Whether in natural populations or between two unrelated members of a species, most phenotypic variation is quantitative. To analyze such quantitative traits, one must first map the underlying quantitative trait loci. Next, and far more difficult, one must identify the quantitative trait genes (QTGs), characterize QTG interactions, and identify the phenotypically relevant polymorphisms to determine how QTGs contribute to phenotype. In this work, we analyzed three Saccharomyces cerevisiae high-temperature growth (Htg) QTGs (MKT1, END3, and RHO2). We observed a high level of genetic interactions among QTGs and strain background. Interestingly, while the MKT1 and END3 coding polymorphisms contribute to phenotype, it is the RHO2 3'UTR polymorphisms that are phenotypically relevant. Reciprocal hemizygosity analysis of the Htg QTGs in hybrids between S288c and ten unrelated S. cerevisiae strains reveals that the contributions of the Htg QTGs are not conserved in nine other hybrids, which has implications for QTG identification by marker-trait association. Our findings demonstrate the variety and complexity of QTG contributions to phenotype, the impact of genetic background, and the value of quantitative genetic studies in S. cerevisiae.
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http://dx.doi.org/10.1371/journal.pgen.0020013 | DOI Listing |
Nat Commun
January 2025
MRC Laboratory of Medical Sciences, London, UK.
Gene enhancers often form long-range contacts with promoters, but it remains unclear if the activity of enhancers and their chromosomal contacts are mediated by the same DNA sequences and recruited factors. Here, we study the effects of expression quantitative trait loci (eQTLs) on enhancer activity and promoter contacts in primary monocytes isolated from 34 male individuals. Using eQTL-Capture Hi-C and a Bayesian approach considering both intra- and inter-individual variation, we initially detect 19 eQTLs associated with enhancer-eGene promoter contacts, most of which also associate with enhancer accessibility and activity.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States of America.
The genetic basis of complex traits involves the function of many genes with small effects as well as complex gene-gene and gene-environment interactions. As one of the major players in complex diseases, the role of gene-environment interactions has been increasingly recognized. Motivated by epidemiology studies to evaluate the joint effect of environmental mixtures, we developed a functional varying-index coefficient model (FVICM) to assess the combined effect of environmental mixtures and their interactions with genes, under a longitudinal design with quantitative traits.
View Article and Find Full Text PDFPlant Genome
March 2025
Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Bari, Italy.
Wheat breeders are constantly looking for genes and alleles that increase grain yield. One key strategy is finding new genetic resources in the wild and domesticated gene pools of related species with genes affecting grain size. This study explored a natural population of Triticum turgidum (L.
View Article and Find Full Text PDFPlant J
January 2025
Unit of Aromatic and Medicinal Plants, Newe Ya'ar Research Center, Volcani Institute, Ramat-Yishay, Israel.
Basil, Ocimum basilicum L., is a widely cultivated aromatic herb, prized for its culinary and medicinal uses, predominantly owing to its unique aroma, primarily determined by eugenol for Genovese cultivars or methyl chavicol for Thai cultivars. To date, a comprehensive basil reference genome has been lacking, with only a fragmented draft available.
View Article and Find Full Text PDFJ Am Heart Assoc
January 2025
Center for Non-Communicable Disease Management Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing China.
Background: The differential impact of serum lipids and their targets for lipid modification on cardiometabolic disease risk is debated. This study used Mendelian randomization to investigate the causal relationships and underlying mechanisms.
Methods: Genetic variants related to lipid profiles and targets for lipid modification were sourced from the Global Lipids Genetics Consortium.
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