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Barbara Peil, Benjamin Meder, Monika Stoll, Hugo A. Katus and Justo Lorenzo Bermejo

Taking advantage of genotype imputation to detect the association between known risk variants and disease subphenotypes in dilated cardiomyopathy. Barbara Peil, Benjamin Meder, Monika Stoll, Hugo A. Katus and Justo Lorenzo Bermejo. Introduction.

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Barbara Peil, Benjamin Meder, Monika Stoll, Hugo A. Katus and Justo Lorenzo Bermejo

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  1. Taking advantage of genotype imputation to detect the association between known risk variants and disease subphenotypes in dilated cardiomyopathy Barbara Peil, Benjamin Meder, Monika Stoll, Hugo A. KatusandJusto Lorenzo Bermejo

  2. Introduction Heterogeneity of patients regarding DCM severity

  3. Strategy Gene selection and association tests [1] Yu W, Gwinn M, Clyne M, Yesupriya A, Khoury M J(2008). A Navigator for Human Genome Epidemiology. Nat Genet 40(2): 124-125 [2] The International HapMap Consortium (2003). The International HapMap Project. Nature 426: 789-796 [3] Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007). A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39: 906-913 NT-proBNP – SNP Association (loglinear regression) Identify genes in connection with “DCM” according to HuGE Navigator [1] Select genes with SNPs called in our GWAS DCM – SNP Association (logistic regression) LVEF – SNP Association (linear regression) Multiple genotype imputation using HapMap II [2,3] NYHA – SNP Association (ordinal regression)

  4. List of Genes associatedwith DCM Identify genes in connection with “DCM” according to HuGE Navigator [1] Select genes with SNPs called in our GWAS Gene selection and association tests

  5. Case-Control-Analysis Identify genes in connection with “DCM” according to HuGE Navigator [1] Select genes with SNPs called in our GWAS DCM – SNP Association (logistic regression) Gene selection and association tests

  6. Case-Control Analysis DCM 65 Genes 16 Genes

  7. Subphenotype-SNP Assocation NT-proBNP – SNP Association (loglinear regression) Identify genes in connection with “DCM” according to HuGE Navigator [1] Select genes with SNPs called in our GWAS DCM – SNP Association (logistic regression) LVEF – SNP Association (linear regression) NYHA – SNP Association (ordinal regression) Gene selection and association tests

  8. Subphenotype-SNP Association DCM LVEF NYHA 2 1 3 2 7 1 NT-proBNP Venn diagram representing the counts of genes which showed associations with DCM and related subphenotypes

  9. Multiple Imputation Gene selection and association tests NT-proBNP – SNP Association (loglinear regression) Identify genes in connection with “DCM” according to HuGE Navigator [1] Select genes with SNPs called in our GWAS DCM – SNP Association (logistic regression) LVEF – SNP Association (linear regression) Multiple genotype imputation using HapMap II [2,3] NYHA – SNP Association (ordinal regression)

  10. Multiple Imputation DCM GWAS Controls, Germany DCM GWAS Patients, Germany African ancestry in Southwest USA Luhya in Webuye, Kenya Maasai in Kinyawa, Kenya Yoruba in Ibadan, Nigeria Utah residents with Northern and Western European ancestry Toscans in Italy Gujarati Indians in Houston, Texas Mexican ancestry in Los Angeles, California Chinese in Metropolitan Denver, Colorado Han Chinese in Beijing, China Japanese in Tokyo, Japan

  11. ExemplaryResult Manhattan plots for one exemplary gene. Associations between genotyped SNPs (colored dots), imputed SNPs (grey dots) and DCM/subphenotype. The plot displays –log10 p-values from a three-genotype regression model which included age and gender. Multiple imputation relied on the CEU population in HapMap II. DCM NT-proBNP LVEF NYHA

  12. Replication using INHERITANCE data?

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