Biology subjects

Baker, S. W.

Publications and source records attributed to Baker, S. W..

2 recordsLinked to original sources

bioRxiv2018

Genetic And Epigenetic Fine Mapping Of Complex Trait Associated Loci In The Human Liver

Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. Although histone modifications are important markers of gene regulatory regions of the genome, any specific histone modification has not been assayed in more than a few individuals in the human liver. As a result, the impacts of genetic variation that direct histone modification states in the liver are poorly understood. Here, we generate the most comprehensive genome-wide dataset of two epigenetic marks, H3K4me3 and H3K27ac, and annotate thousands of putative regulatory elements in the human liver. We integrate these findings with genome-wide gene expression data collected from the same human liver tissues and high-resolution promoter-focused chromatin interaction maps collected from human liver-derived HepG2 cells. We demonstrate widespread functional consequences of natural genetic variation on putative regulatory element activity and gene expression levels. Leveraging these extensive datasets, we fine-map a total of 77 GWAS loci that have been associated with at least one complex phenotype. Our results contribute to the repertoire of genes and regulatory mechanisms governing complex disease development and further the basic understanding of genetic and epigenetic regulation of gene expression in the human liver tissue.

genetics↗

bioRxiv2018

Rapid interpretation of clinical exomes using Phenoxome: a computational phenotype-driven approach

Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies, solving up to 20%-50% of cases depending on indication. Despite rapid advancements in CES analysis, the major challenge still resides in identifying the casual variants among the thousands of variants detected during CES testing, and thus establishing a molecular diagnosis. To improve the clinical exome diagnostic efficiency, we developed Phenoxome, a robust phenotype-driven model that adopts a network-based approach to facilitate automated variant prioritization and subsequent classification. Phenoxome dissects the phenotypic manifestation of a patient in conjunction with their genomic profile to filter and then prioritize putative pathogenic variants. To validate our method, we have compiled a clinical cohort of 105 positive patient samples (i.e. at least one reported pathogenic variant) that represent a wide range of genetic heterogeneity from The Childrens Hospital of Philadelphia. Our approach identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these patient samples respectively. Furthermore, we show that our method is optimized for clinical testing by yielding superior ranking of the pathogenic variants compared to current state-of-art methods. The web application of Phenoxome is available to the public at http://phenoxome.chop.edu/.

bioinformatics↗