Genetic heterogeneity of four MCI/AD neuroanatomical dimensions discovered via deep learning. (20th December 2022)

• Record Type: Journal Article
• Authors: Wen, Junhao; Cui, Yuhan; Yang, Zhijian; Bao, Jingxuan; Chen, Jiong; Erus, Guray; Abdulkadir, Ahmed; Mamourian, Elizabeth; Singh, Ashish; Yang, Shu; Fan, Yong; Saykin, Andrew J.; Thompson, Paul M; Jun, Gyungah R; Ritchie, Marylyn D.; Shen, Li; Wolk, David A.; Shou, Haochang; Nasrallah, Ilya M.; Davatzikos, Christos
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.065223
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Data‐driven unsupervised and semi‐supervised clustering methods have parsed neuroanatomical heterogeneity of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) into multidimensional neuroimaging representations (Vogel et al., 2021; Wen et al., 2021b, 2021a; Yang et al., 2021; Young et al., 2018; Zhang et al., 2016). However, whether this neuroanatomical heterogeneity is partly underpinned by genetic heterogeneity remains unanswered. Method: Whole‐genome sequencing (WGS) data were analyzed for individuals in the Alzheimer's Disease Neuroimaging Initiative. WGS data underwent a standard genetic pipeline. This resulted in 1, 487 participants (428 healthy controls, 489 MCI and 570 AD; age = 78.96 ± 7.72; 44.05% females) with 24, 773, 167 single nucleotide polymorphism (SNP). The deep learning Smile‐GAN model (Yang et al., 2021) generates expression scores (ES) across each of the four subtypes of MCI/AD (Fig. 2 ). The ES of each subtype were used as phenotypes in genome‐wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for age, sex, intracranial volume, disease diagnosis, and the first four genetic principle components using Plink (Purcell et al., 2007). We then performed clumping to define the independent significant variants (ISVs). Result: GWAS discovered 17 ISV‐ES pairwise associations. WGS data allowed us to identify four de no ISVs that were not included in dbSNP (Fig. 1 ). P1 and P4 detected similar AD‐related genetic variants (e.g., APOE, NECTIN2, and TOMM40, Fig. 1 and 2 ), but with opposite directions of effect, i.e., protective factors for P1 and risk factors for P4, consistent with respective patterns of neurodegeneration in these phenotypes. In particular, for rs429358 (APOE‐4), the P4 subtype had more C alleles (minor allele) than the P1 subtype (P‐value < 1e‐20). The four ISVs identified for P2 and P3 were not previously associated with any clinical trait. Conclusion: The current study identified genetic heterogeneity related to the expression of four MCI/AD subtypes previously defined via Smile‐GAN. Notably, our results confirmed previous findings that AD is genetically heterogeneous (Nacmias et al., 2018), identifying known genetic risk factors and several novel variants. Further research is needed to understand how these variants may affect AD pathophysiology and determine if any may provide a therapeutic target.