Полиморфизм генома человека Алма-Ата, 15.04.06 Василий...
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Полиморфизм генома человека
Алма-Ата, 15.04.06
Василий Раменский, Институт молекулярной биологии им. Энгельгардта РАН , Москва
People are different…
…caccagctcctgtgGggggaggccctgct… …caccagctcctgtgGggggaggccctgct… …caccagctcctgtgGggggaggccctgct… …caccagctcctgtgCggggaggccctgct… …caccagctcctgtgCggggaggccctgct…
…and so are their genomes
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%
5’---------------A---------------3’ |||||||||||||||||||||||||||||||3’---------------T---------------5’
5’---------------G---------------3’ |||||||||||||||||||||||||||||||3’---------------C---------------5’
Na
Ng
Na+Ng = N, Na/N ≥0.01, Ng/N ≥0.01
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
Типы полиморфизма в геноме
* однонуклеотидный (SNP)
* короткая вставка/делеция
* микросателлитный повтор различной длины (VNTR,
variable number tandem repeat)
* вставка объекта
* множественный нуклеотидный (MNP)
Некоторые свойства SNPs
• Comprise the ~90% of human genetic variation
• Occur with an average density ~1/600 bp
• 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)
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
SNP в геноме человека
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
Synonymous vs. non-synonymous SNPs:
…CAC CAG CTC CTG TGG GGG GAG GCC CTG CT…
…CAC CAG CTC CTG TGC GGG GAG GCT CTG CT…
HGVBase ID: SNP000003023 G C Hypothetical SNP: C T
… H Q L L W G E A L …
… H Q L L C G E A L …
Example: Lysosomal alpha-glucosidase precursor (SwissProt P10253)
nsSNP Trp746Cys sSNP Ala749Ala
Summary of Annotation on human Genome Build 33 dbSNP Build 124 :
FUNCTION CLASS CODE
SNP COUNTGENE
COUNT
FUNCTIONAL
CLASSIFICATION
1 338787 26210 Locus region
3 39214 14342Allele synonymous to contig nucleotide
4 50772 15710Allele nonsynonymous to contig nucleotide
5 546965 17898 untranslated region
6 2925773 19332 intron
7 832 769 splice site
8 89554 18655 Allele is same as contig nucleotide
9 7111 1006 Coding: synonymy unknown
Жизненный цикл SNP (по Miller&Kwok, 2001)
I. Появление нового аллельного варианта путем мутации
(~100 мутаций на индивидуум)
II. «Выживание» до момента появления гомозигот по этому
аллелю
III. Медленное увеличение частоты в популяции
IV. Фиксация нового аллеля (0 vs. 100%), превращение в
between-species difference
Замечание
Описанный выше жизненный цикл SNP занимает ~0.3 млн
лет. Предполагая, что разделение человека и шимпанзе
произошло ~5 млн лет назад, а выход H.sapiens из 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
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
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
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 a distinction
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
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)
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
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)
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]
PolyPhen: prediction of amino acid substitution effect on protein function
Prediction: benign (neutral), damaging (deleterious)
Data sources:
1. Sequence annotation of the query protein2. PSIC profile matrix values derived from multiple
alignment with homologous proteins3. Structural parameters and contacts of query protein
structure or its >50% homolog
PolyPhen: prediction of amino acid substitution effect on protein function
Prediction: benign (neutral), damaging (deleterious)
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
II. PSIC: profile analysis of homologous sequences
1. Align with homologous proteins with seq. ide. 30..94%
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
SAsn,4 SCys,4
II. PSIC: profile analysis of homologous sequences
3. Analyse difference between profile scores for two a.a. variants:
SAsn,4 SCys,4
AsnCys: = | SAsn,4 – SCys,4 | = 1.591
III. 3D structure analysis1. Residues that are in spatial contact with a
ligand or other “critical” residues
Zen 999
residues in 5Å contact with Zen 999
Bos Taurus trypsin [PDB ID :1ql7]
III. 3D structure analysis2. Residues that form the hydrophobic core of
the protein (buried residues)
Bos Taurus trypsin [PDB ID :1ql7]
Surface residues
Buried residues
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)
RULES (connected with logical AND) PREDICTION
PSIC score difference :
Substitution site properties: Substitution type properties:
arbitraryannotated as a functional* or bond formation** site
arbitrary probably damaging
not consideredin a region annotated or predicted as transmembrane
PHAT matrix difference resulting from substitution is negative
possibly damaging
0.5 arbitrary arbitrary benign
>1.0atoms 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 ACC15%
absolute change of accessible surface propensity is 0.75 orabsolute change of side chain volume is 60
possibly damaging
normed accessibility ACC5%
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
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
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)
Validation: allele frequency
Validation: nsSNPs vs. human-mouse interspecies variation
PolyPhen predictions for dbSNP b.121All: 9,502 unknown27,991 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)
[ Ivan Adzhubei, 2004 ]
PolyPhen predictions for dbSNP b.121All: Filtered: 5 seq. in multiple alignment16,813 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 alignment2,021 benign...............56.6% 499 possibly damaging....14.0%1,050 probably damaging....29.4%3,570 total (2,983 unique rs’s)
[ Ivan Adzhubei, 2004 ]
Hydrophobic core stability parameters are the best predictors
Ramensky et al., Nucleic Acids Res. (2002) 30:3894-90
PolyPhen http://www.bork.embl.de/PolyPhen
PolyPhen input :
Protein identifier OR sequence
Substitution position
Substitution type
PolyPhen http://www.bork.embl.de/PolyPhen
PolyPhen: nsSNPs data collection
DAMAGING nsSNPs
Transphyretin
(PDB: 1tyr, SNP000012365)
Thr118 Asn occurs at the ligand (REA) binding site
Thr 118
REA 130
DAMAGING nsSNPs
Trypsin
(PDB: 1trn, SNP000012965)
Ser142Phe results in the strong side chain volume change at a buried position
Ser 142
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
Development directions
• Better multiple alignment pipeline
• Compensated nsSNPs
• Non-globular structural regions
• Non-coding SNPs
An example of compensated pathogenic deviation
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]
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)