Tom Greenfield

  • Rhythmus – prescribing clarity

    Rhythmus is a new app that makes prescribing natural agents simpler than before when using the Opus23 database of natural agent effects on genes.

    Taking an overview of all SNPs for the client, the main Rhythmus screen shows a list of genes sorted by combined SNP effect power factor, with the color scheme of orange for overall downregulated and green for overall upregulated genes. With over 7,800 individual associations of genes and natural agents in the database, all based on and hyperlinked to PubMed published studies, Opus23 makes visualisation simple for the genes with the highest editor-defined power factor and with associated natural agents that affect the gene function.

    Rhythmus Prescribing App

    Taking the Agency app one step further, Rhythmus also includes the gene visualising ability of Powerspot to help the practitioner choose the best agents for the clientThe AI Pre fills the option of upregulating or downregulating the gene function and the effect required from the agents (although these can be changed manually if required). The numbers of agents associated with the gene function is also listed.

    Rhythmus can be used by checking some genes and other options, then click the Run Rhythmus button. The result is a network map of the genes in the query along with the natural agents that influence their function.

    Rhythmus network map

    The relative size of the agent indicates the confidence of the effect on the gene, and as with other network maps on Opus23, clicking on the gene or agent opens a popup for that element, from where it can be curated to the Client Report or the Protocol Report.

    The AI saves time in selecting the best options according to the available data, but this can alway be overridden by the practitioner, who may have other information about the client. Opus23 subscribes to the philosophy of ‘TMTOWTDI’ (There’s more than one way to do it, pronounced ‘Tim Toady’.) The program was designed with this idea in mind, in that it ‘doesn’t try to tell the practitioner how to parse the data.’ Rather, it presents many different frameworks and cross-sections of the available client data, using a myriad of infographic treatments. Therefore Rhythmus also offers the practitioner a user-defined alternative visualisation of up to five manually entered genes, again with an up- or downregulatory effect and the required effect size. The user-defined visualisation gives a Manhattan map of the combined effect of the possible agents, similar to one side of the Psychic app.

    Result of the user-defined selection in Rhythmus

    As before, clicking on the agent opens the agent popup, where it can be curated. Rhythmus takes Opus23 to another level of utility for data mining and analysing its huge database of PubMed-based gene-agent interactions.

  • Vitamin B12, Secretor Status and Ancestry Raw Data

    The Opus 23 genetic interpretation software [1] checks for ABH secretor status as part of the hereditary genetics algorithms. Knowing whether your client is a secretor or non-secretor is important for many reasons, one being that non-secretors have increased levels of serum vitamin B12. This is a well known association: at least five GWAS studies found SNPs in FUT2 show the strongest statistical association with circulating vitamin B12 [2-6]. Two of these studies also report a 10-25% increase in circulating total vitamin B12 concentration in homozygotes for the common non-secretor alleles, as determined by the FUT2 genotype of the nonsense stop-gain mutation W143X, rs601338. The mechanism for this is however unclear.

    Previously theories have included the influence of H. pylori infection, which has been associated with vitamin B12 deficiency. [7,8] Some authors have proposed that FUT2 genotype can influence the extent to which H. pylori attaches to gastric mucosa and influences vitamin B12 absorption. [9, 10] This was refuted by a subsequent study in 2012, which found that secretor status as determined by FUT2 variation correlates with plasma vitamin B12 concentrations, but is independent of H. pylori serotype. [11]

    Chery et. al. Proposed that FUT2 genotype could affect the glycosylation status of another vitamin B12 transporter, gastric intrinsic factor (GIF) [12]. This was a small study however, and although a potential effect was observed on GIF secretion and glycosylation according to FUT2 rs601338 genotype, the GIF phenotypes of the FUT2 rs601338 GA heterozygotes more closely aligned with those of the non-secretor genotype (AA) than those with the secretor genotype (GG). It is currently unclear to what extent FUT2 genotype influences GIF secretion and thereby alters vitamin B12 concentration in the general population.

    It is important to note that all the above mentioned studies measured only total circulating vitamin B12, which does not distinguish the proportion of B12 bound to its two separate carrier proteins, transcobalamin and haptocorrin. Haptocorrin, also known as transcobalamin-1 (TCN1), is a glycoprotein produced by the salivary glands of the oral cavity in response to ingestion of food. This protein binds strongly to vitamin B12 in the mouth to protect it from the acidic environment of the stomach. Haptocorrin also circulates and binds approximately 80% of circulating B12, rendering it unavailable for cellular delivery by transcobalamin II. These carrier proteins carry significantly different quantities of vitamin B12 in blood, and have different biological properties: transcobalamin II delivers vitamin B12 to all tissues, while vitamin B12 carried by haptocorrin is ultimately returned to the gut. In a recent paper on a GWAS study by Velkova et. al. using The Trinity Student Study population of 2,524 subjects in Ireland, the authors hypothesized that the expression of functional FUT2 enzyme could influence total circulating vitamin B12 concentration by altering the glycosylation of haptocorrin. This is the first study to assess the relationship between ‘active’ B12, total B12 and the FUT2 secretor status variant. [13]

    The authors reported that FUT2 genotype influences the concentration of haptocorrin-bound vitamin B12 to a far greater extent than transcobalamin-bound vitamin B12. This is consistent with FUT2 exerting influence via its fucosylation function, as haptocorrin is a glycosylated protein and transcobalamin is not. They also suggest that FUT2 activity impacts the intra-organismal recycling of vitamin B12, not the absorption and assimilation of the vitamin from the diet.

    In both the H. pylori and the GIF models described above, FUT2 genotype would alter the pool of vitamin B12 absorbed from the gut. As vitamin B12 transported from the gut binds to transcobalamin in plasma, these models are not consistent with the data from Velkova et. al., which shows that FUT2 genotype influences the concentration of haptocorrin-bound vitamin B12 to a far greater extent than transcobalamin-bound vitamin B12.

    The connection between secretor status and B12 levels is consistent with FUT2 exerting influence via its fucosylation function on B12 carriers, as haptocorrin is a glycosylated protein and transcobalamin is not. It also suggests that FUT2 activity impacts the intra-organismal recycling of vitamin B12, not the absorption and assimilation of the vitamin from the diet. This could be the reason why the standard test for vitamin B12 has significant false positive and false negative rates: only ~20% of circulating vitamin B12 (holoTC) represents the “active” bioavailable form, meaning that the most commonly ordered clinical test for vitamin B12 mainly measures the holoHC, which could mask an existing vitamin B12 deficiency. When evaluating or confirming vitamin B12 deficiency, additional markers of vitamin B12-dependent enzyme activity such as methylmalonic acid (MMA) and total homocysteine are also problematic. FUT2 secretor status may therefore be useful when considering the overall B12 status of an individual, and non-secretors may appear to have falsely elevated serum total B12 when compared with active B12.

    Opus 23 handles a range of raw data files, however and Genos data files do not include the rs601338 SNP, which denotes the non-secretor mutation when homozygous. When only these data files are loaded Opus 23 looks for another SNP on FUT2 that is reported in and Genos raw data files, and which is in perfect linkage disequilibrium with rs601338. This will give you the client’s imputed secretor status, and therefore indications for interpreting serum vitamin B12 tests. Opus 23 also checks for another FUT2 non-secretor SNP found only in Asians and not in Caucasians when looking for secretor status.


    1. Opus 23 Pro genetic analysis and reporting software by Dr P. D’Adamo

    2 .Hazra, A., Kraft, P., Selhub, J., Giovannucci, E.L., Thomas, G., Hoover, R.N., Chanock, S.J. and Hunter, D.J. (2008) Common variants of FUT2 are associated with plasma vitamin B12 levels. Nat Genet, 40, 1160-1162. PMID 18776911.

    3. Lin, X., Lu, D., Gao, Y., Tao, S., Yang, X., Feng, J., Tan, A., Zhang, H., Hu, Y., Qin, X. et al. (2012) Genome-wide association study identifies novel loci associated with serum level of vitamin B12 in Chinese men. Hum Mol Genet, 21, 2610-2617. PMID 22367966.

    4. Tanaka, T., Scheet, P., Giusti, B., Bandinelli, S., Piras, M.G., Usala, G., Lai, S., Mulas, A., Corsi, A.M., Vestrini, A. et al. (2009) Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. Am J Hum Genet, 84, 477-482. PMID 19303062.

    5. Grarup, N., Sulem, P., Sandholt, C.H., Thorleifsson, G., Ahluwalia, T.S., Steinthorsdottir, V., Bjarnason, H., Gudbjartsson, D.F., Magnusson, O.T., Sparso, T. et al. (2013) Genetic architecture of vitamin B12 and folate levels uncovered applying deeply sequenced large datasets. PLoS Genet, 9, e1003530. PMID 23754956.

    6. Hazra, A., Kraft, P., Lazarus, R., Chen, C., Chanock, S.J., Jacques, P., Selhub, J. and Hunter, D.J. (2009) Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway. Hum Mol Genet, 18, 4677-4687. PMID 19744961

    7. Kaptan, K., Beyan, C., Ural, A.U., Cetin, T., Avcu, F., Gulsen, M., Finci, R. and Yalcin, A. (2000) Helicobacter pylori–is it a novel causative agent in Vitamin B12 deficiency? Arch Intern Med, 160, 1349-1353. PMID 10809040.

    8. Carmel, R., Perez-Perez, G.I. and Blaser, M.J. (1994) Helicobacter pylori infection and food-cobalamin malabsorption. Dig Dis Sci, 39, 309-314. PMID 8313813.

    9 Ikehara, Y., Nishihara, S., Yasutomi, H., Kitamura, T., Matsuo, K., Shimizu, N., Inada, K., Kodera, Y., Yamamura, Y., Narimatsu, H. et al. (2001) Polymorphisms of two fucosyltransferase genes (Lewis and Secretor genes) involving type I Lewis antigens are associated with the presence of anti-Helicobacter pylori IgG antibody. Cancer Epidemiol Biomarkers Prev, 10, 971-977. PMID 11535550.

    10 Magalhaes, A., Rossez, Y., Robbe-Masselot, C., Maes, E., Gomes, J., Shevtsova, A., Bugaytsova, J., Boren, T. and Reis, C.A. (2016) Muc5ac gastric mucin glycosylation is shaped by FUT2 activity and functionally impacts Helicobacter pylori binding. Sci Rep, 6, 25575. PMID: 27161092.

    11. Oussalah, A., Besseau, C., Chery, C., Jeannesson, E., Gueant-Rodriguez, R.M., Anello, G., Bosco, P., Elia, M., Romano, A., Bronowicki, J.P. et al. (2012) Helicobacter pylori serologic status has no influence on the association between fucosyltransferase 2 polymorphism (FUT2 461 G->A) and vitamin B-12 in Europe and West Africa. Am J Clin Nutr, 95, 514-521. PMID 22237057.

    12. Chery, C., Hehn, A., Mrabet, N., Oussalah, A., Jeannesson, E., Besseau, C., Alberto, J.M., Gross, I., Josse, T., Gerard, P. et al. (2013) Gastric intrinsic factor deficiency with combined GIF heterozygous mutations and FUT2 secretor variant. Biochimie, 95, 995-1001. PMID 23402911.

    13. Velkova A, Diaz JEL, Pangilinan F, et. al; The FUT2 secretor variant p.Trp154Ter influences serum vitamin B12 concentration via holo-haptocorrin (holoHC), but not holo-transcobalamin (holoTC), and is associated with haptocorrin glycosylation, Hum Mol Genet, Volume 26, Issue 24, 15 December 2017, Pages 4975–4988. PMID 29040465.

  • Utopia: Advice, Intelligent interface with Opus23

    Advice: Intelligent Interface with OPUS23

    ADVICE is an app in Utopia that gives algorithmic considerations of multiple bacterial strains in conjunction with the client’s own genome to provide true/false clinical outcomes. ADVICE automatically pulls the client’s Opus23 genetic data where relevant, and incorporates it into algorithmic calculations. (comparable to LUMEN in Opus23).

    Getting around ADVICE

    From the Utopia drop down menu, hover over ‘Algorithms’ until a second list appears, then select ‘Advice’. You are presented with a list of algorithms, those that are true are at the top and display the word ‘TRUE’ in a green box, followed by those that are false, which display the word ‘FALSE’ in an orange box. Each algorithm will display the repute (risk, benefit or neutral), and the magnitude. If an algorithm suggests prescriptive metabolites which enhance or reduce specific bacteria, they will be listed under Encourage or Discourage.

    The client’s relevant genes will be displayed beneath the listed ‘microbiota’ information in the same format found in the Opus23 LUMEN app. Clicking on the colored semicircle will open a pop-up window with a description of the gene and giving details of the client’s SNPs for the gene from their Opus23 data.

    Utopia Advice algorithm

    An ADVICE algorithm for gluten-induced immunopathology

    All metabolites and taxa are clickable. Clicking a metabolite opens a pop-up window with a list of bacteria affected by it. Clicking a bacterium opens a pop-up window as described in previous apps. To add an algorithm to the client report, click on the ‘Add/ Uncurated’ button.

    Individual genera may also be curated and added to the client report for the current dataset by selecting the taxon to open its pop-up information and then clicking the ‘Add/Uncurated’ button.

    Screen shot of Metabolomics pop-up window

    Pop-up window in ADVICE showing metabolomics information

    Using the Algorithm Aggregate Analysis

    At the bottom of the page is the Algorithm Aggregate Analysis. This is a summary of all advice for the client listed under either an ‘increase’ and ‘decrease’ section heading. Each section provides both the microbiota and beneficial/ harmful metabolites that are suggested to be increased or decreased by your client. The ‘result’ information can be a useful tool for streamlining complex treatment strategies. Each metabolite listed in the Algorithm Aggregate Analysis details whether or not the metabolite has mixed results (included as both encourage and discourage in ADVICE algorithms), and whether it is specific to an algorithm.

    Algorithm Aggregate Analysis screenshot

    Algorithm Aggregate Analysis for the ADVICE app

  • Utopia: Pansophia, sequential outcome analysis

    PANSOPHIA – sequential outcome analysis

    The genomic DNA analysis software Opus 23 includes the Utopia suite of apps for analyzing sequential uBiome data, comparing sequencing-based clinical microbiome data with the client’s own genomic DNA.

    Utopia logo

    PANSOPHIA is an Utopia app that gives you the power to sort your client’s data based upon associations with health and disease as well as several other useful categories including keystone, probiotic, core species and butyrate production.  For those clients who have more than one data sample, PANSOPHIA allows you to visualize treatment progress in a graphical format that can be easily added to your client’s report.

    Navigating PANSOPHIA


    From the Utopia drop down menu, hover over ‘analytics’ until a second list appears, then select PANSOPHIA. You will then be presented with the default table which shows ‘everything’ and is sorted by ‘rank’ or ‘percent’.

    At the bottom of the window is a jump screen that allows you to move from one screen to the next. You can control how many rows to display be selecting an option from the ‘Show’ pull down menu. The default is 15.

    Pansophia initial screen

    Filtering and sorting results

    PANSOPHIA allows you to parse the taxon data based upon desired treatment goals. Taxa are sortable by benefit as well as pathogenic potential.  There are two ways to filter the data.

    Using category tabs:

    You can use the category tabs at the top of app’s main screen to select taxa grouped by their associations with health and disease. Once a category is selected, the information will be displayed in graph form.

    Pansophia screenshot

    The PANSOPHIA graph uses the following symbols:

    • An orange dot  denotes average %, the bar graphic represents the client data- specific %
    • For those with multiple data sets, a blue diamond denotes % change

    Click on any desired taxon to open up its information pop—up window for detailed information including taxonomy, an overview of known disease or health benefit associations, interactions and metabolomics. Click ‘add/ curate’ to include it in your clients report. Additionally, the graph itself can be printed or added to your client’s report.

    Using individual taxon selections:

    From the main screen, you can sort the taxa by ‘name’ or ‘client %’ then select desired taxa individually using the display box selections on the left hand side of the screen.  Once you have made your selections, click the orange ‘display selections’ button at the top of the screen. The individual taxon information will be displayed in graph form. Click ‘add/curate’ to include the graph in your client’s report.


  • Utopia Demonstration Video

    Dr. Peter D’Adamo and Dr. Tara Nayak present an introduction to Utopia, the free microbiome analysis add-on module to Opus 23 for uBiome test results.


    To use Utopia, you will need to  have your client’s 23andMe data already uploaded to Opus 23. You can then upload as many raw data files from uBiome tests as you have for that client. Utopia will work with individual uBiome tests, referencing the client’s 23andMe results where appropriate, and also give sequential analysis for multiple uBiome tests.

    The unique combination of Opus 23 and Utopia make this an opportunity for practitioners to get deep insight into their clients on both a genomic and a microbiological level, all sourced from published medical literature. The interaction between the two genomic analyses provides unparalleled informatics tools, and gives the practitioner an edge over any other genomic analysis tool available today.

  • Utopia: Spectrum, visual community organization

    SPECTRUM: Visual community organization

    Utopia logo

    SPECTRUM provides a visual representation of the taxonomic data at both the genus and phylum levels. The app demonstrates the weight, influence and diversity of your client’s microbiome in two helpful display formats, each of which are clickable for a deeper look and easy report curation.

    Navigating  SPECTRUM

    Spectrum logo

    From the Utopia drop down menu, hover over ‘analytics’ until a second list appears, then select SPECTRUM.  Phyla and genus are displayed in pie chart format, which is accompanied by the spectrum profiler found below.

    The pie charts follow the color-coding conventions found in OPUS23 indicating beneficial, neutral as well as pathogenic organisms. Click on any desired section to open up its information pop—up window for detailed information including taxonomy, an overview of known disease or health benefit associations, descendants and metabolomics. Click ‘add/ curate’ to include it in your client’s report.

    Spectrum pie charts

    The spectrum profiler demonstrates the trends in your client’s biome diversity in a graphical format. Clicking on any of the category headings will open a pop-up window listing the organisms found in your client’s sample. Each genus listed is also clickable, opening a pop-up window for detailed information, including taxonomy, an overview of known disease or health benefit associations, descendants and metabolomics. Click ‘add/ curate’ to include it in your client’s report.

    Screenshot of Spectrum Profiler

    Metabolomics is a powerful addition to the spectrum profiler that provides a comprehensive list of metabolites associated with GI biome species. A list of all metabolites active in your client is included. Click on an individual metabolite for detailed information, including genera that are enhanced, inhibited, and those which generate the metabolite as an end product. Click on the Metabolomics link to open a pop-up with all active and inactive genera.

  • Utopia: Loam, a fertile soil

    Announcing the launch of Utopia, the suite of apps within Opus 23 that analyzes and reports on sequential data from uBiome tests. uBiome is the world’s first sequencing-based clinical microbiome screening test, giving the user insight into the bacterial population of multiple body areas. Utopia recognizes all bacteria found by uBiome, but is specifically interested in the gut bacteria and its interaction with the client’s own genomic DNA. Utopia is free for existing clients: Once you have uploaded 23andMe raw data for your client, you can add as many uBiome tests as you want for that client without additional charge. Utopia will then give you access to multiple apps to analyze the data and reference it to the client’s genomic data where appropriate.

    Utopia logo

    LOAM: Adaptable taxon data

    LOAM is a highly flexible search and sort tool that allows you to easily navigate through your client’s Ubiome results by taxon. LOAM allows you to filter taxonomic data based upon several useful parameters as well as sort the filtered results. It is similar to the ARGONAUT app in Opus 23.


    Navigating LOAM

    From the Utopia drop down menu, hover over ‘analytics’ until a second list appears, then select LOAM. You will then be presented with the default table which shows ‘everything’ and is sorted by ‘repute’ or ‘interpretation’.

    At the bottom of the window is a jump screen that allows you to move from one screen to the next. You can control how many rows to display be selecting an option from the ‘Show’ pull down menu. The default is 15.

    Loam Screenshot

    Filtering and sorting results

    LOAM allows you to parse the taxon data based upon desired treatment goals. Taxons are sortable by benefit as well as pathogenic potential.

    The LOAM table displays the following data by column:

    • Taxon name
    • Repute, displaying beneficial !, neutral ! and pathogenic ! 
    • Rank
    • Client-specific % population
    • Average % (if available)
    • Standard deviation (if available)
    • Interpretation (displayed up to +6 times the standard deviation, populations found in a greater abundance are indicated by )
    • Normal variance
    • Order magnitude

    Click on any desired taxon to open up its information pop—up window for detailed information including taxonomy, an overview of known disease or health benefit associations, interactions and metabolomics. Click ‘add/ curate’ to include it in your clients report. The Curated column will then show a green checkmark against all curated taxon after refreshing the page. 

    LOAM columns are sortable. Click on any column title to sort by that column. Click that column again to reverse the sort order.

  • Protection against risk of Parkinson’s disease

    Parkinson’s disease was described in 1817 by Dr James Parkinson, who published an essay reporting six cases of ‘paralysis agitans’ (the disorder that was later renamed after Parkinson). He described the characteristic resting tremor, abnormal posture and gait, paralysis and diminished muscle strength, and the way that the disease progresses over time. [1]

    Since the advent of genetic testing several genes have been found to be associated with Parkinson’s disease (PD), resulting in various classifications. Autosomal dominant Parkinson disease type 8 (PARK8) is caused by heterozygous mutation in LRRK2, the gene encoding the dardarin protein. [2] The G2019S variant is one of the most common genetic causes of PD. Although the clinical motor signs of PD in carriers of the G2019S mutation are largely typical, an earlier age at onset of motor symptoms has been reported in some studies. [3]

    The word dardarin was taken from a Basque word for tremor, as the gene was first identified in families from England and the north of Spain. Mutations in LRRK2 are the most common known cause of familial and sporadic PD, accounting for approximately 5% of individuals with a family history of the disease and 3% of sporadic cases. They account for up to 10% of autosomal dominant familial and 3.6% of sporadic PD. More than 40 different variants, almost all missense, have been found. Seven seem to be proven pathogenic mutations, and are clustered in functionally important regions which are highly conserved through evolution. [4]

    23andMe carried out a privately-funded genome-wide association study (GWAS) to search for novel genetic variants associated with PD. The results, which were published in PLOS in 2011, replicated existing associations and discovered two novel variants. [5] In addition, 23andMe researched genes conferring protection on those with high-risk genes. [6] They found that of the approximately 1 in 10,000 people who have the G2019S  variant, those who also had a mutation in SGK1 were found to have a lower risk of PD than those with just the G2019S variant, conferring protection against the increased risk of PD. [7]

    Other causes of, or factors contributing to PD include pesticide exposure, [8] head trauma, medication, prolonged oxidative stress from infection or high homocysteine. Genetic factors include increased function of MAOB enzymes, high histamine from HNMT mutations, elevated L-dopa from DDC mutations or B6 deficiency. The Opus 23 software contains algorithms for Parkinson’s disease associated with some of these genetic causes, risk or contributory factors found in the 23andMe raw data. A new algorithm added to the Opus 23 Lumen app looks for both the LRRK2 G2019S and the SGK1 variants to assess for both risk of PD and protection from the risk genotype, and lists natural agents associated with gene function.


    1. Parkinson J. An essay on the shaking palsy. London: Sherwood, Neely and Jones; 1817.
    2. Kachergus J, Mata IF, Hulihan M, et. al. Identification of a novel LRRK2 mutation linked to autosomal dominant parkinsonism: evidence of a common founder  across European populations. Am J Hum Genet. 2005 Apr;76(4):672-80. Epub 2005 Feb 22. PMCID: PMC1199304.
    3. Thaler A, Mirelman A, Gurevich T,  et. al. Lower cognitive performance in healthy G2019S LRRK2 mutation carriers. Neurology. 2012 Sep 4;79(10):1027-32. PMCID: PMC3430708.
    4. Davie CA (2008). “A review of Parkinson’s disease”. Br. Med. Bull. 86 (1): 109–27. PMID 18398010
    5. Do CB, Tung JY, Dorfman E, et. al. Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson’s disease. PLoS Genet. 2011 Jun;7(6):e1002141. PMCID: PMC3121750
    6. 23andMe Blog: 23andMe Discovers Genetic Variant That May Protect Those at High Risk for Parkinson’s Disease. Accessed Aug 28, 2016.
    7. Polymorphisms associated with Parkinson’s disease. Patent US8187811 B2.
    8. Van Maele-Fabry G, Hoet P, Vilain F, Lison D. Occupational exposure to pesticides and Parkinson’s disease: a systematic review and meta-analysis of cohort studies. Environ Int. October 2012, 46: 30–43. PMID: 22698719.


  • Kava-kava for panic attacks

    A 2008 paper by Thoeringer et. al., published in the Journal of Neural Transmission [1] described a study of 238 adult Caucasian patients recruited from an Anxiety Disorders Outpatient Clinic in Europe presenting various anxiety disorders, including panic disorder, agoraphobia, social phobia and generalized anxiety disorder. As there are many genetic studies linking the GABA system to anxiety disorders and related personality traits, the patients were genotyped for various polymorphisms in the SLC6A1 (GABA transporter 1), along with 267 controls without anxiety disorder.

    Five polymorphisms in SLC6A1 or in the promoter region were found to be nominally associated with anxiety disorders. Although none were statistically significant alone, the authors found a significant combined effect of all investigated polymorphisms, which strongly suggested a major role of SLC6A1 in the genetic susceptibility of pathological anxiety. Looking at patients with panic disorder, those with the most severe panic disorder were significantly more likely than controls to have two related polymorphisms in the SLC6A1.

    GABA (gamma-aminobutyric acid) is a neurotransmitter that decreases activity in the neurons of the brain and inhibits the excitability of nerve cells. Drugs that block the GABA transporter molecule inhibit the removal of GABA from the nerve synapses, thereby prolonging the action of GABA. Tiagabine, a selective GABA transporter 1 blocker, is used as an antiepileptic, but has off-label use for anxiety disorder. This is thought to be due to the augmentation of GABA function as a neurotransmitter in the brain. This drug has side-effects, however, and other methods of reducing panic disorder have been investigated.

    Kava-kava (Piper methysticum) is a traditional plant-based medicine found in the Western Pacific region which has been shown to reduce anxiety. Kava-kava is legal in most countries, and is generally safe when the root from a ‘noble’ cultivar is used. A study of kava-kava for anxiety reduction using the Hamilton Anxiety Rating Scale (HAMA) as the primary outcome found that patients with generalized anxiety disorder who had polymorphisms in SLC6A1 and in the 5′ flanking region potentially responded to kava-kava supplementation with a more significant reduction in HAMA rating than in patients without the polymorphisms. [2] Treatment consisted of tablets standardized to contain 60 mg of  kavalactones per tablet for a total daily dose of 120 mg of kavalactones for the first 3-week controlled phase, being titrated to 240 mg of kavalactones in nonresponse at the 3-week mark for the second 3-week controlled phase, or placebo.

    An algorithm in the Lumen app in Opus 23 determines how many relevant SNPs a client has in SLC6A1 that are reported in their 23andMe raw data, and which may make treatment with kava-kava more effective in reducing anxiety disorder and panic symptoms.


    1. Thoeringer, C.K., Ripke, S., Unschuld, P.G. et al. The GABA transporter 1 (SLC6A1): a novel candidate gene for anxiety disorders. J Neural Transm (2009) 116: 649. doi:10.1007/s00702-008-0075-y. PMCID: PMC2694916
    2. Sarris J, Stough C, Bousman CA, Kava in the treatment of generalized anxiety disorder: a double-blind, randomized, placebo-controlled study. J Clin Psychopharmacol. 2013 Oct;33(5):643-8. doi: 10.1097/JCP.0b013e318291be67. PMID: 23635869
  • Genetic factors in depression, neuroticism and well-being

    Raining DNA

    Depressive and neurotic behaviours have many potential triggers and contributory factors, but few associated genetic variants have been found, most likely due to the large numbers of subjects needed: Genome-Wide Association Studies (GWAS) require large sample sizes to have sufficient statistical power, which is often achieved by aggregating results in multiple cohorts in a meta-analysis. A paper to be published in Nature Genetics in June 2016 [1] reports on the results of combining several large conducted on three phenotypes:

    • subjective well-being (n = 298,420)
    • depressive symptoms (n = 161,460)
    • neuroticism (n = 170,911).

    Using a meta-analysis on publicly available results from a study performed by the Psychiatric Genomics Consortium together with new results from analyses of the initial release of UK Biobank (UKB) data and the Resource for Genetic Epidemiology Research on Aging cohort, two variants were found to be associated with depressive symptoms. In the UKB cohort  the researchers measured depressive symptoms by combining responses to two questions asked of 23andMe customers with European ancestry. The questionnaire asked about the frequency in the past 2 weeks with which the respondent experienced feelings of low levels of enthusiasm or disinterest, and feelings of depression or hopelessness. The other cohorts included case-control data on major depressive disorder.

    According to Eysenck and Eysenck, [2] neurotic people become easily nervous or upset due to a lowered activation threshold in the sympathetic nervous system, and experience emotional instability in the form of fight-or-flight symptoms resulting from apparently minor stressors. Using twin studies, Eysenck concluded that, “the factor of neuroticism is not a statistical artefact, but constitutes a biological unit which is inherited as a whole….neurotic predisposition is to a large extent hereditarily determined.” [3]

    To analyse this potential genetic association with neuroticism (n = 170,911), the research combined statistics from a published study by the Genetics of Personality Consortium (GPC) with results from a new analysis of UKB data. Eleven variants were found to be associated with neuroticism. The GPC data harmonised different neuroticism batteries, and in the UKB cohort the measure was the respondent’s score on a 12-item version of the Eysenck Personality Inventory Neuroticism.

    This was also the first study to find SNPS that have a significant association with subjective well-being, of which the researchers identified three relevant variants. Questionnaires measured both positive affect (a state of pleasant arousal enthusiasm) and life satisfaction, even though these are different concepts of well-being.

    The SNPs in this study were found mainly in loci regulating expression in tissues of the central nervous system, adrenals or pancreas, including CSE1L , DCC , HNRNPA1P1, KSR2, MTCH2 NMUR2  PAFAH1B1 and RAPGEF6. Previous studies had found a relevant variant in MAGI1, accounting for approximately 15% of the variability in neuroticism, [4] as well as SNPs in TMPRSS9 and GRIN2B. [5]

    23andMe typically reports on up to six of the SNPs in this study related to neuroticism and two related to depression. Two algorithms in the Opus 23 Pro [6] Lumen app can determine the relevant genotypes for these phenotypes from SNPs available in 23andMe raw data.


    1. Okbay A, Baselmans BM, De Neve JE, et. al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet. 2016 Jun;48(6):624-33. doi:10.1038/ng.3552. PMID: 27089181.
    2. Eysenck, H. J. & Eysenck, S. B. G. (1969). Personality Structure and Measurement. London: Routledge.
    3. The Journal of Mental Health, July 1951, Vol. XCVII, “The Inheritance of Neuroticism: An Experimental Study”, H. J. Eysenck and D. B. Prell, p. 402.
    4. Genetics of Personality Consortium, de Moor MH, van den Berg SM, et. al. Meta-analysis of Genome-wide Association Studies for Neuroticism, and the Polygenic Association With Major Depressive Disorder. JAMA Psychiatry. 2015 Jul;72(7):642-50. doi:10.1001/jamapsychiatry.2015.0554. [PMID: 25993607].
    5. Aragam N, Wang KS, Anderson JL, Liu X. TMPRSS9 and GRIN2B are associated with neuroticism: a genome-wide association study in a European sample. J Mol Neurosci. 2013 Jun;50(2):250-6. doi: 10.1007/s12031-012-9931-1. [PMID: 23229837].
    6. Opus 23 Pro software by Dr Peter D’Adamo.
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