MTHFR rs1801131 (A1298C): reading it from your own data

rs1801131 is the MTHFR A1298C variant: a missense change at codon 429 of methylenetetrahydrofolate reductase (NM_005957.5:c.1286A>C, p.Glu429Ala). It is common, roughly 30% minor allele frequency in European-ancestry populations and lower in East Asian and African-ancestry populations. Carrying one or two copies has a small and inconsistent effect on enzyme activity, and on its own it is not associated with elevated plasma homocysteine in people with adequate folate intake. If you want to know something about your folate and one-carbon metabolism, measure homocysteine, serum and red-cell folate, B12, and methylmalonic acid. The genotype is a weak prior. The biochemistry is the measurement.

Where the variant lives, and the strand trap

Coordinates:

• GRCh37: chr1:11,854,476
• GRCh38: chr1:11,794,419

MTHFR is transcribed from the minus strand. The “1298A>C” nomenclature is on the coding sequence, so on the plus-strand reference the alleles are T (ancestral, corresponding to 1298A) and G (corresponding to 1298C). Most VCFs and dbSNP will show REF=T, ALT=G. Consumer array exports report plus-strand calls. A 23andMe or AncestryDNA line reading TT is therefore 1298AA, TG is heterozygous and GG is 1298CC.

The same trap applies to its neighbor rs1801133 (C677T, p.Ala222Val, GRCh37 chr1:11,856,378 / GRCh38 chr1:11,796,321), where the plus-strand alleles are G and A, and the “677T” risk allele shows up as A. Half the confusion on forums about MTHFR results comes from people reading a G and assuming it means the same thing at both sites. It does not.

From a consumer raw file:

grep -P '^rs1801131\t|^rs1801133\t' genome.txt
# rs1801131  1  11854476  TG
# rs1801133  1  11856378  GG

Both SNPs are directly genotyped on essentially every consumer chip, so you are not relying on imputation here.

From your own WGS:

bcftools view -r chr1:11794419,chr1:11796321 sample.vcf.gz \
  | bcftools query -f '%CHROM\t%POS\t%REF\t%ALT[\t%GT\t%DP\t%GQ]\n'

Check the build before you trust the region string. The VCF header tells you: ##contig=<ID=chr1,length=248956422> is GRCh38, length=249250621 is GRCh37. Getting this wrong silently returns a different site or nothing at all.

If the query returns no rows, that is ambiguous in a variant-only VCF. No line means either homozygous reference or no coverage. Resolve it against the BAM or CRAM:

samtools mpileup -f GRCh38.fa -r chr1:11794419-11794419 sample.bam
samtools depth -a -r chr1:11794419-11794419 sample.bam

At 30x mean WGS you should see 20 to 40 reads here. MTHFR sits in a clean, unique region with no pseudogene to confuse short-read alignment, so a DP above 15 with GQ above 30 is a call you can take at face value. If you have a gVCF, the reference block covering the position with a non-variant GQ is the cleaner answer.

What the variant does, and what it does not

The 677T variant is the one with a well-characterized biochemical effect: the p.Ala222Val substitution destabilizes the FAD cofactor binding, and 677TT homozygotes show substantially reduced enzyme activity in vitro, with the effect largest when folate status is low. A1298C sits in the regulatory domain rather than the catalytic domain, and the reported activity reductions are smaller and less consistent across assays. Compound heterozygosity (677CT with 1298AC) has been described as producing a larger reduction than either heterozygote alone, but the population-level association with homocysteine is modest.

What the literature looks like if you read past the abstracts: a long run of candidate-gene association studies, many small, with results that do not replicate cleanly. MTHFR variants have been tested against non-Hodgkin lymphoma and found unassociated, including when DNA methylation at the locus was measured alongside genotype ¹. They have been tested against essential tremor in Han Chinese and found unassociated ². They have been tested against hypertension susceptibility ³, breast cancer, congenital heart disease ⁴, and mercury sensitivity ⁵. Some of these find effects, most are underpowered by modern GWAS standards, and effect sizes where they exist are odds ratios near 1.2 to 1.5. Treat any single-study claim about rs1801131 as a hypothesis. Such a claim is not a result.

There is a practical consequence. Professional medical genetics guidance in the United States has recommended against ordering MTHFR variant testing as part of a routine thrombophilia or recurrent-loss workup, because the variants do not change management. If someone is telling you your A1298C genotype explains a symptom, that is a claim well ahead of the evidence, and it is a conversation for a clinician who can look at the rest of your picture.

What to measure instead

Five analytes give you more information about one-carbon metabolism than the genotype does:

• Plasma total homocysteine. This is the functional readout. Pre-analytics dominate, because red cells keep exporting homocysteine into plasma after draw, on the order of 10% per hour at room temperature. The sample therefore needs to be chilled and the plasma separated within about 30 to 60 minutes, or drawn into a tube with a glycolysis inhibitor. A “high” homocysteine from a tube that sat on a counter is an artifact of the courier. Your MTHFR genotype is not the cause of that number.
• Serum folate. Reflects recent intake, moves within days.
• Red blood cell folate. Reflects tissue stores over roughly the preceding 120 days, the lifespan of the erythrocyte. This is the one worth tracking longitudinally.
• Vitamin B12, and methylmalonic acid as the functional marker. MMA rises with B12 insufficiency and is more sensitive than serum B12 alone. Homocysteine rises with either folate or B12 insufficiency, so you need both to attribute a high value.
• Creatinine and eGFR, because renal function is a major determinant of homocysteine and will confound any interpretation.

If you have RNA-seq from whole blood alongside WGS, you can check allele-specific expression at MTHFR, though coverage at any single transcript in bulk blood RNA is often thin. Neither rs1801131 nor rs1801133 is a splice or expression variant in any well-replicated sense, so expect to see balanced allelic expression.

”Methylation test” means two different things

One meaning is a biochemical panel with these analytes.

• homocysteine
• folate
• B12
• MMA
• sometimes SAM/SAH ratios and betaine

That is the panel above, and it measures flux through the pathway.

The other meaning is DNA methylation: bisulfite sequencing or an array such as Illumina EPIC, producing beta values per CpG. These are different measurements answering different questions. Work on MTHFR has looked at both together, quantifying promoter CpG methylation alongside genotype ¹, and at methylation status of risk variants in congenital heart disease cohorts ⁴. If a vendor sells you a “methylation test” and the report contains SNP genotypes, you received a genotyping array. The product was not a methylation assay.

Weight, diet, and the nutrigenomics claims

The direct question, whether MTHFR genotype makes weight loss hard, does not have support behind it. The clearest body-composition finding points the other way from the folklore: MTHFR polymorphism has been associated with lean body mass but not with fat mass ⁶. Gene-diet interaction work does exist. C677T genotype has been examined for differential response to a dietary intervention in metabolic syndrome ⁷, and MTHFR variants have been reported to modify the relationship between gestational weight gain and adverse birth outcomes ⁸, and to associate with weight change after a psychiatric medication switch ⁹. These are interaction effects in specific contexts. The studies do not make a general claim that your genotype sets your metabolic rate.

The broader nutrigenomics question, whether you can meaningfully eat for your genes, has been reviewed directly, and the conclusion is that the evidence supporting genotype-directed dietary prescriptions is thin for most variants and most outcomes ¹⁰. We would treat rs1801131 the same way, as something interesting to know. The variant is not a basis for changing what you eat. Changes to supplementation, particularly high-dose B vitamins, belong in a conversation with a clinician who has your labs in front of them. The reason is that B12 status has to be established before folate is pushed.

Questions people also ask

Does 23andMe tell you if you have the MTHFR variants? Yes. Both rs1801131 and rs1801133 are on the genotyping chip and appear in the raw data download even when the interpreted reports do not mention them. Grep the raw file by rsID and read the plus-strand alleles, remembering that the 1298C allele appears as G and the 677T allele appears as A.

Is it hard to lose weight with an MTHFR variant? There is no good evidence for that. The body-composition association that has been reported is with lean mass rather than fat mass ⁶. Gene-diet interactions reported for C677T are context-specific ⁷ and do not translate into a general weight-loss penalty.

Can C677T cause weight gain? Not in any established way. Studies linking MTHFR genotype to weight change have been in specific settings, such as weight change after switching antipsychotic medication ⁹ or gestational weight gain and birth outcomes ⁸. Neither supports a claim about everyday weight regulation.

How do I interpret methylation panel results? Look at homocysteine first, then use folate, B12, and MMA to attribute it, then check renal function before concluding anything. A single elevated homocysteine with slow sample handling is most likely a pre-analytic artifact. Two or three draws on the same protocol, spaced out, tell you far more than one. Interpretation that would change what you take belongs with a clinician.

How should I interpret my MTHFR genotype overall? As a small prior on folate handling, most relevant at low folate intake, and largest for 677TT. A1298C alone, with normal homocysteine and normal red-cell folate, is not a finding that needs action.

Footnotes

1. Gabrielle Bradshaw, Heidi G. Sutherland, Emily T. Camilleri, et al. Genetic and epigenetic variants in the MTHFR gene are not associated with non-Hodgkin lymphoma. Meta Gene, 2015. https://doi.org/10.1016/j.mgene.2015.09.004 ↩ ↩²

2. Dan He, Lamei Yuan, Zhi Song, et al. Lack of association between methylenetetrahydrofolate reductase gene variants & essential tremor in Han Chinese. The Indian Journal of Medical Research, 2019. https://doi.org/10.4103/ijmr.ijmr_432_17 ↩

3. Laith Al-Eitan, Ola M. Al-Sanabra, Fouad A. Almomani, et al. Genetic Variants in the MTHFR and WNK1 Genes and Their Contribution to Hypertension Susceptibility. International Journal of Medical Sciences, 2026. https://doi.org/10.7150/ijms.133244 ↩

4. Melissa Calzada-Dávila, Geovana Calvo‐Anguiano, L. Villarreal, et al. Congenital Heart Diseases: Genetic Risk Variants and Their Methylation Status. Genes, 2022. https://doi.org/10.3390/genes13112115 ↩ ↩²

5. B. A. Malyarchuk, N. V. Pokhilyuk. MTHFR Gene Polymorphism in Indigenous Populations of Siberia in Relation to Sensitivity to Mercury. Russian Journal of Genetics, 2026. https://doi.org/10.1134/s1022795426700481 ↩

6. Xiaogang Liu, Lan-Juan Zhao, Yong‐Jun Liu, et al. The MTHFR gene polymorphism is associated with lean body mass but not fat body mass. Human Genetics, 2008. https://doi.org/10.1007/s00439-007-0463-7 ↩ ↩²

7. Laura Di Renzo, Lt Marsella, Francesca Sarlo, et al. C677T gene polymorphism of MTHFR and metabolic syndrome: response to dietary intervention. Journal of Translational Medicine, 2014. https://doi.org/10.1186/s12967-014-0329-4 ↩ ↩²

8. Weixiang Wu, Dan Luo, Xiaolin Ruan, et al. Polymorphisms in gene MTHFR modify the association between gestational weight gain and adverse birth outcomes. Frontiers in Nutrition, 2022. https://doi.org/10.3389/fnut.2022.919651 ↩ ↩²

9. Siti Norsyuhada Roffeei, Gavin P. Reynolds, Nor Zuraida Zainal, et al. Association of ADRA2A and MTHFR gene polymorphisms with weight loss following antipsychotic switching to aripiprazole or ziprasidone. Human Psychopharmacology Clinical and Experimental, 2013. https://doi.org/10.1002/hup.2366 ↩ ↩²

10. Veronica A. Mullins, William Bresette, Laurel Johnstone, et al. Genomics in Personalized Nutrition: Can You “Eat for Your Genes”?. Nutrients, 2020. https://doi.org/10.3390/nu12103118 ↩