KAT2B polymorphism identified for drug abuse in African Americans with regulatory links to drug abuse pathways in human prefrontal cortex.

Drug abuse is a common and heritable set of disorders, but the underlying genetic factors are largely unknown. We conducted genome-wide association studies of drug abuse using 7 million imputed single nucleotide polymorphisms (SNPs) and insertions/deletions in African Americans (AAs; n = 3742) and European Americans (EAs; n = 6845). Cases were drawn from the Urban Health Study of street-recruited people, who injected drugs and reported abusing opioids, cocaine, marijuana, stimulants and/or other drugs 10 or more times in the past 30 days, and were compared with population controls. Independent replication testing was conducted in 755 AAs and 1131 EAs from the Genetic Association Information Network. An intronic SNP (rs9829896) in the K(lysine) acetyltransferase 2B (KAT2B) gene was significantly associated with drug abuse in AAs (P = 4.63 × 10-8 ) and independently replicated in AAs (P = 0.0019). The rs9829896-C allele (frequency = 12%) had odds ratios of 0.68 and 0.53 across the AA cohorts: meta-analysis P = 3.93 × 10-10 . Rs9829896-C was not associated with drug abuse across the EA cohorts: frequency = 36% and meta-analysis P = 0.12. Using dorsolateral prefrontal cortex data from the BrainCloud cohort, we found that rs9829896-C was associated with reduced KAT2B expression in AAs (n = 113, P = 0.050) but not EAs (n = 110, P = 0.39). KAT2B encodes a transcriptional regulator in the cyclic adenosine monophosphate and dopamine signaling pathways, and rs9829896-C was associated with expression of genes in these pathways: reduced CREBBP expression (P = 0.011) and increased OPRM1 expression (P = 0.016), both in AAs only. Our study identified the KAT2B SNP rs9829896 as having novel and biologically plausible associations with drug abuse and gene expression in AAs but not EAs, suggesting ancestry-specific effects.

A multiancestry study identifies novel genetic associations with CHRNA5 methylation in human brain and risk of nicotine dependence.

Nicotine dependence is influenced by chromosome 15q25.1 single nucleotide polymorphisms (SNPs), including the missense SNP rs16969968 that alters function of the α5 nicotinic acetylcholine receptor (CHRNA5) and noncoding SNPs that regulate CHRNA5 mRNA expression. We tested for cis-methylation quantitative trait loci (cis-meQTLs) using SNP genotypes and DNA methylation levels measured across the IREB2-HYKK-PSMA4-CHRNA5-CHRNA3-CHRNB4 genes on chromosome 15q25.1 in the BrainCloud and Brain QTL cohorts [total N = 175 European-Americans and 65 African-Americans (AAs)]. We identified eight SNPs that were significantly associated with CHRNA5 methylation in prefrontal cortex: P ranging from 6.0 × 10(-10) to 5.6 × 10(-5). These SNP-methylation associations were also significant in frontal cortex, temporal cortex and pons: P ranging from 4.8 × 10(-12) to 3.4 × 10(-3). Of the eight cis-meQTL SNPs, only the intronic CHRNB4 SNP rs11636753 was associated with CHRNA5 methylation independently of the known SNP effects in prefrontal cortex, and it was the most significantly associated SNP with nicotine dependence across five independent cohorts (total N = 7858 European ancestry and 3238 AA participants): P = 6.7 × 10(-4), odds ratio (OR) [95% confidence interval (CI)] = 1.11 (1.05-1.18). The rs11636753 major allele (G) was associated with lower CHRNA5 DNA methylation, lower CHRNA5 mRNA expression and increased nicotine dependence risk. Haplotype analyses showed that rs11636753-G and the functional rs16969968-A alleles together increased risk of nicotine dependence more than each variant alone: P = 3.1 × 10(-12), OR (95% CI) = 1.32 (1.22-1.43). Our findings identify a novel regulatory SNP association with nicotine dependence and connect, for the first time, previously observed differences in CHRNA5 mRNA expression and nicotine dependence risk to underlying DNA methylation differences.

Associations of common variants in the BST2 region with HIV-1 acquisition in African American and European American people who inject drugs.

OBJECTIVE: The bone marrow stromal cell antigen 2 (BST2) gene encodes a host restriction factor that acts as an innate immune sensor of HIV-1 exposure and suppresses release of HIV-1 particles. We aimed to identify associations of variants in the BST2 gene region with HIV-1 acquisition and disease progression. DESIGN/METHODS: Using HIV+ cases and HIV- controls from the Urban Health Study (n=3136 African Americans and European Americans who inject drugs), we tested 470 variants in BST2 and its flanking regions for association with HIV-1 acquisition and log-transformed viral load. RESULTS: We found that the single nucleotide polymorphism (SNP) rs113189798 surpassed the P value threshold corrected for multiple testing. The rs113189798-G allele (frequency=16% in African Americans, 4% in European Americans) was associated with increased HIV-1 acquisition risk (meta-analysis P=1.43 × 10): odds ratio (95% confidence interval) of 1.22 (1.01-1.49) in African Americans and 2.17 (1.43-3.33) in European Americans. We also found that the previously reported rs12609479-A allele (frequency=35% in African Americans, 81% in European Americans) was nominally associated with decreased risk of acquiring HIV-1 in our study (meta-analysis P=0.036). Rs12609479-A is predicted to increase BST2 expression and thereby decrease risk of acquiring HIV-1. Rs113189798 and rs12609479 were only weakly correlated [square of the correlation coefficient (r)=0.2-0.4] and represented distinct association signals. None of our tested variants were significantly associated with log-transformed viral load among the HIV-infected cases. CONCLUSION: Our findings support BST2 as a genetic susceptibility factor for HIV-1 acquisition: identifying a novel SNP association for rs13189798 and linking the previously reported regulatory SNP rs12609479 to HIV-1 acquisition.

Novel genetic locus implicated for HIV-1 acquisition with putative regulatory links to HIV replication and infectivity: a genome-wide association study.

Fifty percent of variability in HIV-1 susceptibility is attributable to host genetics. Thus identifying genetic associations is essential to understanding pathogenesis of HIV-1 and important for targeting drug development. To date, however, CCR5 remains the only gene conclusively associated with HIV acquisition. To identify novel host genetic determinants of HIV-1 acquisition, we conducted a genome-wide association study among a high-risk sample of 3,136 injection drug users (IDUs) from the Urban Health Study (UHS). In addition to being IDUs, HIV-controls were frequency-matched to cases on environmental exposures to enhance detection of genetic effects. We tested independent replication in the Women's Interagency HIV Study (N=2,533). We also examined publicly available gene expression data to link SNPs associated with HIV acquisition to known mechanisms affecting HIV replication/infectivity. Analysis of the UHS nominated eight genetic regions for replication testing. SNP rs4878712 in FRMPD1 met multiple testing correction for independent replication (P=1.38x10(-4)), although the UHS-WIHS meta-analysis p-value did not reach genome-wide significance (P=4.47x10(-7) vs. P<5.0x10(-8)) Gene expression analyses provided promising biological support for the protective G allele at rs4878712 lowering risk of HIV: (1) the G allele was associated with reduced expression of FBXO10 (r=-0.49, P=6.9x10(-5)); (2) FBXO10 is a component of the Skp1-Cul1-F-box protein E3 ubiquitin ligase complex that targets Bcl-2 protein for degradation; (3) lower FBXO10 expression was associated with higher BCL2 expression (r=-0.49, P=8x10(-5)); (4) higher basal levels of Bcl-2 are known to reduce HIV replication and infectivity in human and animal in vitro studies. These results suggest new potential biological pathways by which host genetics affect susceptibility to HIV upon exposure for follow-up in subsequent studies.

Cis-Expression Quantitative Trait Loci Mapping Reveals Replicable Associations with Heroin Addiction in OPRM1.

BACKGROUND: No opioid receptor, mu 1 (OPRM1) gene polymorphisms, including the functional single nucleotide polymorphism (SNP) rs1799971, have been conclusively associated with heroin/other opioid addiction, despite their biological plausibility. We used evidence of polymorphisms altering OPRM1 expression in normal human brain tissue to nominate and then test associations with heroin addiction. METHODS: We tested 103 OPRM1 SNPs for association with OPRM1 messenger RNA expression in prefrontal cortex from 224 European Americans and African Americans of the BrainCloud cohort. We then tested the 16 putative cis-expression quantitative trait loci (cis-eQTL) SNPs for association with heroin addiction in the Urban Health Study and two replication cohorts, totaling 16,729 European Americans, African Americans, and Australians of European ancestry. RESULTS: Four putative cis-eQTL SNPs were significantly associated with heroin addiction in the Urban Health Study (smallest p = 8.9 × 10(-5)): rs9478495, rs3778150, rs9384169, and rs562859. Rs3778150, located in OPRM1 intron 1, was significantly replicated (p = 6.3 × 10(-5)). Meta-analysis across all case-control cohorts resulted in p = 4.3 × 10(-8): the rs3778150-C allele (frequency = 16%-19%) being associated with increased heroin addiction risk. Importantly, the functional SNP allele rs1799971-A was associated with heroin addiction only in the presence of rs3778150-C (p = 1.48 × 10(-6) for rs1799971-A/rs3778150-C and p = .79 for rs1799971-A/rs3778150-T haplotypes). Lastly, replication was observed for six other intron 1 SNPs that had prior suggestive associations with heroin addiction (smallest p = 2.7 × 10(-8) for rs3823010). CONCLUSIONS: Our findings show that common OPRM1 intron 1 SNPs have replicable associations with heroin addiction. The haplotype structure of rs3778150 and nearby SNPs may underlie the inconsistent associations between rs1799971 and heroin addiction.

Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.

A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of genotyped SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality.

Assessment of genotype imputation performance using 1000 Genomes in African American studies.

Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina's HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%-93%), but IMPUTE2 had the highest IQS (81%-83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.

Finding relevant references to genes and proteins in Medline using a Bayesian approach.

MOTIVATION: Mining the biomedical literature for references to genes and proteins always involves a tradeoff between high precision with false negatives, and high recall with false positives. Having a reliable method for assessing the relevance of literature mining results is crucial to finding ways to balance precision and recall, and for subsequently building automated systems to analyze these results. We hypothesize that abstracts and titles that discuss the same gene or protein use similar words. To validate this hypothesis, we built a dictionary- and rule-based system to mine Medline for references to genes and proteins, and used a Bayesian metric for scoring the relevance of each reference assignment. RESULTS: We analyzed the entire set of Medline records from 1966 to late 2001, and scored each gene and protein reference using a Bayesian estimated probability (EP) based on word frequency in a training set of 137837 known assignments from 30594 articles to 36197 gene and protein symbols. Two test sets of 148 and 150 randomly chosen assignments, respectively, were hand-validated and categorized as either good or bad. The distributions of EP values, when plotted on a log-scale histogram, are shown to markedly differ between good and bad assignments. Using EP values, recall was 100% at 61% precision (EP=2 x 10(-5)), 63% at 88% precision (EP=0.008), and 10% at 100% precision (EP=0.1). These results show that Medline entries discussing the same gene or protein have similar word usage, and that our method of assessing this similarity using EP values is valid, and enables an EP cutoff value to be determined that accurately and reproducibly balances precision and recall, allowing automated analysis of literature mining results. .