Studies assessing the existence and magnitude of epistatic effects on complex human traits provide inconclusive results. The study of such effects is complicated by considerable increase in computational burden, model complexity, and model uncertainty, which in concert decrease model stability. An additional source introducing significant uncertainty with regard to the detection of robust epistasis is the biological distance between the genetic variation and the trait under study. Here we studied CpG methylation, a genetically complex molecular trait that is particularly close to genomic variation, and performed an exhaustive search for two-locus epistatic effects on the CpG-methylation signal in two cohorts of healthy young subjects. We detected robust epistatic effects for a small number of CpGs (N = 404). Our results indicate that epistatic effects explain only a minor part of variation in DNA-CpG methylation. Interestingly, these CpGs were more likely to be associated with gene-expression of nearby genes, as also shown by their overrepresentation in DNase I hypersensitivity sites and underrepresentation in CpG islands. Finally, gene ontology analysis showed a significant enrichment of these CpGs in pathways related to HPV-infection and cancer.
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http://dx.doi.org/10.1038/s41598-017-13256-9 | DOI Listing |
Nat Commun
January 2025
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods.
View Article and Find Full Text PDFPoult Sci
January 2025
Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China. Electronic address:
Low-coverage whole genome sequencing (lcWGS) is an effective low-cost genotyping technology when combined with genotype imputation approaches. It facilitates cost-effective genomic selection (GS) programs in agricultural animal populations. GS based on lcWGS data has been successfully applied to livestock such as pigs and donkeys.
View Article and Find Full Text PDFJ Appl Genet
January 2025
Department of Plant Protection, Division of Plant Pathology and Mycology, Wrocław University of Environmental and Life Sciences, Grunwaldzki 24A, 50-363, Wrocław, Poland.
Fusarium stalk rot is the main factor reducing the quality of maize grain and leads to significant yield losses, which that ranges from 20 to 100%, depending on the degree of infection and weather conditions. Understanding its genetic mechanism is key to improving grain quality and ultimate yield. An experiment with 26 doubled haploid (DH) lines of maize was conducted in the northern part of the Lower Silesia Province in Poland over a ten-year period (2013-2022).
View Article and Find Full Text PDFGigascience
January 2025
State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Background: Plumage coloration is a distinctive trait in ducks, and the Liancheng duck, characterized by its white plumage and black beak and webbed feet, serves as an excellent subject for such studies. However, academic comprehension of the genetic mechanisms underlying duck plumage coloration remains limited. To this end, the Liancheng duck genome (GCA_039998735.
View Article and Find Full Text PDFMol Biol Evol
January 2025
Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium.
Determining the impact of mutations on the thermodynamic stability of proteins is essential for a wide range of applications such as rational protein design and genetic variant interpretation. Since protein stability is a major driver of evolution, evolutionary data are often used to guide stability predictions. Many state-of-the-art stability predictors extract evolutionary information from multiple sequence alignments of proteins homologous to a query protein, and leverage it to predict the effects of mutations on protein stability.
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