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Gene by environment effects. Elevated Plus Maze (anxiety)

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Presentation on theme: "Gene by environment effects. Elevated Plus Maze (anxiety)"— Presentation transcript:

1 Gene by environment effects

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3 Elevated Plus Maze (anxiety)

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10 Modelling E, G and GxE H 1 : phenotype ~ covariate H 2 : phenotype ~ covariate + LocusX H 3 : phenotype ~ covariate + LocusX + covariate:LocusX H 0 : phenotype ~ 1

11 PRACTICAL: Inclusion of gender effects in a genome scan To start: 1. Copy the folder faculty\valdar\FridayAnimalModelsPractical to your own directory. 2. Start R 3. File -> Change Dir… and change directory to your FridayAnimalModelsPractical directory 4. Open Firefox, then File -> Open File, and open “gxe.R” in the FridayAnimalModelsPractical directory

12 H 0 : phenotype ~ sex H 1 : phenotype ~ sex + marker

13 scan.markers(Phenotype ~ Sex + MARKER, h0 = Phenotype ~ Sex,... etc) H 0 : phenotype ~ sex H 1 : phenotype ~ sex + marker

14 scan.markers(Phenotype ~ Sex + MARKER, h0 = Phenotype ~ Sex,... etc) H 0 : phenotype ~ sex H 1 : phenotype ~ sex + marker

15 scan.markers(Phenotype ~ Sex + MARKER, h0 = Phenotype ~ Sex,... etc) H 0 : phenotype ~ sex H 1 : phenotype ~ sex + marker

16 head(ped.gender0) anova(lm(Phenotype ~ Sex + m1 + Sex:m1, data=ped.gender0)) head(ped.gender1) anova(lm(Phenotype ~ Sex + m1 + Sex:m1, data=ped.gender1)

17 New approaches Advanced intercross lines Genetically heterogeneous stocks

18 F2 Intercross x Avg. Distance Between Recombinations F1 F2 F2 intercross ~30 cM

19 Advanced intercross lines (AILs) F0 F1 F2 F3 F4

20 Chromosome scan for F12 position along whole chromosome (Mb) goodness of fit (logP) 0 100cM significance threshold QTL Typical chromosome

21 PRACTICAL: AILs To start: 1. Open Firefox, then File -> Open File, and open “ail_and_ghosts.R” in the FridayAnimalModelsPractical directory

22 Genetically Heterogeneous Mice

23 F2 Intercross x Avg. Distance Between Recombinations F1 F2 F2 intercross ~30 cM

24 Pseudo-random mating for 50 generations Heterogeneous Stock F2 Intercross x Avg. Distance Between Recombinations: F1 F2 HS ~2 cM F2 intercross ~30 cM

25 Pseudo-random mating for 50 generations Heterogeneous Stock F2 Intercross x Avg. Distance Between Recombinations: F1 F2 HS ~2 cM F2 intercross ~30 cM

26 Genome scans with single marker association

27 High resolution mapping

28 Relation Between Marker and Genetic Effect Observable effect QTL Marker 1

29 Relation Between Marker and Genetic Effect Observable effect QTL Marker 2 Marker 1

30 Relation Between Marker and Genetic Effect No effect observable Observable effect QTL Marker 2 Marker 1

31 Hidden Chromosome Structure Observed chromosome structure Multipoint method (HAPPY) calculates the probability that an allele descends from a founder using multiple markers

32 Haplotype reconstruction using HAPPY chromosome genotypes haplotype proportions predicted by HAPPY

33 HAPPY model for additive effects

34 Genome scans with single marker association

35 Genome scans with HAPPY

36 Results for our HS

37 Many peaks mean red cell volume

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39 How to select peaks: a simulated example

40 Simulate 7 x 5% QTLs (ie, 35% genetic effect) + 20% shared environment effect + 45% noise = 100% variance

41 Simulated example: 1D scan

42 Peaks from 1D scan phenotype ~ covariates + ?

43 1D scan: condition on 1 peak phenotype ~ covariates + peak 1 + ?

44 1D scan: condition on 2 peaks phenotype ~ covariates + peak 1 + peak 2 + ?

45 1D scan: condition on 3 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + ?

46 1D scan: condition on 4 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + ?

47 1D scan: condition on 5 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + ?

48 1D scan: condition on 6 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + ?

49 1D scan: condition on 7 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + ?

50 1D scan: condition on 8 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + ?

51 1D scan: condition on 9 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + peak 9 + ?

52 1D scan: condition on 10 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + peak 9 + peak 10 + ?

53 1D scan: condition on 11 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + peak 9 + peak 10 + peak 11 + ?

54 Peaks chosen by forward selection

55 Bootstrap sampling subjects

56 Bootstrap sampling subjects sample with replacement bootstrap sample from 10 subjects

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58 Forward selection on a bootstrap sample

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61 Bootstrap evidence mounts up…

62 In 1000 bootstraps… Bootstrap Posterior Probability (BPP)

63 Results

64 Tea Break

65 Study design 2,000 mice 15,000 diallelic markers More than 100 phenotypes each mouse subject to a battery of tests spread over weeks 5-9 of the animal’s life

66 101 Phenotypes Anxiety (conditioned and unconditioned tasks) [24] Asthma (plethysmography) [13] Biochemistry [15] Diabetes (glucose tolerance test) [16] Haematology [15] Immunology [9] Weight/size related [8] Wound Healing [1]

67 High throughput phenotyping facility

68 Photo ID

69 Open Field

70 Anxious mouse Non anxious mouse

71 Elevated Plus Maze (anxiety)

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76 Food hyponeophagia (reluctance to try new food)

77 “Home Cage” activity

78 Startle & Conditioning

79 Plethysmograph

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81 Glucose Tolerance Test (diabetes)

82 Wound healing

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86 PRACTICAL:

87 END

88 An individual’s phenotype follows a mixture of normal distributions

89 m Maternal chromosome Paternal chromosome

90 m Chromosome 1 Chromosome 2 ABCDEFABCDEF Strains

91 Markers m ABCDEFABCDEF Strains

92 Markers m ABCDEFABCDEF Strains

93 Markers m 0.5 cM

94 Markers m 0.5 cM 1 cM

95 Markers m 0.5 cM 1 cM

96 Analysis Probabilistic Ancestral Haplotype Reconstruction (descent mapping): implemented in HAPPY

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98 M1 Q M2 m1 q m2 M1 ? m2 recombination

99 M1 Q M2 m1 q m2 m1 Q M2 M1 q m2 M1 q m2

100 M1 Q M2 m1 q m2 M1 Q m2 M1 Q m2 m1 q M2 m1 Q M2 M1 q m2 M1 q m2

101 M1 Q M2 m1 q m2 M1 m2 M1 m2 m1 m2 m1 M2 M1 m2 M1 m2 Qq Qqq Q

102 M1 ? m2 m1 ? m2 cM distances determine probabilities

103 M1 ? m2 m1 ? m2 cM distances determine probabilities Eg,

104 Interval mapping M1 M2 m1 m2 M1 m1 M2 m2 LOD score

105 Interval mapping M1 M2 m1 m2 M1 m1 M2 m2 LOD score Q q

106 Interval mapping M1 M2 m1 m2 M1 m1 M2 m2 LOD score Q q

107 Interval mapping M1 M2 m1 m2 M1 m1 M2 m2 LOD score Q q


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