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CS177 Lecture 10
SNPs and Human Genetic Variation
Tom Madej 11.21.05
Lecture overview
•
Human genetic variation, HapMap project.
•
Experimental methods: PCR, X-ray crystallography,
microarrays.
Motivations to study human genetic variation
• The evolution of our species and its history.
• Understand the genetics of diseases, esp. the more
common complex ones such as diabetes, cancer,
cardiovascular, and neurodegenerative.
• To allow pharmaceutical treatments to be tailored to
individuals (adverse reactions based on genetics).
Genetic variation
• The human genome has approximately 10 million
polymorphisms, i.e. genetic variants that occur at the
level of about 1% or more in the population.
• Many of these polymorphisms are SNPs, single
nucleotide polymorphisms.
• These polymorphisms contribute to our individuality, and
also influence our susceptibility to various diseases.
Mendelian and non-Mendelian diseases
• Geneticists have been very successful in discovering the
variations due to Mendelian disorders. These are
characterized by in that they follow the Mendelian rules
of inheritance.
• The study of particular families using linkage analysis
has been successful for the Mendelian diseases.
• However, the more common complex (i.e. nonMendelian) disorders have been much more difficult to
investigate, even there there are clearly genetic
components to many of these diseases.
Sources of genetic variation (during meiosis)
• Chromosomal reassortment; a human has 23 pairs of
chromosomes, one of each pair is inherited from the
father, and the other one from the mother.
• Mutation; errors in DNA copying. This may result in
SNPs or also larger portions of DNA may be duplicated
or copied incorrectly.
• Genetic recombination; shuffling of segments between
partner chromosomes of a pair.
Reassortment of genetic material during
meiosis
Molecular Biology of the Cell, Alberts et al.
Garland Publishing 2002 (Fig. 20-8)
Single Nucleotide Polymorphisms (SNPs)
• Major source of genetic variation.
• Estimated approx. 7 million SNPs that occur with
frequencies at least 5% in the human population; approx.
11 million with frequencies at least 1%.
• Can we determine the associations between these
variants and diseases?
Other types of genetic variations…
International HapMap project
• Haplotype – set of variants on a chromosome that tend
to inherited as a block.
• Provide a collection of SNPs spanning the genome, and
serving as genetic markers.
• Study correlations (linkage disequilibrium, LD) between
the SNPs.
• Provide a guide for whole genome association studies.
HapMap project
• Project was launched in Oct 2002.
• In the first phase genotyped 1.1 million SNPs in 269
individuals from four ethnic origins.
• Second phase will genotype another 4.6 million SNPs.
• Goal was to find most SNPs that occur with frequencies
of at least 5% in the human population.
Statistics digression: here is an example of a
commonly used correlation measure…
LD and recombination hotspots
Correlated (LD) SNPs and tag SNPs
Nature Genetics: published online Oct 30, 2005; doi:10.1038/ng1688
Haplotype diversity
Nature, v. 437 Oct 27, 2005, p.1306
LD summary
• The human genome consists of regions of low
polymorphism (i.e. low sequence variation) of sizes from
10-100 kb, interspersed with regions of high
polymorphism.
• This seems to be due to “recombination hotspots” in the
chromosomes.
• The inheritance of chromosomal regions without
recombination (haplotypes) means that certain
combinations of genes are widespread across the
human population.
http://www.hapmap.org/
Exercise!
• Go to www.hapmap.org, and select “Browse Project
Data” (link on the left).
• In the “Landmark or Region” box enter: DTNBP1, then
click “Search”.
• Select the NM_032122 link (isoform a).
• Take a look at the Overview and Details.
• Go down to “Tracks”, select “Analysis All on”, and then
“Update Image”.
• Take a look at the LD map, phased haplotypes, and list
of tag SNPs.
Whole genome association study
• Given a sample of people, some with and some without a certain
trait/phenotype (e.g. a certain disease).
• Call the two sets cases and controls.
• Investigate the genetic factors shared by the cases, but absent from
the controls; i.e. find the associations between the genetic factors
and the disease.
• The most straightforward way: genotype all the individuals.
• But this is far too expensive with current technology!
The HapMap data is useful for whole
genome association studies…
• The collection of SNPs give us common genetic
markers.
• By using tag SNPs we can reduce the number of SNPs
that need to be genotyped in the study.
• It is even possible to produce SNP chips with a few
hundred thousand tag SNPs that can be used for the
genotyping.
• But statistical studies need to be done!