Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter in mammalian brain. GABA receptor are involved in a number of complex disorders, including substance abuse. No variants of the commonly studied GABA receptor genes that have been associated with substance dependence have been determined to be functional or pathogenic. To reconcile the conflicting associations with substance dependence traits, we performed a meta-analysis of variants in the GABA_A receptor genes (GABRB2, GABRA6, GABRA1, and GABRG2 on chromosome 5q and GABRA2 on chromosome 4p12) using genotype data from 4739 cases of alcohol, opioid, or methamphetamine dependence and 4924 controls. Then, we combined the data from candidate gene association studies in the literature with two alcohol dependence (AD) samples, including 1691 cases and 1712 controls from the Study of Addiction: Genetics and Environment (SAGE), and 2644 cases and 494 controls from our own study. Using a Bonferroni-corrected threshold of 0.007, we found strong associations between GABRA2 and AD (P=9 × 10⁻⁶ and odds ratio (OR) 95% confidence interval (CI)=1.27 (1.15, 1.4) for rs567926, P=4 × 10⁻⁵ and OR=1.21 (1.1, 1.32) for rs279858), and between GABRG2 and both dependence on alcohol and dependence on heroin (P=0.0005 and OR=1.22 (1.09, 1.37) for rs211014). Significant association was also observed between GABRA6 rs3219151 and AD. The GABRA2 rs279858 association was observed in the SAGE data sets with a combined P of 9 × 10⁻⁶ (OR=1.17 (1.09, 1.26)). When all of these data sets, including our samples, were meta-analyzed, associations of both GABRA2 single-nucleotide polymorphisms remained (for rs567926, P=7 × 10⁻⁵ (OR=1.18 (1.09, 1.29)) in all the studies, and P=8 × 10⁻⁶ (OR=1.25 (1.13, 1.38)) in subjects of European ancestry and for rs279858, P=5 × 10⁻⁶ (OR=1.18 (1.1, 1.26)) in subjects of European ancestry. Findings from this extensive meta-analysis of five GABA_A receptor genes and substance abuse support their involvement (with the best evidence for GABRA2) in the pathogenesis of AD. Further replications with larger samples are warranted.

Oncoviral infection is responsible for 12%–15% of cancer in humans. Convergent evidence from epidemiology, pathology, and oncology suggests that new viral etiologies for cancers remain to be discovered. Oncoviral profiles can be obtained from cancer genome sequencing data; however, widespread viral sequence contamination and noncausal viruses complicate the process of identifying genuine oncoviruses. Here, we propose a novel strategy to address these challenges by performing virome-wide screening of early-stage clonal viral integrations. To implement this strategy, we developed VIcaller, a novel platform for identifying viral integrations that are derived from any characterized viruses and shared by a large proportion of tumor cells using whole-genome sequencing (WGS) data. The sensitivity and precision were confirmed with simulated and benchmark cancer data sets. By applying this platform to cancer WGS data sets with proven or speculated viral etiology, we newly identified or confirmed clonal integrations of hepatitis B virus (HBV), human papillomavirus (HPV), Epstein-Barr virus (EBV), and BK Virus (BKV), suggesting the involvement of these viruses in early stages of tumorigenesis in affected tumors, such as HBV in TERT and KMT2B (also known as MLL4) gene loci in liver cancer, HPV and BKV in bladder cancer, and EBV in non-Hodgkin's lymphoma. We also showed the capacity of VIcaller to identify integrations from some uncharacterized viruses. This is the first study to systematically investigate the strategy and method of virome-wide screening of clonal integrations to identify oncoviruses. Searching clonal viral integrations with our platform has the capacity to identify virus-caused cancers and discover cancer viral etiologies.

Approximately 8% of the human genome is derived from endogenous retroviruses (ERVs). In recent years, an increasing number of human diseases have been found to be associated with ERVs. However, it remains challenging to accurately detect the full spectrum of polymorphic (unfixed) ERVs using whole-genome sequencing (WGS) data. We designed a new tool, ERVcaller, to detect and genotype transposable element (TE) insertions, including ERVs, in the human genome. We evaluated ERVcaller using both simulated and real benchmark WGS datasets. Compared to existing tools, ERVcaller consistently obtained both the highest sensitivity and precision for detecting simulated ERV and other TE insertions derived from real polymorphic TE sequences. For the WGS data from the 1000 Genomes Project, ERVcaller detected the largest number of TE insertions per sample based on consensus TE loci. By analyzing the experimentally verified TE insertions, ERVcaller had 94.0% TE detection sensitivity and 96.6% genotyping accuracy. Polymerase chain reaction and Sanger sequencing in a small sample set verified 86.7% of examined insertion statuses and 100% of examined genotypes. In conclusion, ERVcaller is capable of detecting and genotyping TE insertions using WGS data with both high sensitivity and precision. This tool can be applied broadly to other species. http://www.uvm.edu/genomics/software/ERVcaller.html. Supplementary data are available at Bioinformatics online.

Single-nucleotide polymorphisms that have been associated with opioid dependence (OD) altogether account for only a small proportion of the known heritability. Most of the genetic risk factors are unknown. Some of the ‘missing heritability’ might be explained by copy number variations (CNVs) in the human genome. We used Illumina HumanOmni1 arrays to genotype 5152 African-American and European-American OD cases and screened controls and implemented combined CNV calling methods. After quality control measures were applied, a genome-wide association study (GWAS) of CNVs with OD was performed. For common CNVs, two deletions and one duplication were significantly associated with OD genome-wide (eg, P=2 × 10⁻⁸ and OR (95% CI)=0.64 (0.54–0.74) for a chromosome 18q12.3 deletion). Several rare or unique CNVs showed suggestive or marginal significance with large effect sizes. This study is the first GWAS of OD using CNVs. Some identified CNVs harbor genes newly identified here to be of biological importance in addiction, whereas others affect genes previously known to contribute to substance dependence risk. Our findings augment our specific knowledge of the importance of genomic variation in addictive disorders, and provide an addiction CNV pool for further research. These findings require replication.