Determinants and Mechanisms Involved in Alcohol Consumption and its Health Consequences

Lead Research Organisation: Imperial College London
Department Name: School of Public Health

Abstract

In the current proposed research, I will be using genetic, environmental and metabolite data from the UK Biobank, the Airwave health monitoring study and published genome-wide association studies as well as animal models in order to highlight determinants, mechanisms and pathways involved in alcohol consumption and related diseases. I will use UK Biobank data to study risk factors and determinants of alcohol consumption as well as diseases caused by alcohol consumption. By using alcohol consumption data and metabolites in the Airwave study, I will identify genes that are linked to alcohol-related metabolites. I will then test alcohol-metabolite genes in the UK Biobank study to highlight pathways that are linked to fat accumulation in liver via alcohol consumption. I will additionally test animal models to identify the underlying biology behind the association of genes and alcohol consumption.

Technical Summary

Here, I propose to use genetic and phenotype data to systematically explore genetic and environmental factors as well as gene-environment interactions involved in alcohol consumption and alcohol induced latent changes in liver. I will use large-scale genetic and phenotype data from the UK Biobank study and he Airwave health monitoring study. I will validate the results in collaboration with universities of Liverpool and Leicester. My main aim is to unravel mechanisms involved in alcohol consumption and its consequences. I specifically aim to identify (1) phenotypic and genetic determinants and gene-environment interactions involved in alcohol consumption and its related disease. Here, I will use UK biobank study data and agnostically identify phenotypes associated with alcohol consumption. I will additionally select alcohol-related genetic variants from existing literature and identify phenotypes associated with selected genes in the UK Biobank and consequently test gene-environment interactions. (2) Identify biologic mechanisms involved in alcohol consumption using animal models. This will be done in collaboration with universities of Liverpool and Leicester (3) Identify pathways involved in fat accumulation in liver. Here, I will use metabolites from the Airwave study to identify alcohol-related metabolites. Consequently using the Airwave study, I will identify genes associated with alcohol-related metabolites for further investigation using the UK Biobank genetic and imaging data, to identify pathways involved in fat accumulation in liver through alcohol consumption. (4) Highlight brain areas involved in alcohol consumption using bioinformatics approaches in combination with brain imaging and genetic data (5) Identify novel outcomes caused by alcohol consumption. Here, I will use Mendelian Randomization (MR) study to investigate causality between alcohol consumption with related diseases.
 
Title GWAS summary statistics for urinary sodium excretion 
Description GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits Short title: Genetics of urinary traits Raha Pazoki, MD, PhD; Evangelos Evangelou, PhD; David Mosen-Ansorena, PhD; Rui Climaco Pinto, PhD; Ibrahim Karaman, PhD; Paul Blakeley, PhD; Dipender Gill1, MD; Verena Zuber, PhD; Paul Elliott, MB, PhD; Ioanna Tzoulaki, PhD; Abbas Dehghan, MD, PhD We included participants from the UK Biobank study with ~8.8M single nucleotide polymorphisms (SNPs) imputed to the Haplotype Reference Consortium (HRC) panel at MAF<0.5% from European ancestry participants in UKB (genotyping and imputation [GRCh37] data release 2017). BOLT-LMM association statistics are output in a tab-delimited --statsFile file with the following fields, one line per SNP: SNP: rs number or ID string CHR: chromosome BP: physical (base pair) position GENPOS: genetic position either from bim file or interpolated from genetic map ALLELE1: first allele in bim file (usually the minor allele), used as the effect allele ALLELE0: second allele in bim file, used as the reference allele A1FREQ: frequency of first allele F_MISS: fraction of individuals with missing genotype at this SNP BETA: effect size from BOLT-LMM approximation to infinitesimal mixed model SE: standard error of effect size P_BOLT_LMM_INF: infinitesimal mixed model association test p-value P_BOLT_LMM: non-infinitesimal mixed model association test p-value 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Impact This is a dataset containing association of the whole genome single nucleotide polymorphisms with urinary sodium electrolytes. 
URL http://ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/PazokiR_31409800_GCST008647
 
Title Summary statistics for GWAS of urinary potassium 
Description GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits Short title: Genetics of urinary traits Raha Pazoki, MD, PhD; Evangelos Evangelou, PhD; David Mosen-Ansorena, PhD; Rui Climaco Pinto, PhD; Ibrahim Karaman, PhD; Paul Blakeley, PhD; Dipender Gill1, MD; Verena Zuber, PhD; Paul Elliott, MB, PhD; Ioanna Tzoulaki, PhD; Abbas Dehghan, MD, PhD We included participants from the UK Biobank study with ~8.8M single nucleotide polymorphisms (SNPs) imputed to the Haplotype Reference Consortium (HRC) panel at MAF<0.5% from European ancestry participants in UKB (genotyping and imputation [GRCh37] data release 2017). BOLT-LMM association statistics are output in a tab-delimited --statsFile file with the following fields, one line per SNP: SNP: rs number or ID string CHR: chromosome BP: physical (base pair) position GENPOS: genetic position either from bim file or interpolated from genetic map ALLELE1: first allele in bim file (usually the minor allele), used as the effect allele ALLELE0: second allele in bim file, used as the reference allele A1FREQ: frequency of first allele F_MISS: fraction of individuals with missing genotype at this SNP BETA: effect size from BOLT-LMM approximation to infinitesimal mixed model SE: standard error of effect size P_BOLT_LMM_INF: infinitesimal mixed model association test p-value P_BOLT_LMM: non-infinitesimal mixed model association test p-value 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Impact Whole genome association analysis of single nucleotide polymorphism with urinary potassium. 
URL http://ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/PazokiR_31409800_GCST008648
 
Description Collaboration with Behrooz Alizadeh lab in Groningen, the Netherlands 
Organisation University of Groningen
Country Netherlands 
Sector Academic/University 
PI Contribution We provide genetic variants we identified for increase in the level of liver enzymes.
Collaborator Contribution The collaborators replicates the associations in three cohorts from the Lifelines Study.
Impact Manuscript in preparation
Start Year 2020
 
Description Collaboration with Dr Daniel Bailey at Brunel University, London 
Organisation Brunel University London
Department Department of Life Sciences
Country United Kingdom 
Sector Academic/University 
PI Contribution Dr Daniel Bailey has quantitative expertise in physical activity data specifically reading and interpretation of accelerometer data that is useful in identification of interactions with alcohol consumption.
Collaborator Contribution My partner has agreed to help me with interpretation of accelerometer data within the UK Biobank in order to identify associations and interactions with alcohol consumption in relation to diseases.
Impact The collaboration has just commenced and not yet resulted in any outputs.
Start Year 2020
 
Description Collaboration with the Rotterdam Study in the Netherlands 
Organisation Erasmus University Rotterdam
Department Department of Epidemiology
Country Netherlands 
Sector Academic/University 
PI Contribution We provide genetic variants we identified for increase in the level of liver enzymes.
Collaborator Contribution The collaborators replicates the associations in three cohorts from the Rotterdam Study.
Impact We have a manuscript in preparation at the moment (31 Aug 2019)
Start Year 2019
 
Description Conference (European Society of Hypertension 2019, Milan Italy) 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact This activity was the European Society of hypertension in Milan, Italy. The audience were international students, clinicians, academics, and third sector organizations and industries who attended the conference to showcase their products.
Year(s) Of Engagement Activity 2019
URL https://www.esh2019.eu/