Multiplexed Quantification of Variant Abundance in the Globin Gene Family: Integrating Saturation Mutagenesis with Cross-Paralog Prediction

Widespread genetic testing has expanded the identification of genetic variants, but functional characterization of these variants remains a significant challenge in personalized medicine. In this study, researchers have developed a modified Variant Abundance by Massively Parallel Sequencing (VAMP-seq) platform that integrates both experimental and computational methods to achieve high-resolution abundance profiling of protein variants.

The researchers employed a lentiviral integration system to systematically assess the impact of 2,696 amino acid substitutions in the {zeta}-globin (HBZ) gene in human cells, covering the entire variant spectrum with exceptional reproducibility. Representative variants exhibited strong agreement with orthogonal low-throughput validation assays.

Building upon the VAMP-seq data, the researchers constructed a deep learning framework to predict variant abundance across thalassemia-associated globin paralogs (HBA, HBB, and HBG1), which have been difficult to study experimentally. This hybrid approach combines targeted experimental profiling with AI-driven extrapolation to accelerate the interpretation of variants across protein family members.

By merging experimental and computational techniques, this novel platform represents a significant advancement in the functional characterization of genetic variants, paving the way for improved personalized medicine strategies.