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Genetic Risk “G” (e.g., Risk Score, Candidate Genes). Phenotype/ Risk Factor “X” (e.g., Anxiety ). Outcome “Y” (e.g., CHD, Diabetes. Using GWAS Data for Enhanced Mendelian Randomization Studies. Stefan Walter ( swalter@hsph.harvard.edu ) on behalf of:

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using gwas data for enhanced mendelian randomization studies

Genetic Risk “G”

(e.g., Risk Score, Candidate Genes)

Phenotype/

Risk Factor “X”

(e.g., Anxiety)

Outcome “Y”

(e.g., CHD, Diabetes

Using GWAS Data for Enhanced Mendelian Randomization Studies

Stefan Walter

(swalter@hsph.harvard.edu)

onbehalfof:

Laura Kubzansky, Maria Glymour, Eric J TchetgenTchetgen, Liming Liang, Shun-Chiao Chang, Eric Rimm, Marilyn Cornelis, KarestanKoenen, and Ichiro Kawachi

outline
Outline
  • Setting
  • Projects:
    • Anxiety, Depression, SocialTies
    • Results
  • Methods and Instruments:
    • Using the Genetic Risk Score as an Instrument
    • Leave One Out Approach
    • Instrumental Inequality Tests
  • Future:
    • including interactions in score (GxG, GxE)
setting
Setting
  • NHS I
    • n = 7000 (NHST2D, NHSCGEMS, NHSCHD, NHSKS)
    • age at blooddraw = 59.5 (SD 8)
  • HPFS
    • n = 4125 (HPFST2D, HPFSCHD, HPFSKS)
    • age at blooddraw = 62.2 (SD 9)

Genotypedondifferent chips (Illumina, Affy), HapMap and 1000g imputationavailable.

projects
Projects
  • Anxiety:
    • Crown Crisp Experimental Index (phobic anxiety)
  • Depression
    • long-term composite depressive symptom score (1992-2006).
  • Social Ties
    • social isolation and ability to connect with others (continuously married versus otherwise).
results
Results
  • Depression and Anxiety:
    • No evidence of a geneticinstrumentfromadditiveinternal GWA risk score, candidate genes, orexternal GWA risk score (R2 < 0.1%, mostlynotsignificant)

Butinconsistentresultsfrom chip heritability (GCTA):

NHS_T2D, Affy 6.0, 0.214349

NHS_CGE, Illumina 550k, 0.062074

NHS_CHD, Affy 6.0, 0.305586

NHS_KS, Illumina 610Q, 0.000001

HPFS_T2D, Affy 6.0, 0.214349

HPFS_CHD, Affy 6.0, 0.200122

HPFS_KS, Illumina 610k, 0.046558

slide6

Genetic Risk “G”

(e.g., Risk Score, Candidate Genes)

Phenotype/

Risk Factor “X”

(e.g., BMI)

Outcome “Y”

(e.g., Anxiety, Depression, Social Ties)

  • Advantage:
    • Knowngeneticrelationshipfor BMI (Speliotes et al. Association analyses of 249,796 individuals reveal 18 new loci associated withbodymassindex).
    • Replicates in NHS/HPFS explaining 2% of variance.
  • Challenge:
    • Analysiswithinnested case-control (NCC) samples (allbut KS) requires IP weightingtoallowunbiasedinferencebasedonthe original samplingpopulation.
    • Currently, we are recreatingtherisk sets basedonthepublishedmatchingcriteriausedto derive the NCC.
methods and instruments 1
Methods and Instruments (1):
  • Genetic Risk Score applying allele scoring (Purcell et al., 2009):
    • Derived from internal GWAS
      • Approach: running GWAS in 7 samples, meta-analyzing 6 and scoring in set number 7. Iterative leave one out procedure and subsequent meta-analysis of results.
    • Derived from published GWAS (Speliotes et al, 2010; Demirkan et al., 2010)
    • Derived from Candidate Genes
methods and instruments 2
Methods and Instruments (2):

Glymour, TchetgenTchetgen, Robins., CredibleMendelianRandomizationStudies: Approaches for Evaluating the Instrumental Variable Assumptions, Am J Epi2012

Genetic IVs cannot be proven to be valid. They can sometimes be shown to be invalid, although these tests generally rely on additional assumptions.

  • Four empirical approaches to (in)validation:
    • Leverage prior causal assumptions regarding the confounding of the phenotype-outcome association: 4 equivalent versions of this test.
    • Identify factors that modify the genotype-phenotype association and compare the IV effect estimate across values of the modifier.
    • Instrumental inequality tests: applicable only when the causal phenotype is known to be categorical.
    • Over-identification tests with multiple IVs. Other genes or even polymorphisms of the same gene might provide additional IVs.

*Instrumental Inequality Test

    • Macros available (R, SAS) fordichtomousoutcome, dichotomousphenotype, and ploytomousinstruments
future
Future

Testing and includingGxG, GxE

  • KnownProtein x Proteininteractionstoinformunderlying SNP x SNP interactions. (anxiety)
  • Investigategenderdifferences. (anxiety)
  • QuantileRegressiontoidentify and quantifyinteractionswith (unknown) environmentalfactors.
  • Construct separate scores based on presumed mechanism (e.g., appetite, adipogenesis, cardio-pulmonary fitness) and apply over-identification tests.
  • GetaccesstotheHealthRetirementStudygenetic data fromdbGaPtoincreasesamplesizeforpsycho-social phenotypes.
acknowledgements contact
Acknowledgements & Contact:

NIH/NIHM 1RC4MH092707 (L Kubzansky)

And the entire team:

Laura Kubzansky, Maria Glymour, Eric J TchetgenTchetgen, Liming Liang, Shun-Chiao Chang, Eric Rimm, Marilyn Cornelis, KarestanKoenen, and Ichiro Kawachi

Please feel free to contact us, we are happy to share thoughts, code, macros, etc.

Stefan: swalter@hsph.harvard.edu

Laura: lkubzans@hsph.harvard.edu

Maria: mglymour@hsph.harvard.edu