Literature DB >> 30009200

Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer's disease.

Neha S Raghavan1,2, Adam M Brickman1,2,3, Howard Andrews1,2,4, Jennifer J Manly1,2,3, Nicole Schupf1,2,3,5, Rafael Lantigua1,6, Charles J Wolock7, Sitharthan Kamalakaran7, Slave Petrovski7,8, Giuseppe Tosto1,2,3, Badri N Vardarajan1,2,3,9, David B Goldstein3,6,7, Richard Mayeux1,2,3,4,5.   

Abstract

OBJECTIVE: The genetic bases of Alzheimer's disease remain uncertain. An international effort to fully articulate genetic risks and protective factors is underway with the hope of identifying potential therapeutic targets and preventive strategies. The goal here was to identify and characterize the frequency and impact of rare and ultra-rare variants in Alzheimer's disease, using whole-exome sequencing in 20,197 individuals.
METHODS: We used a gene-based collapsing analysis of loss-of-function ultra-rare variants in a case-control study design with data from the Washington Heights-Inwood Columbia Aging Project, the Alzheimer's Disease Sequencing Project and unrelated individuals from the Institute of Genomic Medicine at Columbia University.
RESULTS: We identified 19 cases carrying extremely rare SORL1 loss-of-function variants among a collection of 6,965 cases and a single loss-of-function variant among 13,252 controls (P = 2.17 × 10-8; OR: 36.2 [95% CI: 5.8-1493.0]). Age-at-onset was 7 years earlier for patients with SORL1 qualifying variant compared with noncarriers. No other gene attained a study-wide level of statistical significance, but multiple top-ranked genes, including GRID2IP,WDR76 and GRN, were among candidates for follow-up studies.
INTERPRETATION: This study implicates ultra-rare, loss-of-function variants in SORL1 as a significant genetic risk factor for Alzheimer's disease and provides a comprehensive dataset comparing the burden of rare variation in nearly all human genes in Alzheimer's disease cases and controls. This is the first investigation to establish a genome-wide statistically significant association between multiple extremely rare loss-of-function variants in SORL1 and Alzheimer's disease in a large whole-exome study of unrelated cases and controls.

Entities:  

Year:  2018        PMID: 30009200      PMCID: PMC6043775          DOI: 10.1002/acn3.582

Source DB:  PubMed          Journal:  Ann Clin Transl Neurol        ISSN: 2328-9503            Impact factor:   4.511


Introduction

Alzheimer's disease (AD) is a highly prevalent disorder that dramatically increases in frequency with age, and has no effective treatment or means of prevention. While three causal genes, Amyloid Precursor Protein (APP), Presenilin 1 and 2 (PSEN1 and PSEN2), have been established for early‐onset AD (age of onset <65 years of age), the rest of the heritability is still unknown. Further, beyond Apolipoprotein E (APOE), which confers the greatest risk for late‐onset AD (age of onset ≥65 years of age), there remains a large gap in the understanding of its causes. Identifying genetic variants that increase risk or protect against AD is considered an international imperative because of the potential therapeutic targets that may be revealed. Recent technological advances in genome‐wide association studies and high throughput next‐generation sequencing may help to implicate variants in genes in specific molecular pathways relevant to AD. In this study, we used whole‐exome sequencing to investigate all protein‐coding genes in the genome focusing on ultra‐rare (allele frequency <0.01%) and putatively deleterious variants. Rare variants are hypothesized to contribute to disease,1, 2 and studies of complex traits in population genetic models indicate an inverse relationship between the odds ratio and effect size conferred by rare variants and low allele frequencies.3 Thus, we searched for large effects conferred by putatively causal ultra‐rare variants. Traditional single variant statistics can be underpowered because patients with similar clinical presentations possess distinct rare variants that inflict similar effects on the gene.4 Gene‐based collapsing analyses increase signal detection by aggregating individual qualifying variants within an a priori region (e.g., a gene), facilitating detection of genes associated with disease through a specific class of genetic variation (e.g., loss‐of‐function variants). In order to maximize the ability to detect ultra‐rare variants associated with AD, exome‐sequencing data of 20,197 cases and controls from the Washington Heights‐Inwood Community Aging Project (WHICAP), the Alzheimer's Disease Sequencing Project (ADSP) and unrelated controls from the Institute of Genomic Medicine were systematically combined and analyzed, using a collapsing method with proven prior success in identifying disease associated genes.5, 6

Methods

The three groups used in this study and their sequencing information are described below.

Washington Heights‐Inwood Community Aging Project

The WHICAP study consisted of a multiethnic cohort of 4,100 individuals followed over several years The cohort participants were nondemented initially, 65 years of age or older, and comprised of non‐Hispanic whites, African Americans, and Caribbean Hispanics from the Dominican Republic. During each assessment, participants received a neuropsychological test battery, medical interview, and were re‐consented for sharing of genetic information and autopsy. A consensus diagnosis was derived for each participant by experienced clinicians based on NINCDS‐ADRDA criteria for possible, probable, or definite AD, or moderate or high likelihood of neuropathological criteria of AD.7, 8 Every individual with whole‐exome sequencing has at least a baseline and one follow‐up assessment and examination, and for those who have died, the presence or absence of dementia was determined using a brief, validated telephone interview with participant informants: the Dementia Questionnaire (DQ)9 and the Telephone Interview of Cognitive Status (TICS).10 3,702 exome‐sequenced WHICAP individuals were designated with case or control status and included in this analysis. From the sequenced cohort, 27% died and <1% were lost at follow‐up.

Alzheimer's Disease Sequencing Project

The ADSP, developed by the National Institute on Aging (NIA) and National Human Genome Research Institute (NHGRI) includes a large case–control cohort of approximately 10,000 individuals.7 The recruitment of these individuals was in collaboration with the Alzheimer's Disease Genetics Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. The details and rationale for the case–control selection process have been previously described.7 All cases and controls were at least 60 years old and were chosen based on sex, age and APOE status: (1) controls were evaluated for their underlying risk for AD and for their likelihood of conversion to AD by age 85, based on age at last examination, sex, and APOE genotype, and those with the least risk for conversion to AD were selected, and (2) cases were evaluated for their underlying risk for AD based on age at onset, sex, and APOE genotype and those with a diagnosis least explained by these factors were selected.7 Cases were determined either because they met NINCDS‐ADRDA clinical criteria for AD, or postmortem findings met moderate or high likelihood of neuropathological criteria of AD.7, 8 Autopsy data was available for 28.7% of the cases and controls used in the analysis. Further, some cases were originally diagnosed clinically, subsequently died and had neuropathological findings available after postmortem examination. Cases had documented age at onset or age at death (for pathologically determined cases). Controls were free of dementia by direct, documented cognitive assessment or neuropathological results. The ADSP group consisted of European‐Americans and Caribbean Hispanics. All data were available for download for approved investigators at The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site website (https://www.niagads.org/adsp/content/home). As part of the ADSP, 116 non‐Hispanic white WHICAP controls and 34 cases previously sequenced were included here.

Additional controls

The Institute for Genomic Medicine (IGM) (Columbia University Medical Center, New York, NY) hosts an internal database of sequencing data collected from previously exome‐sequenced material. In this study, exome‐sequencing data from 6,395 IGM controls were utilized. All data used were previously consented for future control use from multiple studies of various phenotypes. The cohort was made up of 55.7% healthy controls and 46.3% with diseases not comorbid with AD (disease classifications shown in Table S1). Although the cohort of controls were not enriched for any neurological disorder or diseases with a known comorbidity with AD, presence or future possibility of AD could not be excluded based on the available clinical data. Age and APOE status were not available for these participants. The cohort comprised of 70% non‐Hispanic white individuals along with those of African American, Hispanic, Middle Eastern, Asian and unknown descent.

Sequencing, quality control and variant calling

Whole‐exome sequencing of the WHICAP cohort was performed at Columbia University. The additional controls were sequenced at Duke University and Columbia University. Whole‐exome sequencing of the ADSP cohort was performed at The Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas; The Broad Institute Sequencing Platform, The Eli & Edythe L. Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge Massachusetts and Washington University Genome Sequencing Center, Washington University School of Medicine, Saint Louis, Missouri. ADSP raw files in the sequencing read archive format were downloaded from the dbGAP database and decompressed to obtain FASTQ files. All data were reprocessed for a consistent alignment and variant calling pipeline consisting of the primary alignment and duplicate marking using the Dynamic Read Analysis for Genomics (DRAGEN) platform followed by variant calling according to best practices outlined in Genome Analysis Tool Kit (GATK v3.6). Briefly, aligned reads were processed for indel realignment followed by base quality recalibration and Haplotype calling to generate variant calls. Variant calls were then subject to Variant Quality Score Recalibratrion (VQSR), using the known single‐ nucleotide variants (SNVs) sites from HapMap v3.3, dbSNP, and the Omni chip array from the 1000 Genomes Project. SNVs were required to achieve a tranche of 99.9% and indels a tranche of 95%. Finally, read‐backed phasing was performed to determine phased SNVs and merge multinucleotide variants (MNVs) when appropriate. Variants were annotated using Clin‐Eff with Ensembl‐GRCh37.73 annotations. Quality thresholds were set based on previous work,5, 6 such that all resulting exome variants had a quality score of at least 50, quality by depth score of at least 2, genotype quality score of at least 20, read position rank sum of at least −3, mapping quality score of at least 40, mapping quality rank sum greater than −10, and a minimum coverage of at least 10. SNVs had a maximum Fisher's strand bias of 60, while indels had a maximum of 200. For heterozygous genotypes, the alternative allele ratio was required to be greater than or equal to 25% and variant from sequencing artifacts and exome variant server failures (http://evs.gs.washington.edu/EVS) were excluded. Quality control was performed on all sequencing data. Samples with <90% of the consensus coding sequence (CCDS) covered at 10X and samples with sex‐discordance between clinical and genetic data were excluded from the analysis. Cryptic relatedness testing was performed using KING, and second degree or closer (relatedness threshold of 0.0884 or greater) relatives were removed with preferential retention of cases over controls and subsequently samples with higher average read‐depth coverage. The consensus coding sequence11 (CCDS) annotated protein‐coding region for each gene (n = 18,834) was tabulated as either carrying or not carrying a qualifying variant for every individual. Qualifying variants were defined for a loss‐of‐function model: stop gain, frameshift, splice site acceptor, splice site donor, start lost, or exon deleted variants. A negative control analysis was performed defining qualifying variants as synonymous variants to detect potential biases in variant calling between the cases and controls separately for each of the top four genes. The minor allele frequency threshold was 0.01% internally and within African American, Latino, and Non‐Finnish European populations from the Exome Aggregation Consortium12 (ExAC release version 0.3.1). The allele frequency thresholds use a “leave‐one‐out” method for the combined test cohort of cases and controls such that the minor allele frequency of each variant was calculated using all individuals except for the index sample under investigation. Thus, the maximum instances of a single variant a gene in our sample of 20,197 was five. A dominant model was defined such that one or more qualifying variant(s) in a gene qualified the gene. An important aspect of the collapsing analysis methodology is the reduction of variant calling bias due to coverage differences between cases and controls. To ensure balanced sequencing coverage of evaluated sites between cases and controls, we imposed a statistical test of independence between the case/control status and coverage. For a given site, consider total number of cases, total number of controls and number of cases covered at 10X, number of controls covered at 10X. We model the number of covered cases X as a Binomial random variable:If case–control status and coverage status are independent, then we have the following: We can test for this independence by performing a two‐sided Binomial test on the number of covered samples at given site, x. In the collapsing analyses, a binomial test for coverage balance as described above was completed as an additional qualifying criterion. Any site which resulted in a nominal significance threshold of 0.05 was eliminated from further consideration. A Fisher's exact test on qualifying variants in cases and controls for each gene was performed and imbalances in cases and controls within a gene indicated a possible association with the case‐ascertained phenotype. Ultra‐rare variant analyses were conducted using Analysis Tools for Annotated Variants (ATAV), developed and maintained by the Institute for Genomic Medicine at Columbia University. Study‐wise significance was set to 0.05/18,834(# of genes tested) = 2.7 × 10−6. Fisher's Exact Test for the polygenic comparison of International Genetics of Alzheimer's Project (IGAP) loci13 and t‐test for age of onset‐analysis (presented as mean ± standard deviation) were conducted in R v.3.3.1.

Results

We analyzed the exomes of 6,965 individuals meeting with the diagnosis of AD and 13,232 controls (Table 1). Prior to analysis, 570 individuals (91 cases and 479 controls) were removed due to known or cryptic relatedness. For ultra‐rare variant analysis (MAF of 0.01% or lower), conventional population stratification has not been a strong confounder as it can be in common variant analyses; and these results did not significantly differ from meta‐analyses in population stratified data. All variants reported here were found in five or less individuals from the study, and most variants were found in only one person, increasing the confidence that population stratification was not an issue. An important distinction exists between the cases and controls in the ADSP and WHICAP datasets. In the ADSP dataset, the younger cases were preferentially chosen as part of the study design.7 The WHICAP individuals are part of a population‐based cohort followed longitudinally, and thus cases were older than controls.
Table 1

Characteristics of Study Cohort (n = 20,197)

AD CasesControls
WHICAPADSPWHICAPADSPExternal
N13715594233145066395
Combined696513,232
Age (mean ± SD)81.4 ± 6.275.4 ± 8.478.1 ± 6.886.07 ± 4.53N/A
Combined76.7 ± 8.583.4 ± 6.7N/A
Sex (%F)68.557.267.641.147.3
Combined59.445.2
APOE E4 (% Carrier)28.3742.4026.3015.14N/A
Combined39.6418.94N/A

Mean age and APOE E4 carrier % do not include the External controls; Age for cases indicates age at diagnosis, and for controls the age at last assessment or age when last known to be free of dementia.

Characteristics of Study Cohort (n = 20,197) Mean age and APOE E4 carrier % do not include the External controls; Age for cases indicates age at diagnosis, and for controls the age at last assessment or age when last known to be free of dementia. Of the 18,834 genes analyzed, 15,736 contained at least one qualifying variant. Genomic inflation for the analysis was very modest, λ = 1.04 (Fig. 1). Gene‐based, collapsing analyses for loss‐of‐function variants, with allele frequency <0.01% (within the study cohort, and separately within ExAC12) identified SORL1 to be enriched in cases compared to controls at an exome‐wide significance level of P = 2.17 × 10−8 (Table 2). We confirmed the results for SORL1 were not driven by a particular ethnicity by running individual association tests on non‐Hispanic Whites, Caribbean Hispanics, and African Americans as described above, separately and summarizing them in a sample weight meta‐analysis14 (SORL1 P = 2.45 × 10−8). Although no other gene attained the study‐wide level of statistical significance, GRID2IP (P = 2.98 × 10−4), WDR76 (P = 7.39 × 10−4) and GRN (P = 9.56 × 10−4) were highly‐ranked candidate genes that were case‐enriched for loss‐of‐function variants (Table 2). Extended results are found in Table S2. There were no significant differences in synonymous variation in these four genes (1.5% cases, 1.7% of controls; FET P = 0.25).
Figure 1

QQ Plot: Observed vs. expected P‐values. Lambda = 1.04173.

Table 2

Variant counts for the top four AD genes

Gene NameTotal VariantTotal SNVTotal IndelNo. of Cases w/ QVCase FrequencyNo. of Cntrls w/ QVControl FrequencyEnriched directionFet P
SORL117107190.002717.56E‐05Case2.17E‐08
GRID2IP1258110.001621.51E‐04Case2.98E‐04
WDR761037100.001421.51E‐04Case7.39E‐04
GRN1266110.001632.27E‐04Case9.56E‐04

QV, Qualifying variant; FET, Fisher's Exact Test.

QQ Plot: Observed vs. expected P‐values. Lambda = 1.04173. Variant counts for the top four AD genes QV, Qualifying variant; FET, Fisher's Exact Test. There were 19 cases with a loss‐of‐function qualifying variant in SORL1 (Table 3) among 6,965 cases (frequency = 0.27%) and one variant among 13,232 controls (frequency = 0.0076%). Given the rate of SORL1 loss‐of‐function qualifying variants found in our control sample (1/13,232; frequency = 0.0076%), we expected to identify only 0.5 loss‐of‐function variants by chance among our 6965 cases; however, we identified 19. The accompanying odds ratio for AD risk upon identifying a SORL1 loss‐of‐function qualifying variants as defined in this study was 36 [95% CI: 5.8–1493.0]. Targeted investigation into the single control indicated a diagnosis of mild cognitive impairment.15 The SORL1 loss‐of‐function variants were found across the non‐Hispanic white, Caribbean Hispanic, and African American cases. Six of the 19 cases were deceased with autopsy confirmation of the AD diagnosis.16
Table 3

SORL1 variants

Genomic PositionVariant typeVariant classCADD scoreProtein modificationExAC global frequencyCase/ControlSexEthnicityBraak stageAge at onset or last visit
11‐121367577snvSAV26.6NA0CaseFAANA77
11‐121367654snvSG37p.Arg279*0CaseFNHW672
11‐12142134322 23 snvSG39p.Arg744*0CaseMNHWNA65
11‐12142134322 23 snvSG39p.Arg744*0CaseFNHWNA67
11‐121426001IndelFVNAp.Asp850 fs0CaseFNHWNA60
11‐121428047snvSG41p.Arg866*0CaseMNHW665
11‐121430263IndelFVNAp.Ile983 fs0ctrlMAANA64
11‐121440980snvSDV27.6NA4.95E‐05CaseFCHNA80
11‐121456930snvSAV26.8NA0CaseMNHWNA69
11‐121456930snvSAV26.8NA0CaseMNHW662
11‐121461788IndelFVNAp.Cys1431 fs0CaseFNHWNA61
11‐12146648224 25 snvSDV28NA0CaseFNHW390+
11‐12146648224 25 snvSDV28NA0CaseFNHWNA90+
11‐121474911IndelFVNAp.Thr1511 fs0CaseMNHWNA60
11‐121474984snvSG35p.Cys1534*0CaseFNHWNA74
11‐12147756824 25 snvSG46p.Arg1655*0CaseMNHWNA69
11‐121477667snvSDV26.9NA0CaseFAANA68
11‐121485637IndelFVNAp.Asp1828fs0CaseMNHWNA75
11‐121491801IndelFVNAp.Lys1975fs0CaseMNHW661
11‐121500253IndelFVNAp.Met2211fs0CaseMNHW662

Those in bold have previously been identified as indicated by the reference.

SNV, Single‐nucelotide variant; Indel , Insertion or Deletion; CADD , Combined Annotation Dependent Depletion; FV, Frameshift Variant; SAV , Splice Acceptor Variant; SDV, Splice Donor Variant; SG, Stop Gained; AA , African American; CH , Carribean Hispanic; NHW , Non‐hispanic White.

SORL1 variants Those in bold have previously been identified as indicated by the reference. SNV, Single‐nucelotide variant; Indel , Insertion or Deletion; CADD , Combined Annotation Dependent Depletion; FV, Frameshift Variant; SAV , Splice Acceptor Variant; SDV, Splice Donor Variant; SG, Stop Gained; AA , African American; CH , Carribean Hispanic; NHW , Non‐hispanic White. Of relevance to loss‐of‐function variant case‐enrichment, SORL1 is known to be among the protein‐coding genes most significantly depleted of loss‐of‐function variants in the general population (LOF depletion FDR = 2 × 10−7) (Table 2). Of the 17 distinct SORL1 loss‐of‐function qualifying variants, only one (11:121440980, rs200504189) was found in the ExAC database.12 SORL1 was also significantly enriched for functional variants (nonsynonymous and predicted as possibly or probably damaging by PolyPhen‐2 HumVar17) (P = 9.79 × 10−7), 1.8% of cases had a qualifying functional variant compared to 1% controls. There was no difference in the frequency of APOE‐ε4 carriers among cases with qualifying variants in SORL1 compared to those without these variants (40.0% vs. 39.6%). Age‐at‐onset analyses revealed a 6.81 year difference between cases with a SORL1 qualifying variant versus noncarrying cases (AD carriers: 69.86 ± 9.37; AD noncarriers: 76.67 ± 8.53; t(6963), P = 4 × 10−4). Coverage for the 12 qualifying GRID2IP variants was lower in the sequencing performed in this project and in ExAC,12 reducing our confidence of the rare variant calling for this gene because it is likely not represented well by exome capture libraries. The median of mean read‐depth coverage of the GRID2IP variants was 21‐fold and at these exact same sites in ExAC,12 4‐fold. However, read‐depth coverage was higher in the genome aggregation database (gnomAD), with a median of mean read‐depth coverage of 21‐fold, and only two loss‐of‐function variants less than the 0.0001 allele frequency threshold. Two of the 11 cases were deceased with autopsy confirming the pathological diagnosis of AD.16 Coverage for WDR76 and GRN was excellent in this study and in ExAC.12 Three of the 10 individuals clinically diagnosed as AD with loss‐of‐function qualifying variants in WDR76 had undergone autopsy. One met postmortem criteria defined as high likelihood of Alzheimer's disease, a second met intermediate likelihood,16 however, the third had no distinctive pathology and no definitive diagnosis was derived. Two of the 11 individuals with GRN loss‐of‐function qualifying variants had autopsy data; one met criteria for AD and the other for frontotemporal lobar degeneration (FTLD).18 None of the GRN carriers carried variants in any of the other three top genes. We also investigated rare variants in loci that were associated with AD in the IGAP genome wide association study13 along with APP, PSEN1, PSEN2, and TREM2. (Table 4). Qualifying variants in SORL1 and ZCWPW1 (P = 0.02) were more frequent in cases than controls. Overall, there was a slight increase in the frequency of variants in cases compared with controls (Fisher's exact P = 0.002), but after the removal of SORL1, the association was no longer significant (Fisher's exact P = 0.11).
Table 4

Counts of ultra‐rare variant in previously identified or implicated AD genes

Gene NameCases w/ QVCases w/o QVControls w/ QVControls w/o QVFET P‐value
ABCA728693734131980.08
APOE069652132300.55
APP269632132300.61
BIN1169642132301.00
CASS4169641132311.00
CD2AP069656132260.10
CELF1169640132320.34
CLU169641132311.00
CR16695917132150.65
EPHA16695923132090.17
FERMT2069651132311.00
HLA‐DRB59695612132200.46
INPP5D169641132311.00
MEF2C169643132291.00
MS4A6A269637132250.72
NME811695411132210.18
PICALM169643132291.00
PSEN1269630132320.12
PSEN2269630132320.12
PTK2B6695910132220.80
SLC24A4169643132291.00
SORL11969461132312.17E‐08
TREM2469614132280.46
ZCWPW1969565132270.02
Total114685714913087

Qualifying loss‐of‐function variants per gene and combined across the 24 genes.

QV , Qualifying variant, FET , Fisher's exact test.

Counts of ultra‐rare variant in previously identified or implicated AD genes Qualifying loss‐of‐function variants per gene and combined across the 24 genes. QV , Qualifying variant, FET , Fisher's exact test.

Discussion

This study provides strong evidence that ultra‐rare, loss‐of‐function variants in SORL1 represent an important genetic risk factor for AD. This is the first investigation to establish a genome‐wide statistically significant association between ultra‐rare variants in SORL1 and AD in a large, unbiased whole‐exome study of unrelated early‐ and late‐onset cases and controls. SORL1 has previously been implicated in both familial and sporadic, early‐ and late‐onset Alzheimer's disease.19, 20, 21, 22, 23, 24, 25 Common variants in SORL1 were first genetically associated with AD in a candidate gene analysis using 29 common variants.24 Shortly thereafter, nine rare loss‐of‐function variants including nonsense, frameshift and splice site mutations were described in familial and sporadic early onset AD.19, 20 The SORL1 findings in early onset AD were replicated in larger European cohorts of patients.21 Using a targeted, candidate gene approach, SORL1 variants were found by us in familial and sporadic late‐onset AD among Caribbean Hispanics as well as patients with European ancestry with sporadic late‐onset AD.26 Our findings here indicated that cases who possess a SORL1 qualifying variant were on average younger at onset. Yet, only four of the cases with a SORL1 qualifying variant were diagnosed before the age of 65, implicating that the gene is involved in both early‐ and late‐onset AD. Holstege, et al.,23 reported that strongly damaging, but rare variants (ExAC12 MAF < 1 × 10−5) in SORL1 as defined by a Combined Annotation Dependent Depletion (CADD) score of <30, increased the risk of Alzheimer's disease by 12‐fold. The authors proposed that the presence of these variants should be considered in addition to risk variants in APOE, and causal variants in PSEN1, PSEN2 or APP for assessing risk in a clinical setting. Accordingly, only one of the SORL1 variants identified in our study was found in ExAC,12 and was very rare (11:121440980; ExAC AF = 4.95 × 10−5). Furthermore, half of the 10 variants with a CADD score available were over 30, and all were over 25. The depletion of loss‐of‐function variants in the ExAC database lends further evidence to the significance of the higher frequency of loss‐of‐function variants in our AD sample. SORL1, also known as SORLA and LR11, encodes a trafficking protein (sortilin‐related receptor, L(DLR class) A repeats containing protein) that binds the amyloid precursor protein (APP) redirecting it to a nonamyloidogenic pathway within the retromer complex. The major site for expression of SORL1 protein is in the brain especially within neurons and astrocytes. Aβ peptides are also directed to the lysosome by SORL1. Processing of APP requires endocytosis of molecules from the cell surface to endosomes whereby proteolytic breakdown to Aβ occurs. SORL1 acts as a sorting receptor for APP that recycles molecules from endosomes back to the trans‐Golgi network to decrease Aβ production. We found that in the absence of the SORL1 gene, APP was released into the late endosome where it underwent β‐secretase and γ‐secretase cleavage generating Aβ.24 Thus, the mechanisms by which mutations in SORL1 lead to neurodegeration in Alzheimer's disease relates to the disruption of its ability to bind APP. Qualifying variants in other genes were also more prevalent among patients with AD compared with healthy, nondemented controls. Variants in GRID2IP, WDR76 and GRN were four to five times more frequent in cases than in controls, though these genes have not yet achieved genome‐wide significance and thus further studies, including larger patient samples will help determine which contribute to AD risk. Glutamate receptor delta‐2 interacting protein (GRID2IP) is selectively expressed in the cerebellar Purkinje cell‐fiber synapses. The exact role for this gene is not fully understood, but it appears to be a postsynaptic scaffold protein that links to GRID2 with signaling molecules and the actin cytoskeleton.27 There is no known role for GRID2IP in AD despite the fact that mutations were found in two individuals with postmortem confirmed Alzheimer's disease. The gene has not been well represented in existing exome sequencing libraries and the resulting reduced coverage of this gene makes the findings more difficult to interpret. However, the variants driving the signal in our analyses are all well covered in our entire cohort, with more than 96% of samples achieving at least 10X coverage. WDR76 interacts with chromatin components and the cytosolic chaperonin containing TCP‐1 (CCT), allowing for the maintenance of cellular homeostasis by assisting in the identification of folded proteins. WDR76 has low expression in brain and relatively high expression in lymph nodes. Only one of the three individuals with postmortem data met “high likelihood criteria” for AD. GRN mutations in patients with clinically diagnosed AD have been previously reported in large families in the National Institute on Aging family‐based study (NIA‐AD)28 and among large, multiply affected families of Caribbean Hispanic ancestry.29 These loss‐of‐function mutations result in haploinsufficiency, premature stop codons or nonsense variants impairing the secretion or the structure of Progranulin, involved intracellular trafficking and lysosomal biogenesis, and function. Its role in AD is unclear and possibly coincidental.30 The phenotype of FTLD includes unique manifestations allowing it to be distinguished from AD. A family presumed to have Alzheimer's disease phenotypically with a GRN mutation (c.154delA) had FTLD with ubiquitin‐positive, tau‐negative, and lentiform neuronal intranuclear inclusions (‐U NII) with neuronal loss and gliosis, affecting the frontal and temporal lobes, and TDP43 inclusions.31 Only one of the six family members (Patient II:1) had mixed pathology meeting NIA‐Reagan criteria of high likelihood16 and coexisting FTLD‐U N11 with TDP43 inclusions. GRN mutations were also observed in a sporadic patient with postmortem evidence of Alzheimer's disease: NIA‐Reagan criteria of high likelihood16 and coexisting FTLD‐U N11 with TDP43 inclusions.32 Among the patients with GRN mutations in this study, one patient met criteria for definite Alzheimer's disease without coexisting FTLD, while another met pathological criteria for FTLD. The results here indicate that extremely rare, loss‐of‐function variants in SORL1 strongly affect the risk of sporadic AD. While qualifying variants were present in only 0.27% of patients, only a single variant was found among 13,232 controls, and the single control carrier upon a post hoc cognitive evaluation was identified to have a diagnosis of mild cognitive impairment. These results confirm and greatly extend those from sequencing studies in familial and sporadic early onset Alzheimer's disease,19, 20, 21 familial AD families24, 26, 33 and investigations within clinical settings. The resulting impact of the loss‐of‐function variants in SORL1 on recycling of the amyloid precursor protein and the amyloid β protein make this pathway an attractive target for the development of therapies. Beyond implicating SORL1 and highly suggestive candidate genes for AD, this study shows for the first time that the collapsing analysis methodology of ultrarare variants described here that has proven successful for a number of rare diseases also can securely implicate genes in a condition as common as AD.

Author Contributions

Study Design: NSR, CW, SK, SP, GT, BNV, DBG, and RM; Data Collection: AMB, HA, JJM, NS, RL, CW, SK, SP, GT, BNV, DBG, and RM; Data Analysis: NSR, CW, SK, SP, GT, BNV, DBG, and RM Writing and Editing: NSR, AMB, HA, JJM, NS, RL, CW, SK, SP, GT, BNV, DBG, and RM.

Conflict of Interest

SP is a paid employee of and holds stock in AstraZeneca. All other authors have no interests to declare. Table S1. Broad Phenotypes of the external controls in study. Click here for additional data file. Table S2. Genes in which LoF variants reach P < 0.05 in Fisher's Exact Test between cases and controls. Click here for additional data file.
  32 in total

1.  Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants.

Authors:  Ju-Hyun Park; Mitchell H Gail; Clarice R Weinberg; Raymond J Carroll; Charles C Chung; Zhaoming Wang; Stephen J Chanock; Joseph F Fraumeni; Nilanjan Chatterjee
Journal:  Proc Natl Acad Sci U S A       Date:  2011-10-14       Impact factor: 11.205

2.  The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease.

Authors:  Ekaterina Rogaeva; Yan Meng; Joseph H Lee; Yongjun Gu; Toshitaka Kawarai; Fanggeng Zou; Taiichi Katayama; Clinton T Baldwin; Rong Cheng; Hiroshi Hasegawa; Fusheng Chen; Nobuto Shibata; Kathryn L Lunetta; Raphaelle Pardossi-Piquard; Christopher Bohm; Yosuke Wakutani; L Adrienne Cupples; Karen T Cuenco; Robert C Green; Lorenzo Pinessi; Innocenzo Rainero; Sandro Sorbi; Amalia Bruni; Ranjan Duara; Robert P Friedland; Rivka Inzelberg; Wolfgang Hampe; Hideaki Bujo; You-Qiang Song; Olav M Andersen; Thomas E Willnow; Neill Graff-Radford; Ronald C Petersen; Dennis Dickson; Sandy D Der; Paul E Fraser; Gerold Schmitt-Ulms; Steven Younkin; Richard Mayeux; Lindsay A Farrer; Peter St George-Hyslop
Journal:  Nat Genet       Date:  2007-01-14       Impact factor: 38.330

3.  Contribution to Alzheimer's disease risk of rare variants in TREM2, SORL1, and ABCA7 in 1779 cases and 1273 controls.

Authors:  Céline Bellenguez; Camille Charbonnier; Benjamin Grenier-Boley; Olivier Quenez; Kilan Le Guennec; Gaël Nicolas; Ganesh Chauhan; David Wallon; Stéphane Rousseau; Anne Claire Richard; Anne Boland; Guillaume Bourque; Hans Markus Munter; Robert Olaso; Vincent Meyer; Adeline Rollin-Sillaire; Florence Pasquier; Luc Letenneur; Richard Redon; Jean-François Dartigues; Christophe Tzourio; Thierry Frebourg; Mark Lathrop; Jean-François Deleuze; Didier Hannequin; Emmanuelle Genin; Philippe Amouyel; Stéphanie Debette; Jean-Charles Lambert; Dominique Campion
Journal:  Neurobiol Aging       Date:  2017-07-14       Impact factor: 4.673

4.  Neuropathologic diagnostic and nosologic criteria for frontotemporal lobar degeneration: consensus of the Consortium for Frontotemporal Lobar Degeneration.

Authors:  Nigel J Cairns; Eileen H Bigio; Ian R A Mackenzie; Manuela Neumann; Virginia M-Y Lee; Kimmo J Hatanpaa; Charles L White; Julie A Schneider; Lea Tenenholz Grinberg; Glenda Halliday; Charles Duyckaerts; James S Lowe; Ida E Holm; Markus Tolnay; Koichi Okamoto; Hideaki Yokoo; Shigeo Murayama; John Woulfe; David G Munoz; Dennis W Dickson; Paul G Ince; John Q Trojanowski; David M A Mann
Journal:  Acta Neuropathol       Date:  2007-06-20       Impact factor: 17.088

Review 5.  Progranulin mutations as risk factors for Alzheimer disease.

Authors:  David C Perry; Manja Lehmann; Jennifer S Yokoyama; Anna Karydas; Jason Jiyong Lee; Giovanni Coppola; Lea T Grinberg; Dan Geschwind; William W Seeley; Bruce L Miller; Howard Rosen; Gil Rabinovici
Journal:  JAMA Neurol       Date:  2013-06       Impact factor: 18.302

6.  The association between genetic variants in SORL1 and Alzheimer disease in an urban, multiethnic, community-based cohort.

Authors:  Joseph H Lee; Rong Cheng; Nicole Schupf; Jennifer Manly; Rafael Lantigua; Yaakov Stern; Ekaterina Rogaeva; Yosuke Wakutani; Lindsay Farrer; Peter St George-Hyslop; Richard Mayeux
Journal:  Arch Neurol       Date:  2007-04

7.  Coding mutations in SORL1 and Alzheimer disease.

Authors:  Badri N Vardarajan; Yalun Zhang; Joseph H Lee; Rong Cheng; Christopher Bohm; Mahdi Ghani; Christiane Reitz; Dolly Reyes-Dumeyer; Yufeng Shen; Ekaterina Rogaeva; Peter St George-Hyslop; Richard Mayeux
Journal:  Ann Neurol       Date:  2015-02       Impact factor: 10.422

8.  Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer's disease families.

Authors:  Carlos Cruchaga; Gabe Haller; Sumitra Chakraverty; Kevin Mayo; Francesco L M Vallania; Robi D Mitra; Kelley Faber; Jennifer Williamson; Tom Bird; Ramon Diaz-Arrastia; Tatiana M Foroud; Bradley F Boeve; Neill R Graff-Radford; Pamela St Jean; Michael Lawson; Margaret G Ehm; Richard Mayeux; Alison M Goate
Journal:  PLoS One       Date:  2012-02-01       Impact factor: 3.240

9.  Current status and new features of the Consensus Coding Sequence database.

Authors:  Catherine M Farrell; Nuala A O'Leary; Rachel A Harte; Jane E Loveland; Laurens G Wilming; Craig Wallin; Mark Diekhans; Daniel Barrell; Stephen M J Searle; Bronwen Aken; Susan M Hiatt; Adam Frankish; Marie-Marthe Suner; Bhanu Rajput; Charles A Steward; Garth R Brown; Ruth Bennett; Michael Murphy; Wendy Wu; Mike P Kay; Jennifer Hart; Jeena Rajan; Janet Weber; Catherine Snow; Lillian D Riddick; Toby Hunt; David Webb; Mark Thomas; Pamela Tamez; Sanjida H Rangwala; Kelly M McGarvey; Shashikant Pujar; Andrei Shkeda; Jonathan M Mudge; Jose M Gonzalez; James G R Gilbert; Stephen J Trevanion; Robert Baertsch; Jennifer L Harrow; Tim Hubbard; James M Ostell; David Haussler; Kim D Pruitt
Journal:  Nucleic Acids Res       Date:  2013-11-11       Impact factor: 16.971

10.  The UK10K project identifies rare variants in health and disease.

Authors:  Klaudia Walter; Josine L Min; Jie Huang; Lucy Crooks; Yasin Memari; Shane McCarthy; John R B Perry; ChangJiang Xu; Marta Futema; Daniel Lawson; Valentina Iotchkova; Stephan Schiffels; Audrey E Hendricks; Petr Danecek; Rui Li; James Floyd; Louise V Wain; Inês Barroso; Steve E Humphries; Matthew E Hurles; Eleftheria Zeggini; Jeffrey C Barrett; Vincent Plagnol; J Brent Richards; Celia M T Greenwood; Nicholas J Timpson; Richard Durbin; Nicole Soranzo
Journal:  Nature       Date:  2015-09-14       Impact factor: 49.962

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  36 in total

1.  Soluble SORLA Enhances Neurite Outgrowth and Regeneration through Activation of the EGF Receptor/ERK Signaling Axis.

Authors:  Jessica Stupack; Xiao-Peng Xiong; Lu-Lin Jiang; Tongmei Zhang; Lisa Zhou; Alex Campos; Barbara Ranscht; William Mobley; Elena B Pasquale; Huaxi Xu; Timothy Y Huang
Journal:  J Neurosci       Date:  2020-06-29       Impact factor: 6.167

2.  Biochemical Reduction of the Topology of the Diverse WDR76 Protein Interactome.

Authors:  Gerald Dayebgadoh; Mihaela E Sardiu; Laurence Florens; Michael P Washburn
Journal:  J Proteome Res       Date:  2019-08-09       Impact factor: 4.466

3.  Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data.

Authors:  Michael H Guo; Lacey Plummer; Yee-Ming Chan; Joel N Hirschhorn; Margaret F Lippincott
Journal:  Am J Hum Genet       Date:  2018-09-27       Impact factor: 11.025

4.  Rare and de novo variants in 827 congenital diaphragmatic hernia probands implicate LONP1 as candidate risk gene.

Authors:  Lu Qiao; Le Xu; Lan Yu; Julia Wynn; Rebecca Hernan; Xueya Zhou; Christiana Farkouh-Karoleski; Usha S Krishnan; Julie Khlevner; Aliva De; Annette Zygmunt; Timothy Crombleholme; Foong-Yen Lim; Howard Needelman; Robert A Cusick; George B Mychaliska; Brad W Warner; Amy J Wagner; Melissa E Danko; Dai Chung; Douglas Potoka; Przemyslaw Kosiński; David J McCulley; Mahmoud Elfiky; Kenneth Azarow; Elizabeth Fialkowski; David Schindel; Samuel Z Soffer; Jane B Lyon; Jill M Zalieckas; Badri N Vardarajan; Gudrun Aspelund; Vincent P Duron; Frances A High; Xin Sun; Patricia K Donahoe; Yufeng Shen; Wendy K Chung
Journal:  Am J Hum Genet       Date:  2021-09-20       Impact factor: 11.025

5.  Progranulin mutations in clinical and neuropathological Alzheimer's disease.

Authors:  Badri N Vardarajan; Dolly Reyes-Dumeyer; Angel L Piriz; Rafael A Lantigua; Martin Medrano; Diones Rivera; Ivonne Z Jiménez-Velázquez; Eden Martin; Margaret A Pericak-Vance; William Bush; Lindsay Farrer; Jonathan L Haines; Li-San Wang; Yuk Yee Leung; Gerard Schellenberg; Walter Kukull; Philip De Jager; David A Bennett; Julie A Schneider; Richard Mayeux
Journal:  Alzheimers Dement       Date:  2022-02-09       Impact factor: 16.655

Review 6.  Genomics of Alzheimer's disease implicates the innate and adaptive immune systems.

Authors:  Yihan Li; Simon M Laws; Luke A Miles; James S Wiley; Xin Huang; Colin L Masters; Ben J Gu
Journal:  Cell Mol Life Sci       Date:  2021-10-27       Impact factor: 9.207

Review 7.  Rare-variant collapsing analyses for complex traits: guidelines and applications.

Authors:  Gundula Povysil; Slavé Petrovski; Joseph Hostyk; Vimla Aggarwal; Andrew S Allen; David B Goldstein
Journal:  Nat Rev Genet       Date:  2019-10-11       Impact factor: 53.242

8.  Exome sequencing in obsessive-compulsive disorder reveals a burden of rare damaging coding variants.

Authors:  Gerald Nestadt; David B Goldstein; Mathew Halvorsen; Jack Samuels; Ying Wang; Benjamin D Greenberg; Abby J Fyer; James T McCracken; Daniel A Geller; James A Knowles; Anthony W Zoghbi; Tess D Pottinger; Marco A Grados; Mark A Riddle; O Joseph Bienvenu; Paul S Nestadt; Janice Krasnow; Fernando S Goes; Brion Maher
Journal:  Nat Neurosci       Date:  2021-06-28       Impact factor: 24.884

Review 9.  Questions concerning the role of amyloid-β in the definition, aetiology and diagnosis of Alzheimer's disease.

Authors:  Gary P Morris; Ian A Clark; Bryce Vissel
Journal:  Acta Neuropathol       Date:  2018-10-22       Impact factor: 17.088

10.  SORL1 deficiency in human excitatory neurons causes APP-dependent defects in the endolysosome-autophagy network.

Authors:  Christy Hung; Eleanor Tuck; Victoria Stubbs; Sven J van der Lee; Cora Aalfs; Resie van Spaendonk; Philip Scheltens; John Hardy; Henne Holstege; Frederick J Livesey
Journal:  Cell Rep       Date:  2021-06-15       Impact factor: 9.423

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