Download A single-nucleotide polymorphism tagging set for human drug

A single-nucleotide polymorphism
tagging set for human drug
metabolism and transport
Kourosh R Ahmadi, Mike E Weale, Zhengyu Y Xue, Nicole Soranzo,
David P Yarnall, James D Briley, Yuka Maruyama, Mikiro Kobayashi,
Nicholas W Wood, Nigel K Spurr, Daniel K Burns, Allen D Roses,
Ann M Saunders & David B Goldstein
Nature Genetics 37, 84 - 89 ( January 2005)
Presented by Navdeep
A single-nucleotide polymorphism
tagging set for human drug
metabolism and transport
Outline
Background
Aims
Methods
Results
Conclusions
References
Tagging SNPs
A SNP or a set of SNPs that have been
selected on the basis of linkage
disequilibrium (LD) patterns to represent
other SNPs
a, SNPs. four versions of the same chromosome
region in different people showing three bases
where variation occurs. Each SNP has two possible
alleles; the first SNP in panel a has the alleles C
and T.
b, Haplotypes. A haplotype is made up of a
particular combination of alleles at nearby SNPs. a.
For this region, most of the chromosomes in a
population survey turn out to have haplotypes 1–4.
c, Tag SNPs. Genotyping just the three tag SNPs
out of the 20 SNPs is sufficient to identify these four
haplotypes uniquely.
Tagging SNPs
Advantages of tagging SNPs
– Fewer SNPs can be used to construct genome wide linkage
disequilibrium map.
Potential problems
– How well do the selected tags represent undetected variation in
the original sample?
– How well will the tags represent variation (both detected and
undetected) in a new sample from the same population?
– How well do they represent different populations having different
LD patterns?
The ability of tSNP sets to tag
dropped SNPs
Aims
• Identify and evaluate tSNPs for genes involved
in the absorption, distribution, metabolism and
excretion of drugs (ADME genes)
Materials and Methods
• Selection of tSNPs
– haplotype r2 criterion
• Evaluation of tSNPs
– dropped SNP−plus−resampling approach
• Comparison of SNPs with different MAF
– two-tailed Wilcoxon paired-sample rank test
• Genes, SNP selection and choice of populations
– a target density of 1 SNP of MAF > 10% per 2 kb of genomic DNA
• Gene clusters
– no two genes from a cluster to be separated by more than 50 kb
Haplotype r2
• Haplotype r2 is the coefficient of determination (ie.
The proportion of explained variation) obtained
from a standard linear regression of the allelic
state (coded 0/1) of a SNP in question against the
haplotypes determined by the tSNP set. This
regression is equivalent to a one-way analysis of
variance with each tSNP-defined haplotype as a
separate group.
• It allows assessment of the loss of power resulting
from typing a tSNP as opposed to the causal
variant with which it is associated
Long range Linkage Disequilibrium
Minor allelic frequency (MEF)
Indicates the number of occurrences of an
allele seen in the total number of
chromosomes typed at the SNP site
Performance of tags selected
from the full data set
Performance of tags selected
from the reduced* data set
*SNPs with MAFs < 5% excluded
The effect of initial genotyping
density on tag performance
Cosmopolitan tSNP set suitable for both
European and Japanese populations
Performance of selected tSNPs in
representing candidate functional variation
Performance of selected tSNPs in
different population sample
Conclusions
• The effect of MAF on tSNP performance is heavily
dependent on the size of the LD sample
• Comprehensive tagging will require a high genotyping
density (one SNP of MAF ≥5% per 2.5 kb )
• Performance of population specific tSNPs sets in
predicting functional variants is similar to that of random
SNPs.
• Haplotype r2 based tSNP selection is highly effective
even when applied to a population different from LD
sample.
• Rare variants are not well tagged.
References
•
•
•
•
Goldstein, D.B., Ahmadi, K.R., Weale, M.E. & Wood, N.W. Genome scans and candidate gene
approaches in the study of common diseases and variable drug responses. Trends Genet. 19,
615−622 (2003)
Goldstein, D.B., Tate, S.K. & Sisodiya, S.M. Pharmacogenetics goes genomic. Nat. Rev. Genet. 4,
937−947 (2003).
Pritchard, J.P. & Przeworski, M. Linkage disequilibrium in humans: models and data. Am. J. Hum.
Genet. 69, 1−14 (2001)
Carlson, C.S. et al. Selecting a maximally informative set of single-nucleotide polymorphisms for
association analyses using linkage disequilibrium. Am. J. Hum. Genet. 74, 106−120 (2004).