rs11658311 - PEMT

Magnitude 2.2 · 2 studies on file

Reported associations

• Blood metabolic biomarkers and colorectal cancer risk: results from large prospective cohort and Mendelian randomisation analyses - Unknown journal (n.d.) · Unknown authors · PubMed 40307439

  ABSTRACT: Background Emerging evidence suggests metabolic dysregulation may contribute to colorectal cancer (CRC) aetiology. We aimed to identify pre-diagnostic metabolic biomarkers for CRC risk in 230,420 UK Biobank participants. Methods Nuclear magnetic resonance spectroscopy was used to quantify 249 metabolic biomarkers in plasma samples collected at baseline. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals (CIs) for associations of metabolic biomarkers with CRC risk after adjusting for potential confounders. To infer the potential causality of biomarkers that were associated with CRC independent of the others, we performed genome-wide association analyses among 199,732 UK Biobank participants of European ancestry to identify biomarker-as

• Obsessive-compulsive symptoms in a large population-based twin-family sample are predicted by clinically based polygenic scores and by genome-wide SNPs - Unknown journal (n.d.) · Unknown authors · PubMed 26859814

  ABSTRACT: Variation in obsessive-compulsive symptoms (OCS) has a heritable basis, with genetic association studies starting to yield the first suggestive findings. We contribute to insights into the genetic basis of OCS by performing an extensive series of genetic analyses in a homogeneous, population-based sample from the Netherlands. First, phenotypic and genetic longitudinal correlations over a 6-year period were estimated by modeling OCS data from twins and siblings. Second, polygenic risk scores (PRS) for 6931 subjects with genotype and OCS data were calculated based on meta-analysis results from IOCDF-GC, to investigate their predictive value. Third, the contribution of measured single nucleotide polymorphisms (SNPs) to the heritability was estimated using random-effects modeling.

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