The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Functional genomics aims to link genetic variants to molecular phenotypes.
• Current methods are limited by the use of a single haploid reference genome.
• Understanding allele-specific gene regulation is crucial for personalized medicine.

Purpose of the Study:

• To introduce the EN-TEx resource, a comprehensive collection of functional genomics data.
• To develop methods for analyzing allele-specific gene activity in diploid genomes.
• To explore the impact of genetic variants on molecular phenotypes and disease associations.

Main Methods:

• Generation of 1,635 open-access datasets across diverse tissues and assays from four donors.
• Mapping datasets to matched diploid genomes using long-read phasing and structural variant identification.
• Development of a deep-learning transformer model to predict allele-specific activity from sequence context.

Main Results:

• Creation of a catalog of over 1 million allele-specific loci with coordinated haplotype activity.
• Identification of sequence motifs sensitive to variants that influence allele-specific gene expression.
• Demonstration of strong associations between allele-specific loci, GWAS loci, and eQTLs.
• Development of models for transferring eQTL information to under-profiled tissues.

Conclusions:

• EN-TEx provides a valuable resource for studying allele-specific gene regulation in diploid genomes.
• Predictive models based on sequence context can accurately capture allele-specific activity.
• The resource facilitates the integration of functional genomics data with genetic association studies for improved understanding of disease.
• EN-TEx enables more accurate personalized functional genomics and disease risk prediction.