Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread polygenicity complicates biological understanding because no single variant can explain disease etiology. A strategy to advance biological insight is to seek convergent functions among the large set of variants and map them to a smaller set of disease-relevant genes and pathways. Accordingly, functional genomic resources that provide data on intermediate molecular phenotypes, such as gene-expression and methylation status, can be leveraged to functionally annotate variants and map them to genes. Such molecular quantitative trait locus mappings can be integrated with genome-wide association studies to make sense of the polygenic signal that underlies complex disease. Other resources that provide data on the 3-dimensional structure of chromatin and functional importance of specific genomic regions can be integrated similarly. In addition, mapped genes can then be tested for convergence in biological function, tissue, cell type, or developmental stage. In this review, we provide an overview of functional genomic resources and methods that can be used to interpret results from genome-wide association studies, and we discuss current challenges for biological understanding and future requirements to overcome them.

中文翻译：

---

用于神经精神多基因信号生物询问的新兴方法和资源

大多数神经精神疾病是高度多基因的，涉及跨越大部分基因组的成百上千的因果遗传变异。这种广泛的多基因性使生物学理解复杂化，因为没有单一的变异可以解释疾病的病因。提高生物学洞察力的一种策略是在大量变异中寻找收敛功能，并将它们映射到较小的一组疾病相关基因和通路。因此，可以利用提供中间分子表型数据（例如基因表达和甲基化状态）的功能基因组资源对变异进行功能注释并将它们映射到基因。这种分子数量性状基因座映射可以与全基因组关联研究相结合，以理解复杂疾病背后的多基因信号。其他提供染色质 3 维结构和特定基因组区域功能重要性数据的资源也可以类似地进行整合。此外，还可以测试映射基因在生物功能、组织、细胞类型或发育阶段的收敛性。在这篇综述中，我们概述了可用于解释全基因组关联研究结果的功能基因组资源和方法，并讨论了生物学理解的当前挑战和克服这些挑战的未来需求。