Hui Wang1, David A Bennett2,3, Philip L De Jager4,5, Qing-Ye Zhang1, Hong-Yu Zhang6. 1. Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China. 2. Rush University Medical Center, Rush Alzheimer's Disease Center, Chicago, IL, USA. 3. Rush University Medical Center, Department of Neurological Sciences, Chicago, IL, USA. 4. Columbia University Medical Center, Center for Translational and Computational Neuroimmunology, New York, NY, USA. 5. Broad Institute, Cell Circuits Program, Cambridge, MA, USA. 6. Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China. zhy630@mail.hzau.edu.cn.
Abstract
BACKGROUND: Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD. METHODS: We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. RESULTS: We identified 2 candidate genetic interactions with PFDR < 0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR < 0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. CONCLUSIONS: In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.
BACKGROUND: Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD. METHODS: We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of ADpatients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. RESULTS: We identified 2 candidate genetic interactions with PFDR < 0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR < 0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. CONCLUSIONS: In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.
Entities:
Keywords:
Alzheimer’s disease; Association studies in genetics; Gene expression studies; Polygenic risk score
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