1 / 10

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 ) on behalf of:

mark-price
Download Presentation

Using GWAS Data for Enhanced Mendelian Randomization Studies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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)

  3. 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.

  4. 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).

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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

More Related