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Huynh, L.

Publications and source records attributed to Huynh, L..

2 recordsLinked to original sources

Contribution of structural variation to genome structure: TAD fusion discovery and ranking

The significant contribution of structural variants to function, disease, and evolution is widely reported. However, in many cases, the mechanism by which these variants contribute to the phenotype is not well understood. Recent studies reported structural variants that disrupted the three-dimensional genome structure by fusing two topologically associating domains (TADs), such that enhancers from one TAD interacted with genes from the other TAD, and could cause severe developmental disorders. However, no computational method exists for directly scoring and ranking structural variations based on their effect on the three-dimensional structure such as the TAD disruption to guide further studies of their biological function. In this paper, we formally define TAD fusion and provide a combinatorial approach for assigning a score to quantify the level of TAD fusion for each deletion denoted as TAD fusion score. We also show that our method outperforms the approaches which use predicted TADs and overlay the deletion on them to predict TAD fusion. Furthermore, we show that deletions that cause TAD fusion are rare and under negative selection in general population. Finally, we show that our method correctly gives higher scores to deletions reported to cause various disorders (developmental disorder and cancer) in comparison to the deletions reported in the 1000 genomes project.

bioinformatics

Ultra-Accurate Complex Disorder Prediction: Case Study Of Neurodevelopmental Disorders

Early prediction of complex disorders (e.g., autism and other neurodevelopmental disorders) is one of the fundamental goals of precision medicine and personalized genomics. An early prediction of complex disorders can have a significant impact on increasing the effectiveness of interventions and treatments in improving the prognosis and, in many cases, enhancing the quality of life in the affected patients. Considering the genetic heritability of neurodevelopmental disorders, we are proposing a novel framework for utilizing rare coding variation for early prediction of these disorders in subset of affected samples. We provide a novel formulation for the Ultra-Accurate Disorder Prediction (UADP) problem and develop a combinatorial framework for solving this problem. The primary goal of this framework, denoted as Odin (Oracle for DIsorder predictioN), is to make prediction for a subset of affected cases while having very low false positive rate prediction for unaffected samples. Note that in the Odin framework we will take advantage of the available functional information (e.g., pairwise coexpression of genes during brain development) to increase the prediction power beyond genes with recurrent variants. Application of our method accurately recovers an additional 8% of autism cases without a sever variant in a known recurrent mutated genes with a less than 1% false positive rate. Furthermore, Odin predicted a set of 391 genes that severe variants in these genes can cause autism or other developmental delay disorders. Odin is publicly available at https://github.com/HormozdiariLab/Odin{dagger}

bioinformatics