Institute of Fundamental and Frontier Sciences at the University of Electronic Science and Technology of China, Chengdu, China.
College of Computer Science and Engineering, Northeast Forestry University, Harbin, China.
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa263.
Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of diseases, to develop new drugs, to advance precision medicine and to boost breeding. However, the potential of GWAS is still underexploited due to methodological limitations. Many challenges have emerged, including detecting epistasis and single-nucleotide polymorphisms (SNPs) with small effects and distinguishing causal variants from other SNPs associated through linkage disequilibrium. These issues have motivated advancements in GWAS analyses in two contrasting cultures-statistical modelling and machine learning. In this review, we systematically present the basic concepts and the benefits and limitations in both methods. We further discuss recent efforts to mitigate their weaknesses. Additionally, we summarize the state-of-the-art tools for detecting the missed signals, ultrarare mutations and gene-gene interactions and for prioritizing SNPs. Our work can offer both theoretical and practical guidelines for performing GWAS analyses and for developing further new robust methods to fully exploit the potential of GWAS.
在过去的十年中，全基因组关联研究（GWAS）发现了数千个与复杂人类疾病和农业重要性状相关的遗传变异。这些发现被用于剖析疾病的生物学基础，开发新药，推进精准医学，以及促进育种。然而，由于方法学的限制，GWAS 的潜力仍未得到充分利用。出现了许多挑战，包括检测上位性和具有小效应的单核苷酸多态性（SNPs），以及区分因果变异与通过连锁不平衡相关的其他 SNPs。这些问题促使 GWAS 分析在两种截然不同的文化中取得了进展——统计建模和机器学习。在这篇综述中，我们系统地介绍了这两种方法的基本概念、优势和局限性。我们进一步讨论了最近为减轻其弱点所做的努力。此外，我们总结了用于检测遗漏信号、超稀有突变和基因-基因相互作用以及优先考虑 SNPs 的最新工具。我们的工作可以为进行 GWAS 分析以及开发进一步的新稳健方法以充分利用 GWAS 的潜力提供理论和实践指导。
