Genetic architecture of behaviour
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Transcript of Genetic architecture of behaviour
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Genetic architecture of behaviour
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Genetic architecture of behaviour
• How many QTL?
• What is the average effect size of a QTL?
• How do the QTL act?
• What is the molecular basis of QTL action?
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OFA App.- John & Gene
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(From DeFries, Gervais and Thomas, 1978).
OFA/OFD bar graph
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) (From DeFries, Gervais and Thomas, 1978).
OFA line graph
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Inbred Strain Cross
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Intercross experiment
DeFries H1 X DeFries L1 815 animals
DeFries H2 X DeFries L2 821 animals
TOTAL: 1,636
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Loci that influence variation in Open Field Activity
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How many QTL?
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Power to detect a locus
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Undetectable QTL
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QTL estimator
OFA OFD
Number of detected QTL 6 3 NQTL 6.90 4.61
95% CI 3.2-12 1.1-11.9
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What is the effect size of the QTL?
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Average Effect Size of QTL detected in studies of rodent
behaviour
Number of Studies
Number of QTL
42 159
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Average Effect Size
Number of Studies
Number of QTL
Average Effect Size
42 159 5.50%
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Genetic action
• How important are epistatic effects?
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Interaction
A2A1 B2B1
Phenotype 100 100
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Interaction
Phenotype 100 + 100 = 300
Interaction
A2A1 B2B1
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Epistasis: definition
F-All: Y = 0 + 1NA1 + 2NB1 + 3(NA1)(NB1)
F-Part: Y = 0 + 1NA1 + 2NB1
NA1 is the "gene dosage" for the A1 allele in each genotype etc
F-Int2,Fulldf2 = ((F-AllFss – F-PartFss)/F-AllRss)(F-Alldf1-F-Partdf1)/F-Aldf2))
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Circadian Rhythm Interaction QTL
Phenotype Chr 1 Chr 2 F-all F-int LogP F-all LogP F-intPhase 8 12 5.23 5.89 1.94 1.88Amplitude 1 4 6.46 6.88 2.25 2.09Activity 16 X 5.05 7.23 1.90 2.16Dissociation 12 15 5.90 8.80 2.11 2.45
Genome Research Vol. 11, Issue 6, 959-980, June 2001
Genome-Wide Epistatic Interaction Analysis Reveals Complex Genetic Determinants of Circadian Behavior in Mice
Kazuhiro Shimomura,1,2 Sharon S. Low-Zeddies,2 David P. King,1,2 Thomas D.L. Steeves,1 Andrew Whiteley,1 Jani Kushla,1 Peter D. Zemenides,2 Andrew Lin,2 Martha Hotz Vitaterna,2 Gary A. Churchill,3 and Joseph S. Takahashi1,2,4
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Interaction analysis
• All pairs of markers tested for interaction on 23 phenotypes
• total of 86,043 analyses
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Interaction analysis
• All pairs of markers tested for interaction on 23 phenotypes
• total of 86,043 analyses
• 4,048 results gave a -LogP of > 6.7 (significance level for the likelihood under the full regression model (F-all))
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Interaction analysis
• All pairs of markers tested for interaction on 23 phenotypes
• total of 86,043 analyses
• 4,048 results gave a -LogP of > 6.7 (significance level for the likelihood under the full regression model (F-all))
• 0.05 threshold is –LogP 4.9
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Interaction terms less than P-value 0.001 (LogP > 3)
Chr1 Chr2 Phenotype LogP F-all LogP F-int2 7 OFA 11.56 3.561 15 EPM-open entries 9.13 4.331 17 SQ-open entries 9.26 3.235 6 MR-latency 5.97 4.3815 18 MR-latency 7.16 3.22
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Lung Cancer Susceptibility
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Genetic architecture
• Up to 12 QTL
• Effect sizes < 10%
• No evidence for interaction
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What is the molecular basis of the QTL?
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QTL mapping of arthritis susceptibility in rats
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Positional cloning of the QTL
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Reasons for success
• Large effect size: ~25% of phenotypic variance
• Recognizable mutation
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Increasing Generations F0
F1
F2
F3
F4
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Two Strains
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Eight Strains
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A/J AKR Balb C3H C57 DBA IS RIII
HS
HS generations >50
Random Breeding
Genetically Heterogeneous Mice
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High Resolution
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149141
Rgs1Rgs13
Rgs18
147
Uch15 Rgs2
Cfh B3galt2
Ssa2
Glrx2
145143
Physical Map
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Coding sequence variants
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Coding sequence variants
• None
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Relation between Sequence Variants and Genetic Effect
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Strain Sequences Must Be Consistent with QTL Action
AC57BALBAKR
QTL
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Relation between Sequence Variants and Genetic Effect
No effect
observableObservable
effect
QTLMarker 1 Marker 2
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Strain pattern of sequence differences
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Sequence variation
RGS18_MOUSERegion Position Type AJ C3H IS RIII AKR Balb C57BL DBA5'UTR 53 SNP A A G G A A A A5'UTR 164 SNP A A A A G G G G5'UTR 178 SNP T T A A T T T T5'UTR 218 Repeat(T) x4 x4 x4 x4 x2 x2 x2 x25'UTR 222 Repeat(AT) x5 x5 x6 x6 x6 x6 x6 x65'UTR 324 SNP A A T T T T T T5'UTR 418 Repeat(A) x14 x14 x13 x13 x14 x14 x14 x155'UTR 459 SNP C C C C T T T T5'UTR 794 SNP A A A A T T T TNon-coding 1572 SNP G G G G T T T TNon-coding 1578 Repeat(A) x6 x6 x6 x6 x5 x5 x5 x5Non-coding 1615 Repeat(T) x9 x9 x10 x10 x11 x10 x10 x10Non-coding 1645 SNP G G G G A A A ANon-coding 1711 SNP T T T T A T T TNon-coding 2327 Repeat(T) x12 x12 x13 x13 x11 x11 x11 x11Non-coding 2338 SNP T T T T A A A ANon-coding 3244 Insertion AC AC AC ACNon-coding 3246 Repeat(T) x5 x5 x5 x5 x4 x4 x4 x4Non-coding 3535 SNP C C A A C C C CNon-coding 3709 Repeat(A) x8 x8 x8 x8 x7 x7 x7 x7Non-coding 3716 Repeat(T) x3 x3 x3 x3 x2 x2 x2 x2Non-coding 3718 Repeat(A) x4 x4 x4 x4 x5 x5 x5 x5Non-coding 3742 SNP G G T T G G G GNon-coding 4339 SNP G G G G A A A ANon-coding 4736 SNP T T T T C C C CNon-coding 4827 SNP G G G G C C C CNon-coding 5312 Repeat(GT) x22 x22 x24 x24 x23 x23 x23 x23
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Strain Distribution
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Rgs1Rgs13
Rgs18Uch15 Rgs2B3galt2
Ssa2
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Genes
Rgs1Rgs13 Rgs18Rgs2
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Regulators of G Protein signalling
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RGS2 Knock-out shows enhanced fear response
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1 m-1 Mst
Strains
Markersm m+1