Genetic architecture of behaviour

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?

OFA App.- John & Gene

(From DeFries, Gervais and Thomas, 1978).

OFA/OFD bar graph

) (From DeFries, Gervais and Thomas, 1978).

OFA line graph

Inbred Strain Cross

Intercross experiment

DeFries H1 X DeFries L1 815 animals

DeFries H2 X DeFries L2 821 animals

TOTAL: 1,636

Loci that influence variation in Open Field Activity

How many QTL?

Power to detect a locus

Undetectable QTL

QTL estimator

OFA OFD

Number of detected QTL 6 3 NQTL 6.90 4.61

95% CI 3.2-12 1.1-11.9

What is the effect size of the QTL?

Average Effect Size of QTL detected in studies of rodent

behaviour

Number of Studies

Number of QTL

42 159

Average Effect Size

Number of Studies

Number of QTL

Average Effect Size

42 159 5.50%

Genetic action

• How important are epistatic effects?

Interaction

A2A1 B2B1

Phenotype 100 100

Interaction

Phenotype 100 + 100 = 300

Interaction

A2A1 B2B1

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))

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

Interaction analysis

• All pairs of markers tested for interaction on 23 phenotypes

• total of 86,043 analyses

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))

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

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

Lung Cancer Susceptibility

Genetic architecture

• Up to 12 QTL

• Effect sizes < 10%

• No evidence for interaction

What is the molecular basis of the QTL?

QTL mapping of arthritis susceptibility in rats

Positional cloning of the QTL

Reasons for success

• Large effect size: ~25% of phenotypic variance

• Recognizable mutation

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A/J AKR Balb C3H C57 DBA IS RIII

HS

HS generations >50

Random Breeding

Genetically Heterogeneous Mice

High Resolution

149141

Rgs1Rgs13

Rgs18

147

Uch15 Rgs2

Cfh B3galt2

Ssa2

Glrx2

145143

Physical Map

Coding sequence variants

Coding sequence variants

• None

Relation between Sequence Variants and Genetic Effect

Strain Sequences Must Be Consistent with QTL Action

AC57BALBAKR

QTL

Relation between Sequence Variants and Genetic Effect

No effect

observableObservable

effect

QTLMarker 1 Marker 2

Strain pattern of sequence differences

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

Strain Distribution

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0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 1800000

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Rgs1Rgs13

Rgs18Uch15 Rgs2B3galt2

Ssa2

Genes

Rgs1Rgs13 Rgs18Rgs2

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144.8 144.9 145 145.1 145.2 145.3 145.4 145.5 145.6 145.7 145.8

Regulators of G Protein signalling

RGS2 Knock-out shows enhanced fear response

1 m-1 Mst

Strains

Markersm m+1