Context-dependent regulatory variants in Alzheimer's disease.

Ziheng Chen, Yaxuan Liu, Ashley R Brown, Heather H Sestili, Easwaran Ramamurthy, Xushen Xiong, Dmitry Prokopenko, BaDoi N Phan, Lahari Gadey, Peinan Hu, Li-Huei Tsai, Lars Bertram, Winston Hide, Rudolph E Tanzi, Manolis Kellis, Andreas R Pfenning
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Abstract

Noncoding genetic variants underlie many complex diseases, yet identifying and interpreting their functional impacts remains challenging. Late-onset Alzheimer's disease (LOAD), a polygenic neurodegenerative disorder, exemplifies this challenge. The disease is strongly associated with noncoding variation, including common variants enriched in microglial enhancers and rare variants that are hypothesized to influence neurodevelopment and synaptic plasticity. These variants often perturb regulatory sequences by disrupting transcription factor (TF) motifs or altering local TF interactions, thereby reshaping gene expression and chromatin accessibility. However, assessing their impact is complicated by the context-dependent functions of regulatory sequences, underscoring the need to systematically examine variant effects across diverse tissues, cell types, and cellular states. Here, we combined in vitro and in vivo massively parallel reporter assays (MPRAs) with interpretable machine-learning models to systematically characterize common and rare variants across myeloid and neural contexts. Parallel profiling of variants in four immune states in vitro and three mouse brain regions in vivo revealed that individual variants can differentially and even oppositely modulate regulatory function depending on cell-type and cell-state contexts. Common variants associated with LOAD tended to exert stronger effects in immune contexts, whereas rare variants showed more pronounced impacts in brain contexts. Interpretable sequence-to-function deep-learning models elucidated how genetic variation leads to cell-type-specific differences in regulatory activity, pinpointing both direct transcription-factor motif disruptions and subtler tuning of motif context. To probe the broader functional consequences of a locus prioritized by our reporter assays and models, we used CRISPR interference to silence an enhancer within the SEC63-OSTM1 locus that harbors four functional rare variants, revealing its gatekeeper role in inflammation and amyloidogenesis. These findings underscore the context-dependent nature of noncoding variant effects in LOAD and provide a generalizable framework for the mechanistic interpretation of risk alleles in complex diseases.

阿尔茨海默病的环境依赖性调节变异。
非编码遗传变异是许多复杂疾病的基础,但识别和解释其功能影响仍然具有挑战性。迟发性阿尔茨海默病(LOAD),一种多基因神经退行性疾病,就是这一挑战的例证。该疾病与非编码变异密切相关,包括富含小胶质增强子的常见变异和被认为影响神经发育和突触可塑性的罕见变异。这些变异通常通过破坏转录因子(TF)基序或改变局部TF相互作用来扰乱调控序列,从而重塑基因表达和染色质可及性。然而,由于调控序列的上下文依赖功能,评估它们的影响是复杂的,因此需要系统地检查不同组织、细胞类型和细胞状态的变异效应。在这里,我们将体外和体内大规模平行报告基因测定(MPRAs)与可解释的机器学习模型结合起来,系统地表征骨髓和神经环境中的常见和罕见变异。在体外的四种免疫状态和小鼠体内的三个大脑区域中,对变异的平行分析表明,个体变异可以根据细胞类型和细胞状态背景差异甚至相反地调节调节功能。与LOAD相关的常见变异往往在免疫环境中发挥更强的作用,而罕见变异在大脑环境中表现出更明显的影响。可解释的序列-功能深度学习模型阐明了遗传变异如何导致细胞类型特异性的调控活性差异,精确指出了直接转录因子基序破坏和基序上下文的微妙调整。为了探究我们的报告者分析和模型优先考虑的基因座的更广泛的功能后果,我们使用CRISPR干扰沉默SEC63-OSTM1基因座中的一个增强子,该基因座包含四个功能性罕见变异,揭示其在炎症和淀粉样蛋白形成中的守门人作用。这些发现强调了LOAD中非编码变异效应的上下文依赖性,并为复杂疾病中风险等位基因的机制解释提供了一个可推广的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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