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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Related Experiment Video

Updated: Feb 13, 2026

Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood
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AlphaGenome-enabled analysis of non-coding regulatory variants underlying RHD expression with wet-lab validation.

Miao Liu1, Ziyang Shen1, You Kyeong Jeong2

  • 1Department of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

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Artificial intelligence (AI) and base editing efficiently identify functional non-coding variants regulating RHD antigen expression. This AI-guided approach offers a scalable, cost-effective alternative for understanding blood group gene regulation and improving transfusion medicine.

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Area of Science:

  • Human genetics
  • Genomics
  • Bioinformatics

Background:

  • Identifying functional non-coding regulatory variants is crucial but challenging in human genetics.
  • Conventional methods like CRISPR screening and GWAS are costly and time-consuming.
  • The RHD antigen's expression is not fully explained by coding variants alone, necessitating investigation of non-coding regions.

Purpose of the Study:

  • To develop and validate an integrated AI-guided and experimental framework for identifying functional non-coding regulatory variants impacting RHD expression.
  • To demonstrate the utility of AI, specifically AlphaGenome, in prioritizing regulatory variants within the RHD locus.
  • To provide a scalable and cost-effective alternative to traditional screening methods for non-coding variant analysis.

Main Methods:

  • Applied AlphaGenome, a deep-learning model, to systematically analyze the RHD locus using multi-omics data.
  • Utilized in silico deletion and SNP perturbation analyses to predict variant effects on RHD expression.
  • Performed CRISPR-mediated base editing at prioritized non-coding SNP sites in K562 cells, followed by qPCR and flow cytometry for validation.

Main Results:

  • AlphaGenome prioritized regulatory regions in the promoter, 5'UTR, and intragenic regions of the RHD locus.
  • In silico analyses predicted suppressive effects of variants in promoter and intragenic regions.
  • Experimental validation showed strong concordance between AI predictions and observed RHD transcriptional and phenotypic outcomes, with targeted base editing of high-priority variants demonstrating significant functional effects.

Conclusions:

  • The combination of AI-guided variant prioritization and base editing is a powerful, scalable, and cost-effective strategy for identifying functional non-coding variants.
  • This framework has direct implications for genomics-based RHD typing and advancing transfusion medicine.
  • This study represents the first phenotypic validation of AlphaGenome predictions using wet-lab experiments.