Integration of bioinformatics and imaging informatics for identifying rare PSEN1 variants in Alzheimer's disease

Patient with PSEN1 Glu318Gly and Other Possible Disease Risk Mutations, Diagnosed with Early Onset Alzheimer's Disease

In this manuscript, we introduced a French EOAD patient in Korea who carried the presenilin-1 (PSEN1) Glu318Gly mutations with four possible risk variants, including sortilin-related receptor 1 (SORL1) Glu270Lys, ATP-binding cassette subfamily A member 7 (ABCA7) Val1946Met, translocase of outer mitochondrial membrane 40 (TOMM40) Arg239Trp, and granulin (GRN) Ala505Gly. The patient started to present memory decline and behavioral dysfunction in his early 60s. His brain imaging presented amyloid deposits by positron emission tomography (PET-CT). The multimer detection system (MDS) screening test for plasma for amyloid oligomers was also positive, which supported the AD diagnosis. It was verified that PSEN1 Glu318Gly itself may not impact amyloid production. However, additional variants were found in other AD and non-AD risk genes, as follows: SORL1 Glu270Lys was suggested as a risk mutation for AD and could increase amyloid peptide production and impair endosome functions. ABCA7 Val1946Met was a novel variant that was predicted to be damaging. The GRN Ala505Gly was a variant with uncertain significance; however, it may reduce the granulin levels in the plasma of dementia patients. Pathway analysis revealed that PSEN1 Glu318Gly may work as a risk factor along with the SORL1 and ABCA7 variants since pathway analysis revealed that PSEN1 could directly interact with them through amyloid-related and lipid metabolism pathways. TOMM40 and PSEN1 could have common mechanisms through mitochondrial dysfunction. It may be possible that PSEN1 Glu318Gly and GRN Ala505Gly would impact disease by impairing immune-related pathways, including microglia and astrocyte development, or NFkB-related pathways. Taken together, the five risk factors may contribute to disease-related pathways, including amyloid and lipid metabolism, or impair immune mechanisms.

Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies

Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.

The associations of APP, PSEN1, and PSEN2 genes with Alzheimer's disease: A large case-control study in Chinese population

AIM

The associations of non-pathogenic variants of APP, PSEN1, and PSEN2 with Alzheimer's disease (AD) remain unclear. This study is aimed at determining the role of these variants in AD.

METHODS

Our study recruited 1154 AD patients and 2403 controls. APP, PSEN1, PSEN2, and APOE were sequenced using a targeted panel. Variants were classified into common or rare variants with the minor allele frequencies (MAF) cutoff of 0.01. Common variant (MAF≥0.01)-based association test was performed by PLINK 1.9, and gene-based (MAF <0.01) association analysis was conducted using Sequence Kernel Association Test-Optimal (SKAT-O test). Additionally, using PLINK 1.9, we performed AD endophenotypes association studies.

RESULTS

A common variant, PSEN2 rs11405, was suggestively associated with AD risk (p = 1.08 × 10^-2 ). The gene-based association analysis revealed that the APP gene exhibited a significant association with AD (p = 1.43 × 10^-2 ). In the AD endophenotypes association studies, APP rs459543 was nominally correlated with CSF Aβ42 level (p = 7.91 × 10^-3 ).

CONCLUSION

Our study indicated that non-pathogenic variants in PSEN2 and APP may be involved in AD pathogenesis in the Chinese population.

Whole-Exome Sequencing and C9orf72 Analysis in Primary Progressive Aphasia

Primary progressive aphasia (PPA) is mainly considered a sporadic disease and few studies have systematically analyzed its genetic basis. We here report the analyses of C9orf72 genotyping and whole-exome sequencing data in a consecutive and well-characterized cohort of 50 patients with PPA. We identified three pathogenic GRN variants, one of them unreported, and two cases with C9orf72 expansions. In addition, one likely pathogenic variant was found in the SQSTM1 gene. Overall, we found 12%of patients carrying pathogenic or likely pathogenic variants. These results support the genetic role in the pathophysiology of a proportion of patients with PPA.

Whole-genome sequencing reveals new Alzheimer's disease-associated rare variants in loci related to synaptic function and neuronal development

Introduction

Genome‐wide association studies have led to numerous genetic loci associated with Alzheimer's disease (AD). Whole‐genome sequencing (WGS) now permits genome‐wide analyses to identify rare variants contributing to AD risk.

Methods

We performed single‐variant and spatial clustering–based testing on rare variants (minor allele frequency [MAF] ≤1%) in a family‐based WGS‐based association study of 2247 subjects from 605 multiplex AD families, followed by replication in 1669 unrelated individuals.

Results

We identified 13 new AD candidate loci that yielded consistent rare‐variant signals in discovery and replication cohorts (4 from single‐variant, 9 from spatial‐clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2.

Discussion

Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD‐associated variants, which implicate innate immunity and amyloid processing. These loci have not been associated previously with AD, emphasizing the ability of WGS to identify AD‐associated rare variants, particularly outside of the exome.

A Bayesian linear mixed model for prediction of complex traits

MOTIVATION

Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance.

RESULTS

We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive.

AVAILABILITYAND IMPLEMENTATION

The R-package is available at https://github.com/yhai943/BLMM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Amyloid-β1-43 cerebrospinal fluid levels and the interpretation of APP, PSEN1 and PSEN2 mutations

Background

Alzheimer’s disease (AD) mutations in amyloid precursor protein (APP) and presenilins (PSENs) could potentially lead to the production of longer amyloidogenic Aβ peptides. Amongst these, Aβ_1–43 is more prone to aggregation and has higher toxic properties than the long-known Aβ_1–42. However, a direct effect on Aβ_1–43 in biomaterials of individuals carrying genetic mutations in the known AD genes is yet to be determined.

Methods

N = 1431 AD patients (n = 280 early-onset (EO) and n = 1151 late-onset (LO) AD) and 809 control individuals were genetically screened for APP and PSENs. For the first time, Aβ_1–43 levels were analysed in cerebrospinal fluid (CSF) of 38 individuals carrying pathogenic or unclear rare mutations or the common PSEN1 p.E318G variant and compared with Aβ_1–42 and Aβ_1–40 CSF levels.

The soluble sAPPα and sAPPβ species were also measured for the first time in mutation carriers.

Results

A known pathogenic mutation was identified in 5.7% of EOAD patients (4.6% PSEN1, 1.07% APP) and in 0.3% of LOAD patients. Furthermore, 12 known variants with unclear pathogenicity and 11 novel were identified. Pathogenic and unclear mutation carriers showed a significant reduction in CSF Aβ_1–43 levels compared to controls (p = 0.037; < 0.001). CSF Aβ_1–43 levels positively correlated with CSF Aβ_1–42 in both pathogenic and unclear carriers and controls (all p < 0.001). The p.E318G carriers showed reduced Aβ_1–43 levels (p < 0.001), though genetic association with AD was not detected. sAPPα and sAPPβ CSF levels were significantly reduced in the group of unclear (p = 0.006; 0.005) and p.E318G carriers (p = 0.004; 0.039), suggesting their possible involvement in AD. Finally, using Aβ_1–43 and Aβ_1–42 levels, we could re-classify as “likely pathogenic” 3 of the unclear mutations.

Conclusion

This is the first time that Aβ_1–43 levels were analysed in CSF of AD patients with genetic mutations in the AD causal genes. The observed reduction of Aβ_1–43 in APP and PSENs carriers highlights the pathogenic role of longer Aβ peptides in AD pathogenesis. Alterations in Aβ_1–43 could prove useful in understanding the pathogenicity of unclear APP and PSENs variants, a critical step towards a more efficient genetic counselling.

Loss-of-Function CREB3L3 Variants in Patients With Severe Hypertriglyceridemia

Objective:

Genetic determinants of severe hypertriglyceridemia include both common variants with small effects (assessed using polygenic risk scores) plus heterozygous and homozygous rare variants in canonical genes directly affecting triglyceride metabolism. Here, we broadened our scope to detect associations with rare loss-of-function variants in genes affecting noncanonical pathways, including those known to affect triglyceride metabolism indirectly.

Approach and Results:

From targeted next-generation sequencing of 69 metabolism-related genes in 265 patients of European descent with severe hypertriglyceridemia (≥10 mmol/L or ≥885 mg/dL) and 477 normolipidemic controls, we focused on the association of rare heterozygous loss-of-function variants in individual genes. We observed that compared with controls, severe hypertriglyceridemia patients were 20.2× (95% CI, 1.11–366.1; P =0.03) more likely than controls to carry a rare loss-of-function variant in CREB3L3 , which encodes a transcription factor that regulates several target genes with roles in triglyceride metabolism.

Conclusions:

Our findings indicate that rare variants in a noncanonical gene for triglyceride metabolism, namely CREB3L3 , contribute significantly to severe hypertriglyceridemia. Secondary genes and pathways should be considered when evaluating the genetic architecture of this complex trait.

FAM222A encodes a protein which accumulates in plaques in Alzheimer's disease

Alzheimer's disease (AD) is characterized by amyloid plaques and progressive cerebral atrophy. Here, we report FAM222A as a putative brain atrophy susceptibility gene. Our cross-phenotype association analysis of imaging genetics indicates a potential link between FAM222A and AD-related regional brain atrophy. The protein encoded by FAM222A is predominantly expressed in the CNS and is increased in brains of patients with AD and in an AD mouse model. It accumulates within amyloid deposits, physically interacts with amyloid-β (Aβ) via its N-terminal Aβ binding domain, and facilitates Aβ aggregation. Intracerebroventricular infusion or forced expression of this protein exacerbates neuroinflammation and cognitive dysfunction in an AD mouse model whereas ablation of this protein suppresses the formation of amyloid deposits, neuroinflammation and cognitive deficits in the AD mouse model. Our data support the pathological relevance of protein encoded by FAM222A in AD.

Association of Rare Coding Mutations With Alzheimer Disease and Other Dementias Among Adults of European Ancestry

Importance

Some of the unexplained heritability of Alzheimer disease (AD) may be due to rare variants whose effects are not captured in genome-wide association studies because very large samples are needed to observe statistically significant associations.

Objective

To identify genetic variants associated with AD risk using a nonstatistical approach.

Design, Setting, and Participants

Genetic association study in which rare variants were identified by whole-exome sequencing in unrelated individuals of European ancestry from the Alzheimer's Disease Sequencing Project (ADSP). Data were analyzed between March 2017 and September 2018.

Main Outcomes and Measures

Minor alleles genome-wide and in 95 genes previously associated with AD, AD-related traits, or other dementias were tabulated and filtered for predicted functional impact and occurrence in participants with AD but not controls. Support for several findings was sought in a whole-exome sequencing data set comprising 19 affected relative pairs from Utah high-risk pedigrees and whole-genome sequencing data sets from the ADSP and Alzheimer's Disease Neuroimaging Initiative.

Results

Among 5617 participants with AD (3202 [57.0%] women; mean [SD] age, 76.4 [9.3] years) and 4594 controls (2719 [59.0%] women; mean [SD] age, 86.5 [4.5] years), a total of 24 variants with moderate or high functional impact from 19 genes were observed in 10 or more participants with AD but not in controls. These variants included a missense mutation (rs149307620 [p.A284T], n = 10) in NOTCH3, a gene in which coding mutations are associated with cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), that was also identified in 1 participant with AD and 1 participant with mild cognitive impairment in the whole genome sequencing data sets. Four participants with AD carried the TREM2 rs104894002 (p.Q33X) high-impact mutation that, in homozygous form, causes Nasu-Hakola disease, a rare disorder characterized by early-onset dementia and multifocal bone cysts, suggesting an intermediate inheritance model for the mutation. Compared with controls, participants with AD had a significantly higher burden of deleterious rare coding variants in dementia-associated genes (2314 vs 3354 cumulative variants, respectively; P = .006).

Conclusions and Relevance

Different mutations in the same gene or variable dose of a mutation may be associated with result in distinct dementias. These findings suggest that minor differences in the structure or amount of protein may be associated with in different clinical outcomes. Understanding these genotype-phenotype associations may provide further insight into the pathogenic nature of the mutations, as well as offer clues for developing new therapeutic targets.

The Challenge to Search for New Nervous System Disease Biomarker Candidates: the Opportunity to Use the Proteogenomics Approach

Alzheimer's disease, Parkinson's disease, prion diseases, schizophrenia, and multiple sclerosis are the most common nervous system diseases, affecting millions of people worldwide. The current scientific literature associates these pathological conditions to abnormal expression levels of certain proteins, which in turn improved the knowledge concerning normal and affected brains. However, there is no available cure or preventive therapy for any of these disorders. Proteogenomics is a recent approach defined as the data integration of both nucleotide high-throughput sequencing and protein mass spectrometry technologies. In the last years, proteogenomics studies in distinct diseases have emerged as a strategy for the identification of uncharacterized proteoforms, which are all the different protein forms derived from a single gene. For many of these diseases, at least one protein used as biomarker presents more than one proteoform, which fosters the analysis of publicly available data focusing proteoforms. Given this context, we describe the most important biomarkers for each neurodegenerative disease and how genomics, transcriptomics, and proteomics separately contributed to unveil them. Finally, we present a selection of proteogenomics studies in which the combination of nucleotide and proteome high-throughput data, from cell lines or brain tissue samples, is used to uncover proteoforms not previously described. We believe that this new approach may improve our knowledge about nervous system diseases and brain function and an opportunity to identify new biomarker candidates.

Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative

INTRODUCTION

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials.

METHODS

We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/).

RESULTS

(1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies.

DISCUSSION

ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.

Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that causes problems related to brain function. To some extent it is understood on a molecular level how AD arises, however there are a lack of biomarkers that can be used for early diagnosis. Two popular methods to identify AD-related biomarkers use genetics and neuroimaging. Genes and neuroimaging phenotypes have provided some insights as to the potential for AD biomarkers. While the field of imaging-genomics has identified genetic features associated with structural and functional neuroimaging phenotypes, it remains unclear how variants that affect splicing could be important for understanding the genetic etiology of AD.

METHODS

In this study, rare variants (minor allele frequency < 0.01) in splicing regulatory element (SRE) loci from whole genome sequencing (WGS) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, were used to identify genes that are associated with global brain cortical glucose metabolism in AD measured by FDG PET-scans. Gene-based associated analyses of rare variants were performed using the program BioBin and the optimal Sequence Kernel Association Test (SKAT-O).

RESULTS

The gene, EXOC3L4, was identified as significantly associated with global cortical glucose metabolism (FDR (false discovery rate) corrected p < 0.05) using SRE coding variants only. Three loci that may affect splicing within EXOC3L4 contribute to the association.

CONCLUSION

Based on sequence homology, EXOC3L4 is likely a part of the exocyst complex. Our results suggest the possibility that variants which affect proper splicing of EXOC3L4 via SREs may impact vesicle transport, giving rise to AD related phenotypes. Overall, by utilizing WGS and functional neuroimaging we have identified a gene significantly associated with an AD related endophenotype, potentially through a mechanism that involves splicing.

Shared pathological pathways of Alzheimer's disease with specific comorbidities: current perspectives and interventions

Alzheimer's disease (AD) belongs to one of the most multifactorial, complex and heterogeneous morbidity-leading disorders. Despite the extensive research in the field, AD pathogenesis is still at some extend obscure. Mechanisms linking AD with certain comorbidities, namely diabetes mellitus, obesity and dyslipidemia, are increasingly gaining importance, mainly because of their potential role in promoting AD development and exacerbation. Their exact cognitive impairment trajectories, however, remain to be fully elucidated. The current review aims to offer a clear and comprehensive description of the state-of-the-art approaches focused on generating in-depth knowledge regarding the overlapping pathology of AD and its concomitant ailments. Thorough understanding of associated alterations on a number of molecular, metabolic and hormonal pathways, will contribute to the further development of novel and integrated theranostics, as well as targeted interventions that may be beneficial for individuals with age-related cognitive decline.

Influence of low frequency PSEN1 variants on familial Alzheimer's disease risk in Brazil

About 30-70% of familial Alzheimer's disease (AD) cases are related to mutations in presenilin-1 gene (PSEN1). Although the role of mutations and common variants in AD had been extensively investigated, the contribution of rare or low frequency PSEN1 variants on AD risk remains unclear. In the current study, we performed a mutational screening of PSEN1 coding exons and flanking intronic sequences among 53 index cases with familial history of AD from Rio de Janeiro (Brazil). Two missense variants (rs63750592; rs17125721), one rare and a low frequency variant, and two intronic variants (rs3025786; rs165932) were identified. In silico tools were used to predict the functional impact of the variants, revealing no changes in protein functionality by exonic variants. Otherwise, all variants were predicted to alter splicing signals. Prediction results, together with previous reports, suggest a correlation between rs17125721 and AD. So, a subsequent case-control study to evaluate the role of rs1712572 on AD risk was performed in an additional sample of 120 AD sporadic cases and in 149 elderly healthy controls by TaqMan Genotyping Assay. Our data indicates a risk association for rs17125721 in familial AD cases (OR=6.0; IC95%=1.06-33.79; p=0.042). In addition, we tested the multiplicative interaction between allele ε4 of the apolipoprotein E (APOE) and rs17125721 and no statistical association was found. Taken together, our findings provide new insight about the genetic relevance of low frequency PSEN1 variants for familial AD development.

Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease

Background

Rapid advancement of next generation sequencing technologies such as whole genome sequencing (WGS) has facilitated the search for genetic factors that influence disease risk in the field of human genetics. To identify rare variants associated with human diseases or traits, an efficient genome-wide binning approach is needed. In this study we developed a novel biological knowledge-based binning approach for rare-variant association analysis and then applied the approach to structural neuroimaging endophenotypes related to late-onset Alzheimer’s disease (LOAD).

Methods

For rare-variant analysis, we used the knowledge-driven binning approach implemented in Bin-KAT, an automated tool, that provides 1) binning/collapsing methods for multi-level variant aggregation with a flexible, biologically informed binning strategy and 2) an option of performing unified collapsing and statistical rare variant analyses in one tool. A total of 750 non-Hispanic Caucasian participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort who had both WGS data and magnetic resonance imaging (MRI) scans were used in this study. Mean bilateral cortical thickness of the entorhinal cortex extracted from MRI scans was used as an AD-related neuroimaging endophenotype. SKAT was used for a genome-wide gene- and region-based association analysis of rare variants (MAF (minor allele frequency) < 0.05) and potential confounding factors (age, gender, years of education, intracranial volume (ICV) and MRI field strength) for entorhinal cortex thickness were used as covariates. Significant associations were determined using FDR adjustment for multiple comparisons.

Results

Our knowledge-driven binning approach identified 16 functional exonic rare variants in FANCC significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In addition, the approach identified 7 evolutionary conserved regions, which were mapped to FAF1, RFX7, LYPLAL1 and GOLGA3, significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In further analysis, the functional exonic rare variants in FANCC were also significantly associated with hippocampal volume and cerebrospinal fluid (CSF) Aβ_1–42 (p-value < 0.05).

Conclusions

Our novel binning approach identified rare variants in FANCC as well as 7 evolutionary conserved regions significantly associated with a LOAD-related neuroimaging endophenotype. FANCC (fanconi anemia complementation group C) has been shown to modulate TLR and p38 MAPK-dependent expression of IL-1β in macrophages. Our results warrant further investigation in a larger independent cohort and demonstrate that the biological knowledge-driven binning approach is a powerful strategy to identify rare variants associated with AD and other complex disease.