Complex Trait Genetics in Animal Models. Will Valdar Oxford University
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1 Complex Trait Genetics in Animal Models Will Valdar Oxford University
2 Mapping Genes for Quantitative Traits in Outbred Mice Will Valdar Oxford University
3 What s so great about mice? Share ~99% of genes with humans ~90% of the two genomes can be portioned into regions of conserved synteny Shorter lifespans You can do invasive experiments You can breed them as you like control the genetics
4 What is an inbred strain? F20 generation F = 99% BALB/c
5
6 one inbred strain
7 two inbred strains
8 Mouse model of anxiety
9 Mouse model of anxiety Anxious mouse Non anxious mouse
10 F2 cross Generation F0 F1 F2
11 anxious about average non-anxious
12 Quantitative Trait Locus anxious about average non-anxious
13 Quantitative Trait Locus anxious about average non-anxious
14 Quantitative Trait Locus anxious about average non-anxious
15 Quantitative Trait Locus anxious about average non-anxious QTL snp
16 Linear models Also known as ANOVA ANCOVA regression multiple regression linear regression
17 x QTL snp
18 x y ax QTL snp
19 x y ax QTL snp
20 x y ax a a QTL snp
21 Hypothesis testing H 0 : y H 1 : y ax
22 Hypothesis testing H 0 : y ~ 1 y H 1 : y ~ 1 + x y ax
23 Hypothesis testing H 0 : y ~ 1 y H 1 : y ~ 1 + x y ax H 1 vs H 0 : Does x explain a significant amount of the variation?
24 Hypothesis testing H 0 : y ~ 1 y H 1 : y ~ 1 + x y ax H 1 vs H 0 : Does x explain a significant amount of the variation? likelihood ratio LOD score
25 Hypothesis testing H 0 : y ~ 1 y H 1 : y ~ 1 + x y ax H 1 vs H 0 : Does x explain a significant amount of the variation? LOD score likelihood ratio Chi Square test p-value logp
26 Hypothesis testing H 0 : y ~ 1 y H 1 : y ~ 1 + x y ax H 1 vs H 0 : Does x explain a significant amount of the variation? LOD score likelihood ratio Chi Square test p-value logp linear models only SS explained / SS unexplained F-test (or t-test)
27 Hypothesis testing H 0 : y ~ 1 + x1 y a 1 x 1 H 1 : y ~ 1 + x1 + x2 y a x a x H 1 vs H 0 : Does x2 explain a significant amount of the variation after accounting for x1? or is x2 significant conditional on x1?
28 F2 cross Generation F0 F1 F2
29 QTL
30 logp QTL
31 QTL logp highly significant snp non significant snp
32 logp QTL
33 logp QTL
34 Chromosome scan for F2 QTL goodness of fit (logp) significance threshold 0 200Mb position along whole chromosome (Mb) Typical chromosome
35 Advanced intercross lines (AILs) F0 F1 F2 F3 F4 Darvasi & Soller (1995) Genetics
36 F12 cross F0 F1 F2 F12
37 Chromosome scan for F12 QTL goodness of fit (logp) significance threshold 0 200Mb position along whole chromosome (Mb) Typical chromosome
38 Practical 1. Fitting a linear model to test a marker-phenotype association 2. Single marker association on an F2 3. Permutation test 4. Single marker association on an AIL (F12) 5. Conditional modelling of loci Start Firefox, File->Open and go to F:\valdar\ThursdayAfternoonAnimals\practical.R Start R
39 Practical: F2 cross Bonferroni = 2.6 permutation ~ 2.1 uncorrected = 1.3
40 Practical: F2 cross Bonferroni = 2.6 permutation ~ 2.1 uncorrected = 1.3 generalized extreme value (GEV) distribution
41 Practical: F12 phenotype ~ MARKER
42 Practical: F12 phenotype ~ MARKER phenotype ~ m37 + MARKER
43 Practical: F12 phenotype ~ MARKER phenotype ~ m37 + MARKER phenotype ~ MARKER
44 Practical: F12 phenotype ~ MARKER phenotype ~ m37 + MARKER phenotype ~ MARKER phenotype ~ m37 + MARKER
45 Practical: F12 phenotype ~ MARKER phenotype ~ m37 + MARKER phenotype ~ MARKER phenotype ~ m37 + MARKER phenotype ~ m37 + m29 + MARKER
46 F2 F18 F18 multilocus approach
47 F2 F18 F18 multilocus approach
48 F2 breeding in a small population F18 F18 multilocus approach
49 F2 population structure gross genetic differences between groups where groups = families F18 multilocus approach
50 F2 population structure gross genetic differences between groups where groups = families multilocus approach
51 Heterogeneous Stocks
52 Heterogeneous Stock Pseudo-random mating for 50 generations Avg. Distance Between Recombinations: HS ~2 cm
53 Heterogeneous Stock F2 Intercross x Pseudo-random mating for 50 generations F1 Avg. Distance Between Recombinations: HS ~2 cm F2 intercross ~30 cm F2
54 124 Phenotypes Anxiety [24] Asthma [13] Biochemistry [15] Bone Morphology [23] Diabetes [16] Haematology [15] Immunology [9] Weight/size related [8] Wound Healing [1]
55 Intraperitoneal Glucose Tolerance Test
56
57
58 How to select peaks: a simulated example
59 How to select peaks: a simulated example Simulate 7 x 5% QTLs (ie, 35% genetic effect) + 20% shared environment effect + 45% noise = 100% variance
60 Simulated example
61 phenotype ~?
62 phenotype ~ peak 1 +? condition on 1 peak
63 condition on 2 peaks phenotype ~ peak 1 + peak 2 +?
64 condition on 3 peaks phenotype ~ peak 1 + peak 2 + peak 3 +?
65 condition on 4 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 +?
66 condition on 5 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 + peak 5 +?
67 condition on 6 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 +?
68 condition on 7 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 +?
69 condition on 8 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 +?
70 condition on 9 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + peak 9 +?
71 condition on 10 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + peak 9 + peak 10 +?
72 condition on 11 peaks phenotype ~ peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + peak 9 + peak 10 + peak 11 +?
73 Peaks chosen by forward selection
74 Bootstrap sampling 10 subjects
75 Bootstrap sampling sample with replacement subjects bootstrap sample from 10 subjects
76
77 Forward selection on a bootstrap sample
78 Forward selection on a bootstrap sample
79 Forward selection on a bootstrap sample
80 Bootstrap evidence mounts up
81 Model Inclusion Probability In 1000 bootstraps
82
83
84 854 loci in all phenotypes, 84 diabetes loci
85 854 loci in all phenotypes, 84 diabetes loci
86 Servin B, Stephens M (2007)
87 Bayesian Multiple QTL modelling Kilpikari R, Sillanpaa MJ (2003) Bayesian analysis of multilocus association in quantitative and qualitative traits. Genet Epidemiol 25: Yi N (2004) A unified Markov chain Monte Carlo framework for mapping multiple quantitative trait loci. Genetics 167: Servin B, Stephens M (2007) Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet 3: e114 Fridley BL (2008) Bayesian variable and model selection methods for genetic association studies. Genet Epidemiol.
88 The Collaborative Cross Heterogeneous Stocks (HS) Collaborative Cross outbreed outbred population recombinant inbred lines Churchill et al 2004; Broman 2005; Valdar et al 2006
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