A genome-wide survey over the ChIP-on-chip identified androgen receptor-binding genomic regions identifies a novel prostate cancer susceptibility locus at 12q13.13

A genome-wide association scan implicates DCHS2, RUNX2, GLI3, PAX1 and EDAR in human facial variation

We report a genome-wide association scan for facial features in ∼6,000 Latin Americans. We evaluated 14 traits on an ordinal scale and found significant association (P values<5 × 10⁻⁸) at single-nucleotide polymorphisms (SNPs) in four genomic regions for three nose-related traits: columella inclination (4q31), nose bridge breadth (6p21) and nose wing breadth (7p13 and 20p11). In a subsample of ∼3,000 individuals we obtained quantitative traits related to 9 of the ordinal phenotypes and, also, a measure of nasion position. Quantitative analyses confirmed the ordinal-based associations, identified SNPs in 2q12 associated to chin protrusion, and replicated the reported association of nasion position with SNPs in PAX3. Strongest association in 2q12, 4q31, 6p21 and 7p13 was observed for SNPs in the EDAR, DCHS2, RUNX2 and GLI3 genes, respectively. Associated SNPs in 20p11 extend to PAX1. Consistent with the effect of EDAR on chin protrusion, we documented alterations of mandible length in mice with modified Edar funtion.

Humans show great diversity in facial appearance and this variation is highly heritable. Here, Andres Ruiz-Linares and colleagues examined facial features in admixed Latin Americans and identify genome-wide associations for 14 facial traits, including four gene loci (RUNX2, GLI3, DCHS2 and PAX1) influencing nose morphology.

Gene regulatory mechanisms underpinning prostate cancer susceptibility

Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer.

HOXB13, RFX6 and prostate cancer risk

A new study shows that HOXB13 is preferentially recruited to the risk allele of a prostate cancer-associated SNP, enhancing the expression of RFX6, a driver of prostate cancer cell migration and predictor of disease progression. The work illustrates how a single risk locus contributes both to prostate cancer incidence and, through functional follow-up, to disease progression.

Comprehensive assessment and network analysis of the emerging genetic susceptibility landscape of prostate cancer

Background

Recent advances in high-throughput genotyping have made possible identification of genetic variants associated with increased risk of developing prostate cancer using genome-wide associations studies (GWAS). However, the broader context in which the identified genetic variants operate is poorly understood. Here we present a comprehensive assessment, network, and pathway analysis of the emerging genetic susceptibility landscape of prostate cancer.

Methods

We created a comprehensive catalog of genetic variants and associated genes by mining published reports and accompanying websites hosting supplementary data on GWAS. We then performed network and pathway analysis using single nucleotide polymorphism (SNP)-containing genes to identify gene regulatory networks and pathways enriched for genetic variants.

Results

We identified multiple gene networks and pathways enriched for genetic variants including IGF-1, androgen biosynthesis and androgen signaling pathways, and the molecular mechanisms of cancer. The results provide putative functional bridges between GWAS findings and gene regulatory networks and biological pathways.

Utility of genome-wide association study findings: prostate cancer as a translational research paradigm

Genome-wide association studies have identified thousands of consistently replicated associations between genetic markers and complex disease risk, including cancers. Alone, these markers have limited utility in risk prediction; however, when several of these markers are used in combination, the predictive performance appears to be similar to that of many currently available clinical predictors. Despite this, there are divergent views regarding the clinical validity and utility of these genetic markers in risk prediction. There are valid concerns, thus providing a direction for new lines of research. Herein, we outline the debate and use the example of prostate cancer to highlight emerging evidence from studies that aim to address potential concerns. We also describe a translational framework that could be used to guide the development of a new generation of comprehensive research studies aimed at capitalizing on these exciting new discoveries.

Genome-wide two-locus epistasis scans in prostate cancer using two European populations

Approximately 40 single nucleotide polymorphisms (SNPs) that are associated with prostate cancer (PCa) risk have been identified through genome-wide association studies (GWAS). However, these GWAS-identified PCa risk-associated SNPs can explain only a small proportion of heritability (~13%) of PCa risk. Gene-gene interaction is speculated to be one of the major factors contributing to the so-called missing heritability. To evaluate the gene-gene interaction and PCa risk, we performed a two-stage genome-wide gene-gene interaction scan using a novel statistical approach named "Boolean Operation-based Screening and Testing". In the first stage, we exhaustively evaluated all pairs of SNP-SNP interactions for ~500,000 SNPs in 1,176 PCa cases and 1,101 control subjects from the National Cancer Institute Cancer Genetic Markers of Susceptibility (CGEMS) study. No SNP-SNP interaction reached a genome-wide significant level of 4.4E-13. The second stage of the study involved evaluation of the top 1,325 pairs of SNP-SNP interactions (P(interaction) <1.0E-08) implicated in CGEMS in another GWAS population of 1,964 PCa cases from the Johns Hopkins Hospital (JHH) and 3,172 control subjects from the Illumina iControl database. Sixteen pairs of SNP-SNP interactions were significant in the JHH population at a P(interaction) cutoff of 0.01. However, none of the 16 pairs of SNP-SNP interactions were significant after adjusting for multiple tests. The current study represents one of the first attempts to explore the high-dimensional etiology of PCa on a genome-wide scale. Our results suggested a list of SNP-SNP interactions that can be followed in other replication studies.