August 21, 2020 -- The reason why some genetically predisposed individuals may or may not develop a disease is rooted in mutations throughout the genome, according to a new study published in Nature Communications on August 20. The researchers explained how this information can be used to improve disease risk estimations in the clinic.
Genetic variation can predispose someone to disease either by monogenic risk variants that disrupt a single physiologic pathway or polygenic risk that involve many variants with small impacts in different pathways.
Researchers at the Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Massachusetts General Hospital (MGH), and Harvard Medical School, in collaboration with IBM Research and health technology company Color, investigated whether disease risk from a monogenic variant that causes major disruption to a specific pathway can be meaningfully modified by polygenic risk factors that involve small perturbations to a wide range of cellular pathways.
Interplay of monogenic and polygenic risk
"The traditional approach is to focus on a single base pair mutation linked to disease, but there are 3 billion base pairs in the genome," said co-author Akl Fahed, PhD, cardiology fellow at MGH, and a postdoctoral fellow in the Broad's program in Medical and Population Genetics (MPG), in a statement. "So, we asked whether the rest of your genome can help explain the differing rates of disease we see in these patients, and the answer was a clear yes."
The study focused on three Tier 1 genomic conditions that are often associated with incomplete penetrance and variable expressivity and that might be explained by a polygenic background:
In the case studies, the researchers analyzed genetic and clinical information for more than 80,000 individuals and including 61,664 U.K. Biobank participants and 19,264 women tested for breast cancer high-risk variants by Color.
They looked for people with a particular high-risk variant, calculated their polygenic score for the disease (a number that summarizes the estimated effect of many genetic variants on an individual's phenotype), and then determined if the individual developed disease or not through their medical records.
"In trying to do these kinds of studies in the past, there were two main barriers," said senior author Dr. Amit V. Khera, scientist at the MGH Center for Genomic Medicine and associate director of the Broad MPG. "You needed very large datasets of participants with and without high-risk variants, and you needed high-quality polygenic scores calculated in these people to quantify their genetic background. The genetics community is only now beginning to have access to these key tools."
The team found that disease risk from monogenic risk variants can be substantially modified by polygenic background. As an example, they estimated an individual's risk of developing heart disease by the age of 75 and analyzed the impact of their monogenic variants and polygenic background, and computed risks as low as 17% in those with a high-risk variant but low polygenic scores.
But those with a high-risk variant and high polygenic score had a disease risk as high as 78%. In all three diseases, a favorable polygenic background lowered disease risk, bringing it closer to that of an average person without the high-risk variant.
"The changes in risk are striking," Khera noted. "For breast cancer, whether a woman's risk is 13% or 76% may be very important in terms of whether she chooses to get a mastectomy or undergo frequent screening via imaging. Also, for Lynch syndrome, a more precise risk estimate could similarly be a deciding factor for removing the colon entirely or frequent screening colonoscopies."
Bringing an updated view of risk to the clinic
This work provides a new scientific foundation for assessing disease risk by accounting for how likely polygenic background is to increase risk for individuals who inherit a monogenic risk variant. Beyond genetic factors, the researchers plan to build models accounting for additional nongenetic factors that are also associated with disease risk.
"We studied the interplay of monogenic and polygenic disease risk," Fahed said. "But genetics is only part of the story. For heart disease, risk involves other factors like blood pressure and lifestyle risks such as smoking. It is important to account for these as well and develop more fully integrated risk models."
As polygenic scores and disease risk models make their way into the clinic, the researchers said, these powerful clinical tools can empower patients to better understand, predict, and prevent disease using genetic information.
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