Looking Beyond the Structure
The last few years have seen a revolution in the way that diagnosticians evaluate the genetic mechanisms that cause debilitating congenital abnormalities, from heart defects to intellectual disability. Whole genome sequencing (WGS) is just around the corner, and in about a third of cases it finds a strong candidate mutation, sometimes suggesting new treatment options, but otherwise bringing understanding to parents.
But what about all of the other cases?
A study from School of Biology Professor Greg Gibson’s group at the Georgia Institute of Technology, recently published in the American Journal of Human Genetics, argues that we should be looking not just at the structural parts of genes, but also the regulatory regions around them.
The paper, entitled “A Burden of Rare Variants Associated with Extremes of Gene Expression in Human Peripheral Blood,” demonstrates that there is a burden of rare genetic variants in these regions that associates with abnormal gene expression. It does not show that they cause birth defects, but does suggest that they need to be seriously considered as WGS technology develops.
Gibson explains it in the form of a metaphor about building a house.
“There are two critical components, the bricks and mortar, and the plans for where to put them,” says Gibson, a faculty member of the Petit Institute for Bioengineering and Bioscience. “If there is a defect in the glass or a crack in a piece of wood, then sooner or later the structure may fall apart. This is what current approaches focus on, the so-called protein coding-regions. But if the architect’s plans call for more windows than the beams can support, or the contractor doesn’t deliver enough concrete, then the consequences can be just as bad.”
We now know that a lot more of the genetic component related to differences in the way we look and behave (or what makes us susceptible to different diseases) is in the planning than the structural components. This insight is based on studies of common polymorphisms, namely the millions of genetic differences that we all share. The new study argues that it will also be true of rare genetic variants, including new mutations that are specific to a single person.
Graduate student Jing Zhao sequenced the regulatory regions of almost 500 genes from 500 participants in the Georgia Tech-Emory Predictive Health Institute study, and added up the number of rare mutations in people whose expression of those genes was toward the extreme. The result is what she calls a “smile plot,” because the curve has a high number at either end and low number in the middle. It means that the plans can be off in either direction, making too little or too much transcript for each gene.
“It is as if all the houses with crooked window frames are that way not because of the wood quality, but because each builder made different mistakes when putting the frames in,” Gibson says.
Furthermore, Gibson says, there seem to be specific subsets of genes where these events are more or less likely to happen. This is important, because it implies that we may be able to develop algorithms that identify the most likely places for regulation to go wrong, based on the evolutionary conservation of different parts of genes.
Projects such as President Obama’s precision medicine initiative aim to use genomics to help researchers decipher individual causes of disease. In the next few years, Gibson expects that much larger datasets of tens and eventually hundreds of thousands of people, in many different tissues, will appear.
“The challenges,” Gibson says, “are as much in the bioinformatics than the technology. “
In addition to Gibson and Zhao, also contributing to the published study were research scientist Dalia Arafat-Gulick (lab manager for the Gibson lab), T.J. Cradick (former director of the Protein Engineering Facility at Georgia Tech, now head of genome editing for CRISPR Therapeutics in Cambridge, Massachusetts), Cirian Lee (former postdoc at Georgia Tech, now at Rice University), Urko Marigorta (postdoc in the School of Biology), Gang Bao (former Georgia Tech professor, now at Rice University), Idowu Akinsanmi (former researcher in Bao’s lab at Georgia Tech) and Samridhi Banskota, an undergraduate student in the Gibson lab.
Read the study here.
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