Human height and related traits are highly complex, and extensively research has shown that these traits are determined by both genetic and environmental factors. Such factors may partially affect these traits through epigenetic programing. Epigenetic programing is dynamic and plays an important role in controlling gene expression and cell differentiation during (early) development. DNA methylation (DNAm) is the most commonly studied epigenetic feature. In this study we conducted an epigenome-wide DNAm association analysis on height-related traits in a Sub-Saharan African population, in order to detect DNAm biomarkers across four height-related traits. DNAm profiles were acquired in whole blood samples of 704 Ghanaians, sourced from the Research on Obesity and Diabetes among African Migrants study, using the Illumina Infinium HumanMethylation450 BeadChip. Linear models were fitted to detect differentially methylated positions (DMPs) and regions (DMRs) associated with height, leg-to-height ratio (LHR), leg length, and sitting height. No epigenome-wide significant DMPs were recorded. However we did observe among our top DMPs five informative probes associated with the height-related traits: cg26905768 (leg length), cg13268132 (leg length), cg19776793 (height), cg23072383 (LHR), and cg24625894 (sitting height). All five DMPs are annotated to genes whose functions were linked to bone cell regulation and development. DMR analysis identified overlapping DMRs within the gene body of gene, and the gene cluster. In this first epigenome-wide association studies of these traits, our findings suggest DNAm associations with height-related heights, and might influence development and maintenance of these traits. Further studies are needed to replicate our findings, and to elucidate the molecular mechanism underlying human height-related traits.
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http://dx.doi.org/10.1017/S204017442300034X | DOI Listing |
Twin Res Hum Genet
December 2024
Independent researchers.
We analyzed whole-genome sequencing (WGS) data from 51 populations and combined WGS and array data from 89 populations. Multiple types of polygenic scores (PGS) were employed, derived from multi-ancestry, between-family genome-wide association study (GWAS; MIX-Height), European-ancestry, between-family GWAS (EUR-Height), and European-ancestry siblings GWAS (SIB-Height). Our findings demonstrate that both genetic and environmental factors significantly influence adult body height between populations.
View Article and Find Full Text PDFSubmerged aquatic vegetation (SAV) growth can be limited by light and nutrient availability. Infauna are common inhabitants of SAV meadows. Their activity increases nutrient mobility, and they can positively affect plant growth, but we do not know their role in plant trait-biomass production relationships.
View Article and Find Full Text PDFHortic Res
January 2024
State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
J Dev Orig Health Dis
October 2023
Department of Human Genetics, Department of Human Genetics, Genome Diagnostic Laboratory, Amsterdam Reproduction and Development, Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.
Am J Hum Genet
November 2023
Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address:
Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data.
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