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eQTL regulation by NATs in Alzheimer's disease
Oral Sessions: O3-11: Genetics: Gene Expression and Risk for Alzheimer’s Disease ORAL SESSIONS: O3-11 GENETICS: GENE EXPRESSION AND RISK FOR ALZHEIMER’S DISEASE O3-11-01
TITLE NOT AVAILABLE
O3-11-02
EQTL REGULATION BY NATS IN ALZHEIMER’S DISEASE
Amanda Myers, Manuel Ramirez-Restrepo, Anzhelika Engel, University of Miami, Miami, Florida, United States. Background: We have found that w10% of the human brain transcriptome is under genetic control. While these expression quantitative trait loci (eQTLs) exist both in control samples and AD samples, for some of them there is a difference in the SNP-transcript relationship in AD. Our hypothesis is that one process by which eQTL misregulation could occur in AD is through the influence of natural antisense transcripts (NATs). NATs are nonprotein-coding, but fully processed RNAs that are transcribed from the opposite strand of the protein-coding sense transcript. NATs can regulate the expression of their corresponding protein-coding sense transcripts, with beta-secretase regulation being a prime example of relevance to AD. We have examined the top w100 eQTL hits from our screen of w 1200 human samples to determine whether 1. A NAT exists for the eQTL of interest, 2. The mapped NAT is differentially expressed within our series and 3. The NAT appears to regulate the eQTL of interest. Methods: The following criteria were used to pick putative NATs: 1. NATs had to be within the EST database, 2. NATs had to be novel, 3. NATs had to be on the antisense strand and finally, 4. NATs had to be spliced. RT-PCR was performed and differential expression (DE) of each NAT was assessed comparing cases and controls. Initial screening was performed in 6 cases and 5 controls. DE was assessed using standard delta delta CT methods. Further screening was performed using 376 controls and 515 cases. To confirm causation, we will use shRNA to knockdown the NAT of interest and determine the effect on the corresponding eQTL transcript and protein levels. Results: We found that w25% of screened eQTL had at least one corresponding NAT. Of those tested in our initial screens, one NAT showed a significant difference in expression between cases and controls, as well as a significant difference in cases versus controls expression when eQTL genotype group was considered. Conclusions: We have mapped transcripts which are under genomic control and where there is an alteration in eQTL profiles in AD. We now show that NAT transcripts could play a role in this mis-regulation. O3-11-03
GENETIC ASSOCIATION OF VARIANTS WITH LATE-ONSET ALZHEIMER’S DISEASE RISK AND BRAIN GENE EXPRESSION
Mariet Allen1, Fanggeng Zou1, High Seng Chai2, Curtis Younkin1, Julia Crook1, Vernon Pankratz2, Minerva Carrasquillo1, Christopher Rowley1, Asha Nair2, Sumit Middha1, Sooraj Maharjan2, Thuy Nguyen1, Li Ma1, Kimberly Malphrus1, Ryan Palusak1, Sarah Lincoln1, Gina Bisceglio1, Constantin Georgescu1, Christopher Kolbert1, Jin Jen2, Jonathan Haines3, Richard Mayeux4, Margaret Pericak-Vance5, Lindsay Farrer6, Gerard Schellenberg7, The Alzheimer’s Disease Genetics Consortium7, Ronald Petersen2, Neill Graff-Radford1, Dennis Dickson1, Steven Younkin1, Nilufer ErtekinTaner1, 1Mayo Clinic Jacksonville, Jacksonville, Florida, United States; 2 Mayo Clinic Rochester, Rochester, Minnesota, United States; 3Vanderbilt Kennedy Center, Nashville, Tennessee, United States; 4Columbia University, New York, New York, United States; 5University of Miami, Miami, Florida, United States; 6Boston University School of Medicine, Boston, Massachusetts, United States; 7University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States. Background: Recent genome-wide association studies (GWAS) of lateonset Alzheimer’s disease (LOAD) identified nine novel risk loci. There is a need to uncover the actual risk gene and the mechanism of the functional variants at these loci. Further, given that these loci collectively explain part of the risk for LOAD, alternative approaches are required to discover any additional risk genes. Gene expression levels are quantitative
P451
traits that are under strong genetic influence. Regulatory variants that modulate levels of gene expression are thought to underlie many human traits including susceptibility to disease. We hypothesize that there are variants that confer risk for LOAD by influencing brain gene expression. Methods: We measured expression levels of 24,526 transcripts in 773 brain samples from the cerebellum and temporal cortex of autopsied subjects with Alzheimer’s disease (AD, cerebellar n¼197, temporal cortex n¼202) and with other brain pathologies (non-AD, cerebellar n¼177, temporal cortex n¼197). We conducted an expression genome-wide association study (eGWAS) using 213,528 cisSNPs within 6100 kb of the tested transcript. We evaluated the novel LOAD GWAS loci for cisSNPs that associate with brain expression levels of nearby genes. We also compared the significant cisSNPs from our eGWAS for association with AD risk in the Alzheimer’s Disease Genetics Consortium (ADGC) GWAS. Results: We identified cisSNP associations with brain CLU, MS4A4A and ABCA7 levels (p¼7.81x10-4-9.09x10-9) at these respective novel LOAD risk loci. In addition, we found enrichment of significant brain cisSNPs at other loci that also had suggestive AD risk association (p<10-3) in the ADGC GWAS. This enrichment was 2.9-3.3 fold greater than expected by chance and statistically significant (p<10-6). Conclusions: Regulatory variants at the CLU, MS4A4A and ABCA7 loci may confer LOAD risk by influencing brain gene expression. The significant enrichment of brain cisSNPs amongst suggestive LOAD risk loci in the ADGC GWAS could imply presence of additional LOAD risk genes with functional, regulatory variants. Combined assessment of gene expression and disease risk may generate complementary information and aid the discovery of LOAD risk variants with functional implications. O3-11-04
THE ROLE OF DNA METHYLATION IN ALZHEIMER’S SUSCEPTIBILITY GENES WITH ALZHEIMER’S DISEASE PHENOTYPES
Brendan Keenan1, Gyan Srivastava1, Matthew Eaton2, Julie Schneider3, Lori Chibnik4, Alexander Meissner4, Manolis Kellis2, David Bennett5, Philip De Jager1, 1Brigham and Women’s Hospital, Boston, Massachusetts, United States; 2MIT, Cambridge, Massachusetts, United States; 3Rush Alzheimer’s Disease Center, Chicago, Illinois, United States; 4Harvard University, Cambridge, Massachusetts, United States; 5Rush University Medical Center, Chicago, Illinois, United States. Background: The DNA methylome captures the transcriptional potential of a cell or tissue: hyper-methylation of a promoter region is typically a mark of a closed chromatin conformation, which prevents transcription. Differential methylation of validated Alzheimer’s Disease (AD) susceptibility loci could influence their effect on AD phenotypes. We assess the state of chromatin at the regions of the genome around validated loci to identify CpG sites that correlate with AD disease measures. Methods: We utilized data from two longitudinal cohorts, the Religious Order Study and Rush Memory and Aging Project. DNA methylation profiles were generated in samples of dorsolateral prefrontal cortex using Illumina HumanMet450K beadset. We analyzed CpG sites within 11 validated AD susceptibility genes. The outcomes of interest were a count of neuritic amyloid plaques (NP), episodic memory decline (EMD), quantified as residual slope from a linear mixed effects model, and a clinical diagnosis of AD (dxAD). We assessed the association between the outcomes and extent of CpG methylation at each site as well as between the AD SNPs and methylation using linear regression. 257 CpGs were tested with a significance threshold for the primary outcome (dxAD) of p<0.0002. Results: A total of 749 subjects, with a mean (SD) age of death of 88.0 (6.6), were included in the analysis. Intriguing results were seen for AD genes BIN1 and EPHA1. Methylation level of the top CpG site at BIN1 (cg04019522), which lies in the gene body, is significantly associated with all three outcomes, NP (p¼3.4 x10-5), EMD (p¼0.0006), and dxAD (p¼3.6x10-5). The top CpG site at EPHA1 (cg26960083) lies just 313 bp from the transcription start site and is associated with dxAD (p¼3x10-5), marginally with EMD (p¼0.03) but not NP (p¼0.29). In both cases, CpGs are hypermethylated in AD relative to non-demented subjects. Given our modest sample size, the validated SNPs within these genes were not associated
TITLE NOT AVAILABLE
O3-11-02
EQTL REGULATION BY NATS IN ALZHEIMER’S DISEASE
Amanda Myers, Manuel Ramirez-Restrepo, Anzhelika Engel, University of Miami, Miami, Florida, United States. Background: We have found that w10% of the human brain transcriptome is under genetic control. While these expression quantitative trait loci (eQTLs) exist both in control samples and AD samples, for some of them there is a difference in the SNP-transcript relationship in AD. Our hypothesis is that one process by which eQTL misregulation could occur in AD is through the influence of natural antisense transcripts (NATs). NATs are nonprotein-coding, but fully processed RNAs that are transcribed from the opposite strand of the protein-coding sense transcript. NATs can regulate the expression of their corresponding protein-coding sense transcripts, with beta-secretase regulation being a prime example of relevance to AD. We have examined the top w100 eQTL hits from our screen of w 1200 human samples to determine whether 1. A NAT exists for the eQTL of interest, 2. The mapped NAT is differentially expressed within our series and 3. The NAT appears to regulate the eQTL of interest. Methods: The following criteria were used to pick putative NATs: 1. NATs had to be within the EST database, 2. NATs had to be novel, 3. NATs had to be on the antisense strand and finally, 4. NATs had to be spliced. RT-PCR was performed and differential expression (DE) of each NAT was assessed comparing cases and controls. Initial screening was performed in 6 cases and 5 controls. DE was assessed using standard delta delta CT methods. Further screening was performed using 376 controls and 515 cases. To confirm causation, we will use shRNA to knockdown the NAT of interest and determine the effect on the corresponding eQTL transcript and protein levels. Results: We found that w25% of screened eQTL had at least one corresponding NAT. Of those tested in our initial screens, one NAT showed a significant difference in expression between cases and controls, as well as a significant difference in cases versus controls expression when eQTL genotype group was considered. Conclusions: We have mapped transcripts which are under genomic control and where there is an alteration in eQTL profiles in AD. We now show that NAT transcripts could play a role in this mis-regulation. O3-11-03
GENETIC ASSOCIATION OF VARIANTS WITH LATE-ONSET ALZHEIMER’S DISEASE RISK AND BRAIN GENE EXPRESSION
Mariet Allen1, Fanggeng Zou1, High Seng Chai2, Curtis Younkin1, Julia Crook1, Vernon Pankratz2, Minerva Carrasquillo1, Christopher Rowley1, Asha Nair2, Sumit Middha1, Sooraj Maharjan2, Thuy Nguyen1, Li Ma1, Kimberly Malphrus1, Ryan Palusak1, Sarah Lincoln1, Gina Bisceglio1, Constantin Georgescu1, Christopher Kolbert1, Jin Jen2, Jonathan Haines3, Richard Mayeux4, Margaret Pericak-Vance5, Lindsay Farrer6, Gerard Schellenberg7, The Alzheimer’s Disease Genetics Consortium7, Ronald Petersen2, Neill Graff-Radford1, Dennis Dickson1, Steven Younkin1, Nilufer ErtekinTaner1, 1Mayo Clinic Jacksonville, Jacksonville, Florida, United States; 2 Mayo Clinic Rochester, Rochester, Minnesota, United States; 3Vanderbilt Kennedy Center, Nashville, Tennessee, United States; 4Columbia University, New York, New York, United States; 5University of Miami, Miami, Florida, United States; 6Boston University School of Medicine, Boston, Massachusetts, United States; 7University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States. Background: Recent genome-wide association studies (GWAS) of lateonset Alzheimer’s disease (LOAD) identified nine novel risk loci. There is a need to uncover the actual risk gene and the mechanism of the functional variants at these loci. Further, given that these loci collectively explain part of the risk for LOAD, alternative approaches are required to discover any additional risk genes. Gene expression levels are quantitative
P451
traits that are under strong genetic influence. Regulatory variants that modulate levels of gene expression are thought to underlie many human traits including susceptibility to disease. We hypothesize that there are variants that confer risk for LOAD by influencing brain gene expression. Methods: We measured expression levels of 24,526 transcripts in 773 brain samples from the cerebellum and temporal cortex of autopsied subjects with Alzheimer’s disease (AD, cerebellar n¼197, temporal cortex n¼202) and with other brain pathologies (non-AD, cerebellar n¼177, temporal cortex n¼197). We conducted an expression genome-wide association study (eGWAS) using 213,528 cisSNPs within 6100 kb of the tested transcript. We evaluated the novel LOAD GWAS loci for cisSNPs that associate with brain expression levels of nearby genes. We also compared the significant cisSNPs from our eGWAS for association with AD risk in the Alzheimer’s Disease Genetics Consortium (ADGC) GWAS. Results: We identified cisSNP associations with brain CLU, MS4A4A and ABCA7 levels (p¼7.81x10-4-9.09x10-9) at these respective novel LOAD risk loci. In addition, we found enrichment of significant brain cisSNPs at other loci that also had suggestive AD risk association (p<10-3) in the ADGC GWAS. This enrichment was 2.9-3.3 fold greater than expected by chance and statistically significant (p<10-6). Conclusions: Regulatory variants at the CLU, MS4A4A and ABCA7 loci may confer LOAD risk by influencing brain gene expression. The significant enrichment of brain cisSNPs amongst suggestive LOAD risk loci in the ADGC GWAS could imply presence of additional LOAD risk genes with functional, regulatory variants. Combined assessment of gene expression and disease risk may generate complementary information and aid the discovery of LOAD risk variants with functional implications. O3-11-04
THE ROLE OF DNA METHYLATION IN ALZHEIMER’S SUSCEPTIBILITY GENES WITH ALZHEIMER’S DISEASE PHENOTYPES
Brendan Keenan1, Gyan Srivastava1, Matthew Eaton2, Julie Schneider3, Lori Chibnik4, Alexander Meissner4, Manolis Kellis2, David Bennett5, Philip De Jager1, 1Brigham and Women’s Hospital, Boston, Massachusetts, United States; 2MIT, Cambridge, Massachusetts, United States; 3Rush Alzheimer’s Disease Center, Chicago, Illinois, United States; 4Harvard University, Cambridge, Massachusetts, United States; 5Rush University Medical Center, Chicago, Illinois, United States. Background: The DNA methylome captures the transcriptional potential of a cell or tissue: hyper-methylation of a promoter region is typically a mark of a closed chromatin conformation, which prevents transcription. Differential methylation of validated Alzheimer’s Disease (AD) susceptibility loci could influence their effect on AD phenotypes. We assess the state of chromatin at the regions of the genome around validated loci to identify CpG sites that correlate with AD disease measures. Methods: We utilized data from two longitudinal cohorts, the Religious Order Study and Rush Memory and Aging Project. DNA methylation profiles were generated in samples of dorsolateral prefrontal cortex using Illumina HumanMet450K beadset. We analyzed CpG sites within 11 validated AD susceptibility genes. The outcomes of interest were a count of neuritic amyloid plaques (NP), episodic memory decline (EMD), quantified as residual slope from a linear mixed effects model, and a clinical diagnosis of AD (dxAD). We assessed the association between the outcomes and extent of CpG methylation at each site as well as between the AD SNPs and methylation using linear regression. 257 CpGs were tested with a significance threshold for the primary outcome (dxAD) of p<0.0002. Results: A total of 749 subjects, with a mean (SD) age of death of 88.0 (6.6), were included in the analysis. Intriguing results were seen for AD genes BIN1 and EPHA1. Methylation level of the top CpG site at BIN1 (cg04019522), which lies in the gene body, is significantly associated with all three outcomes, NP (p¼3.4 x10-5), EMD (p¼0.0006), and dxAD (p¼3.6x10-5). The top CpG site at EPHA1 (cg26960083) lies just 313 bp from the transcription start site and is associated with dxAD (p¼3x10-5), marginally with EMD (p¼0.03) but not NP (p¼0.29). In both cases, CpGs are hypermethylated in AD relative to non-demented subjects. Given our modest sample size, the validated SNPs within these genes were not associated