- Email: [email protected]
EXOME-SEQUENCING IN LATE-ONSET FAMILIES IDENTIFIED ADDITIONAL CANDIDATES GENES FOR ALZHEIMER'S DISEASE
Oral Sessions: O1-04: Genetics: Next Generation Sequencing in Dementia
O1-04-02
EXOME-SEQUENCING IN LATE-ONSET FAMILIES IDENTIFIED ADDITIONAL CANDIDATES GENES FOR ALZHEIMER’S DISEASE
Carlos Cruchaga1, Zoran Brkanac2, Sheng Chih Jin3, Bruno A. Benitez4, Jan Rehker2, Perry Ridge5, John Kauwe6, John Hardy7, Jose Bras7, Rita Guerreiro7, Andrew Singleton8, Alison Goate3, 1Washington University School of Medicine, Saint Louis, Missouri, United States; 2University of Washington, Seattle, Washington, United States; 3Washington University in St. Louis, St. Louis, Missouri, United States; 4Washington University in Saint Louis, Saint Louis, Missouri, United States; 5BYU, Provo, Utah, United States; 6BYU, Provo, Utah, United States; 7UCL, London, United Kingdom; 8NIA, Bethesda, Maryland, United States. Contact e-mail: [email protected] Background: By performing exome-sequencing in fourteen late-onset families, we found that the phospholipase D3 (PLD3) V232M variant segregated with disease status in two independent families. Additional studies in large case-control datasets confirmed the association of PLD3 with risk for disease. However, on average eight different variants per family segregated with disease status. Methods: In this study, we followed up 400 AD cases and 1080 controls for all the variants/genes that perfectly segregated with disease status in the fourteen sequenced families. A total of 60 genes (DFNB31, NFATC1, LRP4, CACNA1G, CALCR, ZNF30, DMRT2, KIF1A, ZNF341, LRP4, PRKD2, EPHA2, CACNA1E...) were selected. As replication, the most interesting genes were re-sequenced in additional 800 cases and 400 controls. In order to analyze the association of the selected genes in disease risk we performed gene-based test stratifying by minor allele frequency (MAF), and by analyzing variants that were unique to cases or controls. We used TREM2 and PLD3 as positive controls. Results: We found that genebased testing can lead to false negative results especially if several common non-synonymous variants exist and are not associated with disease status. We found that the analyses focus on variants with a MAF<1% provides enough statistical power to find gene-wide significant results. In addition we found that the analyses focus on the variants unique to cases or controls lead to greater effect sizes (odds ratio; OR) but in general with lower p-value due to the very low frequency of these variants. In any case, we hypothesize that this analytical strategy may be used to prioritize gene for follow-up. From all the genes that were selected for the replication datasets, one gene showed a significant gene-based association (only variants MAF<1%) with AD risk after multiple testing, and a strong association when only "unique-variants" were included (OR¼1.97, p¼2.51x10 -4) Conclusions: Our analyses indicated that additional genes harboring low-frequencies risk variants exist, and that family-based studies could help identify such genes and variants. We are currently performing additional sequencing and functional analyses to validate our findings.
O1-04-03
LOW-FREQUENCY VARIANT IMPUTATION IDENTIFIES NOVEL DISEASE-ASSOCIATED LOCI IN A GENOME-WIDE ASSOCIATION STUDY OF LATE-ONSET ALZHEIMER’S DISEASE
Brian W. Kunkle1, Adam C. Naj2, Kara Hamilton-Nelson3, William R. Perry1, Amanda Partch4, Otto Valladares5, Jaeyoon Chung6, Gyungah Jun7, Mike Schmidt1, Gary Beecham3, Li-San Wang8, Eden Martin3, Richard Mayeux9, Jonathan Haines10, Lindsay A. Farrer7, Gerard D. Schellenberg4, Margaret Pericak-Vance3 THE ALZHEIMER’S DISEASE GENETICS CONSORTIUM81University of Miami, Hussman Institute for Human Genomics, Miami, Florida, United States; 2University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States; 3University of Miami, Miami, Florida, United States; 4 University of Pennsylvania, Philadelphia, Pennsylvania, United States; 5 University of Pennsylvania, Philadelphia, Pennsylvania, United States; 6 Boston University, Bioinformatics Graduate Program, Boston, Massachusetts, United States; 7Boston University School of Medicine, Boston, Massachusetts, United States; 8University of Pennsylvania School
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of Medicine, Philadelphia, Pennsylvania, United States; 9Columbia University, New York, New York, United States; 10Case Western Reserve University, Cleveland, Ohio, United States. Contact e-mail: bkunkle@med. miami.edu Background: Recent Genome-wide Association Studies (GWAS) have identified 19 susceptibility loci, in addition to APOE, for Late-onset Alzheimer Disease (LOAD). The Alzheimer Disease Genetics Consortium (ADGC) conducted a GWAS using 31 datasets (including 16 new datasets) increasing our sample to 14,459 cases and 14,556 controls. These data were imputed using the updated 1000 Genomes reference set, which includes over 26 million novel variants, including a large number of lowfrequency variants and indels. Methods: Using IMPUTE2, all 31 datasets were imputed to the 1000 Genomes March 2012 reference panel with the ’all population’ option. Association analysis was conducted adjusting for age, gender and population substructure. Individual datasets were analyzed with SNPTest using the score test (preferred for low-frequency variants), and within-study results were meta-analyzed in METAL. Gene-based testing using rareMETAL is ongoing. Results: The updated imputation increased the number of high quality analyzable variants by 54.2% over the previous analyses done in collaboration with the International Genomics of Alzheimer Project (IGAP) (w15,500,000 variants vs w7,100,000 variants). Approximately 9 million and 6.5 million of the total analyzable variants had a minor allele frequency (MAF) <0.05 and <0.01, respectively. In addition to the previous significantly associated loci reported in the IGAP analysis, several novel loci demonstrated SNP associations with genome-wide statistical significance (P 5310 -8). These included loci centered on Chr12:19,927,847 (rs146334933; P ¼8.08310 -21) and Chr17:58,964,149 (rs149517182; P ¼1.51310 -9). Both of these signals are driven by several variants with MAFs<0.05. Genes near the Chr17 signal include BCAS3, PPM1D and APPBP2, while a lincRNA (RP11-405A12.2) is the only reported transcript near the Chr12 signal, a region previously identified through linkage studies but for which the risk causing gene has not been identified. Conclusions: Using a new imputation reference set and larger sample set, we identified several novel loci associated with LOAD, giving support to the hypothesis that rare and low-frequency variant imputation can identify novel associations with disease. Analyses are ongoing for gene-based analyses and the expansion of the dataset to include the IGAP cohorts (Total sample size: 28,590 cases and 52,531 controls).
O1-04-04
ANALYSIS OF SQSTM1 IN PATIENTS WITH EARLY-ONSET ALZHEIMER’S DISEASE
Elise Cuyvers1, Karolien Bettens2, Sebastiaan Engelborghs3, Mathieu Vandenbulcke4, Celine Merlin1, Lubina Dillen2, Maria Mattheijssens1, Karin Peeters1, Patrick Cras5, Rik R. Vandenberghe6, Peter De Deyn7, Julie van der Zee8, Christine Van Broeckhoven1, Kristel Sleegers1 EU EOD CONSORTIUM11Neurodegenerative Brain Diseases Group, VIB and Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; 2Neurodegenerative Brain Diseases Group, VIB and Institute Born-Bunge, University of Antwerp, Antwerpen, Belgium; 3Institute Born-Bunge, University of Antwerp and Hospital Network Antwerp, Middelheim and Hoge Beuken, Antwerp, Belgium; 4University Hospitals Leuven, Leuven, Belgium; 5Institute Born-Bunge, University of Antwerp and Antwerp University Hospital, Antwerpen, Belgium; 6University Hospitals Leuven and University of Leuven (KUL), Leuven, Belgium; 7Institute Born-Bunge, University of Antwerp and Hospital Network Antwerp Middelheim and Hoge Beuken and University Medical Center Groningen, Antwerpen, Belgium; 8Neurodegenerative Brain Diseases Group, VIB and Institute Bron-Bunge, University of Antwerp, Antwerpen, Belgium. Contact e-mail: [email protected] Background: The p62 protein, which is encoded by the Sequestosome 1 gene (SQSTM1), is commonly found in cytoplasmic inclusions in protein aggregation diseases like Alzheimer’s disease (AD). Previously m utations in SQSTM1 have been associated with Amyotrophic Lateral Sclerosis, frontotemporal lobar degeneration and Paget Disease of Bone (PDB). In a mega
O1-04-02
EXOME-SEQUENCING IN LATE-ONSET FAMILIES IDENTIFIED ADDITIONAL CANDIDATES GENES FOR ALZHEIMER’S DISEASE
Carlos Cruchaga1, Zoran Brkanac2, Sheng Chih Jin3, Bruno A. Benitez4, Jan Rehker2, Perry Ridge5, John Kauwe6, John Hardy7, Jose Bras7, Rita Guerreiro7, Andrew Singleton8, Alison Goate3, 1Washington University School of Medicine, Saint Louis, Missouri, United States; 2University of Washington, Seattle, Washington, United States; 3Washington University in St. Louis, St. Louis, Missouri, United States; 4Washington University in Saint Louis, Saint Louis, Missouri, United States; 5BYU, Provo, Utah, United States; 6BYU, Provo, Utah, United States; 7UCL, London, United Kingdom; 8NIA, Bethesda, Maryland, United States. Contact e-mail: [email protected] Background: By performing exome-sequencing in fourteen late-onset families, we found that the phospholipase D3 (PLD3) V232M variant segregated with disease status in two independent families. Additional studies in large case-control datasets confirmed the association of PLD3 with risk for disease. However, on average eight different variants per family segregated with disease status. Methods: In this study, we followed up 400 AD cases and 1080 controls for all the variants/genes that perfectly segregated with disease status in the fourteen sequenced families. A total of 60 genes (DFNB31, NFATC1, LRP4, CACNA1G, CALCR, ZNF30, DMRT2, KIF1A, ZNF341, LRP4, PRKD2, EPHA2, CACNA1E...) were selected. As replication, the most interesting genes were re-sequenced in additional 800 cases and 400 controls. In order to analyze the association of the selected genes in disease risk we performed gene-based test stratifying by minor allele frequency (MAF), and by analyzing variants that were unique to cases or controls. We used TREM2 and PLD3 as positive controls. Results: We found that genebased testing can lead to false negative results especially if several common non-synonymous variants exist and are not associated with disease status. We found that the analyses focus on variants with a MAF<1% provides enough statistical power to find gene-wide significant results. In addition we found that the analyses focus on the variants unique to cases or controls lead to greater effect sizes (odds ratio; OR) but in general with lower p-value due to the very low frequency of these variants. In any case, we hypothesize that this analytical strategy may be used to prioritize gene for follow-up. From all the genes that were selected for the replication datasets, one gene showed a significant gene-based association (only variants MAF<1%) with AD risk after multiple testing, and a strong association when only "unique-variants" were included (OR¼1.97, p¼2.51x10 -4) Conclusions: Our analyses indicated that additional genes harboring low-frequencies risk variants exist, and that family-based studies could help identify such genes and variants. We are currently performing additional sequencing and functional analyses to validate our findings.
O1-04-03
LOW-FREQUENCY VARIANT IMPUTATION IDENTIFIES NOVEL DISEASE-ASSOCIATED LOCI IN A GENOME-WIDE ASSOCIATION STUDY OF LATE-ONSET ALZHEIMER’S DISEASE
Brian W. Kunkle1, Adam C. Naj2, Kara Hamilton-Nelson3, William R. Perry1, Amanda Partch4, Otto Valladares5, Jaeyoon Chung6, Gyungah Jun7, Mike Schmidt1, Gary Beecham3, Li-San Wang8, Eden Martin3, Richard Mayeux9, Jonathan Haines10, Lindsay A. Farrer7, Gerard D. Schellenberg4, Margaret Pericak-Vance3 THE ALZHEIMER’S DISEASE GENETICS CONSORTIUM81University of Miami, Hussman Institute for Human Genomics, Miami, Florida, United States; 2University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States; 3University of Miami, Miami, Florida, United States; 4 University of Pennsylvania, Philadelphia, Pennsylvania, United States; 5 University of Pennsylvania, Philadelphia, Pennsylvania, United States; 6 Boston University, Bioinformatics Graduate Program, Boston, Massachusetts, United States; 7Boston University School of Medicine, Boston, Massachusetts, United States; 8University of Pennsylvania School
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of Medicine, Philadelphia, Pennsylvania, United States; 9Columbia University, New York, New York, United States; 10Case Western Reserve University, Cleveland, Ohio, United States. Contact e-mail: bkunkle@med. miami.edu Background: Recent Genome-wide Association Studies (GWAS) have identified 19 susceptibility loci, in addition to APOE, for Late-onset Alzheimer Disease (LOAD). The Alzheimer Disease Genetics Consortium (ADGC) conducted a GWAS using 31 datasets (including 16 new datasets) increasing our sample to 14,459 cases and 14,556 controls. These data were imputed using the updated 1000 Genomes reference set, which includes over 26 million novel variants, including a large number of lowfrequency variants and indels. Methods: Using IMPUTE2, all 31 datasets were imputed to the 1000 Genomes March 2012 reference panel with the ’all population’ option. Association analysis was conducted adjusting for age, gender and population substructure. Individual datasets were analyzed with SNPTest using the score test (preferred for low-frequency variants), and within-study results were meta-analyzed in METAL. Gene-based testing using rareMETAL is ongoing. Results: The updated imputation increased the number of high quality analyzable variants by 54.2% over the previous analyses done in collaboration with the International Genomics of Alzheimer Project (IGAP) (w15,500,000 variants vs w7,100,000 variants). Approximately 9 million and 6.5 million of the total analyzable variants had a minor allele frequency (MAF) <0.05 and <0.01, respectively. In addition to the previous significantly associated loci reported in the IGAP analysis, several novel loci demonstrated SNP associations with genome-wide statistical significance (P 5310 -8). These included loci centered on Chr12:19,927,847 (rs146334933; P ¼8.08310 -21) and Chr17:58,964,149 (rs149517182; P ¼1.51310 -9). Both of these signals are driven by several variants with MAFs<0.05. Genes near the Chr17 signal include BCAS3, PPM1D and APPBP2, while a lincRNA (RP11-405A12.2) is the only reported transcript near the Chr12 signal, a region previously identified through linkage studies but for which the risk causing gene has not been identified. Conclusions: Using a new imputation reference set and larger sample set, we identified several novel loci associated with LOAD, giving support to the hypothesis that rare and low-frequency variant imputation can identify novel associations with disease. Analyses are ongoing for gene-based analyses and the expansion of the dataset to include the IGAP cohorts (Total sample size: 28,590 cases and 52,531 controls).
O1-04-04
ANALYSIS OF SQSTM1 IN PATIENTS WITH EARLY-ONSET ALZHEIMER’S DISEASE
Elise Cuyvers1, Karolien Bettens2, Sebastiaan Engelborghs3, Mathieu Vandenbulcke4, Celine Merlin1, Lubina Dillen2, Maria Mattheijssens1, Karin Peeters1, Patrick Cras5, Rik R. Vandenberghe6, Peter De Deyn7, Julie van der Zee8, Christine Van Broeckhoven1, Kristel Sleegers1 EU EOD CONSORTIUM11Neurodegenerative Brain Diseases Group, VIB and Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; 2Neurodegenerative Brain Diseases Group, VIB and Institute Born-Bunge, University of Antwerp, Antwerpen, Belgium; 3Institute Born-Bunge, University of Antwerp and Hospital Network Antwerp, Middelheim and Hoge Beuken, Antwerp, Belgium; 4University Hospitals Leuven, Leuven, Belgium; 5Institute Born-Bunge, University of Antwerp and Antwerp University Hospital, Antwerpen, Belgium; 6University Hospitals Leuven and University of Leuven (KUL), Leuven, Belgium; 7Institute Born-Bunge, University of Antwerp and Hospital Network Antwerp Middelheim and Hoge Beuken and University Medical Center Groningen, Antwerpen, Belgium; 8Neurodegenerative Brain Diseases Group, VIB and Institute Bron-Bunge, University of Antwerp, Antwerpen, Belgium. Contact e-mail: [email protected] Background: The p62 protein, which is encoded by the Sequestosome 1 gene (SQSTM1), is commonly found in cytoplasmic inclusions in protein aggregation diseases like Alzheimer’s disease (AD). Previously m utations in SQSTM1 have been associated with Amyotrophic Lateral Sclerosis, frontotemporal lobar degeneration and Paget Disease of Bone (PDB). In a mega