James E. Hayes

JCoDA: a tool for detecting evolutionary selection.

BackgroundThe incorporation of annotated sequence information from multiple related species in commonly used databases (Ensembl, Flybase, Saccharomyces Genome Database, Wormbase, etc.) has increased dramatically over the last few years. This influx of information has provided a considerable amount of raw material for evaluation of evolutionary relationships. To aid in the process, we have developed JCoDA (Java Codon Delimited Alignment) as a simple-to-use visualization tool for the detection of site specific and regional positive/negative evolutionary selection amongst homologous coding sequences.ResultsJCoDA accepts user-inputted unaligned or pre-aligned coding sequences, performs a codon-delimited alignment using ClustalW, and determines the dN/dS calculations using PAML (Phylogenetic Analysis Using Maximum Likelihood, yn00 and codeml) in order to identify regions and sites under evolutionary selection. The JCoDA package includes a graphical interface for Phylip (Phylogeny Inference Package) to generate phylogenetic trees, manages formatting of all required file types, and streamlines passage of information between underlying programs. The raw data are output to user configurable graphs with sliding window options for straightforward visualization of pairwise or gene family comparisons. Additionally, codon-delimited alignments are output in a variety of common formats and all dN/dS calculations can be output in comma-separated value (CSV) format for downstream analysis. To illustrate the types of analyses that are facilitated by JCoDA, we have taken advantage of the well studied sex determination pathway in nematodes as well as the extensive sequence information available to identify genes under positive selection, examples of regional positive selection, and differences in selection based on the role of genes in the sex determination pathway.ConclusionsJCoDA is a configurable, open source, user-friendly visualization tool for performing evolutionary analysis on homologous coding sequences. JCoDA can be used to rapidly screen for genes and regions of genes under selection using PAML. It can be freely downloaded at http://www.tcnj.edu/~nayaklab/jcoda.

Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong inxa0vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.

Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements

Though numerous polymorphisms have been associated with risk of developing lymphoma, how these variants function to promote tumorigenesis is poorly understood. Here, we report that lymphoma risk SNPs, especially in the non-Hodgkin’s lymphoma subtype chronic lymphocytic leukemia, are significantly enriched for co-localization with epigenetic marks of active gene regulation. These enrichments were seen in a lymphoid-specific manner for numerous ENCODE datasets, including DNase-hypersensitivity as well as multiple segmentation-defined enhancer regions. Furthermore, we identify putatively functional SNPs that are both in regulatory elements in lymphocytes and are associated with gene expression changes in blood. We developed an algorithm, UES, that uses a Monte Carlo simulation approach to calculate the enrichment of previously identified risk SNPs in various functional elements. This multiscale approach integrating multiple datasets helps disentangle the underlying biology of lymphoma, and more broadly, is generally applicable to GWAS results from other diseases as well.

Validation of Prostate Cancer Risk Variants by CRISPR/Cas9 Mediated Genome Editing

GWAS have identified numerous SNPs associated with prostate cancer risk. One such SNP is rs10993994. It is located in the MSMB promoter, associates with MSMB encoded β-microseminoprotein prostate secretion levels, and is associated with mRNA expression changes in MSMB and the adjacent gene NCOA4. In addition, our previous work showed a second SNP, rs7098889, is in LD with rs10993994 and associated with MSMB expression independent of rs10993994. Here, we generate a series of clones with single alleles removed by double guide RNA (gRNA) mediated CRISPR/Cas9 deletions. We demonstrate that each of these SNPs, located in the most prostate-specific enhancer region, independently and greatly alters MSMB expression in an allele-specific manner. We further show that these SNPs have no substantial effect on the expression of NCOA4. These data demonstrate that a single SNP can have a large effect on gene expression and illustrate the importance of functional validation to deconvolute observed correlations. The method we have developed is generally applicable to test any SNP for which a relevant heterozygous cell line is available.

Abstract 2566: Comprehensive analysis to identify functional basis of prostate cancer risk SNPs

Introduction: Genome-wide association studies (GWAS) have identified numerous common single nucleotide polymorphisms (SNPs) associated with the risk of developing prostate cancer. Many of these prostate cancer GWAS hits occur in intergenic regions, distant from any annotated gene. Little is yet known about how these SNPs function to alter an individual9s risk of prostate cancer. Here we test the hypothesis that many of these SNPs, or SNPs with which they are correlated, alter regulatory regions and thus gene expression in the prostate. Materials and Methods: To test if prostate cancer risk SNPs are correlated with gene expression changes, we conducted expression quantitative trait locus (eQTL) association analysis. Using genome-wide high-density genotypes and gene expression data from 56 tumor and 58 adjacent normal prostate tissues we asked if any of the prostate cancer risk SNPs correlate with expression changes in nearby genes using linear regression, adjusting gene expression levels for principal components of ancestry and different batches. We also asked if individuals who have a higher genetic risk of prostate cancer based on the genotype of all of their risk SNPs show different gene expression patterns in the prostate than those at lower risk of prostate cancer. Results: We have identified several novel cis-eQTLs in prostate tissue for prostate cancer risk SNPs including rs1933488 with RGS17. We have also replicated several previously known cis-eQTLs, including those for IRX4, PPP1R14A, and FOXP4. Individuals at higher genetic risk of developing prostate cancer have altered expression of 37 genes (p Conclusions: These data demonstrate that several prostate cancer risk SNPs are associated with expression changes in nearby genes in both pathologically malignant and pathologically benign prostate tissue from patients who underwent radical prostatectomy. Additional genes, including HMOX1, CSGALNACT1, and WDR36 show altered expression in patients with different levels of genetic risk of prostate cancer. Citation Format: Mridu Middha, Xing Xu, Riina-Minna Vaananen, James Hayes, Pekka Taimen, Xiaoni Gao, Hans G. Lilja, Kim Pettersson, Robert J. Klein. Comprehensive analysis to identify functional basis of prostate cancer risk SNPs. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2566.