Exome-wide association study of replicable nonsynonymous variants conferring risk for alcohol dependence

Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information

Alcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast, ADH1C and THRA gene intervals were significant (False discovery rate (FDR) <0.05) using default and empirical weights in the predicted AUDIT-P sample, with the most significant association found using predicted AUDIT-P and empirical weights in the ADH1C gene (SKAT-O P Default = 1.06 x 10 -9 and P Empirical weight = 6.25 x 10 -11 ). These findings provide evidence for rare variant association of the ADH1C gene with the AUDIT-P and highlight the successful leveraging of ML to increase effective sample size and prior empirical functional weights based on common variant GWAS data to refine and increase the statistical significance in underpowered phenotypes.

Genetic variation in the exome: Associations with alcohol and tobacco co-use

Shared genetic factors represent one underlying mechanism thought to contribute to high rates of alcohol and tobacco co-use and dependence. Common variants identified by molecular genetic studies tend to confer only small disease risk, and rare protein-coding variants are posited to contribute to disease risk, as well. However, given that genotyping technologies allowing for their inclusion in association studies have only recently become available, the magnitude of their contribution is poorly understood. The current study examined genetic variation in protein-coding regions (i.e., the exome) for associations with measures of lifetime alcohol and tobacco co-use. Participants from the UCSF Family Alcoholism Study (N = 1,862) were genotyped using an exome-focused genotyping array, and assessed for DSM-IV diagnoses of alcohol and tobacco dependence and quantitative consumption measures using a modified version of the Semi-Structured Assessment for the Genetics of Alcoholism. Analyses included single variant, gene-based, and pathway-based tests of association. One EMR3 variant and a pathway related to genes upregulated in mesenchymal stem cells during the late phase of adipogenesis met criteria for statistical significance. Suggestive associations were consistent with previous findings from studies of substance use and dependence, including variants in the CHRNA5-CHRNA3-CHRNB4 gene cluster with cigarettes smoked per day. Further, several variants and genes demonstrated suggestive association across phenotypes, suggesting that shared genetic factors may underlie risk for increased levels of alcohol and tobacco use, as well as psychopathology more broadly, providing insight into our understanding of the genetic architecture underlying these traits. (PsycINFO Database Record

Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence

BACKGROUND

Previous genomewide association studies (GWASs) have identified a number of putative risk loci for alcohol dependence (AD). However, only a few loci have replicated and these replicated variants only explain a small proportion of AD risk. Using an innovative approach, the goal of this study was to generate hypotheses about potentially causal variants for AD that can be explored further through functional studies.

METHODS

We employed targeted capture of 71 candidate loci and flanking regions followed by next-generation deep sequencing (mean coverage 78X) in 806 European Americans. Regions included in our targeted capture library were genes identified through published GWAS of alcohol, all human alcohol and aldehyde dehydrogenases, reward system genes including dopaminergic and opioid receptors, prioritized candidate genes based on previous associations, and genes involved in the absorption, distribution, metabolism, and excretion of drugs. We performed single-locus tests to determine if any single variant was associated with AD symptom count. Sets of variants that overlapped with biologically meaningful annotations were tested for association in aggregate.

RESULTS

No single, common variant was significantly associated with AD in our study. We did, however, find evidence for association with several variant sets. Two variant sets were significant at the q-value <0.10 level: a genic enhancer for ADHFE1 (p = 1.47 × 10^-5 ; q = 0.019), an alcohol dehydrogenase, and ADORA1 (p = 5.29 × 10^-5 ; q = 0.035), an adenosine receptor that belongs to a G-protein-coupled receptor gene family.

CONCLUSIONS

To our knowledge, this is the first sequencing study of AD to examine variants in entire genes, including flanking and regulatory regions. We found that in addition to protein coding variant sets, regulatory variant sets may play a role in AD. From these findings, we have generated initial functional hypotheses about how these sets may influence AD.

Susceptibility to human cancer: From the perspective of a pathologist

The etiologies of human cancer can only be discerned when the genetic clustering of cancer occurs within a family or when cancer occurs endemically in a particular environment. The possible approaches to solving the nature/nurture problem, especially for human carcinogenesis, posit a fascinating challenge for pathologists. This perspective review presents some examples of how clues to human cancer etiologies and/or susceptibilities reside in the realm of pathology practice. These examples using various omics techniques including adductomics, which I would like to highlight in this article, show that the currently available concepts and methods in human pathology can open a path toward the brave new world of a post-genomic era of medicine for young pathologists, whether their original intention was toward the pursuit of diagnostic or investigative knowledge.

Meta-analysis of six genes (BDNF, DRD1, DRD3, DRD4, GRIN2B and MAOA) involved in neuroplasticity and the risk for alcohol dependence

BACKGROUND

Alcohol-related problems have a large impact on human health, accounting for around 4% of deaths and 4.5% of disability-adjusted life-years around the world. Genetic factors could explain a significant fraction of the risk for alcohol dependence (AD). Recent meta-analyses have found significant pooled odds ratios (ORs) for variants in the ADH1B, ADH1C, DRD2 and HTR2A genes.

METHODS

In the present study, we carried out a meta-analysis of common variants in 6 candidate genes involved in neurotransmission and neuroplasticity: BDNF, DRD1, DRD3, DRD4, GRIN2B and MAOA. We carried out a systematic search for published association studies that analyzed the genes of interest. Relevant articles were retrieved and demographic and genetic data were extracted. Pooled ORs were calculated using a random-effects model using the Meta-Analyst program. Dominant, recessive and allelic models were tested and analyses were also stratified by ethnicity.

RESULTS

Forty two published studies were included in the current meta-analysis: BDNF-rs6265 (nine studies), DRD1-rs4532 (four studies), DRD3-rs6280 (eleven studies), DRD4-VNTR (seven studies), GRIN2B-rs1806201 (three studies) and MAOA-uVNTR (eight studies). We did not find significant pooled ORs for any of the six genes, under different models and stratifying for ethnicity.

CONCLUSIONS

In terms of the number of candidate genes included, this is one of the most comprehensive meta-analyses for genetics of AD. Pooled ORs did not support consistent associations with any of the six candidate genes tested. Future studies of novel genes of functional relevance and meta-analyses of quantitative endophenotypes could identify further susceptibility molecular factors for AD.

Chronic PFOS exposure alters the expression of neuronal development-related human homologues in Eisenia fetida

PFOS is a toxic, persistent environmental pollutant which is widespread worldwide. PFOS contamination has entered the food chain and is interfering with normal development in man and is neurotoxic, hepatotoxic and tumorigenic. The earthworm, Eisenia fetida is one of the organisms which can help to diagnose soil health and contamination at lower levels in the food chain. Studying the chronic effects of sub-lethal PFOS exposure in such an organism is therefore appropriate. As PFOS bioaccumulates and is not easily biodegraded, it is biomagnified up the food chain. Gene expression studies will give us information to develop biomarkers for early diagnosis of soil contamination, well before this contaminant passes up the food chain. We have carried out mRNA sequencing of control and chronically PFOS exposed E. fetida and reconstructed the transcripts in silico and identified the differentially expressed genes. Our findings suggest that PFOS up/down regulates neurodegenerative-related human homologues and can cause neuronal damage in E. fetida. This information will help to understand the links between neurodegenerative disorders and environmental pollutants such as PFOS. Furthermore, these up/down regulated genes can be used as biomarkers to detect a sub-lethal presence of PFOS in soil. Neuronal calcium sensor-2, nucleoside diphosphate kinase, polyadenylate-binding protein-1 and mitochondrial Pyruvate dehydrogenase protein-X component, could be potential biomarkers for sub lethal concentrations of PFOS.