Positive predictive value highlights four novel candidates for actionable genetic screening from analysis of 220,000 clinicogenomic records.

PURPOSE: To identify conditions that are candidates for population genetic screening based on population prevalence, penetrance of rare variants, and actionability. METHODS: We analyzed exome and medical record data from >220,000 participants across two large population health cohorts with different demographics. We performed a gene-based collapsing analysis of rare variants to identify genes significantly associated with disease status. RESULTS: We identify 74 statistically significant gene-disease associations across 27 genes. Seven of these conditions have a positive predictive value (PPV) of at least 30% in both cohorts. Three are already used in population screening programs (BRCA1, BRCA2, LDLR), and we also identify four new candidates for population screening: GCK with diabetes mellitus, HBB with ß-thalassemia minor and intermedia, PKD1 with cystic kidney disease, and MIP with cataracts. Importantly, the associations are actionable in that early genetic screening of each of these conditions is expected to improve outcomes. CONCLUSION: We identify seven genetic conditions where rare variation appears appropriate to assess in population screening, four of which are not yet used in screening programs. The addition of GCK, HBB, PKD1, and MIP rare variants into genetic screening programs would reach an additional 0.21% of participants with actionable disease risk, depending on the population.

Houston Methodist Variant Viewer: An Application to Support Clinical Laboratory Interpretation of Next-generation Sequencing Data for Cancer.

INTRODUCTION: Next-generation-sequencing (NGS) is increasingly used in clinical and research protocols for patients with cancer. NGS assays are routinely used in clinical laboratories to detect mutations bearing on cancer diagnosis, prognosis and personalized therapy. A typical assay may interrogate 50 or more gene targets that encompass many thousands of possible gene variants. Analysis of NGS data in cancer is a labor-intensive process that can become overwhelming to the molecular pathologist or research scientist. Although commercial tools for NGS data analysis and interpretation are available, they are often costly, lack key functionality or cannot be customized by the end user. METHODS: To facilitate NGS data analysis in our clinical molecular diagnostics laboratory, we created a custom bioinformatics tool termed Houston Methodist Variant Viewer (HMVV). HMVV is a Java-based solution that integrates sequencing instrument output, bioinformatics analysis, storage resources and end user interface. RESULTS: Compared to the predicate method used in our clinical laboratory, HMVV markedly simplifies the bioinformatics workflow for the molecular technologist and facilitates the variant review by the molecular pathologist. Importantly, HMVV reduces time spent researching the biological significance of the variants detected, standardizes the online resources used to perform the variant investigation and assists generation of the annotated report for the electronic medical record. HMVV also maintains a searchable variant database, including the variant annotations generated by the pathologist, which is useful for downstream quality improvement and research projects. CONCLUSIONS: HMVV is a clinical grade, low-cost, feature-rich, highly customizable platform that we have made available for continued development by the pathology informatics community.

Correction: Quantitative genetics of CTCF binding reveal local sequence effects and different modes of X-chromosome association.

[This corrects the article DOI: 10.1371/journal.pgen.1004798.].

Quantitative genetics of CTCF binding reveal local sequence effects and different modes of X-chromosome association.

Associating genetic variation with quantitative measures of gene regulation offers a way to bridge the gap between genotype and complex phenotypes. In order to identify quantitative trait loci (QTLs) that influence the binding of a transcription factor in humans, we measured binding of the multifunctional transcription and chromatin factor CTCF in 51 HapMap cell lines. We identified thousands of QTLs in which genotype differences were associated with differences in CTCF binding strength, hundreds of them confirmed by directly observable allele-specific binding bias. The majority of QTLs were either within 1 kb of the CTCF binding motif, or in linkage disequilibrium with a variant within 1 kb of the motif. On the X chromosome we observed three classes of binding sites: a minority class bound only to the active copy of the X chromosome, the majority class bound to both the active and inactive X, and a small set of female-specific CTCF sites associated with two non-coding RNA genes. In sum, our data reveal extensive genetic effects on CTCF binding, both direct and indirect, and identify a diversity of patterns of CTCF binding on the X chromosome.

An eQTL analysis of the human glioblastoma multiforme genome.

In this paper we use eQTL mapping to identify associations between gene dysregulation and single nucleotide polymorphism (SNP) genotypes in glioblastoma multiforme (GBM). A set of 532,954 SNPs was evaluated as predictors of the expression levels of 22,279 expression probes. We identified SNPs associated with fold change in expression level rather than raw expression levels in the tumor. Following adjustment for false discovery rate, the complete set of probes yielded 9257 significant associations (p<0.05). We found 18 eQTLs that were missense mutations. Many of the eQTLs in the non-coding regions of a gene, or linked to nearby genes, had large numbers of significant associations (e.g. 321 for RNASE3, 101 for BNC2). Functional enrichment analysis revealed that the expression probes in significant associations were involved in signal transduction, transcription regulation, membrane function, and cell cycle regulation. These results suggest several loci that may serve as hubs in gene regulatory pathways associated with GBM.

Simultaneous SNP identification and assessment of allele-specific bias from ChIP-seq data.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been associated with many aspects of human development and disease, and many non-coding SNPs associated with disease risk are presumed to affect gene regulation. We have previously shown that SNPs within transcription factor binding sites can affect transcription factor binding in an allele-specific and heritable manner. However, such analysis has relied on prior whole-genome genotypes provided by large external projects such as HapMap and the 1000 Genomes Project. This requirement limits the study of allele-specific effects of SNPs in primary patient samples from diseases of interest, where complete genotypes are not readily available. RESULTS: In this study, we show that we are able to identify SNPs de novo and accurately from ChIP-seq data generated in the ENCODE Project. Our de novo identified SNPs from ChIP-seq data are highly concordant with published genotypes. Independent experimental verification of more than 100 sites estimates our false discovery rate at less than 5%. Analysis of transcription factor binding at de novo identified SNPs revealed widespread heritable allele-specific binding, confirming previous observations. SNPs identified from ChIP-seq datasets were significantly enriched for disease-associated variants, and we identified dozens of allele-specific binding events in non-coding regions that could distinguish between disease and normal haplotypes. CONCLUSIONS: Our approach combines SNP discovery, genotyping and allele-specific analysis, but is selectively focused on functional regulatory elements occupied by transcription factors or epigenetic marks, and will therefore be valuable for identifying the functional regulatory consequences of non-coding SNPs in primary disease samples.

Cell-type specific and combinatorial usage of diverse transcription factors revealed by genome-wide binding studies in multiple human cells.

Cell-type diversity is governed in part by differential gene expression programs mediated by transcription factor (TF) binding. However, there are few systematic studies of the genomic binding of different types of TFs across a wide range of human cell types, especially in relation to gene expression. In the ENCODE Project, we have identified the genomic binding locations across 11 different human cell types of CTCF, RNA Pol II (RNAPII), and MYC, three TFs with diverse roles. Our data and analysis revealed how these factors bind in relation to genomic features and shape gene expression and cell-type specificity. CTCF bound predominantly in intergenic regions while RNAPII and MYC preferentially bound to core promoter regions. CTCF sites were relatively invariant across diverse cell types, while MYC showed the greatest cell-type specificity. MYC and RNAPII co-localized at many of their binding sites and putative target genes. Cell-type specific binding sites, in particular for MYC and RNAPII, were associated with cell-type specific functions. Patterns of binding in relation to gene features were generally conserved across different cell types. RNAPII occupancy was higher over exons than adjacent introns, likely reflecting a link between transcriptional elongation and splicing. TF binding was positively correlated with the expression levels of their putative target genes, but combinatorial binding, in particular of MYC and RNAPII, was even more strongly associated with higher gene expression. These data illuminate how combinatorial binding of transcription factors in diverse cell types is associated with gene expression and cell-type specific biology.

Wingless signaling induces widespread chromatin remodeling of target loci.

How signaling cascades influence gene regulation at the level of chromatin modification is not well understood. We studied this process using the Wingless/Wnt pathway in Drosophila. When cells sense Wingless ligand, Armadillo (the fly beta-catenin) becomes stabilized and translocates to the nucleus, where it binds to the sequence-specific DNA binding protein TCF to activate transcription of target genes. Here, we show that Wingless signaling induces TCF and Armadillo recruitment to a select subset of TCF binding site clusters that act as Wingless response elements. Despite this localized TCF/Armadillo recruitment, histones are acetylated over a wide region (up to 30 kb) surrounding the Wingless response elements in response to pathway activation. This widespread histone acetylation occurs independently of transcription. In contrast to Wingless targets, other active genes not regulated by the pathway display sharp acetylation peaks centered on their core promoters. Widespread acetylation of Wingless targets is dependent upon CBP, a histone acetyltransferase known to bind to Armadillo and is correlated with activation of target gene expression. These data suggest that pathway activation induces localized recruitment of TCF/Armadillo/CBP to Wingless response elements, leading to widespread histone acetylation of target loci prior to transcriptional activation.