Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10− 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10− 4, replication P = 6.7 × 10− 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci. © 2019, The Author(s)

Epidemiology and new developments in the diagnosis of prosthetic joint infection.

Although prosthetic joint infection (PJI) is a rare event after arthroplasty, it represents a significant complication that is associated with high morbidity, need for complex treatment, and substantial healthcare costs. An accurate and rapid diagnosis of PJI is crucial for treatment success. Current diagnostic methods in PJI are insufficient with 10-30% false-negative cultures. Consequently, there is a need for research and development into new methods aimed at improving diagnostic accuracy and speed of detection. In this article, we review available conventional diagnostic methods for the diagnosis of PJI (laboratory markers, histopathology, synovial fluid and periprosthetic tissue cultures), new diagnostic methods (sonication of implants, specific and multiplex PCR, mass spectrometry) and innovative techniques under development (new laboratory markers, microcalorimetry, electrical method, reverse transcription [RT]-PCR, fluorescence in situ hybridization [FISH], biofilm microscopy, microarray identification, and serological tests). The results of highly sensitive diagnostic techniques with unknown specificity should be interpreted with caution. The organism identified by a new method may represent a real pathogen that was unrecognized by conventional diagnostic methods or contamination during specimen sampling, transportation, or processing. For accurate interpretation, additional studies are needed, which would evaluate the long-term outcome (usually >2 years) with or without antimicrobial treatment. It is expected that new rapid, accurate, and fully automatic diagnostic tests will be developed soon

Correction to: Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer (Human Genetics, (2019), 138, 4, (307-326), 10.1007/s00439-019-01989-8)

Every author has erroneously been assigned to the affiliation “62”. The affiliation 62 belongs to the author Graham Casey. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention

Discovery of common and rare genetic risk variants for colorectal cancer

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P &amp;lt; 5 × 10 −8 , bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development. © 2018, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply