Department of Biology, Stanford University, Stanford, USA.
Department of Pathology, Stanford University, Stanford, USA.
Hum Genet. 2020 Jan;139(1):95-102. doi: 10.1007/s00439-019-02044-2. Epub 2019 Jul 17.
A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.
人类遗传学的一个核心目标是鉴定影响多基因疾病风险的变异和基因。在过去的十年中，全基因组关联研究（GWAS）已经确定了数万个与各种疾病相关的遗传位点。由于大多数此类位点位于非编码区域内，并且存在许多连锁不平衡的候选变异，因此准确鉴定特定的因果变异和基因一直具有挑战性。为了帮助发现这些变异和基因，已经开发了各种统计和实验方法。这些方法通常从功能基因组学实验中借用信息，例如 ATAC-seq、ChIP-seq 和 RNA-seq，以注释功能变异并确定变异和基因之间的调控关系。虽然这些方法很强大，但是考虑到细胞类型和环境的多样性，选择与疾病相关的背景进行后续分析至关重要。在这篇综述中，我们讨论了使用来自特定细胞类型的功能基因组学数据确定多基因疾病风险变异因果机制的最新进展、挑战和最佳实践。
