Results 41 to 50 of about 50,815 (199)

Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network

BMC Bioinformatics, 2019
Background Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now,
Yang Guo, Zhiman Zhong, Chen Yang, Jiangfeng Hu, Yaling Jiang, Zizhen Liang, Hui Gao, Jianxiao Liu   +7 more
doaj   +1 more source

Comparison of the Accuracy of Epistasis and Haplotype Models for Genomic Prediction of Seven Human Phenotypes

Biomolecules, 2023
The accuracy of predicting seven human phenotypes of 3657–7564 individuals using global epistasis effects was evaluated and compared to the accuracy of haplotype genomic prediction using 380,705 SNPs and 10-fold cross-validation studies.
Zuoxiang Liang, Dzianis Prakapenka, Yang Da   +2 more
doaj   +1 more source

Evidence of epistasis between Interleukin-1 and Selenoprotein-S with susceptibility to rheumatoid arthritis [PDF]

, 2009
Objective: Selenoprotein-S (SELS) is involved in the stress response within the endoplasmic reticulum (ER) and inflammation. Recently, promoter variants in the SELS gene were shown to be associated with plasma levels of interleukin (IL)6, IL1β and tumour
Binks, M.H., Dickson, M.C., Marinou, L., Montgomery, D.S., Walters, K., Wilson, A.G.   +5 more
core   +2 more sources

Lettuce Genetics: Inheritance, Linkage, and Epistasis [PDF]

Journal of the American Society for Horticultural Science, 1992
Two new lettuce (Luctuca sativa L.) genes are described and named truncated leaf (tn), and sickly (si). A gene for reflexed involucre is identical to that previously described in wild lettuce (L.
openaire   +1 more source

Mechanistic causes of sign epistasis and its applications

Frontiers in Genetics
Mapping genetic variations to phenotypic variations poses a significant challenge, as mutations often combine unexpectedly, diverging from assumed additive effects even in the same environment.
Jinqiu Zhang, Feiyu Chen, Feiyu Chen, Xianghua Li, Xianghua Li, Xianghua Li   +5 more
doaj   +1 more source

Multiallelic epistatic model for an out-bred cross and mapping algorithm of interactive quantitative trait loci

BMC Plant Biology, 2011
Background Genetic mapping has proven to be powerful for studying the genetic architecture of complex traits by characterizing a network of the underlying interacting quantitative trait loci (QTLs).
Tong Chunfa, Zhang Bo, Wang Zhong, Xu Meng, Pang Xiaoming, Si Jingna, Huang Minren, Wu Rongling   +7 more
doaj   +1 more source

Information content of colored motifs in complex networks [PDF]

, 2011
We study complex networks in which the nodes of the network are tagged with different colors depending on the functionality of the nodes (colored graphs), using information theory applied to the distribution of motifs in such networks.
Arend Hintze, Christoph Adami, Jifeng Qian, Kolmogorov A., Matthew Rupp   +4 more
core   +1 more source

Detecting epistasis via Markov bases [PDF]

, 2010
Rapid research progress in genotyping techniques have allowed large genome-wide association studies. Existing methods often focus on determining associations between single loci and a specific phenotype.
Malaspinas, Anna-Sapfo, Uhler, Caroline
core   +1 more source

Concurrent outcomes from multiple approaches of epistasis analysis for human body mass index associated loci provide insights into obesity biology

Scientific Reports, 2022
Genome wide association studies (GWAS) have focused on elucidating the genetic architecture of complex traits by assessing single variant effects in additive genetic models, albeit explaining a fraction of the trait heritability.
Sheldon D’Silva, Shreya Chakraborty, Bratati Kahali   +2 more
doaj   +1 more source

Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. [PDF]

, 2016
Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive.
Bloom, Joshua S, Carlborg, Örjan, Forsberg, Simon KG, Kruglyak, Leonid, Sadhu, Meru J   +4 more
core   +3 more sources