System-Level Analysis of Alzheimer's Disease Prioritizes Candidate Genes for Neurodegeneration.

摘要: Alzheimer's disease (AD) is a debilitating neurodegenerative disorder. Since the advent of genome-wide association study (GWAS) we have come to understand much about genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies increased our ability prioritize weaker effect alleles, while recent development network-based functional prediction has provided mechanism by which can use machine learning reprioritize GWAS hits context relevant brain tissues like hippocampus amygdala. In parallel with these developments, groups Disease Neuroimaging Initiative (ADNI) compiled rich compendia patient data including genotype biomarker information, derived volume measures for structures this wanted identify AD-related atrophy two structures, are often critically impaired over course disease. To do developed combined score prioritization method uses cumulative distribution function gene's positional score, top that not only segregate status, but also hippocampal amygdalar atrophy. Our identified mix had previously been APOE, TOMM40, NECTIN2(PVRL2) several others genetic studies, play integral roles AD-effected pathways IQSEC1, PFN1, PAK2. findings support viability novel as prioritizing region- even cell-specific risk genes.