Biology subjects

Tellam, R. L.

Publications and source records attributed to Tellam, R. L..

2 recordsLinked to original sources

bioRxiv2022

Detecting epistatic interactions in genomic data using Random Forests

Epistatic interactions can play an important role in the genetic mechanisms that control phenotypic variation. However, identifying these interactions in high dimensional genomic data can be very challenging due to the large computational burden induced by the high volume of combinatorial tests that have to be performed to explore the entire search space. Random Forests Decision Trees are widely used in a variety of disciplines and are often said to detect interactions. However, Random Forests models do not explicitly detect variable interactions. Most Random Forests based methods that claim to detect interactions rely on different forms of variable importance measures that suffer when the interacting variables have very small or no marginal effects. The proposed Random Forests based method detects interactions using a two-stage approach and is computationally efficient. The approach is demonstrated and validated through its application on several simulated datasets representing different data structures with respect to genomic data and trait heritabilities. The method is also applied to two high dimensional genomics data sets to validate the approach. In both cases, the method results were used to identify several genes closely positioned to the interacting markers that showed strong biological potential for contributing to the genetic control for the respective traits tested. Contacthawlader.almamun@csiro.au

bioinformatics↗

bioRxiv2017

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics and epigenetics data

Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits, and identifying potential genome editing targets.

genomics↗