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 COUNT GENE 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:  

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

>1.0atoms 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 mutationsStrict set 444 366 3 82.9%Total 2,782 2,047 70 75.4%

Between species substitutionsTotal 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)