TCF7L2 is the strongest genetic predictor of type 2 diabetes risk and carbohydrate metabolism efficiency. It determines how well glucose is cleared after meals and how large the insulin response is to dietary carbohydrate — the core genetic variable in low-carb vs high-carb diet matching.
FADS1 and FADS2 encode fatty acid desaturases that convert short-chain omega-3s (ALA) into the long-chain forms (EPA/DHA) that the brain and cardiovascular system require. Low-activity variants significantly impair this conversion — making dietary and supplemental EPA/DHA essential rather than optional.
The C677T and A1298C variants in MTHFR reduce enzyme activity, impairing conversion of folic acid to its active form (5-MTHF). This affects homocysteine levels, DNA synthesis, and methylation capacity — with direct implications for B12, folate, and methylation supplement protocols.
The LCT gene region determines adult lactase persistence — the ability to digest lactose beyond infancy. This variant has one of the highest effect sizes in nutrigenomics: it directly predicts dairy tolerance with high accuracy across European, African, and Middle Eastern populations.
FTO is the most replicated genetic variant associated with BMI and fat mass. The risk allele influences caloric efficiency, satiety signaling, and appetite regulation — affecting how individuals respond to caloric deficit and what dietary strategies produce the most consistent results.
VDR (vitamin D receptor) and CYP2R1 (25-hydroxylase) variants determine how efficiently vitamin D is activated and used by the body. Low-activity variants dramatically increase the supplemental dose required to achieve adequate serum 25(OH)D — one of the most impactful and underutilized pieces of nutritional genetic data.
Diet genotype matching — identifying whether a low-carbohydrate, moderate-fat, or balanced approach best aligns with the user's metabolic genetics. Based on TCF7L2, FTO, PPARG, and energy metabolism variants.
Structured risk profiles for vitamins B12, D, folate, iron, omega-3s, and magnesium — based on absorption, conversion, and utilization genetics. Specific deficiency risk by genotype, not by population prevalence.
Lactose tolerance, gluten sensitivity predisposition, caffeine metabolism, and alcohol processing — genetic markers that directly inform dietary choices and explain why certain foods have historically caused problems.
The nutrition genomics layer connects directly to supplement recommendations — identifying where dietary insufficiency is compounded by genetic absorption or conversion impairment and what targeted supplementation addresses it.
Whether you're building a nutrition app, a meal planning service, or a functional food brand — tell us about your use case and we'll show you what genomic personalization looks like in your context.