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Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease. Many statistics are used to assess accuracy; some compare imputed to genotyped data and others are calculated without reference to true genotypes. Prior work has shown that the Imputation Quality Score (IQS), which is based on Cohen's kappa statistic and compares imputed genotype probabilities to true genotypes, appropriately adjusts for chance agreement; however, it is not commonly used. To identify differences in accuracy assessment, we compared IQS with concordance rate, squared correlation, and accuracy measures built into imputation programs. Genotypes from the 1000 Genomes reference populations (AFR N = 246 and EUR N = 379) were masked to match the typed single nucleotide polymorphism (SNP) coverage of several SNP arrays and were imputed with BEAGLE 3.3.2 and IMPUTE2 in regions associated with smoking behaviors. Additional masking and imputation was conducted for sequenced subjects from the Collaborative Genetic Study of Nicotine Dependence and the Genetic Study of Nicotine Dependence in African Americans (N = 1,481 African Americans and N = 1,480 European Americans). Our results offer further evidence that concordance rate inflates accuracy estimates, particularly for rare and low frequency variants. For common variants, squared correlation, BEAGLE R2, IMPUTE2 INFO, and IQS produce similar assessments of imputation accuracy. However, for rare and low frequency variants, compared to IQS, the other statistics tend to be more liberal in their assessment of accuracy. IQS is important to consider when evaluating imputation accuracy, particularly for rare and low frequency variants.

Background Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (N total  = 811) and extended the meQTLs to multiple traits. Results We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension ( p -value = 2.35E-08) and decreasing heel bone mineral density ( p -value = 1.92E-19). Conclusions These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits.

Numerous DNA methylation (DNAm) biomarkers of cigarette smoking have been identified in peripheral blood studies, but because of tissue specificity, blood-based studies may not detect brain-specific smoking-related DNAm differences that may provide greater insight as neurobiological indicators of smoking and its exposure effects. We report the first epigenome-wide association study (EWAS) of smoking in human postmortem brain, focusing on nucleus accumbens (NAc) as a key brain region in developing and reinforcing addiction. Illumina HumanMethylation EPIC array data from 221 decedents (120 European American [23% current smokers], 101 African American [26% current smokers]) were analyzed. DNAm by smoking (current vs. nonsmoking) was tested within each ancestry group using robust linear regression models adjusted for age, sex, cell-type proportion, DNAm-derived negative control principal components (PCs), and genotype-derived PCs. The resulting ancestry-specific results were combined via meta-analysis. We extended our NAc findings, using published smoking EWAS results in blood, to identify DNAm smoking effects that are unique (tissue-specific) vs. shared between tissues (tissue-shared). We identified seven CpGs (false discovery rate < 0.05), of which three CpGs are located near genes previously indicated with blood-based smoking DNAm biomarkers: ZIC1, ZCCHC24, and PRKDC. The other four CpGs are novel for smoking-related DNAm changes: ABLIM3, APCDD1L, MTMR6, and CTCF. None of the seven smoking-related CpGs in NAc are driven by genetic variants that share association signals with predisposing genetic risk variants for smoking, suggesting that the DNAm changes reflect consequences of smoking. Our results provide the first evidence for smoking-related DNAm changes in human NAc, highlighting CpGs that were undetected as peripheral biomarkers and may reflect brain-specific responses to smoking exposure.

Opioid use disorder is a highly heterogeneous disease driven by a variety of genetic and environmental risk factors which have yet to be fully elucidated. Opioid overdose, the most severe outcome of opioid use disorder, remains the leading cause of accidental death in the United States. We interrogated the effects of opioid overdose on the brain using ChIP-seq to quantify patterns of H3K27 acetylation in dorsolateral prefrontal cortical neurons isolated from 51 opioid-overdose cases and 51 accidental death controls. Among opioid cases, we observed global hypoacetylation and identified 388 putative enhancers consistently depleted for H3K27ac. Machine learning on H3K27ac patterns predicted case-control status with high accuracy. We focused on case-specific regulatory alterations, revealing 81,399 hypoacetylation events, uncovering vast inter-patient heterogeneity. We developed a strategy to decode this heterogeneity based on convergence analysis, which leveraged promoter-capture Hi-C to identify five genes over-burdened by alterations in their regulatory network or “plexus”: ASTN2, KCNMA1, DUSP4, GABBR2, ENOX1. These convergent loci are enriched for opioid use disorder risk genes and heritability for generalized anxiety, number of sexual partners, and years of education. Overall, our multi-pronged approach uncovers neurobiological aspects of opioid use disorder and captures genetic and environmental factors perpetuating the opioid epidemic.

Many candidate genes have been studied for asthma, but replication has varied. Novel candidate genes have been identified for various complex diseases using genome-wide association studies (GWASs). We conducted a GWAS in 492 Mexican children with asthma, predominantly atopic by skin prick test, and their parents using the Illumina HumanHap 550 K BeadChip to identify novel genetic variation for childhood asthma. The 520,767 autosomal single nucleotide polymorphisms (SNPs) passing quality control were tested for association with childhood asthma using log-linear regression with a log-additive risk model. Eleven of the most significantly associated GWAS SNPs were tested for replication in an independent study of 177 Mexican case-parent trios with childhood-onset asthma and atopy using log-linear analysis. The chromosome 9q21.31 SNP rs2378383 (p = 7.10x10(-6) in the GWAS), located upstream of transducin-like enhancer of split 4 (TLE4), gave a p-value of 0.03 and the same direction and magnitude of association in the replication study (combined p = 6.79x10(-7)). Ancestry analysis on chromosome 9q supported an inverse association between the rs2378383 minor allele (G) and childhood asthma. This work identifies chromosome 9q21.31 as a novel susceptibility locus for childhood asthma in Mexicans. Further, analysis of genome-wide expression data in 51 human tissues from the Novartis Research Foundation showed that median GWAS significance levels for SNPs in genes expressed in the lung differed most significantly from genes not expressed in the lung when compared to 50 other tissues, supporting the biological plausibility of our overall GWAS findings and the multigenic etiology of childhood asthma.

Background Despite evidence for a genetic component, few genetic associations with lung function decline have been identified. We aimed to evaluate genome-wide associations and putative downstream functionality of genetic variants for lung function decline. Methods We conducted genome-wide association study (GWAS) analyses of decline in FEV 1 , FVC, and FEV 1 /FVC in 52,056 White ( N  = 44,988), Black ( N  = 5,788), Hispanic ( N  = 550), and Chinese American ( N  = 730) participants across seven general population cohorts. GWAS analyses were stratified by cohort, ancestry, and sex. Results were combined in cross-ancestry and ancestry-specific meta-analyses. Significant variants available in two independent COPD-enriched cohorts were tested for replication. Results We identified 361 distinct genome-wide significant ( p  < 5E-08) variants for one or more of the FEV 1 , FVC, and FEV 1 /FVC decline phenotypes, which overlapped with previously reported genetic signals for pulmonary traits. Four variants, or 10.3% of variants available for replication testing, were nominally associated ( p  < 0.05) with at least one decline phenotype in COPD-enriched cohorts. Gene-level analysis of GWAS results implicated 38 genes, many with consistent associations across ancestries or decline phenotypes. Annotation class analysis revealed enrichment of regulatory processes for corticosteroid biosynthesis and metabolism. Drug repurposing analysis identified 43 approved compounds targeting eight implicated genes. Conclusions Our GWAS meta-analyses identified numerous genetic loci associated with lung function decline. These findings contribute knowledge to the genetic architecture of lung function decline, provide evidence for a role of corticosteroids in the etiology of lung function decline, and identify drug targets meriting further study for potential repurposing to slow lung function decline and mitigate lung disease.

ObjectivesTo investigate causality of the association of serum vitamin D with the risk and severity of COVID-19 infection.DesignTwo-sample Mendelian randomisation study.SettingSummary data from genome-wide analyses in the population-based UK Biobank and SUNLIGHT Consortium, applied to meta-analysed results of genome-wide analyses in the COVID-19 Host Genetics Initiative.Participants17 965 COVID-19 cases including 11 085 laboratory or physician-confirmed cases, 7885 hospitalised cases and 4336 severe respiratory cases, and 1 370 547 controls, primarily of European ancestry.ExposuresGenetically predicted variation in serum vitamin D status, instrumented by genome-wide significant single nucleotide polymorphisms (SNPs) associated with serum vitamin D or risk of vitamin D deficiency/insufficiency.Main outcome measuresSusceptibility to and severity of COVID-19 infection, including severe respiratory infection and hospitalisation.ResultsMendelian randomisation analysis, sufficiently powered to detect effects comparable to those seen in observational studies, provided little to no evidence for an effect of genetically predicted serum vitamin D on susceptibility to or severity of COVID-19 infection. Using SNPs in loci related to vitamin D metabolism as genetic instruments for serum vitamin D concentrations, the OR per SD higher serum vitamin D was 1.04 (95% CI 0.92 to 1.18) for any COVID-19 infection versus population controls, 1.05 (0.84 to 1.31) for hospitalised COVID-19 versus population controls, 0.96 (0.64 to 1.43) for severe respiratory COVID-19 versus population controls, 1.15 (0.99 to 1.35) for COVID-19 positive versus COVID-19 negative and 1.44 (0.75 to 2.78) for hospitalised COVID-19 versus non-hospitalised COVID-19. Results were similar in analyses using SNPs with genome-wide significant associations with serum vitamin D (ie, including SNPs in loci with no known relationship to vitamin D metabolism) and in analyses using SNPs with genome-wide significant associations with risk of vitamin D deficiency or insufficiency.ConclusionsThese findings suggest that genetically predicted differences in long-term vitamin D nutritional status do not causally affect susceptibility to and severity of COVID-19 infection, and that associations observed in previous studies may have been driven by confounding. These results do not exclude the possibility of low-magnitude causal effects or causal effects of acute responses to therapeutic doses of vitamin D.

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies. Evaluation of 17 published functional weighting methods for improving GWAS statistical power demonstrates a tradeoff between high sensitivity and high positive predictive value.