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Single Nucleotide Polymorphism And Association Studies
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  1. Single Nucleotide PolymorphismAnd Association Studies Stat 115 Dec 12, 2006

  2. Outline • Definition and motivation • SNP distribution and characteristics • Allele frequency, LD, population stratification • SNP discovery (unknown) and genotyping(known) • SNP association studies • Case control studies, and family based association studies • Issues related to association studies

  3. Mode of inheritance

  4. Polymorphism • Polymorphism: sites/genes with “common” variation, less common allele frequency >= 1%, otherwise called rare variant and not polymorphic • First discovered (early 1980): restriction fragment length polymorphism • Some definitions: • Locus: position on chromosome where sequence or gene is located • Allele: alternative form of DNA on a locus

  5. Fundamental rules of genetics • Law of Segregation: a diploid parent is equally likely to pass along either of its two alleles P(pass copy 1) = P(pass copy 2) = ½ • Law of Random Union gametes unite in a random fashion, so allele A1 is no more likely to unite with allele A1 than A2, for example P(offspring is A1A1) = P(father passes A1) × P(mother passes A1) P(offspring is A1A2) = P(father passes A1) × P(mother passes A2)+ P(mother passes A1) × P(father passes A2) Slides from Karin S. Dorman

  6. Hardy-Weinberg Equilibrium • Consider a single locus where there are two alleles segregating in a diploid population. Make the Hardy-Weinberg (HW) assumptions: • No difference in genotype proportions between the sexes. • Synchronous reproduction at discrete points in time (discrete generations) • Infinite population size (so that small variabilities are erased in the average) • No mutation. • No migration • No selection • Random mating Slides from Karin S. Dorman

  7. Deriving HWE • Let genotypes at generation t be P11(t), P12(t), and P22(t). Then, • Genotype in the next generation will be • And p1(t+1)=p1(t); p2(t+1)=p2(t) • So in one step it returns to the equilibrium! Slides from Karin S. Dorman

  8. A simple example • Consider this “population” Slides from Karin S. Dorman

  9. Slides from Karin S. Dorman

  10. Slides from Karin S. Dorman

  11. SNP • Three classes of polymorphic markers: • Biallelic: SNPs and indels, less informative but more frequent & stable • Multiallelic: micro and mini satellites, more dynamic, high copy number loci have high mutation rate • Combination of above two • Single Nucleotide Polymorphism • Occasionally short (1-3 bp) indels are considered SNPs too • Come from DNA-replication mistake individual germ line cell, then transmitted

  12. ATGGTAAGCCTGAGCTGACTTAGCGT-AT ATGGTAAACCTGAGTTGACTTAGCGTCAT    SNP SNP indel SNPs result from replication errors and DNA damage They are a ‘polymorphic’ bit state at a nucleoside address What are Single Nucleotide Polymorphisms (SNPs)?

  13. Why Should We Care • Personalized Medicine • Aithal et al., 1999, Lancet • Warfarin anticoagulant drug • CYP2C9 gene metabolizes warfarin, CYP2C9*1 (wild type) has two allelic variants: CYP2C9*2 & CYP2C9*3 (both single AA change) • Patients with variant alleles are poor warfarin metabolisers, often at higher risk of bleeding • Disease gene discovery • Association studies • Chromosome aberrations (copy number changes)

  14. Disease resistant population Disease susceptible population Genotype all individuals for thousands of SNPs ATGATTATAG geneX ATGTTTATAG Resistant people all have an ‘A’ at position 4 in geneX, while susceptible people have a ‘T’ (A/T are the SNPs)

  15. SNP Applications in Medicine • Gene discovery and allele mapping • Association-based (drug) candidate • polymorphism testing of a trait pool • Diagnostics / risk profiling • Drug response prediction • Homogeneity testing / study design • Gene function identification

  16. Population Assignment– assessing competing hypotheses • The likelihood ratio method • Definition of competing hypotheses is essential Adapted from a slide of Steve DiFazio

  17. Adapted from a slide of Steve DiFazio

  18. Hypothesis testing in statistics … • Null hypothesis – assumed true unless there is an overwhelming evidence against it. • P-value – under the null hypothesis assess how “odd” aparticular aspect of the data is – the probability of seeing values as extreme or more extreme than the one we saw. • Using the likelihood ratio to find an effective aspect of the data to tell the two hypotheses apart – a way to guide your choice

  19. SNP Distribution • Most common, > 1 SNP / 1KB • Balance between mutation introduction rate and polymorphism lost rate • Most mutations lost within a few generations • Often more transitions (A/G, C/T) than transversions (A/T, A/C, G/T, G/C) • In non-coding regions, often fewer SNPs at more conserved regions • In coding regions, often more synonymous than non-synonymous SNPs

  20. SNP Characteristics: Allele Frequency Distribution • Most alleles are rare (minor allele frequency < 10%) • Allele frequency in different genomes have a large variation • Human > 1 SNP / 600-1KB, • Fly and maize have an order of magnitude greater number of polymorphism (1 SNP / 50-100 bp) • Nucleotide diversity is positively correlated with recombination rate

  21. International HapMap Project • The International HapMap project is a recent, large-scale effort to facilitate GWAS studies: • Phase 1: 269 samples, 1.1 M SNPs • Phase 2: 270 samples, 3.9 M SNPs • Phase 3: 1115 samples, 1.6 M SNPs • Phase 3 platforms: • Illumina Human1M (by Wellcome Trust Sanger Institute) • Affymetrix SNP 6.0 (by Broad Institute)