Significance Associated with Phenotype Score Aids in Variant Prioritization for Exome Sequencing Analysis.

Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduced a novel metric Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing an individual's observed phenotypes against existing gene-phenotype associations. To evaluate the SAP score, a retrospective analysis was performed on 219 exomes.

Methods for fine-mapping with chromatin and expression data.

Recent studies have identified thousands of regions in the genome associated with chromatin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression.

Improved methods for multi-trait fine mapping of pleiotropic risk loci.

Motivation: Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

DNA glycosylase activity and cell proliferation are key factors in modulating homologous recombination in vivo.

Cancer susceptibility varies between people, affected by genotoxic exposures, genetic makeup and physiological state. Yet, how these factors interact among each other to define cancer risk is largely unknown. Here, we uncover the interactive effects of genetical, environmental and physiological factors on genome rearrangements driven by homologous recombination (HR).