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

Shannon L Risacher,Arthur W Toga,Kwangsik Nho,John Q Trojanowski,Clifford R Jack,Dokyoon Kim,Leslie M Shaw,Michael W Weiner,Sungeun Kim,Tatiana Foroud,Ronald C Petersen,Paul S Aisen,Andrew J Saykin,Emrin Horgusluoglu,Robert C Green

doi:10.1186/s12920-016-0190-9

Abstract

BackgroundPathogenic mutations in PSEN1 are known to cause familial early-onset Alzheimer’s disease (EOAD) but common variants in PSEN1 have not been found to strongly influence late-onset AD (LOAD). The association of rare variants in PSEN1 with LOAD-related endophenotypes has received little attention. In this study, we performed a rare variant association analysis of PSEN1 with quantitative biomarkers of LOAD using whole genome sequencing (WGS) by integrating bioinformatics and imaging informatics.MethodsA WGS data set (N = 815) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort was used in this analysis. 757 non-Hispanic Caucasian participants underwent WGS from a blood sample and high resolution T1-weighted structural MRI at baseline. An automated MRI analysis technique (FreeSurfer) was used to measure cortical thickness and volume of neuroanatomical structures. We assessed imaging and cerebrospinal fluid (CSF) biomarkers as LOAD-related quantitative endophenotypes. Single variant analyses were performed using PLINK and gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O).ResultsA total of 839 rare variants (MAF < 1/√(2 N) = 0.0257) were found within a region of ±10 kb from PSEN1. Among them, six exonic (three non-synonymous) variants were observed. A single variant association analysis showed that the PSEN1 p. E318G variant increases the risk of LOAD only in participants carrying APOE ε4 allele where individuals carrying the minor allele of this PSEN1 risk variant have lower CSF Aβ1–42 and higher CSF tau. A gene-based analysis resulted in a significant association of rare but not common (MAF ≥ 0.0257) PSEN1 variants with bilateral entorhinal cortical thickness.ConclusionsThis is the first study to show that PSEN1 rare variants collectively show a significant association with the brain atrophy in regions preferentially affected by LOAD, providing further support for a role of PSEN1 in LOAD. The PSEN1 p. E318G variant increases the risk of LOAD only in APOE ε4 carriers. Integrating bioinformatics with imaging informatics for identification of rare variants could help explain the missing heritability in LOAD.

Highlights

Pathogenic mutations in PSEN1 are known to cause familial early-onset Alzheimer’s disease (EOAD) but common variants in PSEN1 have not been found to strongly influence late-onset AD (LOAD)
The Alzheimer’s Disease Neuroimaging Initiative (ADNI)-1 participants were recruited from 59 sites across the U.S and Canada and include approximately 200 cognitively normal older individuals (healthy controls (HC)), 400 patients diagnosed with mild cognitive impairment (MCI), and 200 patients diagnosed with early probable AD aged 55–90 years
The APOE ε4 allele frequency was significantly higher in patients with AD

Summary

Introduction

Pathogenic mutations in PSEN1 are known to cause familial early-onset Alzheimer’s disease (EOAD) but common variants in PSEN1 have not been found to strongly influence late-onset AD (LOAD). We performed a rare variant association analysis of PSEN1 with quantitative biomarkers of LOAD using whole genome sequencing (WGS) by integrating bioinformatics and imaging informatics. With the heritability of LOAD estimated to be as high as 80 %, a better understanding of the genetic susceptibility factors of LOAD would advance strategies for early detection and treatment [1, 2]. Recent large-scale genome-wide association studies (GWAS) have identified and confirmed approximately twenty-one LOAD-associated genes in addition to APOE, whose ε4 allele is the best established and the most significant genetic risk factor [3]. Rapid advancement of generation sequencing technologies has facilitated the search for genetic susceptibility factors that influence disease risk and become a key technique for detecting pathogenic variants in human diseases [4]. Several sequencing-based association studies could identify functional risk variants with large effects on LOAD pathogenesis within TREM2, ABCA7, and PLD3 genes [7,8,9,10,11]

Methods

Results

Discussion

Conclusion