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Introduction to QTL mapping

Introduction to QTL mapping. Manuel Ferreira. Boulder Introductory Course 2006. Outline. 1. Aim. 2. The Human Genome. 3. Principles of Linkage Analysis. 4. Parametric Linkage Analysis. 5. Nonparametric Linkage Analysis. 1. Aim. QTL mapping.

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Introduction to QTL mapping

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  1. Introduction to QTL mapping Manuel Ferreira Boulder Introductory Course 2006

  2. Outline 1. Aim 2. The Human Genome 3. Principles of Linkage Analysis 4. Parametric Linkage Analysis 5. Nonparametric Linkage Analysis

  3. 1. Aim

  4. QTL mapping LOCALIZE and then IDENTIFY a locusthat regulates a trait (QTL) Nucleotide or sequence of nucleotides with variation in the population, with different variants associated with different trait levels.

  5. For a heritable trait... Linkage: localize region of the genome where a QTL that regulates the trait is likely to be harboured Family-specific phenomenon: Affected individuals in a family share the same ancestral predisposing DNA segment at a given QTL Association: identify a QTL that regulates the trait Population-specific phenomenon: Affected individuals in a population share the same ancestral predisposing DNA segment at a given QTL

  6. 2. Human Genome

  7. DNA structure A DNA molecule is a linear backbone of alternating sugar residues and phosphate groups Attached to carbon atom 1’ of each sugar is a nitrogenous base: A, C, G or T Two DNA molecules are held together in anti-parallel fashion by hydrogen bonds between bases [Watson-Crick rules] Antiparallel double helix A gene is a segment of DNA which is transcribed to give a protein or RNA product Only one strand is read during gene transcription Nucleotide: 1 phosphate group + 1 sugar + 1 base

  8. DNA polymorphisms Microsatellites >100,000 Many alleles, (CA)n, very informative, even, easily automated SNPs 10,054,521 (25 Jan ‘05) 10,430,753 (11 Mar ‘06) Most with 2 alleles (up to 4), not very informative, even, easily automated A B

  9. DNA organization 22 + 1 2 (22 + 1) 2 (22 + 1) 2 (22 + 1) ♂ ♁ ♂ A - A - A - ♁ B - ♂ ♁ ♁ ♂ Mitosis B - B - chr1 A - A - A - - A A - - A ♁ ♁ ♂ B - B - B - - B B - - B A - - A - A B - - B - B chr1 G1 phase S phase M phase Haploid gametes Diploid zygote 1 cell Diploid zygote >1 cell

  10. DNA recombination 22 + 1 22 + 1 A - NR (♂) B - A - - A chr1 2 (22 + 1) 2 (22 + 1) B - - B - A ♁ Meiosis R chr1 (♂) (♁) ♂ ♁ - B A - A - - A - A chr1 B - B - - B - B A - R chr1 chr1 chr1 chr1 (♁) A - - A B - chr1 Diploid gamete precursor cell B - - B - A chr1 NR - B Haploid gamete precursors chr1 Hap. gametes

  11. DNA recombination between linked loci 22 + 1 A - NR B - (♂) A - - A B - - B 2 (22 + 1) - A ♁ Meiosis NR - B (♂) (♁) ♂ ♁ A - A - - A - A B - B - - B - B A - NR B - (♁) A - - A B - - B Diploid gamete precursor - A - B NR Haploid gamete precursors Hap. gametes

  12. Human Genome - summary DNA is a linear sequence of nucleotides partitioned into 23 chromosomes Two copies of each chromosome (2x22 autosomes + XY), from paternal and maternal origins. During meiosis in gamete precursors, recombination can occur between maternal and paternal homologs Recombination fraction between loci A and B (θ) Proportion of gametes produced that are recombinant for A and B If A and B are very far apart: 50%R:50%NR - θ = 0.5 If A and B are very close together: <50%R - 0 ≤ θ < 0.5 Recombination fraction (θ) can be converted to genetic distance (cM) Haldane: eg. θ=0.17, cM=20.8 Kosambi: eg. θ=0.17, cM=17.7

  13. 3. Principles of Linkage Analysis

  14. Linkage Analysis requires genetic markers Q M1 Mn M2 .3 .4 .4 .3 0.5 0.5 θ 0.5 .15 M1 Mn M2 .35 .22 .35 .26 0.5 θ 0.5 0.5 .4 .3 .3 .4 .1 M1 Mn M2

  15. Linkage Analysis: Parametric vs. Nonparametric Gene Chromosome Recombination Genetic factors M Q A Mode of inheritance Correlation D Phe C E Environmental factors Adapted from Weiss & Terwilliger 2000

  16. 4. Parametric Linkage Analysis

  17. Linkage with informative phase known meiosis Gene Chromosome ♂ ♁ M1..6 Q1,2 Autosomal dominant, Q1 predisposing allele M2M5Q2Q2 M1M6Q1Q? M1 Q1 Informative Phase known M1Q1/M2Q2 M3M4Q2Q2 M1M2Q1Q2 M2 Q2 M1Q1/M3Q2 M2Q2/M3Q2 M1Q1/M4Q2 M1Q1/M4Q2 M2Q2/M4Q2 M2Q1/M3Q2 NR: M1Q1 NR: M2Q2 (~20.8 cM) θMQ = 1/6 = 0.17 R: M1Q2 R: M2Q1

  18. Linkage with informative phase unknown meiosis M1 Q1 M1 Q2 Q2Q2 Q1Q? M2 M2 Q2 Q1 Informative Phase unknown M1Q1/M2Q2 M1M2Q1Q2 M3M4Q2Q2 M1Q2/M2Q1 M1Q1/M3Q2 M2Q2/M3Q2 M1Q1/M4Q2 M1Q1/M4Q2 M2Q2/M4Q2 M2Q1/M3Q2 M1Q2/M2Q1 M1Q1/M2Q2 P P N N 3 3 ½θ ½(1-θ) R: M1Q1 NR: M1Q1 R: M2Q2 ½θ NR: M2Q2 2 2 ½(1-θ) NR: M1Q2 R: M1Q2 ½θ 0 0 ½(1-θ) NR: M2Q1 R: M2Q1 1 1 ½(1-θ) ½θ + +

  19. Parametric LOD score calculation Overall LOD score for a given θ is the sum of all family LOD scores at θ eg. LOD=3 for θ=0.28

  20. Parametric Linkage Analysis - summary Q M1 Mn M2 .3 .4 .4 .3 θ 0.5 0.5 0.5 .1 For each marker, estimate the θ that yields highest LOD score across all families This θ (and the LOD) will depend upon the mode of inheritance assumed MOI determines the genotype at the trait locus Q and thus determines the number of meiosis which are recombinant or nonrecombinant. Limited to Mendelian diseases. Markers with a significant parametric LOD score (>3) are said to be linked to the trait locus with recombination fraction θ

  21. Outline 1. Aim 2. The Human Genome 3. Principles of Linkage Analysis 4. Parametric Linkage Analysis 5. Nonparametric Linkage Analysis

  22. 5. Nonparametric Linkage Analysis

  23. Approach Parametric: genotype marker locus & genotype trait locus (latter inferred from phenotype according to a specific disease model) Parameter of interest: θbetween marker and trait loci Nonparametric: genotype marker locus & phenotype If a trait locus truly regulates the expression of a phenotype, then two relatives with similar phenotypes should have similar genotypes at a marker in the vicinity of the trait locus, and vice-versa. Interest: correlation between phenotypic similarity and marker genotypic similarity No need to specify mode of inheritance, allele frequencies, etc...

  24. Phenotypic similarity between relatives Squared trait differences Squared trait sums Trait cross-product Trait variance-covariance matrix Affection concordance T2 T1

  25. Genotypic similarity between relatives IBSAlleles shared Identical By State “look the same”, may have the same DNA sequence but they are not necessarily derived from a known common ancestor M3 M1 M2 M3 Q3 Q1 Q2 Q4 IBDAlleles shared Identical By Descent are a copy of the same ancestor allele M1 M2 M3 M3 Q1 Q2 Q3 Q4 IBD IBS M1 M3 M1 M3 2 1 Q1 Q4 Q1 Q3 0 0 1 0 1 Inheritance vector (M)

  26. Genotypic similarity between relatives - Inheritance vector (M) Number of alleles shared IBD Proportion of alleles shared IBD - M2 M3 M1 M3 0 0 0 1 1 0 Q2 Q4 Q1 Q3 M1 M3 M1 M3 0 0 1 0 0.5 1 Q1 Q4 Q1 Q3 M1 M3 M1 M3 0 0 0 0 2 1 Q1 Q3 Q1 Q3

  27. Genotypic similarity between relatives - D A B C 22n

  28. Practical Aim (1) Estimate IBD with MERLIN; (2) IBD estimation can be influenced by genotyped individuals and allele frequencies; (3) compute H:\manuel - Copy folder “Linkage” to C:\ 1. Open with Notepad: pr1.ped pr1.dat pr1.map pr1.freq 2. Start>Run>C:/Linkage/pfe32.exe 3. Run Command Prompt 4. Keep a File Explorer window open Exercice1 (1) Estimate IBD for pedigrees A, B and C in the previous slide (2) Change allele frequencies (pr1.freq) from 0.25 0.25 0.25 0.25 to (i) 0.45 0.25 0.25 0.05 and (ii) 0.05 0.25 0.25 0.45

  29. Practical A1A2 A3A4 A1A3 A2A4 A1A3 Exercice 2 (1) Modify pr1.ped and estimate IBD probabilities and between twin 1 and twin 2 for pedigrees E, F and G: E F G A1A2 A1A3 A1A3 A1A3 A1A3 A2A4 A1A3 A1A3 P(IBD=0) 0.08 0.00 0.00 P(IBD=1) 0.31 0.20 0.00 P(IBD=2) 0.61 0.80 1.00 0.77 0.90 1.00 Allele frequencies on pr1.freq: 0.25 0.25 0.25 0.25

  30. M1 Mn M2 IBD at a marker Singlepoint IBD 5 cM M1 Mn M2 IBD at a ‘grid’ Multipoint IBD

  31. Statistics that incorporate both phenotypic and genotypic similarities Phenotypic similarity 0.5 1 0 Genotypic similarity ( )

  32. Haseman-Elston regression – Quantitative traits 0.5 1 0 Phenotypic dissimilarity = b × Genotypic similarity + c

  33. VC ML – Quantitative & Categorical traits method 0.5 1 0 H1: H0: e.g. LOD=3

  34. Genome-wide linkage analysis (e.g. VC) Individual LOD scores can be expressed as P values (Pointwise) LOD Chi-sq (n-df) P value 2.1 9.67 0.0009 (x4.6) True positive Theoretical(Lander & Kruglyak 1995) k LOD LOD = 3.6, Chi-sq = 16.7, P = 0.000022 Type I error

  35. Nonparametric Linkage Analysis - summary No need to specify mode of inheritance Models phenotypic and genotypic similarity of relatives Expression of phenotypic similarity, calculation of IBD HE and VC are the most popular statistics used for linkage of quantitative traits Other statistics available, specially for affection traits

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