Decoding polygenic diseases: advances in noncoding variant prioritization and validation

• Published in: Trends in cell biology 2024-06, Vol.34 (6), p.465-483
• Main Authors: Chin, Iris M., Gardell, Zachary A., Corces, M. Ryan
• Format: Article
• Language: English
• Subjects: Animals; fine-mapping; functional genomics; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; GWAS; Humans; Multifactorial Inheritance - genetics; noncoding variant; variant effect prediction; variant prioritization
• ISSN: 0962-8924; 1879-3088; 1879-3088
• DOI: 10.1016/j.tcb.2024.03.005

Advances in analytical methods such as in machine learning have enabled rapid prioritization of noncoding variant effects.Emerging high-throughput screening methods such as massively parallel reporter assays and CRISPR-based approaches provide orthogonal interpretation of noncoding variant effects.Bona fide validation of noncoding variant effects remains challenging and necessitates observing an impact at the endogenous genomic locus. Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease. Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.