Ask about this productRelated genes to: MTRR Blocking Peptide
- Gene:
- MTRR NIH gene
- Name:
- 5-methyltetrahydrofolate-homocysteine methyltransferase reductase
- Previous symbol:
- -
- Synonyms:
- cblE
- Chromosome:
- 5p15.31
- Locus Type:
- gene with protein product
- Date approved:
- 1998-04-20
- Date modifiied:
- 2018-07-04
Related products to: MTRR Blocking Peptide
Related articles to: MTRR Blocking Peptide
- Beijing's 2024 school physical education reforms have substantially increased activity demands on children, yet population-level genotype data for Chinese schoolchildren remain scarce. - Source: PubMed
Publication date: 2026/07/29
Peng XinruiDing WangxinKang XinYan YuhaoChen YanjingZhang Yan - ObjectiveTo assess the association and interaction between rs1805087 and rs1801394 polymorphisms and nonsyndromic cleft lip with or without cleft palate (NSCL ± P) in a Chilean population.DesignA retrospective case-control study.MethodsRs1805087 and rs1801394 genotypes were extracted from an array for 248 cases and 479 controls. Association analysis was performed by logistic regression for additive, dominant and recessive models and gene-gene interaction was evaluated by a Multifactor Dimensionality Reduction algorithm.ResultsNo association was detected for rs1805087 (870) or rs1801394 (919) and NSCL/P or for their interaction (197).ConclusionOur findings in Chile may be explained by no effect of these markers on NSCL ± P risk, as was described in other populations. However, we must be cautious with this conclusion due to the low statistical power for association (<10%). Thus, replication studies may include higher sample sizes. In addition, other genes or variants for the methionine cycle as well as the interaction with environmental factors must be considered in future studies. - Source: PubMed
Publication date: 2026/08/14
Suazo JoséScapoli LucaSalamanca CarlosLeiva Villagra NoemiPantoja RobertoPardo Rosa - Non-invasive prenatal testing (NIPT) generates vast amounts of low-depth sequencing data, offering a valuable resource for studying maternal genetic traits. However, standard NIPT genotype imputation workflows include time-consuming post-alignment processing steps from GATK, whose benefits for low-depth data remain uncertain. Additionally, merging imputation results from large, batch-processed cohorts presents a challenge, particularly for accurately combining imputation information scores (INFO). This study therefore aimed to develop an efficient imputation pipeline for NIPT data by evaluating the necessity of standard post-alignment steps and validating a batch-merging strategy, using maternal folate metabolism genotyping as a clinical application. The omission of GATK post-alignment steps, including duplicate marking and base quality score recalibration, did not compromise imputation accuracy across multiple simulated low depths but substantially reduced computational time. A sample-size weighted averaging method enabled accurate merging of imputation INFO scores from batch-processed data, yielding results nearly identical to single-cohort imputation for high-quality variants. Applying this optimized pipeline to 517 real-world NIPT samples demonstrated high genotype and allele concordance for the MTHFR rs1801131 and MTRR rs1801394 loci when compared to a sequencing capture method, with both metrics exceeding 96% at GP80. In conclusion, this study validates a simplified, computationally efficient imputation workflow for low-depth NIPT data. It enables accurate assessment of maternal folate metabolism genotypes, offering a cost-effective strategy for large-scale genetic screening of specific maternal traits without additional experimental burden, using existing clinical sequencing data. - Source: PubMed
Publication date: 2026/08/12
Wu KaixinZheng MeiHe PengWang DechengZhao MiFeng YanchunLiang GuangqingXiong JunZhu BiqingLin Guo-Wang - Background and AimsAtherosclerosis (AS) is associated with high residual cardiovascular risk despite standard treatment. Abnormal homocysteine metabolism and MTHFR polymorphisms are involved in AS progression, but few prognostic models integrate genetic and multidimensional biochemical indicators. This study aimed to develop and validate a prognostic model for major adverse cardiovascular events (MACE) in patients with AS.MethodsThis single-center observational cohort study enrolled 580 patients with AS confirmed by coronary angiography between January 2023 and January 2026. Baseline data included clinical characteristics, imaging indices, serum biochemical markers, and MTHFR/MTRR genotypes. The primary outcome was MACE. Predictors were screened by LASSO regression, and a nomogram was constructed using multivariable Cox regression. Model performance was evaluated by C-index, calibration curves, and decision curve analysis.ResultsOver a median follow-up of 24.5 months, 135 patients (23.3%) developed MACE. Independent predictors included MTHFR 677TT mutation, elevated Hcy, low serum folate, Gensini score, CIMT, Lp-PLA2, and diabetes. The model achieved a C-index of 0.885, showing excellent discrimination, good calibration, and favorable net clinical benefit.ConclusionThis integrated prognostic model demonstrated good internal discrimination and calibration for predicting MACE in patients with AS. The nomogram provides a practical risk-stratification framework for identifying individuals at high residual cardiovascular risk. However, given the lack of external validation and the inherent risk of optimism bias in single-center studies, these findings should be considered preliminary. Rigorous external validation in diverse, multicenter cohorts is strictly required before this tool can be recommended for routine clinical implementation. - Source: PubMed
Publication date: 2026/08/11
Dou XiaohuiZhang XijuanZeng LiangHu XiaoyanZeng LihengWan YahuiJiang XuhongGe MingxiaHuang Xiaoyan - To characterize the whole-genome features of clinical isolates collected from a tertiary medical institution in Beijing, with a focus on the genomic basis of ceftriaxone non-susceptibility and multidrug resistance. - Source: PubMed
Publication date: 2026/07/15
Qin Jia-HaoLyu Qiu-MinZhao Xiu-YingLiu LinXiao Nan