Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA.
Curr Top Behav Neurosci. 2024;68:109-128. doi: 10.1007/7854_2024_513.
Population-scale genetic association studies of complex neurologic diseases have identified the underlying genetic architecture as multifactorial. Despite the study sample sizes reaching the millions, the identified disease-related genes explain only a small fraction of the phenotypic variance. Notable advancements in statistical methods now enable researchers to gain insights even from genomic regions where genotype-phenotype associations do not reach statistical significance. Such studies confirm a highly interconnected molecular network comprising a core group of genes directly involved in the disease process, alongside an expanded peripheral network, each contributing a small but potentially important (modulatory) effect. Additionally, causal inference methods, utilizing genetic instruments, have shed light on putative causal links between risk factors and clinical endpoints. In light of the pervasive genetic overlap or pleiotropy, however, caution is warranted in interpreting causal relationships inferred from these analyses. In this chapter, I will introduce the genetic association model, provide insights into the current state of genetic association studies, and discuss potential future directions.
大规模的复杂神经系统疾病的遗传关联研究表明，其遗传结构是多因素的。尽管研究样本量已达到数百万，但所确定的与疾病相关的基因仅能解释表型变异的一小部分。现在，统计方法的显著进步使研究人员即使在基因型-表型关联未达到统计学意义的基因组区域也能获得深入的见解。此类研究证实了一个高度相互关联的分子网络，其中包括直接参与疾病过程的核心基因群，以及一个扩展的外围网络，每个网络都有一个小但潜在重要的（调节）作用。此外，利用遗传工具的因果推断方法揭示了风险因素与临床终点之间可能存在的因果关系。然而，鉴于普遍存在的遗传重叠或多效性，需要谨慎解释从这些分析中推断出的因果关系。在这一章中，我将介绍遗传关联模型，深入了解遗传关联研究的现状，并讨论潜在的未来方向。
