Genetic Testing: The Key to Truly Personalized Medicine

by Matthew Pratt-Hyatt, PhD

Personalized medicine has been called the future of medicine since the inception of the Human Genome Project (HGP) in the early 1990s, a project set up by the US government to sequence the complete human genome. The HGP was completed in 2003.¹ This new wealth of knowledge allowed scientists to develop tests that sequence the 3 billion base pairs and the 20,000 to 25,000 genes in the human genome.² Over those 25,000 genes there are over 80 million variants in the human genome.³ These variations include single nucleotide polymorphisms (SNPs), as well as small deletions and insertions throughout the genome, and many of those variants play a significant role in patient health. The dream of personalized health care is to use genetic testing to understand a patient’s predisposition for developing different conditions, and then undergo molecular diagnostic tests to determine how the environment is interacting with these genes.

At the Great Plains Laboratory Inc., we have been primarily focused on looking at the second half of this equation: finding the root cause of patient symptoms in a wide variety of chronic disorders. We have developed tests that look at hundreds of different analytes and have worked with doctors to help them interpret how these data can be used to personalize treatment for patients. Even though traditional medicine has mostly followed the philosophy that one size fits most, functional medicine says that each person is unique and deserves unique care. That is why we have developed our new genetic test, GPL-SNP_1000, which now allows us to have a more complete picture of what contributes to a patient’s health status. In partnership with the genetic company Courtagen, we have developed what we think will be the next great tool for personalized medicine.

GPL-SNP₁₀₀₀ uses the most recent advance in sequencing technology, Next Generation Sequencing (NGS). NGS machines can monitor what nucleotide is added at each place during the DNA chain prolongation reaction. This principle has been labeled “sequencing-by-synthesis.” NGS allows for sequencing to move from about 1000 nucleotides long to about 1000 billion bases per run. This gives researchers the ability to perform a very in-depth sequence for one patient or sequence several dozen patients at a time using more pinpointed analysis.⁴

GPL-SNP₁₀₀₀ is a genetic screen that covers 1048 SNPs over 144 different genes. These genes are broken up into nine different groups: DNA methylation, mental health, drug metabolism/chemical detoxification, autism risk, oxalate metabolism, cholesterol metabolism, acetaminophen toxicity, and the transporter genes (see Figure 1).

The GPL-SNP₁₀₀₀ test report (see Figure 2) is programmed to only depict the SNPs that are mutated. We are including the gene symbol, the RS number (or reference SNP number), which indicates which SNP is mutated (so that you can look up new research on that mutation), a pathogenicity number (we look at all available research on each SNP and predict how severe a mutation at that SNP would be), genotype (what is the change in nucleotide), phenotype (whether the patient is heterozygous or homozygous [one of two mutated copies], and the disease(s) associated with that mutation (we have listed the most common conditions associated with every SNP in our assay). The report also has interpretations that are autogenerated for genes found to be mutated in the assay. One additional feature that our report has is hyperlinks to the references on PubMed used to make the interpretations. This allows both patients and health-care practitioners to review the literature about those particular mutations, without having to search the Internet for these articles.

We were very strategic about selecting the nine specific groups of genes and SNPs that our test evaluates. We talked to dozens of functional medicine professionals and asked them what groups of genes would help them the most in their practices. The top answer was the DNA methylation pathway, which was not surprising, because the most utilized genetic tests on the market are currently the MTHFR tests. The MTHFR pathway is a process by which carbons are added onto folic acid from amino acids and redistributed onto other compounds throughout the body. This process is responsible for the formation of methionine, S-adenosylmethionine (SAMe), and thymidylate monophosphate (dTMP). These compounds play critical roles in nucleotide synthesis, neurotransmitter function, detoxification, and numerous other processes.⁵ We believed that we could provide better coverage of these genes than previously done by other genetic tests. We knew that no other test had more than 35 SNPs in its assay for the MTHFR gene, so we redesigned our existing DNA Methylation Profile by increasing the number of SNPs from 32 to 105. One reason why this test is so popular is the very common occurrence of one of the more serious SNPs of the MTHFR gene, rs1801133 (C667T). This mutation has a mutant allele frequency of 39% for the heterozygous genotype and a 17% frequency for the homozygous mutant. It can decrease the enzyme’s functionality by 90%, causing patients to have an increased risk of developmental delay, mental retardation, vascular disease, and stroke.⁶

Our second most requested group of genes was those that correlate with mental health. Mutations to these genes can predispose patients to a variety of ailments, including depression, schizophrenia, anxiety, and bipolar disorder. We designed this group to include the 9 genes and 53 SNPs that are most commonly the cause of mental disorders. One of the more important genes in this group is the catechol-O-methyltransferase (COMT) gene. This enzyme is responsible for the degradation of catecholamines, which include dopamine, epinephrine, and norepinephrine. Mutations to COMT can lead to bipolar disorder, anxiety, obsessive compulsive disorder, and attention deficit/hyperactivity disorder. One of the more common mutations of COMT is the Val108Met mutation (rs4680), which can cause a heightened risk of developing anxiety.⁷

The next gene group we focus on is the group for drug metabolism/chemical detoxification. These enzymes include the cytochrome P450s, sulfur transferases, glutathione transferases, and the methyltransferases. The P450s are important for multiple molecular functions including drug metabolism, hormone production, toxicant detoxification, and more. The P450s are expressed throughout the body, but primarily in the liver. There are 57 different genes for the cytochrome P450 enzymes, however eight are responsible for most of the drug metabolism done by the body. The P450 enzymes are responsible for 75% of all drug metabolism.⁸ Mutations to P450s can cause changes in the rate of metabolism of some medications, causing decreased effectiveness and other dangerous complications. Some medications known to be affected by drug mutations include but are certainly not limited to warfarin, diazepam, antiarrhythmic drugs, antidepressants, and antipsychotics.^12,13 P450s that are known to have alleles in the population that dramatically affect drug metabolism include CYP2C9, CYP2C19, and CYP2D6.⁹ Besides the P450s, which are considered phase I detoxification, GPL-SNP₁₀₀₀ covers phase II detoxification enzymes that include glutathione S-transferse, Sulfotranferase 1a1, betaine-homocysteine methyltransferase 2, and UDP glucuronosyltransferease 1A1.

The next group of genes that we analyze tells parents if they or their children may have a mutation that is commonly found in autistic patients. It has been reported that the prevalence of autism has increased dramatically in the last two decades.¹⁰ We looked at many different studies to determine what mutations are more commonly found in autistic patients, but not found in the neurotypical, nonautistic public. Three large studies that were done using over 3000 participants were very useful in developing this panel.^11–13 We selected 252 SNPs that cover 33 genes that were found in these three studies. These genes cover many different pathways, including glucose metabolism, ion and calcium channels, DNA transcription regulation, and nervous system genes.

Next, we included a group of genes that are involved with oxalate metabolism. Oxalate and its acidic form, oxalic acid, are formed from diet, human metabolism, and yeast/fungal overgrowth. Oxalates are known to combine with calcium to form crystals that can cause kidney stones. These crystals may also form in the bones, joints, blood vessels, lungs, and even brain.¹⁴ The oxalate group from our test analyzes 32 SNPs that cover 5 different genes. One of these genes is alanine-glyoxylate aminotransferase (AGXT). Mutations to AGXT can lead to kidney stones and primary hyperoxaluria.¹⁵

In addition to these groups of genes, our new test also looks at genes for cholesterol metabolism, as well as transporters. Both of these pathways are important for the body to regulate itself properly. Cholesterol is important because it is critical for producing cellular membranes, hormones, and bile acids. There are numerous recent articles discussing the importance of these cholesterol-produced molecules that regulate sugar metabolism and our metabolic rate. Transporters are also necessary because they move large molecules and other chemicals into and out of the cell, which are not able to move across cellular membranes without assistance. Without transporters, cells are not able to attain the proper building blocks necessary for optimum functionality or dispose of toxic cellular waste.

Truly personalized medicine may not be a reality today; however, I believe the recent developments in genetic testing are the biggest leaps that we’ve made in a long time. GPL-SNP₁₀₀₀ helps health-care professionals know what problems their patients may have now or in the future due to genetic mutations, as well as what specific treatments may be beneficial. The Great Plains Laboratory Inc. offers cutting-edge diagnostic tools that help identify underlying causes of many chronic conditions and provides recommendations for treatment based on test results. In addition to our new genetic test, we offer other comprehensive biomedical testing, including our organic acids test (OAT), IgG food allergy test, GPL-TOX (our toxic organic chemical profile), and many more. Utilizing a combination of our genetic and molecular diagnostics, we can now see a more complete picture of a patient’s overall health, both at present and potential problems for the future, which can all be addressed now. I truly believe that the sun is now rising on a new horizon of health.

Notes

1. Biello D, Harmon K. Tools for life. Sci Am. 2010;303:17–18.

2. Marian AJ. Sequencing your genome: what does it mean? Methodist Debakey Cardiovasc J. 2014;10(1):3–6.

3. McCarthy DJ, Humburg P, Kanapin A, et al. Choice of transcripts and software has a large effect on variant annotation. Genome Med. 2014;6(3):26.

4. Lin B, Wang J, Cheng Y. Recent patents and advances in the Next-Generation Sequencing Technologies. Recent Pat Biomed Eng. 2008;2008(1):60–67.

5. Wiemels JL, Smith RN, Taylor GM, et al. Methylenetetrahydrofolate reductase (MTHFR) polymorphisms and risk of molecularly defined subtypes of childhood acute leukemia. Proc Natl Acad Sci U S A. 2001;98(7):4004–4009.

6. Deloughery TG, Evans A, Sadeghi A, et al. Common mutation in methylenetetrahydrofolate reductase. Correlation with homocysteine metabolism and late-onset vascular disease. Circulation. 1996;94(12):3074–3078.

7. Craddock N, Owen MJ, O’Donovan MC. The catechol-O-methyl transferase (COMT) gene as a candidate for psychiatric phenotypes: evidence and lessons. Mol Psychiatry. 2006;11(5):446–458.

8. Guengerich FP. Mechanisms of drug toxicity and relevance to pharmaceutical development. Drug Metab Pharmacokinet. 2011;26(1):3–14.

9. Kalra BS. Cytochrome P450 enzyme isoforms and their therapeutic implications: an update. Indian J Med Sci. 2007;61(2):102–116.

10. Rutter M. Incidence of autism spectrum disorders: changes over time and their meaning. Acta Paediatr. 2005;94(1):2–15.

11. Sanders SJ, He X, Willsey AJ, et al. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron. 2015;87(6):1215–1233.

12. Iossifov I, O’Roak BJ, Sanders SJ, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515(7526):216–221.

13. De Rubeis S, He X, Goldberg AP, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515(7526):209–215.

14. Hall BM, Walsh JC, Horvath JS, Lytton DG. Peripheral neuropathy complicating primary hyperoxaluria. J Neurol Sci. 1976;29(2–4):343–349.

15. Poore RE, Hurst CH, Assimos DG, Holmes RP. Pathways of hepatic oxalate synthesis and their regulation. Am J Physiol. 1997;272(1 Pt 1):C289–294.

Figure 1:

GPL-SNP₁₀₀₀ Gene Groups and SNPs

Figure 2: GPL-SNP₁₀₀₀ Report