A Method for Detecting Pleiotropy

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A Method for Detecting Pleiotropy. Ingrid Borecki, Qunyuan Zhang, Michael Province Division of Statistical Genomics Washington University School of Medicine. Biological question : Does a genetic variant have independent effects on both of two traits? . Pleiotropy. Statistical question :

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A Method for Detecting Pleiotropy

Ingrid Borecki, Qunyuan Zhang, Michael Province

Division of Statistical Genomics

Washington University School of Medicine

Biological question:

Does a genetic variant have independent effects on both of two traits?

Pleiotropy

Statistical question:

Can the correlation or a portion of the correlation between two traits be explained by a genetic variant?

Compound null:

no pleiotropy

Alternative:

pleiotropy

Y1

Y2

X

Y1

Y2

Y1

Y2

Y1

Y2

X

X

X

Compound null:

no pleiotropy

Alternative:

pleiotropy

Estimating δ

Two traits are simultaneously fit into a mixed model

T is the trait indicating variable; R is block diagonal covariance matrix (after re-ordering by individuals), with blocks corresponding to the individuals and each block having the compound-symmetry structure

When excluding X from the model

When including X in the model

Q-Q Plot under the null

Testing δ

Pleiotropy Estimation Test (PET)

Estimated by bootstrapre-sampling 100 times

with replacement

-LOG10(P)

=Residual of Y1 adjusted by Y2

=Residual of Y2 adjusted by Y1

• MANOVA (Wilks' test, wrong null)
• FCP: Fisher’s combined p-value test (meta-analysis ignoring correlations, wrong null)
• RCM: Reverse compound model (two tests)
• SUM: Simple univariate model (two tests)

Other Methods for Comparison

Testing if β1≠0 and β2≠0

PET

FCP

MANOVA

RCM

SUM

Power Comparison

PET

FCP

MANOVA

RCM

SUM

Correlation (WC, HOMA)= 0.542

Application

Correlation (TG, CAC)= 0.089

The PET Method

• Tests proper compound null for pleiotropy;
• Gives estimation of covariance due to pleiotropy;
• Has greater power other alternatives;
• Under mixed model framework, can easily be expanded to other data (covariates, family data etc.) ;
• Practical to GWAS data (with 300 blades, R version takes less than 1 day for the analysis of 2M SNPs and ~3000 subjects) ;
• Must be fit to primary phenotype and (typed or imputed) genotype data.

Acknowledgement

Ling-Yun Chang (programming & testing)

Mary Feitosa (GWAS data and application)