Describing DNA Variants (Mutation Nomenclature)

2017 ◽  
pp. 13-22
Author(s):  
J.T. den Dunnen ◽  
P.E.M. Taschner
Keyword(s):  
Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1160
Author(s):  
Atsuko Okazaki ◽  
Sukanya Horpaopan ◽  
Qingrun Zhang ◽  
Matthew Randesi ◽  
Jurg Ott

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.


Author(s):  
Claudia Calabrese ◽  
Aurora Gomez-Duran ◽  
Aurelio Reyes ◽  
Marcella Attimonelli

2006 ◽  
Vol 38 (11) ◽  
pp. 1261-1268 ◽  
Author(s):  
Raquel Moreno-Loshuertos ◽  
Rebeca Acín-Pérez ◽  
Patricio Fernández-Silva ◽  
Nieves Movilla ◽  
Acisclo Pérez-Martos ◽  
...  

1988 ◽  
Vol 79 (3) ◽  
pp. 195-195
Keyword(s):  

Author(s):  
Flavia Palombo ◽  
Camille Peron ◽  
Leonardo Caporali ◽  
Angelo Iannielli ◽  
Alessandra Maresca ◽  
...  

2021 ◽  
pp. 100106
Author(s):  
Qiang Zhang ◽  
Li-Jie Zhang ◽  
Sha-Sha Yuan ◽  
Xiao-Jiang Quan ◽  
Bao-Yu Zhang ◽  
...  

2017 ◽  
Vol 1 (3) ◽  
pp. 245-248 ◽  
Author(s):  
Chris P. Ponting

With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate function, and molecular interactions are often irrelevant to a cell's or organism's survival or propagation. Merely correlative relationships found in big data fail to provide answers to the Why questions of human biology. Instead, those answers are expected from methods that causally link DNA changes to downstream effects without being confounded by reverse causation. These approaches require the controlled measurement of the consequences of DNA variants, for example, either those introduced in single cells using CRISPR/Cas9 genome editing or that are already present across the human population. Inferred causal relationships between genetic variation and cellular phenotypes or disease show promise to rapidly grow and underpin our knowledge base.


2002 ◽  
Vol 29 (1) ◽  
pp. 102-104 ◽  
Author(s):  
Laurent Andréoletti ◽  
Laurence Weiss ◽  
Ali Si-Mohamed ◽  
Christophe Piketty ◽  
Thierry Prazuck ◽  
...  

2008 ◽  
Vol 99 (12) ◽  
pp. 2088-2093 ◽  
Author(s):  
E Webb ◽  
P Broderick ◽  
I Chandler ◽  
S Lubbe ◽  
S Penegar ◽  
...  

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