VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies: Applied to Alzheimer's disease

Abstract Background Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. Results In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. Conclusions After validating the performance of our method using simulation experiments, we further apply it to Alzheimer’s disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer’s disease.

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P1-262: Genetic Variants Influencing Human Longevity from Late-Onset Alzheimer's Disease (LOAD) Genome-Wide Association Studies (GWAS)

Alzheimer s & Dementia ◽

10.1016/j.jalz.2011.05.542 ◽

2011 ◽

Vol 7 ◽

pp. S195-S195

Author(s):

Hui Shi ◽

Christopher Medway ◽

Kristelle Brown ◽

Noor Kalsheker ◽

Alison Goate ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genetic Variants ◽

Late Onset ◽

Association Studies ◽

Genome Wide Association ◽

Human Longevity ◽

Genome Wide Association Studies ◽

Genome Wide

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Monocyte-specific changes in gene expression implicate LACTB2 and PLIN2 in Alzheimer’s disease

10.1101/2020.06.05.136275 ◽

2020 ◽

Author(s):

Janet C. Harwood ◽

Ganna Leonenko ◽

Rebecca Sims ◽

Valentina Escott-Price ◽

Julie Williams ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Association Studies ◽

Genetic Data ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Risk Genes ◽

Genome Wide ◽

Immune Pathways

AbstractMore than 50 genetic loci have been identified as being associated with Alzheimer’s disease (AD) from genome-wide association studies (GWAS) and many of these are involved in immune pathways and lipid metabolism. Therefore, we performed a transcriptome-wide association study (TWAS) of immune-relevant cells, to study the mis-regulation of genes implicated in AD. We used expression and genetic data from naive and induced CD14+ monocytes and two GWAS of AD to study genetically controlled gene expression in monocytes at different stages of differentiation and compared the results with those from TWAS of brain and blood. We identified nine genes with statistically independent TWAS signals, seven are known AD risk genes from GWAS: BIN1, PTK2B, SPI1, MS4A4A, MS4A6E, APOE and PVR and two, LACTB2 and PLIN2/ADRP, are novel candidate genes for AD. Three genes, SPI1, PLIN2 and LACTB2, are TWAS significant specifically in monocytes. LACTB2 is a mitochondrial endoribonuclease and PLIN2/ADRP associates with intracellular neutral lipid storage droplets (LSDs) which have been shown to play a role in the regulation of the immune response. Notably, LACTB2 and PLIN2 were not detected from GWAS alone.

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