Year: 2025
Type: article
Abstract: Rare genetic variants play a pivotal role in shaping the genetic architecture of complex traits, yet their identification and characterization remain challenging. Traditionally, genome-wide association studies (GWAS) have focused on common variants, leaving a substantial portion of heritability unexplained—a phenomenon known as the ‘missing heritability problem’. Advances in next-generation sequencing have significantly improved our ability to detect low-frequency variants, shedding light on their contributions to disease susceptibility, evolutionary processes, and agriculturally significant traits. However, the statistical power of traditional GWAS models is often insufficient to capture the impact of rare variants on quantitative trait variability, necessitating the development of alternative approaches. We highlight the limitations of classical association models and evaluate emerging methodologies, including machine learning-driven frameworks, to enhance the detection of rare variants. Despite their importance, rare variants remain underrepresented in human and agricultural genetics due to analytical challenges and the lack of targeted methodologies. By leveraging whole-genome sequencing, population genetics insights, and advanced computational techniques, researchers can better harness the power of rare variants to improve both human health and crop resilience.
Source: in silico Plants
Authors: Talissa Floriani, Alexander E Lipka
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Genetics
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,297
Awards: G1957999595, G2926720356, G5620962805
OpenAlex ID: W4412688661