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Genetic Variability in Tau Dephosphorylation Pathway and Alzheimer’s Disease Risk
Poster Presentations P1 P1-268
DISCOVERY AND REPLICATION OF GENE-GENE INTERACTIONS IN MULTIPLE INDEPENDENT DATASETS FROM THE ALZHEIMER DISEASE GENETICS CONSORTIUM
Tricia Thornton-Wells1, Eric Torstenson1, Scott Dudek1, Marylyn Ritchie1, Eden Martin2, Margaret Pericak-Vance2, Jonathan Haines1 The Alzheimer’s Disease Genetics Consortium3, 1 Vanderbilt University, Nashville, Tennessee, United States; 2University of Miami, Miami, Florida, United States; 3University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States. Background: Alzheimer disease (AD) has a complex genetic etiology, involving heterogeneity and gene-gene interactions. Recent GWAS in AD have led to the discovery of novel genetic risk factors; however, the investigation of gene-gene interactions in GWAS has been limited. Methods: We conducted a gene-gene interaction analysis in one discovery dataset from University of Miami, Vanderbilt University and Mt. Sinai School of Medicine (UM/VU/MSSM) and two replication datasets: (a) the Translational Genomics Research Institute series 2 (TGEN2), and (b) the Alzheimer Disease Neuroimaging Initiative (ADNI). All cohorts were genotyped using Illumina or Affymetrix SNP microarrays, and each dataset was imputed using MaCH with HapMap Phase 2 CEU samples. We used a biological knowledge-driven approach (Biofilter) to select SNP-SNP models with a priori evidence their genes interact or participate in common biological pathways or processes. (SNPs within 50kb of APOE were excluded.) We analyzed each SNP-SNP model using the multifactor dimensionality reduction (MDR) method and selected models with a testing balanced accuracy (testBA) ¼55%. We then used a gene-centric approach for model replication. For each replication dataset, we ran MDR on all SNP-SNP models comprising genes from the selected discovery dataset models, and we selected all models with a testBA ¼ 55% in at least 2 of 3 datasets. Results: Six gene-gene models discovered in the UM/VU/MSSM dataset were replicated in both replication datasets, each with an average testBA ¼ 58% and a maximum testBA (for the best SNP-SNP model in a single dataset) of up to 72%. Seven gene-gene models replicated in only one dataset with average testBA ¼ 60% and a maximum testBA of up to 79%. ABCC9 appeared in 2 models that replicated in both datasets–one with ABCB1 (a major component of the blood-brain barrier that purportedly plays a role in Aß clearance) and one with TF (which has been associated with iron overload and oxidative stress in AD). SMAD3 (which is bound by pTau in AD brain) appeared in 2 models, one of which replicated in both datasets. Conclusions: Using a biological knowledge-driven approach, we identified SNP-SNP models of interest that replicated at the gene level across multiple datasets.
P1-269
GENETIC VARIABILITY IN TAU DEPHOSPHORYLATION PATHWAY AND ALZHEIMER’S DISEASE RISK
Jose Luis V azquez-Higuera1, Ana Pozueta2, Ignacio Mateo1, Eloy Rodrıguez-Rodrıguez2, Pascual Sanchez-Juan2, Miguel Calero3, Jose Luis Dobato3, Maria Jesus Bullido4, Jose Berciano2, Onofre Combarros2, 1Neurology Service and CIBERNED, University Hospital “Marques de Valdecilla”, Santander, Spain; 2Neurology Service and CIBERNED, University Hospital “Marques de Valdecilla”, Santander, Spain; 3Alzheimer Disease Research Unit, CIEN Foundation, Carlos III Institute of Health, Alzheimer Center Reina Sofia Foundation, Madrid, Spain; 4Molecular Biology Department and CIBERNED, Centro de Biologıa Molecular Severo Ochoa (C.S.I.C.-U.A.M.), Madrid, Spain. Background: Abnormal tau hyperphosphorylation is one of the central events in the development of neurofibrillary tangles in Alzheimer’s disease (AD). Tau phosphorylation depends on the coordinated and reciprocal ac-
S197
tion of kinases and phosphatases. A downregulation of tau phosphatases is thought to play an important role in the abnormal hyperphosphorylation of tau. PP2A is the major tau phosphatase and comprises a structural, a catalytic (PPP2CA)and a regulatory (PPP2R2A) subunit. PP2A activity is regulated by an endogenous inhibitor (ANP32A) and by methylation of its catalytic subunit (LCMT1 andPPME1). In addition, PIN1 is a cis/trans isomerase that is co-localized with phosphorylated tau in AD brains and promotes tau dephosphorylation via PP2A. We studied the role of genetic variability in the tau dephosphorylation pathway on the risk of AD by analysing 24 polymorphisms across 6 PP2Aphosphatase-related genes (PPP2CA, PPP2R2A, ANP32A, LCMT1, PPME1 and PIN1). Methods: The study included 729 AD patients (67% women; mean age at onset 73.3;SD 8.0; range 60-100 years) who met NINCDS/ADRDA criteria for probable AD. Control subjects were 670 unrelated individuals (64% women; mean age 78.3; SD9.4; range 60-104 years) with Mini-Mental State Examination scores of 28 or more. Analysed genetic variants are tag-SNPs that captured at least 90% of genetic variability in each gene. SNP genotyping was performed using SequenomiPLEX technology. Results: The presence of a T allele (rs1077220 C/T) in PIN1 gene was associated with a significant increase of risk for AD (T allele frequency, 21% in AD patients vs 12% in controls; OR ¼ 1.98, 95%CI ¼ 1.60-2.44; p ¼ 2.4x109 Bonferroni corrected). However, genetic variants of the PPP2CA, PPP2R2A, ANP32A, LCMT1 and PPME1 genes were not associated with AD risk. Conclusions: A common genetic variant (rs1077220) in PIN1 gene modifies the risk of AD. By increasing the activity of tau phosphatases, it could be possible to prevent neuronal degeneration in tauopathies, and thereby, PIN1 might be one of the potential therapeutic targets for AD.
P1-270
IDENTIFICATION OF GENE-GENE INTERACTIONS IN ALZHEIMER DISEASE USING CO-OPERATIVE GAME THEORY
Badri Narayan Vardarajan1, Gyungah Jun1, Jacqueline Buros1, Kathryn Lunetta1, Lindsay Farrer1 The Alzheimer’s Disease Genetics Consortium2, 1Boston University, Boston, Massachusetts, United States; 2 University of Pennsylvania, Philadelphia, Pennsylvania, United States. Background: Much of the unexplained heritability of Alzheimer disease (AD) is likely hidden within complex gene-gene interactions. Contemporary approaches to detect such interactions in genome wide data are mathematically and computationally challenging. Methods: We investigated gene-gene interactions for AD using an novel algorithm based on cooperative game theory in a discovery dataset of 8,309 AD cases and 7,366 cognitively normal elder (CNE) controls and in a replication dataset containing 3,531 AD cases and 3,565 CNE’s from the Alzheimer Disease Genetics Consortium. We utilize the framework of ‘simple games’ which have been widely used to analyze the power of players in interaction situations such as Councils and Parliament. This approach computes a Shapely Value statistic for every candidate SNP to detect the best coalitions of SNPs that contribute most in predicting AD risk. Results: Initial analyses in a subset of the discovery sample containing 770 cases and 480 controls from the TGEN dataset were performed using 64,885 SNPs showing evidence of marginal association with AD (p < 0.2). In a pruned list of the 799 SNPs with highest Shapley values, APOE was ranked 1 and two GAB2 SNPs (which were shown previously in this dataset to be associated with AD in APOE e4 carriers) were ranked 196 and 197 respectively. We also find evidence of novel interaction between SNPs in PALM2 and DAGLB (p ¼ 5.47E-06). Conclusions: The game-theory based algorithm confirmed a known interaction in the TGEN dataset and identified novel interactions which would have been missed if only SNPs with strong marginal association with AD were tested. This algorithm is currently being applied to the entire ADGC GWAS dataset to elucidate and replicate novel interactions in a large cohort. To our knowledge this is one of the largest cohorts being used to study epistasis in AD.
DISCOVERY AND REPLICATION OF GENE-GENE INTERACTIONS IN MULTIPLE INDEPENDENT DATASETS FROM THE ALZHEIMER DISEASE GENETICS CONSORTIUM
Tricia Thornton-Wells1, Eric Torstenson1, Scott Dudek1, Marylyn Ritchie1, Eden Martin2, Margaret Pericak-Vance2, Jonathan Haines1 The Alzheimer’s Disease Genetics Consortium3, 1 Vanderbilt University, Nashville, Tennessee, United States; 2University of Miami, Miami, Florida, United States; 3University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States. Background: Alzheimer disease (AD) has a complex genetic etiology, involving heterogeneity and gene-gene interactions. Recent GWAS in AD have led to the discovery of novel genetic risk factors; however, the investigation of gene-gene interactions in GWAS has been limited. Methods: We conducted a gene-gene interaction analysis in one discovery dataset from University of Miami, Vanderbilt University and Mt. Sinai School of Medicine (UM/VU/MSSM) and two replication datasets: (a) the Translational Genomics Research Institute series 2 (TGEN2), and (b) the Alzheimer Disease Neuroimaging Initiative (ADNI). All cohorts were genotyped using Illumina or Affymetrix SNP microarrays, and each dataset was imputed using MaCH with HapMap Phase 2 CEU samples. We used a biological knowledge-driven approach (Biofilter) to select SNP-SNP models with a priori evidence their genes interact or participate in common biological pathways or processes. (SNPs within 50kb of APOE were excluded.) We analyzed each SNP-SNP model using the multifactor dimensionality reduction (MDR) method and selected models with a testing balanced accuracy (testBA) ¼55%. We then used a gene-centric approach for model replication. For each replication dataset, we ran MDR on all SNP-SNP models comprising genes from the selected discovery dataset models, and we selected all models with a testBA ¼ 55% in at least 2 of 3 datasets. Results: Six gene-gene models discovered in the UM/VU/MSSM dataset were replicated in both replication datasets, each with an average testBA ¼ 58% and a maximum testBA (for the best SNP-SNP model in a single dataset) of up to 72%. Seven gene-gene models replicated in only one dataset with average testBA ¼ 60% and a maximum testBA of up to 79%. ABCC9 appeared in 2 models that replicated in both datasets–one with ABCB1 (a major component of the blood-brain barrier that purportedly plays a role in Aß clearance) and one with TF (which has been associated with iron overload and oxidative stress in AD). SMAD3 (which is bound by pTau in AD brain) appeared in 2 models, one of which replicated in both datasets. Conclusions: Using a biological knowledge-driven approach, we identified SNP-SNP models of interest that replicated at the gene level across multiple datasets.
P1-269
GENETIC VARIABILITY IN TAU DEPHOSPHORYLATION PATHWAY AND ALZHEIMER’S DISEASE RISK
Jose Luis V azquez-Higuera1, Ana Pozueta2, Ignacio Mateo1, Eloy Rodrıguez-Rodrıguez2, Pascual Sanchez-Juan2, Miguel Calero3, Jose Luis Dobato3, Maria Jesus Bullido4, Jose Berciano2, Onofre Combarros2, 1Neurology Service and CIBERNED, University Hospital “Marques de Valdecilla”, Santander, Spain; 2Neurology Service and CIBERNED, University Hospital “Marques de Valdecilla”, Santander, Spain; 3Alzheimer Disease Research Unit, CIEN Foundation, Carlos III Institute of Health, Alzheimer Center Reina Sofia Foundation, Madrid, Spain; 4Molecular Biology Department and CIBERNED, Centro de Biologıa Molecular Severo Ochoa (C.S.I.C.-U.A.M.), Madrid, Spain. Background: Abnormal tau hyperphosphorylation is one of the central events in the development of neurofibrillary tangles in Alzheimer’s disease (AD). Tau phosphorylation depends on the coordinated and reciprocal ac-
S197
tion of kinases and phosphatases. A downregulation of tau phosphatases is thought to play an important role in the abnormal hyperphosphorylation of tau. PP2A is the major tau phosphatase and comprises a structural, a catalytic (PPP2CA)and a regulatory (PPP2R2A) subunit. PP2A activity is regulated by an endogenous inhibitor (ANP32A) and by methylation of its catalytic subunit (LCMT1 andPPME1). In addition, PIN1 is a cis/trans isomerase that is co-localized with phosphorylated tau in AD brains and promotes tau dephosphorylation via PP2A. We studied the role of genetic variability in the tau dephosphorylation pathway on the risk of AD by analysing 24 polymorphisms across 6 PP2Aphosphatase-related genes (PPP2CA, PPP2R2A, ANP32A, LCMT1, PPME1 and PIN1). Methods: The study included 729 AD patients (67% women; mean age at onset 73.3;SD 8.0; range 60-100 years) who met NINCDS/ADRDA criteria for probable AD. Control subjects were 670 unrelated individuals (64% women; mean age 78.3; SD9.4; range 60-104 years) with Mini-Mental State Examination scores of 28 or more. Analysed genetic variants are tag-SNPs that captured at least 90% of genetic variability in each gene. SNP genotyping was performed using SequenomiPLEX technology. Results: The presence of a T allele (rs1077220 C/T) in PIN1 gene was associated with a significant increase of risk for AD (T allele frequency, 21% in AD patients vs 12% in controls; OR ¼ 1.98, 95%CI ¼ 1.60-2.44; p ¼ 2.4x109 Bonferroni corrected). However, genetic variants of the PPP2CA, PPP2R2A, ANP32A, LCMT1 and PPME1 genes were not associated with AD risk. Conclusions: A common genetic variant (rs1077220) in PIN1 gene modifies the risk of AD. By increasing the activity of tau phosphatases, it could be possible to prevent neuronal degeneration in tauopathies, and thereby, PIN1 might be one of the potential therapeutic targets for AD.
P1-270
IDENTIFICATION OF GENE-GENE INTERACTIONS IN ALZHEIMER DISEASE USING CO-OPERATIVE GAME THEORY
Badri Narayan Vardarajan1, Gyungah Jun1, Jacqueline Buros1, Kathryn Lunetta1, Lindsay Farrer1 The Alzheimer’s Disease Genetics Consortium2, 1Boston University, Boston, Massachusetts, United States; 2 University of Pennsylvania, Philadelphia, Pennsylvania, United States. Background: Much of the unexplained heritability of Alzheimer disease (AD) is likely hidden within complex gene-gene interactions. Contemporary approaches to detect such interactions in genome wide data are mathematically and computationally challenging. Methods: We investigated gene-gene interactions for AD using an novel algorithm based on cooperative game theory in a discovery dataset of 8,309 AD cases and 7,366 cognitively normal elder (CNE) controls and in a replication dataset containing 3,531 AD cases and 3,565 CNE’s from the Alzheimer Disease Genetics Consortium. We utilize the framework of ‘simple games’ which have been widely used to analyze the power of players in interaction situations such as Councils and Parliament. This approach computes a Shapely Value statistic for every candidate SNP to detect the best coalitions of SNPs that contribute most in predicting AD risk. Results: Initial analyses in a subset of the discovery sample containing 770 cases and 480 controls from the TGEN dataset were performed using 64,885 SNPs showing evidence of marginal association with AD (p < 0.2). In a pruned list of the 799 SNPs with highest Shapley values, APOE was ranked 1 and two GAB2 SNPs (which were shown previously in this dataset to be associated with AD in APOE e4 carriers) were ranked 196 and 197 respectively. We also find evidence of novel interaction between SNPs in PALM2 and DAGLB (p ¼ 5.47E-06). Conclusions: The game-theory based algorithm confirmed a known interaction in the TGEN dataset and identified novel interactions which would have been missed if only SNPs with strong marginal association with AD were tested. This algorithm is currently being applied to the entire ADGC GWAS dataset to elucidate and replicate novel interactions in a large cohort. To our knowledge this is one of the largest cohorts being used to study epistasis in AD.