Download Single nucleotide polymorphisms

Single nucleotide
polymorphisms
Usman Roshan
SNPs
• DNA sequence variations that occur when a
single nucleotide is altered.
• Must be present in at least 1% of the
population to be a SNP.
• Occur every 100 to 300 bases along the 3
billion-base human genome.
• Many have no effect on cell function but some
could affect disease risk and drug response.
Toy example
SNPs on the chromosome
SNP
Chromosome
Gene
Perl exercise
• Determining SNPs from a pairwise
genome alignment:
– Can we solve this problem with a Perl
script?
Bi-allelic SNPs
• Most SNPs have one of two nucleotides
at a given position
• For example:
– A/G denotes the varying nucleotide as
either A or G. We call each of these an
allele
– Most SNPs have two alleles (bi-allelic)
Perl exercise
• Determining SNP type from a multiple
genome alignment.
SNP genotype
• We inherit two copies of each chromosome
(one from each parent)
• For a given SNP the genotype defines the
type of alleles we carry
• Example: for the SNP A/G one’s genotype
may be
–
–
–
–
AA if both copies of the chromosome have A
GG if both copies of the chromosome have G
AG or GA if one copy has A and the other has G
The first two cases are called homozygous and
latter two are heterozygous
SNP genotyping
Perl exercise
• SNP encoding:
– Convert SNP genotype from a character
sequence to numeric one
Real SNPs
• SNP consortium: snp.cshl.org
• SNPedia: www.snpedia.com
Application of SNPs:
association with disease
• Experimental design to detect cancer
associated SNPs:
– Pick random humans with and without
cancer (say breast cancer)
– Perform SNP genotyping
– Look for associated SNPs
– Also called genome-wide association study
Case-control example
• Study of 100 people:
– Case: 50 subjects with
cancer
– Control: 50 subjects
without cancer
• Count number of
dominant and recessive
alleles and form a
contingency table
#Recessive
alleles
#Dominant
alleles
Case
10
40
Control
2
48
Perl exercise
• Contingency table:
– Compute contingency table given case and
control SNP genotype data
Odds ratio
• Odds of recessive in
cancer = a/b = e
• Odds of recessive in
no-cancer = c/d = f
• Odds ratio of recessive
in cancer vs no-cancer
= e/f
#Recessive
alleles
#Dominant
alleles
Cancer
a
b
No cancer
c
d
Risk ratio (Relative risk)
• Probability of recessive
in cancer = a/(a+b) = e
• Probability of recessive
in no-cancer = c/(c+d) =
f
• Risk ratio of recessive
in cancer vs no-cancer
= e/f
#Recessive
alleles
#Dominant
alleles
Cancer
a
b
No cancer
c
d
Odds ratio vs Risk ratio
• Risk ratio has a natural interpretation
since it is based on probabilities
• In a case-control model we cannot
calculate the probability of cancer given
recessive allele. Subjects are chosen
based disease status and not allele type
• Odds ratio shows up in logistic
regression models
Example
• Odds of recessive in case =
15/35
• Odds of recessive in control
= 2/48
• Odds ratio of recessive in
case vs control =
(15/35)/(2/48) = 10.3
• Risk of recessive in case =
15/50
• Risk of recessive in control =
2/50
• Risk ratio of recessive in
case vs control = 15/2 = 7.5
#Recessive
alleles
#Dominant
alleles
Case
15
35
Control
2
48
Odds ratios in genome-wide
association studies
• Higher odds ratio means stronger
association
• Therefore SNPs with highest odds
ratios should be used as predictors or
risk estimators of disease
• Odds ratio generally higher than risk
ratio
• Both are similar when small
Statistical test of association
(P-values)
• P-value = probability of the observed data (or
worse) under the null hypothesis
• Example:
– Suppose we are given a series of co in-tosses
– We feel that a biased coin produced the tosses
– We can ask the following question: what is the probability
that a fair coin produced the tosses?
– If this probability is very small then we can say there is a
small chance that a fair coin produced the observed tosses.
– In this example the null hypothesis is the fair coin and the
alternative hypothesis is the biased coin