Genetic Architecture of Neurological Disorders and Their Endophenotypes: Insights from Genetic Association Studies.

Muralidharan Sargurupremraj
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Abstract

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.

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神经系统疾病的遗传结构及其内表型:遗传关联研究的启示
对复杂的神经系统疾病进行的人群规模遗传关联研究发现,潜在的遗传结构是多因素的。尽管研究样本量高达数百万,但已确定的疾病相关基因只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甚至能从基因型与表型关联未达到统计学意义的基因组区域中获得见解。这些研究证实了一个高度相互关联的分子网络,其中包括一组直接参与疾病过程的核心基因,以及一个扩大的外围网络,每个基因都有微小但潜在的重要(调节)作用。此外,利用基因工具的因果推断方法也揭示了风险因素与临床终点之间的潜在因果联系。然而,鉴于普遍存在的遗传重叠或多效性,在解释从这些分析中推断出的因果关系时需要谨慎。在本章中,我将介绍遗传关联模型,深入探讨遗传关联研究的现状,并讨论未来可能的研究方向。
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来源期刊
Current topics in behavioral neurosciences
Current topics in behavioral neurosciences Neuroscience-Behavioral Neuroscience
CiteScore
4.80
自引率
0.00%
发文量
103
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