Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Version 1 : Received: 21 July 2021 / Approved: 26 July 2021 / Online: 26 July 2021 (11:14:06 CEST)

A peer-reviewed article of this Preprint also exists.

Okazaki, A.; Horpaopan, S.; Zhang, Q.; Randesi, M.; Ott, J. Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits. Genes 2021, 12, 1160. Okazaki, A.; Horpaopan, S.; Zhang, Q.; Randesi, M.; Ott, J. Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits. Genes 2021, 12, 1160.

Journal reference: Genes 2021, 12, 1160
DOI: 10.3390/genes12081160

Abstract

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.

Keywords

pattern mining; digenic traits; genotype pattern; diplotype

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