single nucleotide polymorphism

11. Lecture WS 2003/04

Bioinformatics III 1

Single Nucleotide Polymorphism

Some people have blue eyes, some are great artists or athletes, and others are

afflicted with a major disease before they are old.

Many of these kinds of differences among people have a genetic basis -

alterations in the DNA. Sometimes the alterations involve a single base pair and are

shared by many people. Such single base pair differences are called "single

nucleotide polymorphisms", or SNPs for short.

Nonetheless many SNPs, perhaps the majority, do not produce physical changes in

people with affected DNA. (On average, SNPs occur in the human population more

than 1 % of the time. Since only 3-5% of the genome code for proteins, most SNPs

are found outside of coding regions. Those within a coding region are of course of

particular interest.)

Why then are genetic scientists eager to identify as many SNPs as they can,

distributed on all 23 human chromosomes?



Reasons for studying SNPs

1 Even SNPs that do not themselves change protein expression and cause

disease may be close on the chromosome to deleterious mutations.

Because of this proximity, SNPs may be shared among groups of people with

harmful but unknown mutations and serve as markers for them. Such markers

help unearth the mutations and accelerate efforts to find therapeutic drugs.

2 Analyzing shifts in SNPs among different groups of people will help

population geneticists to trace the evolution of the human race down through

the millenia and to unravel the connections between widely dispersed ethnic

groups and races.

3 Most human sequence variation is attributable to SNPs, with the rest

attributable to insertions or deletions of one or more bases, repeat length

polymorphisms and rearrangements.



The SNP Consortium

These motives motivated a number of pharmaceutical and technology companies

and academic sequencing centers to join forces to identify thousands of SNPs.

- The task is smaller than sequencing the whole human genome

- 4 major centers for genetics involved: Cold Spring Harbor Lab, Sanger Centre,

Wash Univ St. Louis, Whitehead/MIT Center for Genome Research

The fruits of this research are made available in the database dbSNP:

www.ncbi.nlm.nih.gov/SNP/

Main publication:

A map of human genome sequence variation containing 1.42 million SNPs,

The international SNP Map Working Group (41 authors) Nature 409, 928 (2001).

In this work, single base differences were detected using two validated algorithms:

Polybayes and the neighbourhood quality standard (NQS).



POLYBAYES

Develop + test with EST clones from 10 genomic clones that are aligned against a

fragment of the finished sequence of human (less than 1 error per 10.000 bp).

Task is to identify SNPs from the genomic sequences of multiple individuals

(e.g. 10 genomic clones).

First organize sequences:

- fragment clustering

- identification of paralogues (induction of sequences representing highly similar

regions duplicated elsewhere in the genome may give rise to false SNP predictions)

- multiple alignment of sequences

- analyze differences among sequences (e.g. using Polybayes)

Marth et al. Nature Gen. 23, 452 (1999)



Application of the POLYBAYES procedure to EST data

Regions of known human

repeats in a genomic

sequence are masked.

b, Matching human ESTs

are retrieved from dbEST

and traces are re-called.

c, Paralogous ESTs are

identified and discarded.

d, Alignments of native

EST reads are screened

for candidate variable

sites.

e, An STS is designed for

the verification of a

candidate SNP.


f, The uniqueness of the genomic location is determined by

sequencing the STS in CHM1 (homozygous DNA). g, The

presence of a SNP is analysed by sequencing the STS from

pooled DNA samples.



Paralogue identification

Identify paralogous sequences by determining if the number of mismatches

observed between the genomic reference sequence and a matching EST is

consistent with polymorphic variation opposed to sequence difference between

duplicated chromosomal locations, taking into account sequence quality.

Observation: paralogous sequences exhibit a pair-wise dissimilarity rate higher than

PPAR = 0.02 (2%) compared with the average pair-wise polymorphism rate,

PPOLY,2 = 0.001 (0.1%)

In a pair-wise match of length L we therefore expect L PPOLY,2 mismatches due to

polymorphism, versus L PPAR mismatches due to paralogous difference.

In both cases, an additional number, E, of mismatches are expected to arise from

sequencing errors.

Expect DNAT = L PPOLY,+ E or DPAR = L PPAR+ E mismatches.




Paralogue identification

The probability of observing d discrepancies in the pairwise alignment is

approximated by a Poisson distribution, with parameter = DNAT for ModelNAT

and = DPAR for ModelPAR.

In the absence of reliable a priori knowledge of the expected proportions of native

versus paralogous ESTs, uninformed (flat) priors were used.

The posterior probability PNAT = P(ModelNAT|d) that the EST represents native

sequence is estimated as:


NAT

PARDDNAT

DD

edModelP

PARNAT

1

1|

ESTs that scored above a cutoff value, PNAT,MIN, were considered native;

sequences scoring below the threshold were declared paralogous.



Paralogue discrimination

Example probability distributions for a

matching sequence with (hypothetical)

uniform base quality values of 20, in pair-

wise alignment with base perfect genomic

anchor sequence (quality values 40), over a

length of 250 bp.

PPOLY,2 = 0.001, PPAR = 0.02, E = 2.525,

DNAT = 2.775 and DPAR = 7.525.

Note: the error rate E is quite similar to the

frequency of true polymorphisms DNAT !

If the posterior probability, PNAT, is higher

than PNAT,MIN, the EST is considered native;

otherwise, it is considered paralogous.




Distribution of the posterior probability

values, PNAT, calculated for 1,954 cluster

members from real EST data anchored to

ten genomic clone sequences.

The bimodal distribution indicates that one

can distinguish between less accurate

sequences that nevertheless originate from

the same underlying genomic location, and

more accurate sequences with high-quality

discrepancies that are likely to be

paralogous.

Using PNAT,MIN = 0.75, 23% of the cluster

members were declared as paralogous.


Paralogue discrimination



SNP detection

The Polybayes algorithm identifies polymorphic locations by evaluating the likelihood

of nucleotide heterogeneity within (perpendicular) cross-sections of a multiple

alignment = single nucleotide positions.

Each of the nucleotides S1, ..., SN, in a cross-section of N sequences, R1, ..., RN, can

be any of the four DNA bases, for a total of 4N nucleotide permutations.

The likelihood P(Si|Ri) that a nucleotide Si is A, C, G, or T is estimated from the error

probability PError,i obtained from the base quality value.

(1 - PError,i) is assigned to the called base, and (PError,i/3) to each of the three uncalled

bases.

In the absence of likelihood estimates, insertions and deletions are not considered.




SNP detection in multiple alignments

Each heterogenous (polymorphic) permutation is classified according to its

nucleotide multiplicity, the specific variation, and the distribution of alleles.

The value PPOLY = 0.003 (1 polymorphic site in 333 bp) was used as the total a

priori probability that a site is polymorphic.

The values PPoly have to be distributed to assign a prior probability PPrior(S1, ..., SN)

to each permutation.

Here: assign equal values to different variation types.

PPrior = (1 - PPOLY)/4 is assigned to each of the four non-polymorphic permutations,

corresponding to a uniform base composition, PPrior(Si).





The Bayesian posterior probability of a particular nucleotide permutation is

calculated considering the 4N different permutations (for N positions) as the set of

conflicting models:


iNi SSeveryiNiPrior

iNPrior

NiN

iPrior

ii

NPriorNPrior

NN

PriorNN

SSPSP

RSP

SP

RSP

SSPSP

RSP

SP

RSP

RRSSP

,...,1

1

11

11

11

11

1

,...,...

,...,...

,...,,...,

The Bayesian posterior probability of a SNP, PSNP, is the sum of posterior

probabilities of all heterogeneous permutations observed in the cross section.

The computation is performed with a recursive algorithm.

A site within a multiple alignment is reported as a candidate SNP if the

corresponding posterior probability exceeds a set threshold value, PSNP,MIN.



POLYBAYES

- Mask known repeats in genomic sequences with RepeatMasker.

search against dbEST

- process sequence traces with PHRED base-calling program

- register distinct groups of matching ESTs as clusters.

- Each cluster member was first pair-wise aligned to the genomic anchor sequence

- Then a multiple alignment was produced by propagating gaps and insertions in the

pair-wise alignments into all remaining sequences.




SNP probability scores

Bayesian statistical model takes into account

- depth of coverge

- base quality values of the sequences

- a priori expected rate of polymorphic sites in region.

a, Distribution of the posterior probability value that a

site is polymorphic, PSNP, for 69,756 sites in multiple

alignments of native ESTs.

b, Correlation between PSNP score and confirmation rate

in experiments. The fraction of confirmed candidate

SNPs (light bars) and the fraction of candidate SNPs

that were not detected in population-specific DNA pools

(dark bars) are shown.

Overall confirmation rate: 56%.

Higher SNP probability scores correspond to higher

confirmation rates. Marth et al. Nature Gen. 23, 452 (1999)



Sensitivity of the SNP detection algorithm

Algorithm successfully detected

variations in clusters containing a

single EST aligned to the reference

sequence.

a, Minimum base quality requirement

for the detection of minor alleles of a

given frequency, in alignments of

depth N=20, 40, 60, at a detection

threshold value PSNP,MIN = 0.40.

b, Base quality requirement for the

detection of a single minor allele in

alignments of depth N = 2,...,10, and

SNP probability threshold values

PSNP,MIN = 0.20, 0.40, 0.60 and 0.80. In

(a,b), the quality value for each base

was assumed to be uniform.Marth et al. Nature Gen. 23, 452 (1999)



Evaluate performance of POLYBAYES

on „working-draft“ quality genome

sequence.

Fractions of ESTs recovered (white

bars) and SNPs recovered (grey bars)

are shown. Percentages were based on

the 733 ESTs anchored by 5 of 10

genomic clones in the primary

experiment, and the 14 confirmed SNPs

detected among these sequences. Error

bars indicate standard deviation among

20 consecutive experiments.


This shows that Polybayes does

not require base-perfect

reference sequence to be

effective.

SNP detection with assembled shotgun genomic reference sequence



Neighbourhood quality standard

For the particular case of finding SNPs in human chromosome 22, the NQS

„algorithm“ was used with the following selection criteria for candidate SNPs:

(1) the quality value (Q) of the SNP base is 23,

the Q value for the 5 bases on either side of the SNP is 15

(2) At least nine of the flanking ten bases matched between reads.

(3) The cluster depth is no greater than e.g. 8 reads, on the basis that deeper

clusters might comprise a low-copy repeat.

(4) The number of candidate SNPs in a cluster is 4, on the basis that clusters

with more divergent sequences might be composed of low-copy repeats (recently

diverged paralogous sequences, accumulating sequence differences between

them.)

Mullikin et al. Nature 407, 516 (2000)



validation of SNP detection in dbSNP


TSC contributed 1.023.950 SNPs.

1.585 TSC samples from 24 DNA samples were experimentally verified:

95% were polymorphic

4% non-polymorphic (false positives)

1% previously unrecognized repeats.

These very high validation rates were observed separately for subsets of SNPs

discovered by reduced representation shotgun and genomic alignment,

and for subsets identified either with Polybayes and the NQS.

Candidate SNPs are included in the final map (dbSNP) only if they map to a single

location in the genome assembly.



A map of human genome sequence variation containing 1.42 million SNPs

Distribution of SNP coverage across

intervals of finished sequence.

Windows of defined size (in

chromosome coordinates) were

examined for whether they

contained one or more SNPs.

Analysis was restricted to the

900 Mb of available finished

sequence.

- Most of the genome contains

SNPs at high density.

90% of contiguous 20-kb windows

contain one or more SNPs

The SNP Consortium Nature 409, 928 (2001)




- SNP density is relatively constant

across the autosomes.

- two exonic SNPs per gene are

estimated

- density of SNPs in exons (1 SNP

per 1.08 kb) is higher than in the

genome as a whole; this reflects the

fact that sequencing efforts focus on

exonic regions.

The SNP Consortium, Nature 409, 928 (2001)



Analysis of nucleotide diversity

Describing the underlying pattern of nucleotide diversity requires a

polymorphism survey performed at high density, in a single, defined population

sample, and analyzed with a uniform set of tools.

Analyze 4.5 M passing sequence reads using genomic alignment using the

NQS. Set contains 1.2 billion aligned bases and 920,752 heterozygous

positions.

Measure nucleotide sequence variation using the normalized measure of

heterozygosity (), representing the likelihood that a nucleotide position will

be heterozygous when compared across two chromosomes selected

randomly from a population.




Analysis of nucleotide diversity

also estimates the population genetic parameter = 4 Ne in a model in

which sites evolve neutrally, with mutation rate in a constant-sized

population of effective size Ne.

For the human genome, = 7.51 10-4, or one SNP for every 1331 bp

surveyed in two chromosomes (NIH panel).




Nucleotide diversity by chromosome


The autosomes are quite

similar to one another.

The most striking difference

is the lower diversity of the

sex chromosomes X and Y.

This may be explained by a

lower effective population

size (Ne) and a lower

mutation rate .



Distribution of heterozygosity

a, The genome was divided into

contiguous bins of 200,000 bp based on

chromosome coordinates, they are

randomly shuffled, and the number of

high-quality bases examined and

heterozygosity calculated for each.

The heterozygosities are quite different

b, Heterozygosity was calculated across

contiguous 200,000-bp bins on

Chromosome 6. The blue lines represent

the values within which 95% of regions

fall: 2.010-4 -15.810-4.

Red, bins falling outside this range.





One measure of the spread in the data is the coefficient of variation (CV), the

ratio of the standard deviation () to the mean () of the heterozygosity of each

individual read.

(Each nucleotide position has its own value compute average () and

standard deviation () for each read.)

For the observed data, the CV (observed / observed) was 1.93, considerably larger

than would be expected if every base had uniform diversity, corresponding to a

Poisson sampling process (Poisson / Poisson) = 1.73.

This high variability can be expected because both biochemical and evolutionary

forces cause diversity to be nonuniform across the genome.






Population genetic forces are likely to be even more important.

Each locus has its own history, with samples at some loci tracing back to a

recent common ancestor, and other loci describing more ancient genalogies.

Biological factors may include rates of

mutation and recombination at each locus.

The figure shows that heterozygosity is

correlated with the GC content for each

read, reflecting the high frequency of CpG

to TpG mutations arising from deamination

of methylated 5-methylcytosine.





To assess whether gene history would account for the observed variation in

heterozygosity, the observed CV was compared to that expected under a

standard coalescent population genetic model.

For each read, was adjusted on the basis of its per cent GC and length, and

the genealogical histories were simulated under the assumption of a constant-

sized population with Ne = 10.000. The CV determined under this model (constant-

size/constant-size) = 1.96 is a close match to the observed data.



does gene history account for variations in heterozygosity


The results indicate that the

observed pattern of

genome-wide heterozygosity

is broadly consistent with the

predictions of this standard

population genetic model.



does gene history account for variations in heterozygosity


Future work required to assess additional factors that could influence this

distribution:

biological factors such as variation in mutation and recombination rates, historical

forces such as bottlenecks, expansions or admixture of differentiated populations,

evolutionary selection, and methodological artefacts.

Certain regions of low diversity (e.g. sex chromosomes) may be explained by

higher selective pressure.

Combined with single-protein phylogeny and genome rearrangement phylogeny

(breakpoint trees) this is another puzzle stone is disecting evolution.



Implications for medical and population genetics

(1) First genome-wide view of how human DNA sequence varies in the population.

Allows studying biological and population genetic influences on human genetic

diversity.

(2) Insights into human evolutionary history by using SNPs from the map to

characterize haplotype diversity throughout the genome.

(3) Where a gene has been implicated in causing disease it is desirable exhaustively

to survey allelic variation for any association to disease.

Using the SNP map, evaluate the extent to which common haplotypes contribute to

disease risk.

Deepen understanding of disease, methods of diagnosis, and ultimately the

development of new and more effective therapies.


single nucleotide polymorphism

Documents

thousands of snps

human population

human repeats

human sequence variation

human race

human chromosomes

matching human

finished sequence of