Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits
| Autor: | Qingrun Zhang, Sukanya Horpaopan, Jurg Ott, Atsuko Okazaki, Matthew Randesi |
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| Rok vydání: | 2021 |
| Předmět: |
Genotype
Mutant Datasets as Topic QH426-470 Biology Dna variants Polymorphism Single Nucleotide Article 03 medical and health sciences 0302 clinical medicine Retinitis pigmentosa Genetics medicine Permutation testing Data Mining Humans biochemistry Gene Genetics (clinical) 030304 developmental biology 0303 health sciences digenic traits Blindness Genetic Diseases Inborn DNA medicine.disease Phenotype Case-Control Studies genotype pattern diplotype 030217 neurology & neurosurgery pattern mining |
| Zdroj: | Genes Volume 12 Issue 8 Genes, Vol 12, Iss 1160, p 1160 (2021) |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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