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. Among them, 82 family-based and 35 singleton exomes had at least one disease-causing variant that explained the patient's clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared with a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77 of 82) of the family-based exomes and in first place for 73% of these cases. For singleton exomes, the SAP score analysis ranked the known pathogenic variants within the top 10 for 80% (28 of 35) of cases. The SAP score, which is independent of detected variants, demonstrates comparable performance with Exomiser, which considers both phenotype and variant-level evidence simultaneously. Among 102 cases with negative results or variants of uncertain significance, SAP score analysis revealed two cases with a potential new diagnosis based on rank. The SAP score, a phenotypic quantitative metric, can be used in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis.

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. In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions.

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. RESULTS: In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data. AVAILABILITY AND IMPLEMENTATION: The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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). Using FYDR mice to quantify HR-driven rearrangements in pancreas tissue, we show that DNA methylation damage (induced by methylnitrosourea) and cell proliferation (induced by thyroid hormone) each induce HR and together act synergistically to induce HR-driven rearrangements in vivo. These results imply that developmental or regenerative proliferation as well as mitogenic exposures may sensitize tissues to DNA damaging exposures. We exploited mice genetically deficient in alkyl-adenine DNA glycosylase (Aag) to analyse the relative contributions of unrepaired DNA base lesions versus intermediates formed during base excision repair (BER). Remarkably, results show that, in the pancreas, Aag is a major driver of spontaneous HR, indicating that BER intermediates (including abasic sites and single strand breaks) are more recombinogenic than the spontaneous base lesions removed by Aag. Given that mammals have about a dozen DNA glycosylases, these results point to BER as a major source of pressure on the HR pathway in vivo. Taken together, methylation damage, cell proliferation and Aag interact to define the risk of HR-driven sequence rearrangements in vivo. These data identify important sources of sequence changes in a cancer-relevant organ, and advance the effort to identify populations at high-risk for cancer.