{'fraction_non_alphanumeric': 0.03378593161073521, 'fraction_numerical': 0.024022523954407158, 'mean_word_length': 5.147962032384143, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 3, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Independent and epistatic effects of variants in VPS10-d receptors on Alzheimer disease risk and processing of the amyloid precursor protein (APP)

Genetic variants in the sortilin-related receptor (SORL1) and the sortilin-related vacuolar protein sorting 10 (VPS10) domain-containing receptor 1 (SORCS1) are associated with increased risk of Alzheimer's disease (AD), declining cognitive function and altered amyloid precursor protein (APP) processing. We explored whether other members of the (VPS10) domain-containing receptor protein family (the sortilin-related VPS10 domain-containing receptors 2 and 3 (SORCS2 and SORCS3) and sortilin (SORT1)) would have similar effects either independently or together. We conducted the analyses in a large Caucasian case control data set (n=11 840 cases, 10 931 controls) to determine the associations between single nucleotide polymorphisms (SNPs) in all the five homologous genes and AD risk. Evidence for interactions between SNPs in the five VPS10 domain receptor family genes was determined in epistatic statistical models. We also compared expression levels of SORCS2, SORCS3 and SORT1 in AD and control brains using microarray gene expression analyses and assessed the effects of these genes on γ-secretase processing of APP. Several SNPs in SORL1, SORCS1, SORCS2 and SORCS3 were associated with AD. In addition, four specific linkage disequilibrium blocks in SORCS1, SORCS2 and SORCS3 showed additive epistatic effects on the risk of AD (P⩽0.0006). SORCS3, but not SORCS2 or SORT1, showed reduced expression in AD compared with control brains, but knockdown of all the three genes using short hairpin RNAs in HEK293 cells caused a significant threefold increase in APP processing (from P<0.001 to P<0.05). These findings indicate that in addition to SORL1 and SORCS1, variants in other members of the VPS10 domain receptor family (that is, SORCS1, SORCS2, SORCS3) are associated with AD risk and alter APP processing. More importantly, the results indicate that variants within these genes have epistatic effects on AD risk.


Introduction
A central event in the pathogenesis of Alzheimer's disease (AD) is the deposition of amyloid b (Ab) 1-40 and Ab1-42 peptides generated by proteolytic cleavage by b-and g-secretase from a larger membrane-bound protein, the amyloid precursor protein (APP). 1 APP and the secretases are integral transmembrane proteins dynamically sorted through the plasma membrane. Modulation of APP sorting through the membrane or altering APP cleavage by secretase enzymes could affect the regulation of Ab production or processing.
Variants in two members of the vacuolar protein sorting 10 (VPS10) domain-containing receptor protein family, sortilinrelated receptor (SORL1) and sortilin-related VPS10 domaincontaining receptor 1 (SORCS1), are associated with lateonset AD presumably through effects on APP sorting and cleavage. [2][3][4] The VPS10 domain-containing receptor protein family contains five type I membrane homologs (SORL1, sortilin (SORT1), SorCS1, SorCS2 and SorCS3), [5][6][7][8][9] that are expressed in the central nervous system. All contain a single Vps10p-D situated at the N-terminus of their luminal/extracellular moiety. The VPS10 motif functions as a sorting 1 receptor in the Golgi compartment required for the intracellular sorting and delivery of proteins, including APP. In SORT1, also known as neurotensin receptor-3, the Vps10p-D makes up the entire luminal extracellular part of the receptor, but the other four receptors have additional modules. In SORL1, the Vps10p-D is followed by five lowdensity lipoprotein receptor class B repeats flanked by an epidermal growth factor precursor-type repeat, a cluster of 11 low-density lipoprotein receptor class A repeats and 6 fibronectin type-III repeats. The mutually highly homologous SorCS1, SorCS2 and SorCS3 contain a leucine-rich segment between the Vps10p-D and the transmembrane domain. Structure prediction of the leucine-rich segment suggests a beta-sandwich fold and relates the domain to the immunoglobulin-like fold (E-set) superfamily. Following the extracellular and transmembrane segment, each receptor carries a short (40-80 amino acids) cytoplasmic domain comprising typical motifs for interaction with cytosolic adaptor molecules. In genomic DNA, members of this family are large with many exons but the coding sequence lengths are usually o3700 nucleotides. Very large introns (introns 1-2) typically separate the exons encoding the VPS10 domain; the remaining exons are separated by much smaller introns. Exons 1-3 encode the VPS10 domain.
Previously, we demonstrated that SORL1 modulates the translocation and retention of APP in subcellular compartments, which are less favorable for secretase processing, thereby reducing the extent of proteolytic breakdown into both amyloidogenic and non-amyloidogenic products. 3 Furthermore, we showed that under-expression of SORL1 leads to overexpression of Ab and an increased risk of AD. Subsequently, we demonstrated that genetic variation in SORCS1 also influences AD risk, cognitive performance, APP processing and Ab40 and Ab42 levels through an effect on g-secretase processing of APP. 2,10 Overexpression of SorCS1 reduced Ab40 and Ab42 levels, whereas suppression of SorCS1 increased g-secretase processing of APP. The association of SORL1 with AD has been supported by a metaanalysis of Caucasian and Asian data sets that included a total of 12 464 cases and 17 929 controls 11 and has been further validated in various ethnic groups, including African Americans, Israeli Arabs and Caribbean Hispanics, although with some degree of allelic heterogeneity. 3,[11][12][13][14][15][16][17][18] In addition, these data are supported by a study in which overexpression of SorCS1cb-myc in cultured cells caused a significant reduction in A generation, whereas, conversely, endogenous murine A40 andA42 levels were increased in the brains of Sorcs1 hypomorphic mice. 19 We hypothesized that variants in other members of the sortilin-related VPS10 domain containing receptor family, namely SORCS2, SORCS3 and SORT1, would also be associated with AD risk either independently or through epistatic effects. These homologous genes are expressed in different brain regions with different subcellular localisations, [20][21][22] but there are many brain regions, such as the hippocampus, in which these genes are co-expressed albeit at low levels. 20 We conducted single-marker association and epistasis analyses of all the five homologous genes in a large Caucasian case-control data set, with sufficient power to detect modest effect sizes and interactive effects. In addition, we conducted microarray gene expression analyses and g-secretase assays for SORCS1, SORCS2, SORCS3 and SORT1.

Participants and methods
Ethics statement. Informed consent was obtained from all the participants using procedures approved by institutional review boards at each of the clinical research centers collecting human subjects for the ADGC project.
Participants. The data set included 11 840 cases and 10 931controls from the ADGC data set. 23 The clinical characteristics are summarized in Table 1. The diagnoses of 'probable' or 'possible' AD were defined based on the National Institute of Neurological and Communication Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) diagnosis criteria at clinics specializing in memory disorders or in clinical investigations. Persons were classified as 'controls' when they were without cognitive impairment or dementia at last visit.
Genotyping. HapMap2-imputed genotypic data for single nucleotide polymorphisms (SNPs) in SORCS1 (n ¼ 648), SORCS2 (n ¼ 740), SORCS3 (n ¼ 742), SORL1 (n ¼ 160) and SORT1 (n ¼ 40) was obtained from the previously published genome-wide association study. 23 The SNPs assessed included both intronic and exonic SNPs. The SORCS1 SNPs were not identical to the SNPs assessed in our previous study, which had been selected based on previous reports. 2 Details regarding apolipoprotein E (APOE) genotyping are described in the Supplementary methods.
Cell culture and transfection. Using HEK293 cell lines, reverse transcriptase-PCR (RT-PCR) and western analysis were used to detect all five VPS10 proteins and to verify the knockdown and specificity of each short hairpin RNA (shRNA) as previously described. 2 The corresponding shRNA DNA sequences are shown in Supplementary  Table 3.
APP-GV Assay. The g-secretase activity and nuclear translocation of the APP/Fe65/TIP60 protein complex was monitored with the APP-GV assay. 24 The APP-GV assay is a luciferase-based assay 24 consisting of the APP gene's C-terminus (AICD) fused to a transcription factor composed of the GAL4 DNA-binding domain with VP16 transcriptional activator (GV). In addition, the AICD fragment is fused to the GV domains as a positive control of AICD generation and allows for the evaluation of the AICD-specific contribution to the observed modulation in the APP-GV assay. Briefly, SorCS2 cDNA or SorCS2 shRNAs transiently transfected were evaluated in either the APP-GV or the AICD-GV assay, as previously described 24 in the HEK293 cell line. SorCS3 cDNA or SorCS3 shRNAs, and SORT1 cDNA or SORT1 shRNAs, were evaluated in a similar fashion.
Microarray gene expression and quantitative RT-PCR.
Expression profiling was performed separately for the cerebellum, parietal-occipital neocortex and amygdala regions from 19 AD and 10 control brains from the New York Brain Bank (www.nybb.hs.columbia.edu). This threeregion approach allowed us to enhance the signal-to-noise ratio 25 and to determine those changes in expression that are specific for late-onset AD and consistent with the distribution of AD pathology. For the expression profiling of AD and control brains, the Affymetrix GeneChip Human Exon 1.0 ST Arrays (Affymetrix, Santa Clara, CA, USA) were used. Frozen brain tissue was ground over liquid nitrogen and stored at À 80 1C until use. Total RNA was extracted and purified using the TRIzol Plus RNA purification kit (Invitrogen, Life Technologies, Grand Island, NY, USA). All RNA preparations were analyzed using an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA; RNA 6000 nano-kit) to determine RNAquantity/quality and only samples with RNA integrity number 48 were used in the subsequent RNA amplification and hybridization steps. The Genechip expression two-cycle target labeling kit (Affymetrix) was used for all samples according to Affymetrix's protocols. Briefly, the procedure consists of an initial ribosomal RNA reduction step and two cycles of reverse transcription followed by in vitro transcription. For each sample, 1 mg of total RNA is initially subjected to removal of ribosomal RNA using the RiboMinus Transcriptome Isolation Kit (Invitrogen) and spiked with Eukaryotic PolyA RNA controls (Affymetrix). The ribosomal RNA-depleted fraction was used for cDNA synthesis by reverse transcription primed with T7-random hexamer primers, followed by second strand synthesis. This cDNA served as the template for in vitro transcription to obtain amplified antisense cRNA. Subsequently, cRNA from the first round was reverse transcribed using random primers to obtain single-stranded sense DNA. In this second reverse transcription, dUTP (2 0 -deoxyuridine, 5 0 -triphosphate) is incorporated into the DNA to allow for subsequent enzymatic fragmentation using a combination of UDG (uracil-DNA glycosylase) and APE1 (apyrimidinic endonuclease 1). All reverse and in vitro transcription steps were performed using the GeneChip WT cDNA synthesis and amplification Kit (Affymetrix). The resulting fragmented DNA was labeled with Affymetrix DNA Labeling Reagent. Labeled fragmented DNA was hybridized to Affymetrix Human Exon 1.0 ST arrays, washed and stained using the GeneChip Hybridization, Wash and Stain Kit (Affymetrix).
Fluorescent images were recorded on a Genechip scanner 3000 and analyzed with the GeneChip operating software. Significant results obtained from the microarray study were validated by quantitative RT-PCR using the same set of AD and control samples. Total RNA (1 mg) from of the amygdala region was used to generate cDNAs using the AffinityScript first-strand synthesis kit (Agilent Stratagene, CA, USA). RT-PCR primers were designed for three randomly selected exons of SorCS3 (10, 17 and 21). The housekeeping gene, TBP (TATA-binding protein) , was used as the endogenous control; and samples were analyzed in triplicate. The primers used in the quantitative RT-PCR are available from Supplementary Table 1. Real-time RT-PCR was done using SYBR Green reagent (TaKaRa Mirus Bio, Madison, WI, USA) on an ABI7500 system (Applied Biosystems, Foster City, CA, USA).
Statistical methods. Extensive quality review of SNPs and samples were previously completed. 23 Then multivariate logistic regression analyses in PLINK (http://pngu.mgh. harvard.edu/Bpurcell/plink/) were used to assess additive genotypic and allelic associations with AD risk in the casecontrol data sets, and generalized estimating equation models were used for family-based data sets. All models were first adjusted for age at examination, sex and population stratification and subsequently for APOE-e4 (additive effect) as well. For adjustment for population stratification, the first two, three or four estimated principal components were used, as described previously. 23 Logistic generalized estimating equation models 26,27 were used to evaluate association in the family-based data sets, using the same adjustments. Then, a meta-analysis of the individual study results was performed using inverse variance weights for the effect estimates as implemented in METAL (http:// www.sph.umich.edu/csg/abecasis/Metal/). In order to take linkage disequilibrium (LD) between the markers into account, the P-value threshold for multiple testing correction was, in both single-marker and epistasis analyses, determined by applying the algorithm by Li and Ji. 28 As this was a candidate gene study with the a priori hypothesis of an association between each of the explored genes and AD, the calculation was done separately for each gene.
Epistasis. Using only the SNPs that were associated with AD in the single-marker analyses (Pp0.05), we tested for an interaction between SNPs in the five homologous genes. The analysis was carried out using PLINK (http://pngu.mgh. harvard.edu/Bpurcell/plink/) adjusting for population stratification. The model based on generalized estimating equations yields a list of SNP-by-SNP comparisons with beta coefficients and P-values. Based on the number of independent interactions tested, we accepted a P-value of p0.0001 as statistically significant. As described above, LD between the markers was taken into account applying the algorithm by Li and Ji. 28 Statistical analysis for the gene expression and quantitative RT-PCR data. To determine in which genes expression levels differ between affected and unaffected brain regions, as well as between AD and control brains, we performed both within-and between-group factors' analysis of variance using PARTEK GENOMICS SUITE 6.4 (http://www.partek.com/partekgs). Before the expression analysis, we log 10 -transformed the Rank invariant normalized expression data. False discovery rate was used to account for the error in multiple comparisons. The real-time RT-PCR data were analyzed by the comparative CT method integrated in the DataAssist Software (Life Technologies). 29 Statistical analysis for the cell biology assays. Mean expression levels were compared by analysis of variance with post hoc correction using Graphpad Statistical software (Graphpad, Inc., San Diego, CA, USA). All data were normalized to transfection efficiency (for example, green fluorescent protein) and then to the control values on each plate for every assay to allow for comparisons across experiments.

Results
Single-marker analyses. Table 1 shows the characteristics of the study populations. In all, 15 SNPs in SORL1, 23 SNPs in SORCS1, 18 SNPs in SORCS2 and 12 SNPs in SORCS3 were associated with AD (Table 2). These SNPs belonged to distinct LD blocks in these genes (Supplementary Figure S1). All SNPs in SORL1 reached statistical significance after correction for multiple testing and taking LD between the markers into account. One of these SNPs, rs1784933, corresponds to SNP26 in the original study by Rogaeva et al. 3 and is located 6 kb from SNP 25, which is part of one of the two SORL1 clusters that have been repeatedly associated with AD in different ethnic groups. 3,18 The SNPs in SORCS1, SORCS2 and SORCS3 were close, but not statistically significant. Interestingly, in line with previous reports, 2 most of the significant SNPs in SORCS1 and SORCS2 are located in intron 1, which is adjacent to the exons encoding the VPS10 domain. In addition, in all the four homologs (SORL1, SORCS1, SORCS2, SORCS3) some of the disease-associated SNPs were close to splice sites ( Table 2). None of the genotyped SNPs in SORT1 were significantly associated with AD (Supplementary Table 2).
Cell culture and transfection. cDNA transfection of Vps10 family members in Hek 293 cells demonstrated a significant (0.01pPp0.05) decrease of g-secretase (APP-GV), whereas there was no effect on the AICD-GV translocation assay (Figure 3).

Discussion
Taken together with previous studies, 2,3 the findings here indicate that variants in SORL1, SORCS1, SORCS2 and SORCS3 of the VPS10-d receptor family are associated with AD risk. The results are consistent with previous studies showing associations between the SNPs in SORL1 and SORCS1 with AD 2,3,11,13-16,18 and with cognitive performance. 10 Similar to previous reports, the associated SNPs in SORCS1, SORCS2 and SORCS3 were mostly located in introns 1-3, implicating the VPS10 domain.
The effect sizes of the associated SNPs were small (b: À 0.45 to 0.36), but this is consistent with previous observations for the homologous genes SORL1 and SORCS1 2,3,18 as well as all recently detected novel AD susceptibility loci identified by large genome-wide association studies. 23,[31][32][33][34] The epistasis models of SNPs significant in single-marker analyses further revealed pairwise SNP associations between specific LD blocks in the highly homologous SORCS1, SORCS2 and SORCS3. One single LD block in SORCS3 showed epistasis with both a single LD block in SORCS1 and two specific LD blocks in SORCS2. In addition, the same two regions of SORCS2 and SORCS3 interacted. Of note, the epistasis b ranging from À 0.94 to 0.94 reflected larger effects (additive) than the corresponding single-marker analyses ( À 0.15obo0.20), and the interacting SNPs are almost exclusively located in introns 1 and 2, adjacent to the exons encoding the VPS10 domain. This region has also been demonstrated to include the majority of disease-associated SNPs for both SORCS1 and SORL1. 2,3,10,18,20 Our findings indicate that there are sequences within these specific LD blocks that are biologically important and that are interacting. The mechanism underlying these interactions is presently unclear. It could arise from direct interaction between the homologs, interactions with a mutual binding partner or interactions with a common substrate, such as APP or APP-CTF. However, it could also result from quite remote interactions that do not require a first-or second-order interaction between these proteins.
Suppression of SORCS2, SORCS3 or SORT1 increased g-secretase processing of APP, findings consistent with reported effects by SORL1 and SORCS1 on g-secretase processing of APP and changes in Ab40 and Ab42 levels. 2,3 Although SorCS3 and SorCS1 do not convey trans-Golgi network to late endosome sorting, 35,36 SORT1 is-similar to SORL1-also capable of mediating sorting of ligands from the trans-Golgi network to late endosomes or lysosomes. 22 Interestingly, sortilin-mediated endocytosis has been shown to determine levels of progranulin involved in frontotemporal dementia. 37 Results from the current genome-wide association studies suggest that other genes PICALM, BIN1 and CD2AP, modulate intracellular trafficking of cell surface proteins. Thus, it is appears that in addition to their effect on gsecretase processing of APP, some members of the VPS10-d receptor family exert their effect on AD through modulation of APP trafficking. Reduced expression of SORCS3 may be a secondary effect of the disease, but it is consistent with the g-secretase assays, which indicated that suppression of SORCS3 activates Ab production. If correct, this would provide a potential explanation for how downregulation of SORCS3 might increase risk for AD. We are unable to see any difference in the expression of SORCS2 and SORT1. However, we cannot yet exclude the possibility that this was the result of the small sample size or chosen phenotype. In a previous study by Mufson et al., 38 SORT1 levels wereconsistent with our findings-not associated with clinical diagnosis or antemortem cognitive test scores. However, there was an association with severity of neuropathology by Braak and NIA-Reagan diagnoses.

APP-GV
The significant strengths of this study are the large sample size, allowing us to detect small effects and explore epistasis. Limitations include that the SNPs assessed were derived from available genome-wide arrays. Thus, they do not cover the complete genetic variation in these genes, and it is possible that there are additional disease-associated markers that have not been genotyped. It is also possible that we lacked the power to detect additional disease-associated markers or interactions of SNPs with lower allele frequencies or effect sizes.
Taken together, our results indicate that in addition to SORL1 and SORCS1, the variants in other members of the VPS 10-D receptor family (SORCS2, SORCS3 and SORT1) are associated with AD either independently or through epistatic mechanisms.