Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals.

Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging.

Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders.

We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

神经发育障碍 (NDD) 影响个体功能的多个方面，包括社交互动、沟通和行为。NDD 的潜在生物学机制尚未完全了解，药物治疗的有效性受到限制，部分原因是这些疾病的复杂性和个体症状的异质性。

识别与 NDD 相关的遗传位点有助于了解生物学机制，并有可能促进新疗法的开发。然而，这些复杂疾病的多基因性质使得从全基因组关联研究（GWAS）中确定新的治疗靶点具有挑战性。

大数据和高通量工具领域的最新进展为 NDD 的潜在生物学机制提供了全新的见解。本文回顾了各种大数据方法，包括深度学习等经典技术和最新技术，这些技术可以从 GWAS 和其他组学数据中识别潜在的治疗目标，特别强调 NDD。我们还强调可解释的因果机器学习 (ML) 方法越来越重要，这些方法可以帮助识别基因、分子途径和更复杂的生物过程，这些过程可能是未来干预这些疾病的目标。

我们的结论是，遗传学和机器学习方面的这些新进展有望增进我们对 NDD 的理解并确定新的治疗靶点。