Expressive

rs11000212 - ASCC1

Magnitude 2.0 · 3 studies on file

Reported associations

  • Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes - Unknown journal (n.d.) · Unknown authors · PubMed 31513605

    ABSTRACT: Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses. To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover,

  • Progressive effects of single-nucleotide polymorphisms on 16 phenotypic traits based on longitudinal data - Unknown journal (n.d.) · Unknown authors · PubMed 31902109

    ABSTRACT: Background There are many research studies have estimated the heritability of phenotypic traits, but few have considered longitudinal changes in several phenotypic traits together. Objective To evaluate the progressive effect of single nucleotide polymorphisms (SNPs) on prominent health-related phenotypic traits by determining SNP-based heritability () using longitudinal data. Methods Sixteen phenotypic traits associated with major health indices were observed biennially for 6843 individuals with 10-year follow-up in a Korean community-based cohort. Average SNP heritability and longitudinal changes in the total period were estimated using a two-stage model. Average and periodic differences for each subject were considered responses to estimate SNP heritability. Furthermore, a ge

  • Causal association of body mass index with hypertension using a Mendelian randomization design - Unknown journal (n.d.) · Unknown authors · PubMed 30045251

    ABSTRACT: Supplemental Digital Content is available in the text Abstract Observational studies have shown that obesity is a major risk factor for hypertension, but unmeasured confounding factors may exist. We used Mendelian randomization (MR) to assess the causal effect of obesity on hypertension. The MR analysis was performed in a well-defined community cohort study of 8832 middle-aged (40-69 years) adults in Korea enrolled from 2001 to 2013. We used baseline hypertension and newly diagnosed hypertension during the 10-year follow-up period as the outcome variable. Genetic risk score associated with body mass index (BMI GRS) was used as the instrumental variable (IV) to measure the causal relationship between obesity and hypertension. The IV estimate of causal odds ratio (OR) was derived

Auto-generated from study metadata. AI-synthesised commentary is added when this entry is regenerated through content-service's LLM mode.