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| http://dx.doi.org/10.1038/ejhg.2014.287 | DOI Listing |
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Deep learning captures the effect of epistasis in multifactorial diseases.
Front Med (Lausanne)
January 2025
International Laboratory of Bioinformatics, AI and Digital Sciences Institute, Faculty of Computer Science, HSE University, Moscow, Russia.
Background: Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weights of individual SNPs are estimated by the linear regression model that assumes independent and linear contribution of each SNP to the phenotype. However, for complex multifactorial diseases such as Alzheimer's disease, diabetes, cardiovascular disease, cancer, and others, association between individual SNPs and disease could be non-linear due to epistatic interactions.
View Article and Find Full Text PDFGenotype by year interaction and additive and epistasis gene effects for Fusarium stalk rot resistance in doubled haploid lines of maize (Zea mays L.).
J 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 PDFGenome-wide association study of rice (Oryza sativa L.) inflorescence architecture.
Plant Sci
January 2025
Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran.
Rice yield strongly depends on panicle size and architecture but the genetics underlying these traits and their coordination with environmental cues through various signaling pathways have remained elusive. A genome-wide association study (GWAS) was performed to pinpoint the underlying genetic determinants for rice panicle architecture by analyzing 20 panicle-related traits using a data set consisting of 44,100 SNPs. We defined QTL windows around significant SNPs by the rate of LD decay for each chromosome and used these windows to identify putative candidate genes associated with the trait.
View Article and Find Full Text PDFGenetic interaction network of quantitative trait genes for heading date in rice.
J Genet Genomics
January 2025
Shanghai Key Laboratory of Plant Molecular Sciences, Shanghai Engineering Research Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China. Electronic address:
Several quantitative trait genes (QTGs) related to rice heading date, a key factor for crop development and yield, have been identified, along with complex interactions among genes. However, a comprehensive genetic interaction network for these QTGs has not yet been established. In this study, we use 18K-rice lines to identify QTGs and their epistatic interactions affecting rice heading date.
View Article and Find Full Text PDFEpistatic hotspots organize antibody fitness landscape and boost evolvability.
Proc Natl Acad Sci U S A
January 2025
Department of Physics and Astronomy, University of California, Los Angeles, CA 90095.
The course of evolution is strongly shaped by interaction between mutations. Such epistasis can yield rugged sequence-function maps and constrain the availability of adaptive paths. While theoretical intuition is often built on global statistics of large, homogeneous model landscapes, mutagenesis measurements necessarily probe a limited neighborhood of a reference genotype.
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