MTHFR (methylenetetrahydrofolate reductase) is part of the folate pathway implicated in the autism spectrum, neurodevelopmental disorders, mood disorders, cervical dysplasia, cardiovascular disease, several types of cancer, and epigenetic modification of DNA.

Genomic research over the last 10 years has primarily focused on MTHFR, however, other genes encoding enzymes downstream from MTHFR in the transmethylation (methyl groups) and transsulfuration (methionine-homocysteine-L-glutathione) pathways have received little attention. This is changing, and so is the paradigm of using a single gene SNP to predict chronic disease risk. The monogenic approach, while is has been key in understanding gene function, has limited ability to predict disease risk.

Powered by millions dollars to uncover the origins of disease, research is shifting from a monogenic to polygenic approach. Single nucleotide polymorphisms (SNPs), in contrast to genetic mutations, have a weak effect on a person’s phenotype. These weak effects can be additive when multiple gene SNPs exist in a single biochemical pathway or are part of a biochemical system. Studies are now starting to look at how multiple gene SNPs interact within a system to cause dysfunction and disease.

Example 1: Mavaddat and his co-workers published a study that predicted the risk of breast cancer in women of all ages, profiling 77 common genetic variants linked to all breast cancer, ER+ breast cancer, and ER- breast cancer. Stratification of breast cancer risk without a family history versus those with a family history is extremely important when it comes to prevention and treatment options.

SERMs (selected estrogen receptor modulators) such as tamoxifen or raloxifene are often recommended in accordance with the NICE guidelines as primary breast cancer prevention for women at moderate to high risk of ER+ breast cancer. These risk assessments were based on risk of overall breast cancer for women with a family history of breast cancer.

As most women who develop breast cancer have no family history, this approach has significant limitations. Risk assessments incorporating ER+ polygenic risk scores may be an important tool to better define the subset(s) of women most likely to benefit from SERM treatment.

Example 2: A polygenic risk assessment would be advantageous would be for women considering hormone replacement therapy.

Women who are carriers for multiple gene SNPs associated with breast cancer and had a very high polygenic risk score could be guided away from HRT or at the very least counseled as to the risks of breast cancer versus benefits of this treatment in a more precise way.

Evaluating these 77 gene SNPs in younger women might facilitate better identification, detection, and emphasis on preventive lifestyle choices. The alternative—getting diagnosed with breast cancer without having the foreknowledge that could have led to earlier proactive, preventive dietary, and lifestyle interventions.

A similar model capturing the polygenic nature of the transmethylation and transulfuration pathways could provide risk scores for elevated homocysteine levels, cardiovascular disease, neural tube defects, deep vein thrombosis, and the myriad of diseases currently associated with poor folic acid metabolism.

When you stratify prevention and treatment strategies for each individual based on polygenic versus monogenic genomic testing, personalized health is more precise and individualized.

The next time you read a genomic test report, keep this in mind. Analyze genes both individually and as a group. There is more power evaluating many gene SNPs in a pathway versus one, such as in the folate pathway, glucose metabolism, or detoxification pathway. The potential health impact is likely to be greater.

Nutrigenomic interventions can differ for each of the gene SNPs downstream from the one gene SNP being evaluated.

A polygenic approach also means analyzing groups of genes that may be impacting different biochemical pathways that intersect to create disease. Weight management, cardiovascular disease, neurodegenerative diseases, mental health conditions, and even cancer risks are all examples of complex diseases that benefit from a polygenic approach.

Although ahead of the times, we believe that the advanced polygenic model that we are using will soon become the new standard for genomic medicine.

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