QTL analysis / Mutagenesis

QTL analysis / Mutagenesis

candidate genes

Humanpopulations

functionalstudies

Genetic basis of atherosclerotic organ damage:

From animal genetics to human cohorts

functionalstudies

atherosclerosisrenal damage

HDL cholesterolPLTP activity

Definitions

A quantitative trait is one that has measurable phenotypic variation owing to genetic and/or environmental influences.

A QTL is a genetic locus, the alleles of which affect this variation.

QTL analysis is a statistical method to map these loci in the species under investigation

Members of the Complex Trait Consortium (2003) Nature Reviews Genetics 4:911-916

Principle of QTL analysis (1)

Advantages of QTL analysis

QTL analysis allows identification of novel genes involved in the phenotype.

QTL mapping is more likely to find mutations in rate limiting or regulatory genes, which will be very important therapeutic targets.

Comparison between species as a search strategy (1):Comparison between species as a search strategy (1):Concordance of Concordance of HDL QTLHDL QTL between human and mouse between human and mouse

atherogenic diet

Apoe or Ldlr KO

human concordance

rabbit concordance

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Comparison between species as a search strategy (2):Comparison between species as a search strategy (2):Concordance of Concordance of atherosclerosis QTLatherosclerosis QTL between human and mouse between human and mouse

Locating Ath8, a locus for murine atherosclerosis susceptibility and testing several of its candidate genes in mice and humans.

Korstanje R, Eriksson P, Samnegard A, Olsson, Forsman-Semb K, Sen S, Churchill G,Rollins J, Harris S, Hamsten A, Paigen B

Atherosclerosis, 2004, 177:443-450

1. QTL for atherosclerosis susceptibility was identified in mouse

2. Candidate genes were tested

3. Gene was identified with likely functional difference

4. Association was found between human homolog and cohort

X

SM/J NZB/B1NJ

F1

F2

258 females

Angptl3 polymorphisms between NZB and SM mice.

Effect on proteinEffect on protein

PositionPosition bpbpaa NZBNZB SMSM domaindomain AAAAbb NZBNZB SMSM

Exon 3Exon 3 736736 AA GG linkerlinker 233233 GluGlu GlyGly

740740 AA CC linkerlinker 234234 ThrThr ThrThr

Exon 4Exon 4 788788 AA TT FHDFHD 250250 TyrTyr PhePhe

abasepair numbering based on Genbank No. XM_131498.bamino acid numbering based upon the first ATG as +1.

Controls Patients P

ANGPTL3_1 AA 153 171A/G 183 143G/G 39 48 <0.05

ANGPTL3_2 G/G 152 170G/A 183 138A/A 39 59 <0.01

Age (years) 53.0±4.8 52.3±5.6 NSSex (men/women) 307/66 304/63 NSBMI (kg/m2) 26.5±11.9 27.4±4.0 NS Plasma triglycerides (mmol/L) 1.37±0.74 1.95±1.21 <0.0001LDL cholesterol (mmol/L) 3.50±0.88 3.23±0.99 <0.0001HDL cholesterol (mmol/L) 1.39±0.41 1.10±0.30 <0.0001Plasma insulin (pmol/L) 42.05±23.67 57.13±40.49 <0.0001Plasma proinsulin (pmol/L) 4.48±3.48 7.21±7.69 <0.0001

ANGPTL3 genotype distribution in patients and controls.

Numbers of genotypes are shown for the two polymorphisms. Continuous values are means±SD

ANGPTL3 genotypes and severity of angiographically determined coronary artery disease in postinfarction patients.

ANGPTL3ANGPTL3_1_1

A/AA/A A/GA/G G/GG/G pp

NN 105105 9292 2929

Plaque areaPlaque area 0.24±0.080.24±0.08 0.22±0.080.22±0.08 0.20±0.050.20±0.05 <0.05 <0.05<0.05 <0.05aa

Min. lumen diameter (mm)Min. lumen diameter (mm) 2.32±0.452.32±0.45 2.32±0.452.32±0.45 2.34±0.512.34±0.51 NSNS

Mean stenosis (%)Mean stenosis (%) 32.44±8.3332.44±8.33 31.63±10.2531.63±10.25 32.45±9.8932.45±9.89 NSNS

Mean segment diameter (mm)Mean segment diameter (mm) 3.02±0.473.02±0.47 3.02±0.433.02±0.43 3.02±0.583.02±0.58 NSNS

Values are shown as means±SD. awhen adjusted for plasma triglyceride concentration. BMI, body mass index; LDL, low density lipoprotein; HDL, high density lipoprotein, insulin, proinsulin and smoking.

Conclusions (part 1)Conclusions (part 1)

Animal models (rat and mouse in particular) can make identifying candidate genes easier because of less genetic complexity, better genetic tools, and a controlled environment

Concordance between mouse, rat, and human QTL allow the use of animal models for candidate gene selection.

HaplotypesHaplotypes

Yalcin, B. et al. (2004) Proc. Natl. Acad. Sci. USA 101, 9734-9739

Haplotype structure of 1,450 diallelic variants with SDP frequency >1% between eight inbred strains (A/J, AKR, BALB/c, C3H, C57BL/6, DBA/2, I, and RIII) across a 4.8-Mb region of mouse chromosome 1

In silicoIn silico mapping (1) mapping (1)

Pletcher et al: 48 inbred strains, 10.990 SNPs

Presence or absence of the retinal degeneration and albino phenotypes was given a numericalvalue of 1 or 0 for use in the mapping algorithm. The most significant P value was obtained for the region that contains the gene known to produce these phenotypes.


Comparison of in silico QTL with experimentally derived QTL


Analysis of Adcy7 haplotypes reveals amino acid associated with HDL phenotypes

Kidney disease QTL are concordant in rat, human, and mouse

Korstanje and DiPetrillo, AJP Renal, 287:F347-F352

Promising regions for renal damage Promising regions for renal damage are our current focusare our current focus

Korstanje and DiPetrillo, AJP Renal, 287:F347-F352

Analysis of QTL studies and haplotypesAnalysis of QTL studies and haplotypes

D9Rat29

D9Rat26

D9Mit3

D9Mco6

UAE16LOD 8.0SSxSHR16 wkmalesGarrett et al

UAE25LOD 3.5MWFxSHR14 wkmalesSchulz et al

SSxSHR8 wkmalesMehr et al

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100

D9Rat64

D9Rat3

NO QTL

MWFxLew14 wkmales/femalesSchulz et al

Haplotype analysis narrows QTL intervalHaplotype analysis narrows QTL intervaland excludes many genes and excludes many genes

Test candidate genes for sequence Test candidate genes for sequence and expression differences between strains and expression differences between strains

Lewis MWF

SHR Dahl SS

Lewis

8 wks

Lewis

16 wks

SHR

8 wks

SHR

16 wks

Dahl SS

8 wks

Dahl SS

16 wks

MWF

16 wks

MWF

16 wks

Quantifying Complex Traits:Quantifying Complex Traits:

Epidemiology of Phenotype-Genoype InteractionsEpidemiology of Phenotype-Genoype Interactions

Mike W. ZuurmanMike W. Zuurman

P

PG

G G

EF

Environment

Individual

EF

Complex Traits Diagram

FP

EF

EF

1

1. env. response to phenotype (treatment), 2. env. influence on gene expression, 3. env. inducing internal phenotype, 4. env. influencing expression of unknown gene, 5. phenotypes combine to one detectable phenotype, 6. phenotype is only partly surfacing, 7. internal feedback mechanisms 8. unidentified gene influencing detectable phenotype, 9. un- visible/detectable/known phenotype 10. gene-phenotype-phenotype-gene interaction

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P FP P+ + = trait

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= intermediate phenotype

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Complex Traits Example: Cholesterol Metabolism

Genetic Complex Traits End organ damage

Some end-points of interest in the clinic

-Hypertension-Ischemia-Infarction-Diminished/Elevated filtration-Albuminuria-Mortality-Immunological abnormalities-Metabolic Syndrome-Other lipid-related phenotypes-Etc.

Confounders

-associated with exposureassociated with exposure-related to the outcomerelated to the outcome-not part of the causal pathway-not part of the causal pathway

Mother highly educatedMother highly educated Child Down syndromeChild Down syndrome

Alcohol intakeAlcohol intake Lung cancer Lung cancer

Blood pressure Blood pressure UAE UAE

Age

Smoking

Gender

CETP Taq 1B single nucleotide polymorphism

B1/B1 B1/B2 B2/B2

GG AG AA

CETP Taq1B

1,30

1,35

1,40

HD

L (

AZ

G)

Means

B1B1 B1B2 B2B2

CETP Taq B1B2 (R) Coefficient p

CETP Taq1B B1B1 -0,07097 >0.00001

CETP Taq1B B2B2 0,063692 >0.00001

Age 0,000906 >0.00001

Sex 0,329274 >0.00001

BMI nc1 -0,02786 >0.00001

Linear regression (HDL)

PREVEND-data

HDL Levels Low

(criterium Metabolic

Syndrome))

pOdds ratio

CETP Taq B1B2 Reference Reference 11

CETP Taq1B B1B1 0,005 1,377

CETP Taq1B B2B2 0,017 0,669

Age 0,0001 1,019

Logistic regression

Clinical relevance

Metabolic Syndrome p Odds ratio

CETP Taq1B B1B1 0,39 0,86


Age >0,00001 1,06

Sex >0,00001 2,73

Significant effect on clinical intermediate phenotype

Borderline effect on clinical phenotype

Mortality p Odds ratio



Age >0,00001 1,11

Sex >0,00001 0,55

Borderline effect on clinical end point

co

mp

lex

ity

Complex Traits : 2 or more genotypes confound phenotype

Conceptual thinking:

Given a parameter measured in a population one is able to detectdifferences in frequency of a combination of geno- or phenotypesalong the range of the parameter when compared to the prevalanceof that combination in the whole population.

Visualization of complexity

Complexity and novel tools

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f1g f2g f3g f4g

f1h f2h f3h f4h

Visualization of complexity

0% 20% 40% 60% 80% 100%

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Example 1: Hypothesis driven visualization

Phenotype : HDL-cholesterolComplex Trait: CETP-Taq1B + CETP-I405V

B1B1II

B1B2VV

B1B2II

Chi-square Test

cc p

B1B1II 0.0008

B1B2VV 0.0073

B1B2II 0.0069

PREVEND-data

FGClustor 1.0, XeNTaX

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

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Phenotype : Metabolic syndromeComplex Trait: Gender + Quintile Age + CETP-Taq1B + CETP-I405V

B1B2IV B1B2II B1B1II females in the highest quintile of ageFGClustor 1.0, XeNTaX

PREVEND-data

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Phenotype : MortalityComplex Trait: Gender + Quintile Age + CETP-Taq1B + CETP-I405V

B1B2IV B1B2II B1B1II B1B1IV males in the highest quintile of age


PREVEND-data

In the general population, the CETP B1 and I allele, known to lead to lower HDL levels and less CETP activity, are associated with metabolic syndrome and higher mortality in the elderly.

Standard statistics and novel visualization of complexitycombined to generate the following working theory:

0% 20% 40% 60% 80% 100%

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Example 2 – Strong confounders

Phenotype: Systolic blood pressureComplex Trait: Quartiles Cholesterol + Gender Chi-square Test

F2 F1

M1 M2

F3 F4

M3 M4

F2 0,000217

M1 0,000703

M2 0,000606

F3 0,019796

F4 0,001564

M3 0,000205

M4 0,000192

F1 6,67E-05

PREVEND-data


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

30

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54

66

Age

ran

ge

Example 3: Non-hypothesis driven“Phenotype” : Age

Complex Trait : 5 Renin-aldosteron-angiotensin System genotypes

Chi-square Test

No low p values

Increasingly complex system requires huge sample size besides solid hypothesis

PREVEND-data

P

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Concluding remarks on the epidemiology of Breedtestrategie

-Powerful statistical methods help uncover “mild”complex genetical pathways leading to clinically relevant phenotypes.

-Yet, increasing complexity of the trait shows limitations of standard epidemiological statistics when applied on current population sizes;

-Development of novel tools help identify undocumented traits of greater complexity

-Interdisciplinary communication and exchange of expertise in dealing with complex traits will boost research

Complex Trait Centre Groningen (CTCG)

http://www.ctcg.org

•CTCG is an interdisciplinary community of researchers that CTCG is an interdisciplinary community of researchers that deal with genetic traits in the broad sense of the worddeal with genetic traits in the broad sense of the word

•CTCG will organize seminars by people in the field that will CTCG will organize seminars by people in the field that will educate researchers in their approach educate researchers in their approach

– – join the mailinglist to receive emails informing of join the mailinglist to receive emails informing of upcoming events!upcoming events!- propose speakers for seminars at [email protected]!- propose speakers for seminars at [email protected]!

•CTCG aims at communicating problem solving in the daily CTCG aims at communicating problem solving in the daily routine of complex trait researchroutine of complex trait research

•CTCG is cooperative and not restricted to set departmentsCTCG is cooperative and not restricted to set departments

•CTCG invites anyone that is interested in complex trait CTCG invites anyone that is interested in complex trait research to join the CTCG communityresearch to join the CTCG community

QTL analysis / Mutagenesis

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Transcript of QTL analysis / Mutagenesis