A team of international researchers developed the most extensive catalog of allele-specific variants, the EN-TEx resource. The project, led by Cold Spring Harbor Laboratory’s Thomas Gingeras and Yale University’s Mark Gerstein, employed a variety of techniques to characterize genetic variants from different tissue types of four individuals.
“We mapped over a million allele-specific variants in each of the four sequenced individuals,” said Gingeras. “Our findings indicate that parts of the genome, called cis-regulatory elements, can be particularly sensitive to these genetic variants. Overall, EN-TEx provides rich data and models for more accurate personal genomics.”
Previous studies only focused on using the general reference genome and didn’t directly apply the variations detected in the individual’s diploid sequence. This project sought to develop generalized models to understand how genetic variants impact molecular phenotypes.
Development of EN-TEx
The project began by sequencing the genomes of four individuals using Illumina’s short-read technologies, linked-reads from 10x Genomics, and long-read sequencing data from PacBio and Oxford Nanopore. From the generated sequences, they were able to assemble genomes differentiating the maternal and paternal haplotypes.
In addition, epigenome, transcriptome, and proteome assays were performed on different tissues obtained from the four individuals, including chromatin immunoprecipitation sequencing (ChIP-seq), assay for transposase-accessible chromatin sequencing (ATAC-seq), DNase sequencing (DNase-seq), methylation arrays, and short-read RNA-seq. The researchers coupled the assays with the genome assemblies to identify complete sets of structural variants (SVs). Reads mapped from the assays had the benefit of being applied directly to diploid genomes, giving the researchers a more accurate view of expression, variation, and epigenetic changes.
Combined with corresponding statistical and deep learning models, the team anticipates that this information will be able to assist other studies with annotating and elucidating tissue-specific variant effects in other individual genomes.
“With the high-quality genomes and the matched assays and tissues in EN-TEx, the catalog of allele-specific events can help ascertain variant impact in an extremely precise fashion because one has a 'natural control' in comparing the maternal and paternal haplotypes," said Gerstein explained, adding that the resource "lets us develop generalizable models for variant impact."
Key Findings
Using these specific models, the team discovered that they could highlight overrepresented regulatory elements at expression quantitative trait loci (eQTL) and loci identified through genome-wide association studies (GWAS), meaning they were able to pinpoint genomic regions where variants had more distinct regulatory impacts.
"Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription factor-binding motifs particularly sensitive to variants," explained the authors in the published report. "Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci."
The authors also believe that these models will provide an opportunity for translating the eQTL effects discovered from easily obtained tissue (e.g., blood) to more difficult tissues like organs. This offers an important expansion to the GTEx eQTL catalog, allowing researchers to connect uncharacterized variants to genes with known functions.
Tissues used for the current EN-TEx resource don’t include certain tissues like those from the brain. Additionally, the datasets are limited to individuals of European descent. However, Gerstein mentioned, "It would be powerful to extend the EN-TEx approach to additional individuals, allowing one to develop QTL-like studies, and then include individuals with different ancestries."
The authors concluded the study with these forward-looking statements. "We envision that in the near future, with the decreased cost of sequencing, generating a matched personal genome sequence as an accompaniment to each functional genomics experiment will become the norm. Thus, the EN-TEx personalized epigenomics approach for analyzing the impact of genome variation will necessarily become commonplace, potentially providing benefits for precision medicine.”
Read the full study here.
“We mapped over a million allele-specific variants in each of the four sequenced individuals,” said Gingeras. “Our findings indicate that parts of the genome, called cis-regulatory elements, can be particularly sensitive to these genetic variants. Overall, EN-TEx provides rich data and models for more accurate personal genomics.”
Previous studies only focused on using the general reference genome and didn’t directly apply the variations detected in the individual’s diploid sequence. This project sought to develop generalized models to understand how genetic variants impact molecular phenotypes.
Development of EN-TEx
The project began by sequencing the genomes of four individuals using Illumina’s short-read technologies, linked-reads from 10x Genomics, and long-read sequencing data from PacBio and Oxford Nanopore. From the generated sequences, they were able to assemble genomes differentiating the maternal and paternal haplotypes.
In addition, epigenome, transcriptome, and proteome assays were performed on different tissues obtained from the four individuals, including chromatin immunoprecipitation sequencing (ChIP-seq), assay for transposase-accessible chromatin sequencing (ATAC-seq), DNase sequencing (DNase-seq), methylation arrays, and short-read RNA-seq. The researchers coupled the assays with the genome assemblies to identify complete sets of structural variants (SVs). Reads mapped from the assays had the benefit of being applied directly to diploid genomes, giving the researchers a more accurate view of expression, variation, and epigenetic changes.
Combined with corresponding statistical and deep learning models, the team anticipates that this information will be able to assist other studies with annotating and elucidating tissue-specific variant effects in other individual genomes.
“With the high-quality genomes and the matched assays and tissues in EN-TEx, the catalog of allele-specific events can help ascertain variant impact in an extremely precise fashion because one has a 'natural control' in comparing the maternal and paternal haplotypes," said Gerstein explained, adding that the resource "lets us develop generalizable models for variant impact."
Key Findings
Using these specific models, the team discovered that they could highlight overrepresented regulatory elements at expression quantitative trait loci (eQTL) and loci identified through genome-wide association studies (GWAS), meaning they were able to pinpoint genomic regions where variants had more distinct regulatory impacts.
"Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription factor-binding motifs particularly sensitive to variants," explained the authors in the published report. "Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci."
The authors also believe that these models will provide an opportunity for translating the eQTL effects discovered from easily obtained tissue (e.g., blood) to more difficult tissues like organs. This offers an important expansion to the GTEx eQTL catalog, allowing researchers to connect uncharacterized variants to genes with known functions.
Tissues used for the current EN-TEx resource don’t include certain tissues like those from the brain. Additionally, the datasets are limited to individuals of European descent. However, Gerstein mentioned, "It would be powerful to extend the EN-TEx approach to additional individuals, allowing one to develop QTL-like studies, and then include individuals with different ancestries."
The authors concluded the study with these forward-looking statements. "We envision that in the near future, with the decreased cost of sequencing, generating a matched personal genome sequence as an accompaniment to each functional genomics experiment will become the norm. Thus, the EN-TEx personalized epigenomics approach for analyzing the impact of genome variation will necessarily become commonplace, potentially providing benefits for precision medicine.”
Read the full study here.