An intronic PICALM polymorphism, rs588076, is associated with allelic expression of a PICALMisoform
© Parikh et al.; licensee BioMed Central Ltd. 2014
Received: 24 July 2014
Accepted: 21 August 2014
Published: 29 August 2014
Although genome wide studies have associated single nucleotide polymorphisms (SNP)s near PICALM with Alzheimer’s disease (AD), the mechanism underlying this association is unclear. PICALM is involved in clathrin-mediated endocytosis and modulates Aß clearance in vitro. Comparing allelic expression provides the means to detect cis-acting regulatory polymorphisms. Thus, we evaluated whether PICALM showed allele expression imbalance (AEI) and whether this imbalance was associated with the AD-associated polymorphism, rs3851179.
We measured PICALM allelic expression in 42 human brain samples by using next-generation sequencing. Overall, PICALM demonstrated equal allelic expression with no detectable influence by rs3851179. A single sample demonstrated robust global PICALM allelic expression imbalance (AEI), i.e., each of the measured isoforms showed AEI. Moreover, the PICALM isoform lacking exons 18 and 19 (D18-19 PICALM) showed significant AEI in a subset of individuals. Sequencing these individuals and subsequent genotyping revealed that rs588076, located in PICALM intron 17, was robustly associated with this imbalance in D18-19 PICALM allelic expression (p = 9.54 x 10-5). This polymorphism has been associated previously with systolic blood pressure response to calcium channel blocking agents. To evaluate whether this polymorphism was associated with AD, we genotyped 3269 individuals and found that rs588076 was modestly associated with AD. However, when both the primary AD SNP rs3851179 was added to the logistic regression model, only rs3851179 was significantly associated with AD.
PICALM expression shows no evidence of AEI associated with rs3851179. Robust global AEI was detected in one sample, suggesting the existence of a rare SNP that strongly modulates PICALM expression. AEI was detected for the D18-19 PICALM isoform, and rs588076 was associated with this AEI pattern. Conditional on rs3851179, rs588076 was not associated with AD risk, suggesting that D18-19 PICALM is not critical in AD. In summary, this analysis of PICALM allelic expression provides novel insights into the genetics of PICALM expression and AD risk.
KeywordsPICALM Alzheimer’s disease Next-generation sequencing Allelic expression imbalance Single nucleotide polymorphism
Phosphatidylinositol binding clathrin assembly protein (PICALM) facilitates clathrin-mediated endocytosis. PICALM binds phosphatidylinositol 4,5- bisphosphate (PIP2), adaptor protein 2 (AP2) and clathrin to mediate endocytic clathrin coated vesicle formation at the plasma membrane. Although PICALM is ubiquitously expressed, PICALM expression is more pronounced in microvessels [1, 2]. Previous studies have shown PICALM co-localizes with APP and modulates amyloid beta (Aß) generation [3–5]. Accumulation of Aß deposits is a hallmark of Alzheimer’s Disease (AD) pathology.
Genome wide association studies in multiple cohorts have identified single nucleotide polymorphisms (SNP)s near the PICALM gene as significantly associated with AD risk [6–10]. Studies were first conducted with Caucasian populations and then independently verified in several although not all Asian populations [11–15]. These studies report that the rs3851179 A allele reduces AD risk with an odds ratio of 0.88 . This SNP is located approximately 80 kb 5′ of PICALM.
Understanding how rs3851179 alters PICALM to impact AD risk may lead to novel insights into AD mechanisms and potential treatments. Since rs3851179 is not in linkage disequilibrium (LD) with a SNP that alters a PICALM amino acid (r2 < 0.1), we hypothesize that rs3851179 is associated with changes in mRNA transcription or processing. Allelic expression imbalance (AEI), which is an expression difference between allelic transcripts within an individual, has been used to detect cis-regulatory effects [16–20]. Here, we performed an AEI analysis by comparing allelic expression through the use of two exonic SNPs, rs76719109 and rs592297, in AD and non-AD brain samples. These studies included 35 samples that were heterozygous for rs76719109 and 19 samples that were heterozygous for rs592297. While PICALM expression did not show AEI overall, one individual showed robust PICALM AEI, with an allelic ratio of 0.76. Additionally, significant AEI was detected for the PICALM isoform lacking exons 18 and 19 (D18-19 PICALM). Sequencing and additional genotyping established that rs588076 was robustly associated with this AEI pattern. Interestingly, rs588076 has been associated with blood pressure response to Ca++ channel blocking agents . We discuss these overall results in the context that genetic regulation of PICALM isoforms relative to AD risk is highly complex with further work necessary to elucidate the mechanisms modulating genetic risk.
Interestingly, cDNA from the individual termed AD40 showed significant unequal allelic mRNA expression with a T:G ratio of 0.76 (Figure 4a, Additional file 1: Table S1). This ratio is based on a total of four experiments that detected a total of 68141 copies of the T allele and 88400 copies of the G allele. This AEI was not due to genomic normalization as the genomic T:G ratio was 1.01. These data are based upon the rs76719109 SNP because this individual was homozygous for rs592297. A similar result was obtained when each of four separate replicates were analyzed individually, i.e., when each replicate was analyzed individually, the T:G ratio was 0.73 ± 0.04 (mean ± SD). Hence, significant AEI was observed in a single individual among the 42 unique samples.
D18-19 PICALM shows significant AEI in nine samples
D18-19 T:G Counts
Ratio (Normalized to genomic ratio)
8.66 x 10-3
7.91 x 10-34
2.78 x 10-3
7.06 x 10-25
9.47 x 10-3
3.05 x 10-5
5.27 x 10-5
2.64 x 10-14
1.96 x 10-65
Samples with robust AEI are heterozygous for three SNPs
MAF in CEU
LD with rs3851179
LD with rs588076
LD with rs645299
LD with rs618629
Logistic regression modeling of rs3851179 and/or rs588076 effect(s) on AD
0.7151 - 0.9898
0.6819 - 0.8891
rs3851179 + rs588076
0.6604 - 0.9136
rs3851179 + rs588076
0.8238 - 1.227
The primary findings of this report include (i) overall PICALM expression shows no evidence of global AEI even when parsed by AD-associated SNPs, (ii) robust global AEI was detected in one sample, suggesting the existence of a rare SNP that strongly modulates PICALM expression, and (iii) eight individuals show AEI for the D18-19 PICALM isoform that is associated with rs588076. However, rs588076 was not associated with AD risk when considered in a model that also included rs3851179. In summary, analysis of allelic expression has proved a useful tool for the evaluation of cis-acting regulatory polymorphisms and AD risk.
A consistent pattern of AEI in overall PICALM expression was not detected. This was unexpected since Xu et al. reported consistent and robust PICALM AEI . The reason for different results in these two studies is unclear. The studies are similar in that both used rs76719109 as a reporter SNP and similar although not identical PCR primers. The studies differ in that Xu et al. used an Asian population while this report studied Caucasians. One explanation that would account for the difference in the studies was the presence of a confounding SNP in the Asian population in the genomic primer sequence because much of the AEI in Xu et al. was due to correction for imbalance in gDNA , although such a SNP has not yet been reported. We previously reported that the AD-associated SNP rs3851179 was associated with a modest difference in PICALM expression when analyzed relative to cell-type specific mRNAs; the minor rs3851179A allele appeared to be expressed modestly higher than the G allele . A similar difference was not observed here. One possible interpretation of these findings is that rs3851179 or its proxy AD SNP acts in a cell-type specific fashion that was discernible in our analysis that included cell-type specific markers. The current AEI study had smaller sample size because only the samples that were heterozygous for rs76719019 or rs592297 were suitable for analysis. However, this would not be expected to affect the AEI results because they rely upon an intra-individual analysis. We interpret these results overall as suggesting that the AD-associated SNP, or its functional proxy, acts in a cell-type specific fashion to modulate PICALM expression. This cell-type specific action was not detectable in this AEI study of mRNA derived from multiple cell types.
The second major finding was that robust AEI was detected for all PICALM isoforms in one individual, arguing for the existence of a rare functional SNP that strongly modulates total PICALM expression. For this individual, the rs76719109G allele was consistently more abundant than the T allele for each PICALM isoform. We hypothesize that AD40 is unique among the 42 samples in showing AEI because this sample is heterozygous for a causal SNP. If this causal allele is present in the heterozygous state in 1 of 42 people, this SNP has a minor allele frequency of ~1.2%. Although current sequencing studies of the PICALM promoter region have not yet identified candidate functional SNPs for AEI in this sample, these studies are on-going and a SNP that strongly modulates PICALM expression would be expected to be a robust AD risk factor.
The third major finding was that the D18-19 PICALM isoform showed robust AEI. There was a strong skew towards increased expression of the rs76719109T allele. Sequencing identified several candidate SNPs including rs588076, which is 509 bp downstream of exon 17. This SNP was found to be robustly associated with AEI for D18-19 PICALM. There are three possible ways rs588076 could influence D18-19 PICALM splicing efficiency: (i) rs588076 is in high LD with a functional SNP that modulates splicing, (ii) rs588076 and other SNPs influence D18-19 PICALM splicing in a cooperative manner, and/or (iii) rs588076 directly influences splicing. Further studies are necessary to discern among these possibilities.
The biological significance of the rs588076 association with D18-19 PICALM is complex. D18-19 PICALM transcripts account for 1-2% of total PICALM expression . Thus rs588076 is significantly associated with AEI for a PICALM isoform that is relatively rare in brain. Exons 18 and 19 encode a total of 27 amino acids that are part of the carboxyl terminal region required for clathrin binding and endocytosis . Hence, the protein encoded by D18-19 PICALM is likely to have reduced function . However, rs588076 was not associated with AD risk and did not enhance the logistic regression model for the rs3851179 association with AD. This leads us to conclude that the rs588076 and D18-19 PICALM isoform may be too rare in the brain to influence AD pathogenesis. Interestingly, rs588076 was recently associated with the blood pressure response to Ca++ channel blocking agents . Since rs588076 is associated only with D18-19 PICALM, we speculate that this isoform may be more abundant in other tissues and rs588076 actions upon D18-19 PICALM mediate this systolic blood pressure phenotype.
In summary, analysis of allelic expression has shown that compelling PICALM AEI was not observed in most brain RNA samples. Strong global AEI was documented in one sample, suggesting the existence of a rare PICALM regulatory SNP. A pattern of AEI was clearly discerned for the D18-19 PICALM isoform and rs588076 was significantly associated with this pattern. Rs588076 was not associated with AD risk although this SNP has been associated with a blood pressure-related phenotype. Allele-dependent expression studies may provide further insights into additional AD-associated polymorphisms.
DNA and RNA extraction from human brain tissue
The RNA and DNA samples for this study were from de-identified AD and non-AD human brain anterior cingulate specimens provided by the University of Kentucky AD Center Neuropathology Core and have been described previously [2, 26, 27]. The overall dataset included 30 AD samples (14 male, 16 female) and 30 non-AD samples (15 male, 15 female). The age at death for individuals that were cognitively intact, i.e., non-AD, was 82 ± 8 years (mean ± SD, n = 30) while age at death for AD individuals was 82 ± 6 (n = 30). The average post-mortem interval (PMI) for non-AD individuals was 2.8 ± 0.9 hours (mean ± SD, n = 30) while the PMI for AD individuals was similar at 3.4 ± 0.6 hours (n = 30). For the rs76719109 AEI assay, a subset of 35 samples were heterozygous for this SNP and included 18 non-AD (9 male, 9 female) and 17 AD (9 male, 8 female). For the rs592297 AEI assay, a total of 19 out of 60 samples were heterozygous, 13 non-AD (7 male, 6 female) and 6 AD (3 male, 3 female). Preparation of gDNA, RNA and cDNA was performed as described in previous studies [2, 26, 27]. Although RNA integrity analyses were not performed prior to reverse transcription, others have demonstrated that for qPCR with short amplicons, normalized expression differences are comparable between samples with moderate RNA degradation and those with high integrity RNA . We recognize that the absence of RNA integrity analysis constitutes a caveat of this study.
Genotyping and sequencing
DNA samples were genotyped for rs3851179, rs76719109, rs592297 and rs588076 by using unlabeled PCR primers and two allele-specific TaqMan FAM and VIC dye-labeled MGB probes (Pre-designed TaqMan SNP Genotyping Assay, Applied Biosystems) on a real-time PCR machine (Chromo4, MJ Research PTC-200).
Allelic imbalance assay
PCR primers for rs76719109 and rs592297 AEI assay
Data extraction and analysis of allelic mRNA expression
Allelic counts were extracted from DNA sequences by using Perlscript in a three-step fashion: (i) sequences corresponding to each sample were separated based on their barcode, (ii) gDNA and cDNA were then separated based on the presence of intronic and exonic sequences, respectively, and (iii) allele counts were obtained by using sequences that bridged the SNP of interest.
Standard curve generation
One rs76719109 homozygous major (GG) and one homozygous minor (TT) individual was selected based on similar qPCR copy numbers. Five dilutions were prepared with different ratios of each individual’s cDNA: 1:4, 1:2, 1:1, 2:1, and 4:1. These samples were PCR amplified and subjected to sequencing as described above.
Analysis of allelic counts was based upon the assumption that transcript read counts follow a Poisson distribution . As such, each allele from the heterozygous SNP was used to define two random variables. Following the rs76719109 example of a G/T SNP, we denote the pair of transcript counts as G ~ Poisson (λ G ) and T ~ Poisson (λ T ). That is, G and T are Poisson-distributed random variables with means λ G and λ T , respectively. It can then be readily shown that for a given pair of realized transcripts counts, G = g and T = t, the transcript count of either allele is binomially distributed with success probability equal to a ratio of component means. That is, . Testing for AEI then simplifies to an examination of the null hypothesis that the pair of transcript counts comes from the same distribution, i.e., that λ G = λ T which is equivalent to testing . This null hypothesis agrees with the intuition that when the total of transcript counts is known, the number generated from a specific allele is essentially a sequence of independent, equally probable trials. Thus, rejection of this null hypothesis corresponds to AEI.
Measuring transcripts from genomic DNA is one way of correcting for the possibility of differential experimental error between allele transcript counts. Conceptually, one could adapt methods for determining AEI by an appropriate adjustment with the ratio of reads from gDNA as these reads should theoretically come from the same distribution regardless of AEI (Fardo et al., unpublished). Alternatively, it can be assumed that one allele is derived from a distribution with an inflated mean solely due to experimental error (i.e., under the null hypothesis of no AEI). In this case, we have that the means of the transcript reads satisfy either λ G = (1 + δ)λ T or (1 + δ)λ G = λ T . Here, the probability parameter, p, for the count probability in the AEI test becomes or , respectively. For our gDNA data, we have a maximum 8.5% increase of one allele over the other and chose to conservatively assume a 20% mean increase (i.e., δ = 0.2). We then calculate the AEI test p-value from the lesser-significant test of and .
Genotype association with AD risk
The Mayo Clinic dataset has been described previously [31, 32]. Briefly, the Mayo Clinic dataset contained 1789 cases and 2529 non-ADs collected from six centers from the US and Europe as described . Direct genotyping of rs3851179 and rs588076 was performed using a TaqMan SNP genotyping assay in an ABI PRISM 7900HT Sequence Detection System with 384-well block module from Applied Biosystems (California, USA). First-pass genotype cluster calling was analyzed using the SDS software version 2.2.3 (Applied Biosystems, California, USA). Variants passed Hardy-Weinburg (P > 0.05) and minor allele frequencies are consistent with public databases (EVS, HapMap, 1000G). Association testing for rs3851179, with and without rs588076, was carried out in PLINK  by using an additive logistic regression model corrected for appropriate covariates; diagnosis age, APOE 4, APOE 2, sex and contributing center.
This work is funded by National Institutes of Health [P01-AGO30128 and R01-AG045775 (SE), P30-AG028383, R25GM093044 and K25-AG043546 (DWF)] and Alzheimer’s Research UK US travel fellowship (CWM).
- Baig S, Joseph SA, Tayler H, Abraham R, Owen MJ, Williams J, Kehoe PG, Love S: Distribution and expression of picalm in Alzheimer disease. J Neuropathol Exp Neurol. 2010, 69: 1071-1077.PubMed CentralView ArticlePubMedGoogle Scholar
- Parikh I, Fardo DW, Estus S: Genetics of PICALM expression and Alzheimer’s disease. PLoS One. 2014, 9: e91242-PubMed CentralView ArticlePubMedGoogle Scholar
- Xiao Q, Gil SC, Yan P, Wang Y, Han S, Gonzales E, Perez R, Cirrito JR, Lee JM: Role of phosphatidylinositol clathrin assembly lymphoid-myeloid leukemia (PICALM) in intracellular amyloid precursor protein (APP) processing and amyloid plaque pathogenesis. J Biol Chem. 2012, 287: 21279-21289.PubMed CentralView ArticlePubMedGoogle Scholar
- D’Angelo F, Vignaud H, Di Martino J, Salin B, Devin A, Cullin C, Marchal C: A yeast model for amyloid-beta aggregation exemplifies the role of membrane trafficking and PICALM in cytotoxicity. Dis Model Mech. 2013, 6: 206-216.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanatsu K, Morohashi Y, Suzuki M, Kuroda H, Watanabe T, Tomita T, Iwatsubo T: Decreased CALM expression reduces Abeta42 to total Abeta ratio through clathrin-mediated endocytosis of gamma-secretase. Nat Commun. 2014, 5: 3386-View ArticlePubMedGoogle Scholar
- Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Williams A, Jones N, Thomas C, Stretton A, Morgan AR, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Morgan K, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Holmes C, et al: Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet. 2009, 41: 1088-1093.PubMed CentralView ArticlePubMedGoogle Scholar
- Jun G, Naj AC, Beecham GW, Wang LS, Buros J, Gallins PJ, Buxbaum JD, Ertekin-Taner N, Fallin MD, Friedland R, Inzelberg R, Kramer P, Rogaeva E, St George-Hyslop P, Cantwell LB, Dombroski BA, Saykin AJ, Reiman EM, Bennett DA, Morris JC, Lunetta KL, Martin ER, Montine TJ, Goate AM, Blacker D, Tsuang DW, Beekly D, Cupples LA, Hakonarson H, Kukull W, et al: Meta-analysis confirms CR1, CLU, and PICALM as alzheimer disease risk loci and reveals interactions with APOE genotypes. Arch Neurol. 2010, 67: 1473-1484.PubMed CentralView ArticlePubMedGoogle Scholar
- Lambert JC, Heath S, Even G, Campion D, Sleegers K, Hiltunen M, Combarros O, Zelenika D, Bullido MJ, Tavernier B, Letenneur L, Bettens K, Berr C, Pasquier F, Fievet N, Barberger-Gateau P, Engelborghs S, De Deyn P, Mateo I, Franck A, Helisalmi S, Porcellini E, Hanon O, de Pancorbo MM, Lendon C, Dufouil C, Jaillard C, Leveillard T, Alvarez V, Bosco P, et al: Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet. 2009, 41: 1094-1099.View ArticlePubMedGoogle Scholar
- Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, Bis JC, Smith AV, Carassquillo MM, Lambert JC, Harold D, Schrijvers EM, Ramirez-Lorca R, Debette S, Longstreth WT, Janssens AC, Pankratz VS, Dartigues JF, Hollingworth P, Aspelund T, Hernandez I, Beiser A, Kuller LH, Koudstaal PJ, Dickson DW, Tzourio C, Abraham R, Antunez C, Du Y, Rotter JI, et al: Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA. 2010, 303: 1832-1840.PubMed CentralView ArticlePubMedGoogle Scholar
- Pedraza O, Allen M, Jennette K, Carrasquillo M, Crook J, Serie D, Pankratz VS, Palusak R, Nguyen T, Malphrus K, Ma L, Bisceglio G, Roberts RO, Lucas JA, Ivnik RJ, Smith GE, Graff-Radford NR, Petersen RC, Younkin SG, Ertekin-Taner N: Evaluation of memory endophenotypes for association with CLU, CR1, and PICALM variants in black and white subjects. Alzheimers Dement. 2014, 10: 205-213.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu G, Zhang S, Cai Z, Ma G, Zhang L, Jiang Y, Feng R, Liao M, Chen Z, Zhao B, Li K: PICALM gene rs3851179 polymorphism contributes to Alzheimer’s disease in an Asian population. Neuromolecular Med. 2013, 15: 384-388.View ArticlePubMedGoogle Scholar
- Miyashita A, Koike A, Jun G, Wang LS, Takahashi S, Matsubara E, Kawarabayashi T, Shoji M, Tomita N, Arai H, Asada T, Harigaya Y, Ikeda M, Amari M, Hanyu H, Higuchi S, Ikeuchi T, Nishizawa M, Suga M, Kawase Y, Akatsu H, Kosaka K, Yamamoto T, Imagawa M, Hamaguchi T, Yamada M, Moriaha T, Takeda M, Takao T, Nakata K, et al: SORL1 is genetically associated with late-onset Alzheimer’s disease in Japanese, Koreans and Caucasians. PloS one. 2013, 8: e58618-PubMed CentralView ArticlePubMedGoogle Scholar
- Yu JT, Song JH, Ma T, Zhang W, Yu NN, Xuan SY, Tan L: Genetic association of PICALM polymorphisms with Alzheimer’s disease in Han Chinese. J Neurol Sci. 2011, 300: 78-80.View ArticlePubMedGoogle Scholar
- Li HL, Shi SS, Guo QH, Ni W, Dong Y, Liu Y, Sun YM, Bei W, Lu SJ, Hong Z, Wu ZY: PICALM and CR1 variants are not associated with sporadic Alzheimer’s disease in Chinese patients. J Alzheimers Dis. 2011, 25: 111-117.PubMedGoogle Scholar
- Chen LH, Kao PY, Fan YH, Ho DT, Chan CS, Yik PY, Ha JC, Chu LW, Song YQ: Polymorphisms of CR1, CLU and PICALM confer susceptibility of Alzheimer’s disease in a southern Chinese population. Neurobiol Aging. 2012, 33: 210-e211-217PubMedGoogle Scholar
- Mondal AK, Sharma NK, Elbein SC, Das SK: Allelic expression imbalance screening of genes in chromosome 1q21-24 region to identify functional variants for Type 2 diabetes susceptibility. Physiol Genomics. 2013, 45: 509-520.PubMed CentralView ArticlePubMedGoogle Scholar
- Jones BL, Swallow DM: The impact of cis-acting polymorphisms on the human phenotype. The HUGO journal. 2011, 5: 13-23.PubMed CentralView ArticlePubMedGoogle Scholar
- Pham MH, Bonello GB, Castiblanco J, Le T, Sigala J, He W, Mummidi S: The rs1024611 regulatory region polymorphism is associated with CCL2 allelic expression imbalance. PLoS One. 2012, 7: e49498-PubMed CentralView ArticlePubMedGoogle Scholar
- Jentarra GM, Rice SG, Olfers S, Saffen D, Narayanan V: Evidence for population variation in TSC1 and TSC2 gene expression. BMC Med Genet. 2011, 12: 29-PubMed CentralView ArticlePubMedGoogle Scholar
- Smith RM, Webb A, Papp AC, Newman LC, Handelman SK, Suhy A, Mascarenhas R, Oberdick J, Sadee W: Whole transcriptome RNA-Seq allelic expression in human brain. BMC Genomics. 2013, 14: 571-PubMed CentralView ArticlePubMedGoogle Scholar
- Kamide K, Asayama K, Katsuya T, Ohkubo T, Hirose T, Inoue R, Metoki H, Kikuya M, Obara T, Hanada H, Thijs L, Kuznetsova T, Noguchi Y, Sugimoto K, Ohishi M, Morimoto S, Nakahashi T, Takiuchi S, Ishimitsu T, Tsuchihashi T, Soma M, Higaki J, Matsuura H, Shinagawa T, Sasaguri T, Miki T, Takeda K, Shimamoto K, Ueno M, Hosomi N, et al: Genome-wide response to antihypertensive medication using home blood pressure measurements: a pilot study nested within the HOMED-BP study. Pharmacogenomics. 2013, 14: 1709-1721.View ArticlePubMedGoogle Scholar
- Xu X, Wang H, Zhu M, Sun Y, Tao Y, He Q, Wang J, Chen L, Saffen D: Next-generation DNA sequencing-based assay for measuring allelic expression imbalance (AEI) of candidate neuropsychiatric disorder genes in human brain. BMC Genomics. 2011, 12: 518-PubMed CentralView ArticlePubMedGoogle Scholar
- Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O’Donnell CJ, de Bakker PI: SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics. 2008, 24: 2938-2939.PubMed CentralView ArticlePubMedGoogle Scholar
- Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001, 29: 308-311.PubMed CentralView ArticlePubMedGoogle Scholar
- Scotland PB, Heath JL, Conway AE, Porter NB, Armstrong MB, Walker JA, Klebig ML, Lavau CP, Wechsler DS: The PICALM protein plays a key role in iron homeostasis and cell proliferation. PLoS One. 2012, 7: e44252-PubMed CentralView ArticlePubMedGoogle Scholar
- Malik M, Simpson JF, Parikh I, Wilfred BR, Fardo DW, Nelson PT, Estus S: CD33 Alzheimer’s Risk-Altering Polymorphism, CD33 Expression, and Exon 2 Splicing. J Neurosci. 2013, 33: 13320-13325.PubMed CentralView ArticlePubMedGoogle Scholar
- Ling IF, Bhongsatiern J, Simpson JF, Fardo DW, Estus S: Genetics of clusterin isoform expression and Alzheimer’s disease risk. PLoS One. 2012, 7: e33923-PubMed CentralView ArticlePubMedGoogle Scholar
- Fleige S, Pfaffl MW: RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med. 2006, 27: 126-139.View ArticlePubMedGoogle Scholar
- Thomas M, William ML, Mattieu M, Rishi N, Bert O, Miguel P, Bethan P, Emily P, Harpreet Singh R, Graham RS R, Magali R, Michael S, Daniel S, Daniel S, Kieron T, Anja T, Stephen T, Simon W, Wilder SP, Aken BL, Ewan B, Fiona C, Ian D, Jennifer H, Javier H, Tim JP H, Nathan J, Rhoda K, Anne P, Giulietta S, et al: Ensembl 2014. Nucleic Acids Research. 2014, 42 (Database issue): D749-D755.Google Scholar
- Jiang H, Wong WH: Statistical inferences for isoform expression in RNA-Seq. Bioinformatics. 2009, 25: 1026-1032.PubMed CentralView ArticlePubMedGoogle Scholar
- Ridge PG, Mukherjee S, Crane PK, Kauwe JS, Alzheimer’s Disease Genetics C: Alzheimer’s disease: analyzing the missing heritability. PLoS One. 2013, 8: e79771-PubMed CentralView ArticlePubMedGoogle Scholar
- Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, Gallins PJ, Buxbaum JD, Jarvik GP, Crane PK, Larson EB, Bird TD, Boeve BF, Graff-Radford NR, De Jager PL, Evans D, Schneider JA, Carrasquillo MM, Ertekin-Taner N, Younkin SG, Cruchaga C, Kauwe JS, Nowotny P, Kramer P, Hardy J, Huentelman MJ, Myers AJ, Barmada MM, Demirci FY, Baldwin CT, et al: Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet. 2011, 43: 436-441.PubMed CentralView ArticlePubMedGoogle Scholar
- Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007, 81: 559-575.PubMed CentralView ArticlePubMedGoogle Scholar
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