume change at a buried position Ser 142 DAMAGING nsSNPs

45. Damaging nsSNPs • We estimate that ~20% of non-synonymous cSNPs from databases are damaging • Average allele frequency of non-synonymous cSNPs predicted to be damaging is twice lower than for benign non-synonymous cSNPs • We propose to use these predictions for prioritisation of candidates for association studies

46. Development directions • Better multiple alignment pipeline • Compensated nsSNPs • Non-globular structural regions • Non-coding SNPs

47. An example of compensated pathogenic deviation

48. Polyphenism: the ability of a single genome to produce two or more alternative morphologies within a single population in response to an environmental cue (such as temperature, photoperiod, or nutrition). [Dr. Ehab Abouheif, McGill University,Montréal Québec] The seasonal morphs of the buckeye butterfly, Precis coenia (Nymphalidae). The ventral surfaces are shown. The Summer morph ("linea") is on the left; the Fall morph ("rosa") is on the right. [Scott F.Gilbert, A Companion to Developmental Biology. Chapter 22, Seasonal Polyphenism in Butterfly Wings]

49. People Shamil Sunyaev(1), Vasily Ramensky(2), Steffen Schmidt(1), Ivan Adzhubei(1) (1) Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA) (2) Engelhardt Institute of Molecular Biology Moscow Russia) Peer Bork, Yan P. Yuan (European Molecular Biology Laboratory, Heidelberg, Germany)