- Email: [email protected]
THE POLYGENIC EPISTASIS RISK SCORE DEMONSTRATES SIGNIFICANT ROLE OF GENE INTERACTION IN BIPOLAR DISORDER
EXOME-WIDE ASSOCIATION STUDY IDENTIFIES NOVEL SUSCEPTIBILITY GENES FOR PERSONALITY TRAITS
Disclosure: Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2017.08.046
46. THE POLYGENIC EPISTASIS RISK SCORE DEMONSTRATES SIGNIFICANT ROLE OF GENE INTERACTION IN BIPOLAR DISORDER
S807
Discussion: The highly significant enrichment of the TGEN sample for interactions found in the GAIN sample argues for a substantial contribution of interaction in bipolar disorder. It also argues for the model of a very large number of interactions of small effect that add or otherwise combine together. This score can be combined with conventional PRS in order to capture a larger portion of the genetic variance and improve the power of such analyses.
n
John Kelsoe , The Bipolar Genome Study
Disclosure: Pathway Genomics – Consultant, Self
University of California, San Diego http://dx.doi.org/10.1016/j.euroneuro.2017.08.047
Background: Gene-gene interaction occurs when the association of one locus to a phenotype depends on the genotype at a different locus. GWAS has made possible the discovery of dozens of genes for psychiatric illnesses generally using a model in which there is no gene-gene interaction. Even a modest GWAS may include 1 M SNPs, making an exhaustive search for interaction vastly underpowered. Yet, interaction effects are frequently found in defined biological systems, though they may have significant effect sizes, none can survive correction for genome-wide comparisons. Though polygenic models such as the polygenic risk score, achieve high reproducibility, they explain only a small portion of the genetic variance and do not incorporate interaction. A more realistic model is that in addition to a large number of independent marginal effects, there are also a very large number of interaction effects. In order to test this, I propose an extension to the polygenic risk score and demonstrate a significant contribution of gene-gene interaction in bipolar disorder. Methods: The 1500 most significant SNPs in the PGCBD1 GWAS were selected for interaction testing. The model dataset was the GAIN bipolar sample of 995 cases and 1,023 controls. The test sample was a similarly collected set of subjects termed the TGEN sample of 1,199 cases and 403 controls. Interactions among these SNPs were detected using a case-case analysis as implemented in the plink –fast epistasis option. A routine was written in R, to estimate the odds ratios of each genotypic combination in each interaction detected. This array of odds ratios was then used to weight the nine genotypic combinations as seen in each individual in the test dataset. The log10 of these odds ratios then were summed over all interactions to produce a cumulative score representing genetic loading due to interactions. This I have termed the Polygenic Epistasis Risk Score (PERS). An R package will be made available. Results: 11,605 interactions were detected in the GAIN sample amongst the 1500 SNPs tested that met a criterion of po0.01. The odds ratios for the interactions ranged from 6.6 to 30.8 with a mean of 8.4 and median 7.8. The PERS score estimating interaction effects was calculated for each individual in the TGEN sample, then the score distribution in bipolar subjects was compared to that of controls. PERS scores were normally distributed for both bipolars and controls. The mean PERS score for cases was 21, while that for the controls -34 (p o2.2 10–16). ROC analysis was used to assess the PERS as a binary diagnostic predictor, and the AUC was found to be 0.92 indicating excellent predictive power.
47. METHYLOME-WIDE ASSOCIATION STUDIES FOR MAJOR DEPRESSIVE DISORDER IN BLOOD OVERLAP WITH METHYLATION RESULTS FROM BRAIN AND LARGE-SCALE GWAS n
Karolina Aberg ,1, Brian Dean2, Andrey Shabalin1, Min Zhao1, Robin Chan1, Mohammad Hattab1, Gerard van Grootheest3, Laura Han3, Moji Aghajani3, Yuri Milaneschi3, Rick Jansen3, Lin Xie1, Shaunna Clark1, Brenda Penninx3, Edwin van den Oord1 1
Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University 2 The Molecular Psychiatry Laboratory, The Florey Institute of Neuroscience and Mental Health 3 VU Medical Center Background: Epigenetic modifications such as DNA methylation provide stability and diversity to the cellular phenotype and aberrant methylation has been implicated in processes underlying psychiatric disorders. Therefore, studies combining DNA methylation and genotype information provide a promising approach to study disorders where genotype information alone has failed to reveal the full etiology. Methods: We applied an optimized MBD-seq protocol to assay the complete CpG methylome in cases with Major Depressive Disorder (MDD) and controls using blood samples (N=1,132) from Netherlands Study of Depression and Anxiety and brain samples (N=64) from the Victorian Brain Bank Network. Data were analyzed with RaMWAS, a novel Bioconductor package specifically designed for Methylome-Wide Association Studies (MWAS). To study the overlap between top MWAS findings in blood and brain, we used a permutation based enrichment test (shiftR) that accounted for the dependency between adjacent CpG sites. Furthermore, we utilized the methylation data in combination with existing genotype information from the same individuals in a MWAS of CpGs created or destroyed by SNPs. Next, we tested whether top results from this CpG-SNP MWAS overlapped with recent largescale GWAS to identify robust associations with genomic loci of importance for MDD etiology.
Disclosure: Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2017.08.046
46. THE POLYGENIC EPISTASIS RISK SCORE DEMONSTRATES SIGNIFICANT ROLE OF GENE INTERACTION IN BIPOLAR DISORDER
S807
Discussion: The highly significant enrichment of the TGEN sample for interactions found in the GAIN sample argues for a substantial contribution of interaction in bipolar disorder. It also argues for the model of a very large number of interactions of small effect that add or otherwise combine together. This score can be combined with conventional PRS in order to capture a larger portion of the genetic variance and improve the power of such analyses.
n
John Kelsoe , The Bipolar Genome Study
Disclosure: Pathway Genomics – Consultant, Self
University of California, San Diego http://dx.doi.org/10.1016/j.euroneuro.2017.08.047
Background: Gene-gene interaction occurs when the association of one locus to a phenotype depends on the genotype at a different locus. GWAS has made possible the discovery of dozens of genes for psychiatric illnesses generally using a model in which there is no gene-gene interaction. Even a modest GWAS may include 1 M SNPs, making an exhaustive search for interaction vastly underpowered. Yet, interaction effects are frequently found in defined biological systems, though they may have significant effect sizes, none can survive correction for genome-wide comparisons. Though polygenic models such as the polygenic risk score, achieve high reproducibility, they explain only a small portion of the genetic variance and do not incorporate interaction. A more realistic model is that in addition to a large number of independent marginal effects, there are also a very large number of interaction effects. In order to test this, I propose an extension to the polygenic risk score and demonstrate a significant contribution of gene-gene interaction in bipolar disorder. Methods: The 1500 most significant SNPs in the PGCBD1 GWAS were selected for interaction testing. The model dataset was the GAIN bipolar sample of 995 cases and 1,023 controls. The test sample was a similarly collected set of subjects termed the TGEN sample of 1,199 cases and 403 controls. Interactions among these SNPs were detected using a case-case analysis as implemented in the plink –fast epistasis option. A routine was written in R, to estimate the odds ratios of each genotypic combination in each interaction detected. This array of odds ratios was then used to weight the nine genotypic combinations as seen in each individual in the test dataset. The log10 of these odds ratios then were summed over all interactions to produce a cumulative score representing genetic loading due to interactions. This I have termed the Polygenic Epistasis Risk Score (PERS). An R package will be made available. Results: 11,605 interactions were detected in the GAIN sample amongst the 1500 SNPs tested that met a criterion of po0.01. The odds ratios for the interactions ranged from 6.6 to 30.8 with a mean of 8.4 and median 7.8. The PERS score estimating interaction effects was calculated for each individual in the TGEN sample, then the score distribution in bipolar subjects was compared to that of controls. PERS scores were normally distributed for both bipolars and controls. The mean PERS score for cases was 21, while that for the controls -34 (p o2.2 10–16). ROC analysis was used to assess the PERS as a binary diagnostic predictor, and the AUC was found to be 0.92 indicating excellent predictive power.
47. METHYLOME-WIDE ASSOCIATION STUDIES FOR MAJOR DEPRESSIVE DISORDER IN BLOOD OVERLAP WITH METHYLATION RESULTS FROM BRAIN AND LARGE-SCALE GWAS n
Karolina Aberg ,1, Brian Dean2, Andrey Shabalin1, Min Zhao1, Robin Chan1, Mohammad Hattab1, Gerard van Grootheest3, Laura Han3, Moji Aghajani3, Yuri Milaneschi3, Rick Jansen3, Lin Xie1, Shaunna Clark1, Brenda Penninx3, Edwin van den Oord1 1
Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University 2 The Molecular Psychiatry Laboratory, The Florey Institute of Neuroscience and Mental Health 3 VU Medical Center Background: Epigenetic modifications such as DNA methylation provide stability and diversity to the cellular phenotype and aberrant methylation has been implicated in processes underlying psychiatric disorders. Therefore, studies combining DNA methylation and genotype information provide a promising approach to study disorders where genotype information alone has failed to reveal the full etiology. Methods: We applied an optimized MBD-seq protocol to assay the complete CpG methylome in cases with Major Depressive Disorder (MDD) and controls using blood samples (N=1,132) from Netherlands Study of Depression and Anxiety and brain samples (N=64) from the Victorian Brain Bank Network. Data were analyzed with RaMWAS, a novel Bioconductor package specifically designed for Methylome-Wide Association Studies (MWAS). To study the overlap between top MWAS findings in blood and brain, we used a permutation based enrichment test (shiftR) that accounted for the dependency between adjacent CpG sites. Furthermore, we utilized the methylation data in combination with existing genotype information from the same individuals in a MWAS of CpGs created or destroyed by SNPs. Next, we tested whether top results from this CpG-SNP MWAS overlapped with recent largescale GWAS to identify robust associations with genomic loci of importance for MDD etiology.