Determining the reason why people with similar genetic makeups can have different risk levels for disease could help scientists develop more accurate tools that clinical laboratories and pathology groups can use for diagnosis and prognosis
It’s been a big question for genetic scientists that now may have part of an answer. Why do individuals who carry identical gene mutations for a particular disease often experience different disease symptoms and severity? The question relates to variable penetrance and a new study suggests some reasons why this is often true.
Researchers at the New York Genome Center (NYGC) and the Columbia University Department of Systems Biology performed the study by examining the important implications of a genetic irregularity known as variable penetrance in human disease.
The researchers published their findings in the scientific journal Nature Genetics.
Disease Risk Determined by Combination of Coding and Regulatory Gene Variants
The phenomenon of variable penetrance refers to the severity of the effects of disease-causing variants and how they may differ among individuals who carry those genetic variants. Variable penetrance has proven to be a challenge when predicting the severity of a disease even when a strong genetic association is present.
The researchers developed a hypothesis for modified penetrance, where genetic variants that regulate gene activity can alter the disease risk caused by protein-coding gene variants. The study links modified penetrance to specific diseases at the genome level, which could help predict the severity of some diseases.
“Our findings suggest that a person’s disease risk is potentially determined by a combination of their regulatory and coding variants, and not just one or the other,” stated Tuuli Lappalainen, PhD, Group Leader at the New York Genome Center and Assistant Professor at Columbia University, in a news release. “Most previous studies have focused on either looking for coding variants or regulatory variants that affect disease in these individuals or potentially looking at common variants that could affect disease. We have merged these two fields into one clear hypothesis that uses data from both of them, which was fairly unheard of before.”
Tuuli Lappalainen, PhD (top photo left), and Stephane Castel, PhD (bottom photo left), of the New York Genome Center (NYGC) and Columbia University, co-led the new study. The hypothesis of the study is illustrated here with an example in which an individual is heterozygous for both a regulatory variant and a pathogenic coding variant. The two possible haplotype configurations would result in either decreased penetrance of the coding variant, if it was on the lower-expressed haplotype, or increased penetrance of the coding variant, if it was on the higher-expressed haplotype. (Image and caption copyrights: NYGC.)
The researchers first tested their modified penetrance hypothesis by analyzing data from the Genotype-Tissue Expression (GTEx) Project, a database created by the National Institutes of Health (NIH) to increase our understanding of how genes contribute to diseases. By evaluating the interactions of regulatory and coding variants in people without severe genetic disorders, they found an enrichment of haplotypes, a group of alleles of different genes on a single chromosome that are closely enough linked to be inherited.
Haplotypes protect against disease by decreasing the penetrance of coding variants associated with disease development. Because the researchers were looking at individuals without severe genetic diseases, the presence of enhanced haplotypes was expected.
The scientists then tested their hypothesis of modified penetrance in a disease-specific population of patients. They analyzed data from The Cancer Genome Atlas (TCGA), a database complied by the NIH, along with information from the Simons Simplex Collection (SSC).
The SSC is a project of the Simons Foundation Autism Research Initiative (SFARI) that has a permanent repository of genetic samples from 2,600 families, each of which has one child affected with autism spectrum disorder (ASD), and unaffected parents and siblings.
In both the cancer patients and individuals with ASD, the researchers discovered an enrichment of haplotypes forecasted to increase the penetrance of coding variants associated with the two disorders.
The team then designed an experiment using CRISPR/Cas9 genome editing technology to test their modified penetrance hypothesis. For this portion of the experiment, they chose a coding variant associated with Birt-Hogg-Dube´ Syndrome, a rare genetic disorder that can cause susceptibility to certain types of tumors.
By editing the single-nucleotide polymorphism (SNP) into a cell line on different haplotypes with a regulatory variant, they were able to prove that the regulatory variant did modify the effect of the coding disease-causing variant.
“Now that we have demonstrated a mechanism for modified penetrance, the long-term goal of the research is better prediction of whether an individual is going to have a disease using their genetic data by integrating the regulatory and coding variants,” said Lappalainen in the news release.
New Tools for More Precise Diagnosis/Prognosis
This discovery should help provide a framework for scientists to test disease SNPs to assess if they could be affected by modified penetrance, which could help medical professionals better predict an individual’s potential risk of disease development and severity.
“In the future, studies of the genetic causes of severe diseases should take into account this idea that regulatory variants need to be considered alongside coding variants,” said Stephane Castel, PhD, Senior Research Fellow at NYGC, in the news release. “This should eventually lead to a more fine-grained understanding of the risk of coding variants associated with disease.”
Of course, such test are years away from clinical use. However, the NYGC/Columbia University study highlights how much more precise diagnosis/prognosis could become with these types of tools.
Should further research validate these early insights, clinical laboratories could soon have new genetic tests that better predict and identify which health outcomes patients should expect based on their unique genetic makeup.