scholarly journals Multivariate Detection of Gene-Gene Interactions

2012 ◽  
Vol 36 (6) ◽  
pp. 622-630 ◽  
Author(s):  
Indika Rajapakse ◽  
Michael D. Perlman ◽  
Paul J Martin ◽  
John A. Hansen ◽  
Charles Kooperberg
Keyword(s):  
Gene Interactions ◽  
Multivariate Detection

Genetic Variation: Impact on Folate (and Choline) Bioefficacy

2010 ◽  
Vol 80 (45) ◽  
pp. 319-329 ◽  
Author(s):  
Allyson A. West ◽  
Marie A. Caudill
Keyword(s):  
Carbon Metabolism ◽  
Genetic Variants ◽  
Plasma Homocysteine ◽  
Water Soluble ◽  
Gene Interactions ◽  
Dietary Folate ◽  
Methyl Donors ◽  
Common Genetic Variants ◽  
One Carbon Metabolism ◽  
The Impact

Folate and choline are water-soluble micronutrients that serve as methyl donors in the conversion of homocysteine to methionine. Inadequacy of these nutrients can disturb one-carbon metabolism as evidenced by alterations in circulating folate and/or plasma homocysteine. Among common genetic variants that reside in genes regulating folate absorptive and metabolic processes, homozygosity for the MTHFR 677C > T variant has consistently been shown to have robust effects on status markers. This paper will review the impact of genetic variants in folate-metabolizing genes on folate and choline bioefficacy. Nutrient-gene and gene-gene interactions will be considered along with the need to account for these genetic variants when updating dietary folate and choline recommendations.

Within- and Inter- gene interactions in FKBP5 and GR genes and its association with early response

2005 ◽  
Vol 38 (05) ◽  
Author(s):  
D Salyakina ◽  
EB Binder ◽  
M Ising ◽  
M Uhr ◽  
S Lucae ◽  
...  
Keyword(s):  
Early Response ◽  
Gene Interactions

Machine learning: Assisted multivariate detection and visual image matching to build broad-specificity immunosensor

2021 ◽  
Vol 339 ◽  
pp. 129872
Author(s):  
Aori Qileng ◽  
Hongshuai Zhu ◽  
Siqian Liu ◽  
Liang He ◽  
Weiwei Qin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Matching ◽  
Visual Image ◽  
Broad Specificity ◽  
Multivariate Detection

INHERITANCE AND LINKAGE STUDIES WITH THE GRANDPA GENE IN BARLEY, HORDEUM VULGARE L.

1971 ◽  
Vol 13 (3) ◽  
pp. 489-498
Author(s):  
R. W. Matchett ◽  
H. G. Nass ◽  
D. W. Robertson
Keyword(s):  
Hordeum Vulgare ◽  
Chromosomal Location ◽  
Gene Interactions ◽  
Chromosome 2 ◽  
Linkage Studies ◽  
Hordeum Vulgare L ◽  
Barley Genome ◽  
Chlorophyll Mutants ◽  
Mutant Genes ◽  
Linkage Association

This study was initiated to determine the chromosomal location of the grandpa (gp) gene within the barley genome. The gp gene was placed on the long arm of chromosome 2 as indicated by linkage association with liguleless (li).Tests of allelism showed the gp gene to the allelic with the gp-2 gene. Seven sources of "yellow" chlorophyll mutants when crossed to grandpa plants gave albino double recessive seedlings. Three other sources of "yellow" chlorophyll mutants in the double recessive combination with grandpa exhibited yellow and white bands on the leaves. Double recessive individuals carrying the mottled (mt2) and grandpa genes were also albino. This is evidence of gene interactions between chlorophyll mutant genes.

scholarly journals GENECFE-ANFIS: A NEURO-FUZZY INFERENCE SYSTEM TO INFER GENE-GENE INTERACTIONS BASED ON RECOGNITION OF MICROARRAY GENE EXPRESSION PATTERNS

2007 ◽  
Vol 19 (02) ◽  
pp. 71-78 ◽  
Author(s):  
Cheng-Long Chuang ◽  
Chung-Ming Chen ◽  
Grace S. Shieh ◽  
Joe-Air Jiang
Keyword(s):  
Gene Expression ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expression Patterns ◽  
Gene Interactions ◽  
Microarray Gene Expression ◽  
Inference System ◽  
Qrt Pcr ◽  
Neuro Fuzzy ◽  
Microarray Gene

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.

scholarly journals A Methodological Framework to Discover Pharmacogenomic Interactions Based on Random Forests

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 933
Author(s):  
Salvatore Fasola ◽  
Giovanna Cilluffo ◽  
Laura Montalbano ◽  
Velia Malizia ◽  
Giuliana Ferrante ◽  
...  
Keyword(s):  
Random Forests ◽  
Cancer Cell Line ◽  
Predictive Performance ◽  
Computational Time ◽  
Gene Interactions ◽  
Methodological Framework ◽  
Concordance Correlation ◽  
Driver Genes ◽  
Tumor Tissues ◽  
Gene Alterations

The identification of genomic alterations in tumor tissues, including somatic mutations, deletions, and gene amplifications, produces large amounts of data, which can be correlated with a diversity of therapeutic responses. We aimed to provide a methodological framework to discover pharmacogenomic interactions based on Random Forests. We matched two databases from the Cancer Cell Line Encyclopaedia (CCLE) project, and the Genomics of Drug Sensitivity in Cancer (GDSC) project. For a total of 648 shared cell lines, we considered 48,270 gene alterations from CCLE as input features and the area under the dose-response curve (AUC) for 265 drugs from GDSC as the outcomes. A three-step reduction to 501 alterations was performed, selecting known driver genes and excluding very frequent/infrequent alterations and redundant ones. For each model, we used the concordance correlation coefficient (CCC) for assessing the predictive performance, and permutation importance for assessing the contribution of each alteration. In a reasonable computational time (56 min), we identified 12 compounds whose response was at least fairly sensitive (CCC > 20) to the alteration profiles. Some diversities were found in the sets of influential alterations, providing clues to discover significant drug-gene interactions. The proposed methodological framework can be helpful for mining pharmacogenomic interactions.

scholarly journals RIL-StEp: epistasis analysis of rice recombinant inbred lines (RILs) reveals candidate interacting genes that control seed hull color and leaf chlorophyll content

2021 ◽  
Author(s):  
Toshiyuki Sakai ◽  
Akira Abe ◽  
Motoki Shimizu ◽  
Ryohei Terauchi
Keyword(s):  
Chlorophyll Content ◽  
Recombinant Inbred ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Gene Interactions ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Seed Hull

Abstract Characterizing epistatic gene interactions is fundamental for understanding the genetic architecture of complex traits. However, due to the large number of potential gene combinations, detecting epistatic gene interactions is computationally demanding. A simple, easy-to-perform method for sensitive detection of epistasis is required. Due to their homozygous nature, use of recombinant inbred lines (RILs) excludes the dominance effect of alleles and interactions involving heterozygous genotypes, thereby allowing detection of epistasis in a simple and interpretable model. Here, we present an approach called RIL-StEp (recombinant inbred lines stepwise epistasis detection) to detect epistasis using single nucleotide polymorphisms in the genome. We applied the method to reveal epistasis affecting rice (Oryza sativa) seed hull color and leaf chlorophyll content and successfully identified pairs of genomic regions that presumably control these phenotypes. This method has the potential to improve our understanding of the genetic architecture of various traits of crops and other organisms.

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