Single Nucleotide Polymorphisms (SNPs), Haplotypes, Linkage Disequilibrium, and the Human Genome

1
Single Nucleotide Polymorphisms (SNPs),
Haplotypes, Linkage Disequilibrium, and the Human
Genome

• Manish Anand
• Nihar Sheth
• Jim Costello
• 24th November, 2003

2
Overview

• Biological Background
• Terminology
• SNP related general information
• SNP detection techniques
• SNP Applications
• References

3
Biological Background

• How can researchers hope to identify and study
  all the changes that occur in so many different
  diseases?
• How can they explain why some people respond to
  treatment and not others?

4

• SNP is the answer to these questions
• So what exactly are SNPs?
• How are they involved in so many different
  aspects of health?

5
What is SNP ?

• A SNP is defined as a single base change in a DNA
  sequence that occurs in a significant proportion
  (more than 1 percent) of a large population.

6
Variations in Genome
7
Terminology

• Polymorphism
• Linkage Disequilibrium
• Correlation of characters states among
  polymorphic sites
• Insufficient passage of time to randomize
  character states by meiotic recombinations
• Haplotype

8
Some Facts

• In human beings, 99.9 percent bases are same.
• Remaining 0.1 percent makes a person unique.
• Different attributes / characteristics / traits
• how a person looks,
• diseases he or she develops.
• These variations can be
• Harmless (change in phenotype)
• Harmful (diabetes, cancer, heart disease,
  Huntington's disease, and hemophilia )
• Latent (variations found in coding and regulatory
  regions, are not harmful on their own, and the
  change in each gene only becomes apparent under
  certain conditions e.g. susceptibility to lung
  cancer)

9
SNP facts

• SNPs are found in
• coding and (mostly) noncoding regions.
• Occur with a very high frequency
• about 1 in 1000 bases to 1 in 100 to 300 bases.
• The abundance of SNPs and the ease with which
  they can be measured make these genetic
  variations significant.
• SNPs close to particular gene acts as a marker
  for that gene.
• SNPs in coding regions may alter the protein
  structure made by that coding region.

10
SNPs may / may not alter protein structure
11
SNPs act as gene markers
12
SNP maps

• Sequence genomes of a large number of people
• Compare the base sequences to discover SNPs.
• Generate a single map of the human genome
  containing all possible SNPs gt SNP maps

13
SNP Maps
14
SNP Profiles

• Genome of each individual contains distinct SNP
  pattern.
• People can be grouped based on the SNP profile.
• SNPs Profiles important for identifying response
  to Drug Therapy.
• Correlations might emerge between certain SNP
  profiles and specific responses to treatment.

15
SNP Profiles
16
Techniques to detect known Polymorphisms

• Hybridization Techniques
• Micro arrays
• Real time PCR
• Enzyme based Techniques
• Nucleotide extension
• Cleavage
• Ligation
• Reaction product detection and display
• Comparison of Techniques used

17
Hybridization Techniques

• Micro Arrays
• Sequencing by hybridization
• utilize a set of tiling oligonucleotides
• somewhat complex
• pooling and processing of PCR amplicons that are
  subsequently hybridized to a DNA micro array and
  visualized.
• Theoretically capable of genotyping thousands of
  polymorphisms simultaneously
• Success rate 97 (Somewhat low for this kind of
  analysis)
• High False rates 1121
• Design and fabrication of micro arrays is
  expensive, hence users are confined to the set of
  genotypes established by the manufacturer.

18

• Real Time PCRs
• Utilizes TaqmanTM DNA probes to detect PCR
  products in real-time
• TaqmanTM probe contains a fluorescent reporter at
  the 5' end and a fluorescence resonance energy
  transfer (FRET) moiety at the 3' end, which
  quenches the fluorescent signal of the reporter.
• The probe sequence is complementary to the PCR
  amplicon and is designed to anneal at the
  extension temperature.
• During extension, the 5' 3' exonuclease activity
  of Taq DNA polymerase I cleaves the probe,
  emitting signal due to the separation of the
  reporter from the quencher.
• Polymorphism is determined solely by
  hybridization and not by the ability of the
  enzyme to discriminate.
• Because the enzyme does not confer specificity in
  detection, this technique is classified as
  hybridization-based.
• Depending on optical thermocycler platform 384
  reactions can be monitored for each cycle without
  removing any sample
• amenable to robotic automation.

19
Real Time PCRs
20
Enzyme based Techniques

• Nucleotide extension
• Simplest techniques for known polymorphism
  detection
• Existing in numerous variations (also known as
  minisequencing, SNuPE, GBA, APEX, AS-PE capture,
  FNC, TDI or PROBE) this assay typically involves
  the single base extension of an oligonucleotide
  by a polymerase
• Oligonucleotide is designed to anneal immediately
  upstream of the polymorphism locus and
  differentially labeled fluorescent
  dideoxynucleotides are utilized as substrates for
  polymerase extension.
• The fluorescent signal emitted corresponds to the
  nucleotide incorporated and thus the sequence of
  the polymorphism.
• Simplicity and accuracy in distinguishing between
  heterozygous and homozygous genotypes.
• Targets need to be PCR amplified PCR reagents
  must be removed.
• False negatives due to mis-priming

21
Nucleotide Extension
22

• Cleavage
• The InvaderTM assay utilizes the exonuclease
  activity of Cleavase VIII on overlapping
  oligonucleotide strands.
• Two oligonucleotides, an invader probe and
  either a wild-type or mutant primary probe,
  overlap each other at a single nucleotide
  position on the template only if they are
  complementary to the polymorphism being queried.
• Cleavage occurs when the specific overlapping
  conformation is present, freeing an
  oligonucleotide referred to as a flap.
• This flap can be detected in a multiplex manner
  by size, mass or sequence
• Commonly the flap participates in a second
  cleavage assay with another complementary target,
  causing release of a fluorescent signal.
• Advantage - the same flap may bind to many
  targets, generating a cascading signal
  amplification and thereby obviating the need for
  PCR amplification.
• Single-tube one-step reaction.

23
Cleavage
24

• Ligation
• One of the most specific assays due to the high
  specificity of T4 ligase (oligo ligation assay)
  and even higher specificity of thermostable
  ligases (ligation detection reaction, LDR)
• Two primers are designed to anneal adjacent to
  one another on the target of interest
• Generally, the upstream primer (discriminating
  primer) contains a fluorescent label at the 5'
  end, with the 3' nucleotide overlapping the
  polymorphic base.
• The fluorescent signal corresponds to the allele
  being queried at the 3' position of the
  discriminating primer
• When the discriminating primer forms a perfect
  complement with the target at the junction, the
  ligase covalently attaches the adjacent
  downstream primer (common primer)
• The resulting product is approximately twice as
  long as each of the individual primers and can be
  easily monitored for detection by means of
  capillary electrophoresis or by display on a
  microarray
• Advantage Very good sensitivity and specificity

25
Techniques to detect unknown Polymorphisms

• Direct Sequencing
• Microarray
• Cleavage / Ligation
• Electrophoretic mobility assays
• Comparison of Techniques used

26
Direct Sequencing

• Sanger dideoxysequencing can detect any type of
  unknown polymorphism and its position, when the
  majority of DNA contains that polymorphism.
• Misses polymorphisms and mutations when the DNA
  is heterozygous
• limited utility for analysis of solid tumors or
  pooled samples of DNA due to low sensitivity
• Once a sample is known to contain a polymorphism
  in a specific region, direct sequencing is
  particularly useful for identifying a
  polymorphism and its specific position.
• Even if the identity of the polymorphism cannot
  be discerned in the first pass, multiple
  sequencing attempts have proven quite successful
  in elucidating sequence and position information.

27
Microarray

• Variation detection arrays (VDA) scans large
  sequence blocks and identify regions containing
  unknown polymorphisms.
• This methodology suffers from the same
  limitations in fabrication and design as observed
  in known polymorphism analysis, but has
  demonstrated much greater success in the context
  of unknown polymorphism detection for both SNP
  and tumor analysis.
• With respect to SNP analysis, a recent study of
  chromosome 21 successfully identified
  approximately half of the estimated number of
  common SNPs (frequency of 1050) across the
  entire chromosome.
• The experimental design required a sacrifice in
  sensitivity in order to minimize false positives.
• This explains the decrease in successful
  identification from 80 to 50.

28
Cleavage/Ligation

• Unknown polymorphisms can also be identified by
  the cleavage of mismatches in DNADNA
  heteroduplexes.
• This can be achieved either chemically chemical
  cleavage method (CCM) or enzymatically (T4 Endo
  nuclease VII, MutY cleavage or Cleavase).
• Typically, at least two samples are PCR amplified
  (one sample can be sufficient for solid tumor
  samples with high levels of stromal
  contamination), denatured and then hybridized to
  create DNADNA heteroduplexes of the variant
  strands.
• Enzymes cleave adjacent to the mismatch and
  products are resolved via gel or capillary
  electrophoresis.
• Unfortunately, the cleavage enzymes often nick
  complementary regions of DNA as well. This
  increases background noise, lowers specificity,
  and reduces the pooling capacity of the assay.

29
Cleavage / Ligation
30
SNP Applications

• Gene discovery and mapping
• Association-based candidate polymorphism testing
• Diagnostics/risk profiling
• Response prediction
• Homogeneity testing/study design
• Gene function identification

31
High-resolution haplotype structure in the human
genome

• Mark J. Daly, John D. Rioux, Stephen F.
  Schaffner, Thomas J. Hudson Eric S. Lander

32
Abstract

• Authors are describing a high-resolution analysis
  of the haplotype structure across 500 KB on
  chromosome 5q31 using 103 SNPs in a European
  derived population.
• They developed an analytical model for Linkage
  disequilibrium (LD) mapping based on
  high-resolution haplotype blocks, which offers a
  coherent framework for creating a haplotype map
  of the human genome.

33
Data used

• 500 kb region on human chromosome 5q31 that is
  implicated as containing a genetic risk factor
  for Crohn disease.
• Rioux, J. D et al. Hierarchical linkage
  disequilibrium mapping of a susceptibility gene
  for Crohn s disease to the cytokine cluster on
  chromosome 5. Nature Gene. 29, 223-228(2001)
• 103 common (gt5 minor allele frequency) SNPs
  genotyped from a European-derived population.
  Study describe 258 chromosomes transmitted to
  individuals with Crohn disease and 258
  untransmitted chromosomes.

34
Data used

• The genotype data used in study provides the
  highest-resolution picture of the patterns of
  genetic variation across a large genomic region,
  with a market density of 1 SNP roughly every 5
  kb.

35
Study

• Focus on identifying the underlying haplotypes.
• Authors initial focus was on untransmitted
  control chromosomes, however, the same haplotype
  structure was seen in the chromosomes transmitted
  to individuals with Crohn disease, with the only
  difference being that one of the haplotypes was
  enriched in frequency, reflecting its association
  with Crohn disease.

36
Study

• It became evident during the study that the
  region could be largely decomposed into discrete
  haplotype blocks, each with a lack of diversity.
• As haplotype block structure was the same in both
  groups, they presented combined data from all
  chromosomes (transmitted and untransmitted).

37
Haplotype block structure on 5q31
38
Haplotype block structure on 5q31
a. Common haplotype patterns in each block of low
diversity. Dashed lines indicate locations where
more than 2 of all chromosomes are observed to
transition one common haplotype to a different
one.
39
Haplotype block structure on 5q31
b. Percentage of observed chromosomes that match
one of the common patterns exactly (total
chromosomes 258 transmitted 258
untransmitted).
40
Haplotype block structure on 5q31
c. Percentage of each of the common patterns
among 258 untransmitted chromosomes.
41
Haplotype block structure on 5q31
d. Rate of haplotype exchange between the blocks
as estimated by the HMM.
42
Haplotype block structure on 5q31

• The haplotype blocks span up to 100 kb and
  contain multiple (five or more) common SNPs.
• The blocks have only few (2-4) haplotypes, which
  show no evidence of being derived from one
  another by recombination, and which account for
  nearly all chromosomes (gt90) in all cases in the
  sample.

43
Haplotype block structure on 5q31
For example, an 84 kb block shows only two
distinct haplotypes that together account for 95
of the observed chromosomes (table -1).
44
Study

• The discrete blocks are separated by intervals in
  which several independent historical
  recombination event seem to have occurred, giving
  rise to greater haplotype diversity for regions
  spanning the blocks.
• The most common recombination events are
  indicated in previous figure by lines connecting
  the haplotypes.
• The recombination events appear to be clustered
  multiple obligate exchanges must have occurred
  between most blocks, with little or no exchange
  within block.

45
Study

• Although there is detectable recombination
  between blocks, it is modest enough for there to
  be clear long-range correlation among (that is,
  LD) blocks.
• The haplotypes at the various blocks can be
  readily assigned to one of the four ancestral
  long-range haplotypes.
• Indeed, 38 of the chromosomes studies carried
  one of these four haplotypes across the entire
  length of the region.

46
Study

• Using HMM, they developed an approach to define
  the block structure formally.
• The HMM simultaneously assigns every position
  along each observed chromosome to one of the four
  ancestral haplotypes and estimates the
  maximum-likelihood values of the historical
  recombination frequency (T) between each pair of
  markers.

47
Study

• The quantity T provides a convenient summary of
  the degree of haplotype exchange across
  inter-marker intervals and relates directly toe
  conventional measures of LD.
• In this study, T is estimated at less than 1 for
  73 of the inter-marker intervals, 1-4 for 14 of
  the intervals, and more than 4 for only 9 of the
  intervals.

48
Methods Individuals and market selection

• The individuals studies, Canadians from
  metropolitan Toronto of predominantly European
  descent and the genotyping methodologies are
  described in the paper
• Rioux, J. D et al. Hierarchical linkage
  disequilibrium mapping of a susceptibility gene
  for Crohn s disease to the cytokine cluster on
  chromosome 5. Nature Gene. 29, 223-228(2001)
• To ensure the ability to reconstruct multi-marker
  haplotypes, SNPs for haplotype analysis were
  selected from the set of markers for which full
  genotypes were available for all members.
• SNPs at CpG sites were not included to prevent
  potential confounding of common haplotype
  patterns from recurrent mutations.

49
Methods Haplotype counting

• Haplotype percentages in Haplotype block
  structure in 5q31 figure were computed using
  haplotypes generated by the transmission
  disequilibrium test (TDT) implementation in
  Genehunter 2.0 (ref. 22 in the paper), followed
  by use of an EM-type algorithm (ref. 23,24 in
  paper), to include the minority of chromosomes
  that had one or more markers with ambiguous phase
  or where one marker was missing genotype data.

50
Methods Hidden Markov model

• The observation that over long distances most
  haplotypes can be described either as belonging
  to one of a small number of common haplotypes
  categories suggested the use of an HMM in which
  haplotype categories were defined as state.
• Authors assigned observed chromosomes to those
  hidden states and simultaneously estimated the
  transition probability in each map interval by
  using an EM algorithm and by making the
  simplifying assumption that there was any
  transition probability for each map interval
  rather than allowing specific transition
  probabilities from each state to each state.
• The output of this method was a
  maximum-likelihood assignment to haplotype
  category at each position and ML estimates of T
  indicating how significantly recombination has
  acted to increase haplotype diversity in each map
  interval.

51
Discussion of Study

• The region of chromosome 5q31 may be largely
  divided into discrete blocks of 10-100 kb each
  block has only a few common haplotypes and the
  haplotype correlation between blocks gives rise o
  long-range LD.
• Focusing on haplotype blocks greatly clarifies LD
  analyses. Once the haplotype blocks are
  identified, they can be treated as alleles and
  tested for LD (instead of single-marker analyses
  of LD).

52
Discussion of Study

• In analogous fashion, the haplotype structure
  provides a crisp approach for testing the
  association of genomic segments with disease. By
  contrast, disease association studies
  transitionally involve testing individual SNPs in
  and around a gene.
• Once the haplotype blocks are defined, it is
  straightforward to examine a subset of SNPs that
  uniquely distinguish the common haplotypes in
  each block. This allows the common variation in a
  gene to tested exhaustively for association with
  disease.

53
Discussion of Study

• This approach provides a precise framework for
  creating a comprehensive haplotype map of the
  human genome.
• By testing a sufficiently large collections of
  SNPs, it should be possible to define all of the
  common haplotypes underlying blocks of LD. Once
  such a map is created, it will be possible to
  select an optimal reference set of SNPs for any
  subsequent genotyping study.
• This detailed understanding of common human
  variation represents an important step in the
  Human genome project.

54
Linkage Disequilibrium

• Uses unrelated individuals
• Good for fine scale mapping because there is
  greater opportunity for recombination to occur.
• Map of loci that contribute to inherited genetic
  disorders
• States can not be considered independent because
  they are related by distance and recombination,
  so individual haplotypes may not be the cause of
  disease, but rather a combination of several
  haplotypes in blocks

55
Linkage Disequilibrium

• Greater distance between genes, the greater
  chance of recombination
• Lesser distance between genes, the less chance of
  recombination
• Knowing the above and observing inherited
  alleles, one can estimate the relative distance
  between genes

56
Measures of Linkage Disequilibrium

• cM centiMorgans
• 50cM would mean that two genes have a 50 chance
  of recombination occurring.
• Genes are relatively far apart

57
Importance of Linkage Disequilibrium

• Offers us a way to measure the distance between
  genes.
• Non-random
• Measure of relation between markers and disease
  mutations.
• Possibly used to map disease genes because high
  LD areas would be related to recombination and
  formation of new alleles

58
Data Mining Applied to Linkage Disequilibrium
Mapping

• HPM - Haplotype Pattern Mining
• Method of data mining LD-based gene mapping
• Uses haplotypes as inputs which can be obtained
  from genetic simulation programs such as
  GENEHUNTER
• Extension of traditional association analysis
• Search for shared and flexible haplotypes and
  find out which ones are strongly associated with
  a disease.
• Uses non-parametric statistical model without
  any genetic models on the basis of the locations
  of the haplotypes

59
What we know

• LD, which has a non-random association of
  haplotypes to a disease, is likely strongest
  around the DS(Disease Susceptibility) gene.
• A locus will most likely be where the strongest
  associations are.

60
Notation

• Haplotype Map M has k parameters (m1,,mk)
• The haplotype pattern P on M consists of the
  vector space (p1,,pk), where each pi is an
  allele of mi or a wild-card ()
• P occurs on the haplotype vector, which is simply
  the chromosome (H), so H (h1,,hk) where hi
  pi or hi
• Example
• P1 (, 2, 5, , 3, , , , , )
• PC (4, 2, 5, 1, 3, 2, 6, 4, 5, 3)

61
Issues in Shape of Haplotype Pattern

• Length of the pattern
• Defined as maximal distance between any 2 markers
  measured in centiMorgans
• Extremely long sequences dont give us much
  information, so the size of the P is constrained
  in HPM
• Gaps in sequences
• Accounts for mutations, errors, missing data, and
  recombination
• Gap size and number can be controlled in HPM

62
Procedure

• Depth-first search finds all haplotype patterns
  that exceed the lower bound threshold and meets
  the association measure
• Calculate the frequency f(mi) of marker mi with
  respect to (M, H, Y, x), where Y phenotype and x
  positive association threshold
• Markers with highest frequencies are predicted to
  be the area of the DS gene, assuming a DS gene is
  present.
• Prediction of granularity of marker density
• Ranked based on frequency

63
Results Simulated Data

• Founder population which grows from 300 to 100,
  000 in 500 years was simulated in the Populus
  simulator package
• Simulated data used because it is cheaper and can
  be easily manipulated

64

• List of 11 most strongly disease-associated
  haplotype patterns in the simulated data
• Chromosome has 101 markers
• Dashed line indicates the true gene location

65

• Frequency histogram of previous slides data, but
  with patterns exceeding the threshold of
  association
• Dashed line indicates the true gene location
• Marker 5 now has the highest frequency

66

• The actual vs. predicted locations for 100 data
  sets

67

• Mutation carrying chromosomes, denoted by A
• Sample founder population size
• Corrupted data
• Missing data

68
Real Data HLA complex

• Data consisting of affected sib-pair families
  with type 1 diabetes from the UK that were
  genotyped for 25 markers was used
• Markers covered 14-Mb and covered the entire HLA
  complex
• The HLA-DQB1 and HLA-DRB1 loci, which are located
  in the middle of these 14-Mb, are known to be the
  primary factors for type 1 diabetes
• Randomly selected 200 from 385 sample space to
  compare with simulated results

69

• Frequency vs. Map Location of HLA markers
• ___ HPM calculated frequencies
• ----- Background LD frequencies
• Vertical lines indicates true locations of
  markers

70
Discussion of HPM Technique

• Robust to lost and erroneous data
• Applicable to complex gene mapping
• Works well with small data sets, but accuracy is
  increased with the increase of data
• Works with real and simulated data
• Does not include any previously derived models

71
References

• Introduction to SNPs Discovery of Markers of
  Disease
• SNP seeking long term association with complex
  diseases
• SNP mapping using Genome-wide Unique Sequences
• The Structure of Haplotypes Blocks in Human
  Genome
• Using Haplotype blocks to map human complex trait
  loci
• High Resolution haplotype structure in human
  genome
• Detection of regulatory variation in mouse genes
• http//linkage.rockefeller.edu/wli/lld.html
• http//statwww.epfl.ch/davison/teaching/Microarray
  s/snp.ppt
• http//www.cs.helsinki.fi/u/htoivone/pubs/ajhg_200
  0.pdf
• Resolution of Haplotypes and Haplotype
  Frequencies from SNP Genotype of Pooled Samples
• http//www.journals.uchicago.edu/AJHG/journal/issu
  es/v71n6/024386/024386.html
• http//www.ncbi.nlm.nih.gov/entrez/utils/fref.fcgi
  ?http//www.sciencemag.org/cgi/pmidlookup?viewful
  lpmid11452081
• http//www.genome.gov/10001665
• http//walnut.usc.edu/magnus/papers/tig.pdf