Summary: Researchers are leading efforts to understand the human genome at an individual level, identifying specific genetic mutations that lead to illness based on a patient’s own genome.
Life on Earth comes in a beautiful assortment of shapes, sizes and colors, thanks to genetic mutations. Some mutations are beneficial, some are dangerous, and some do nothing.
Each person has about 4.5 million genetic variations. But are these forms helpful or harmful? Geneticists have been trying to find the answer for half a century. Their biggest obstacle these days? Standard Human Genome Sequence Reference Data.
The original human genome sequence is a combination of 13 individual donors, with little to no ethnic variation among them. More individual genome sequencing is needed to understand which mutations cause disease in a single individual. Professor Thomas Gingeras of Cold Spring Harbor Laboratory and Professor Mark Gerstein of Yale University are leading an international, multi-institutional effort to address this need.
Gingeras said, “It’s very clear, for a long time, the ideal would be to get everyone’s genome sequenced and analyze the cause-and-effect variations (on) as a basis for diagnosing diseases and treating them. That’s where medicine is going. And to provide a paradigm for doing that.” an attempt.”
They have now sequenced the genomes of four people and tracked the mutations in each of them, along with their genetic consequences.
The team has created the world’s largest catalog of genetic mutations called allele-specific variants. Using this catalog—EN-TEx—they developed an algorithm to predict how variants affect tissues and an individual’s risk for developing certain diseases. Catalogs and algorithms provide an unprecedented tool for personalized medicine. The work is published in the journal Cell.
“We mapped more than 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, may be particularly sensitive to these genetic variants. Overall, EN-TEx provides rich data and models for more accurate personal genomics.”
For scientists, one of the key features of this new approach is the ability to study the effects of genetic mutations in tissues that are difficult to obtain without surgery. For example, if someone has a heart or brain condition, genomic analysis of those tissues will be challenging unless there is a clinical need to operate. But with this new method, a person’s blood can be analyzed using it as a “surrogate”.
Gingeras hopes his work will bring us one step closer to personalized medicine. Collecting and mining thousands of genomic data points is a daunting task. Gingeras’ “blueprint” can make it much more manageable.
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“EN-TEx resource on multi-tissue individual epigenome and variant-effect models“By Mark Gerstein et al. cell
EN-TEx resource on multi-tissue individual epigenome and variant-effect models
- EN-TEx 4 includes 1,635 datasets mapped to individual genomes, ∼30 tissues × 15 assays
- An extensive catalog of allele-specific activity, sequencing regulatory elements
- Model of translocation of known eQTLs from solid to profile tissues (eg, skin → heart)
- Transformer models for predicting allelic activity based on local sequence context
Understanding how genetic variants affect molecular phenotypes is a key goal of functional genomics, currently hampered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays).
The datasets are matched to long-read phasing and structural variants, mapped to the diploid genome, generating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity across haplotypes and are less conserved than similar, non-allele-specific ones.
Surprisingly, a deep-learning transformer model can predict allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs that are particularly sensitive to morphology.
Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. This enables models to transfer known EQTLs to hard-to-profile tissues (eg, skin to heart).
Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.