Biology subjects

Bartels, M.

Publications and source records attributed to Bartels, M..

4 recordsLinked to original sources

bioRxiv2018

A genetic perspective on the relationship between eudaimonic -and hedonic well-being

Whether hedonism or eudaimonism are two distinguishable forms of well-being is a topic of ongoing debate. To shed light on the relation between the two, large-scale available molecular genetic data were leveraged to gain more insight into the genetic architecture of the overlap between hedonic and eudaimonic well-being. Hence, we conducted the first genome-wide association studies (GWAS) of eudaimonic well-being (N = [~]108K) and linked it to a GWAS of hedonic well-being (N = [~] 222K). We identified the first two genome-wide significant independent loci for eudaimonic well-being and 6 independent loci for hedonic well-being. Joint analyses revealed a moderate phenotypic correlation (r = 0.53), but a high genetic correlation (rg = 0.78) between eudaimonic and hedonic well-being. For both traits we identified enrichment in the frontal cortex -and cingulate cortex as well as the cerebellum to be top ranked. Bi-directional Mendelian Randomization analyses using two-sample MR indicated some evidence for a causal relationship from hedonic well-being to eudaimonic well-being whereas no evidence was found for the reverse. Additionally, genetic correlations patterns with a range of positive and negative related phenotypes were largely similar for hedonic -and eudaimonic well-being. Our results reveal a large genetic overlap between hedonism and eudaimonism.

genetics↗

bioRxiv2018

Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use

Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2-4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

genetics↗

bioRxiv2017

Multivariate Genome-Wide and Integrated Transcriptome and Epigenome-Wide Analyses of the Well-being Spectrum.

Phenotypes related to well-being (life satisfaction, positive affect, neuroticism, and depressive symptoms), are genetically highly correlated (| rg | > .75). Multivariate genome-wide analyses (Nobs = 958,149) of these traits, collectively referred to as the well-being spectrum, reveals 63 significant independent signals, of which 29 were not previously identified. Transcriptome and epigenome analyses implicate variation in gene expression at 8 additional loci and CpG methylation at 6 additional loci in the etiology of well-being. We leverage an anatomically comprehensive survey of gene expression in the brain to annotate our findings, showing that SNPs within genes excessively expressed in the cortex and part of the hippocampal formation are enriched in their effect on well-being.

genetics↗

bioRxiv2017

Stratified Linkage Disequilibrium Score Regression reveals enrichment of eQTL effects on complex traits is not tissue specific

Both gene expression levels and eQTLs (expression quantitative trait loci) are partially tissue-specific, complicating the detection of eQTLs in tissues with limited sample availability, such as the brain. However, eQTL overlap between tissues might be non-trivial, allowing for inference of eQTL functioning in the brain via eQTLs measured in readily accessible tissues, e.g. whole blood. Using Stratified Linkage Disequilibrium Score Regression (SLDSR), we quantify the enrichment in GWAS signal of blood and brain eQTLs in genome-wide association study (GWAS) on 11 complex traits (schizophrenia, BMI, educational attainment, Crohns disease, rheumatoid arthritis, ulcerative colitis, age at menarche, coronary artery disease, height, LDL levels, and smoking behavior). Our analyses established significant enrichment of blood and brain eQTLs in their effects across all traits. As we do not know the true number of causal eQTLs, it is difficult to determine the precise magnitude of enrichment. We found no evidence for tissue-specific enrichment in GWAS signal for either eQTLs uniquely seen in the brain or whole blood. To follow up on our findings, we tested tissue-specific enrichment of eQTLs discovered in 44 tissues by the Genotype-Tissue Expression (GTEx) consortium, and, again, found no tissue-specific eQTL effects. We further integrated the GTEx eQTLs with SNPs associated with tissue-specific histone modifiers, and interrogate its effect on rheumatoid arthritis and schizophrenia. We observed substantially enriched effects on schizophrenia, though again not tissue-specific. Finally, we extracted eQTLs in tissue-specific differentially expressed genes, and determined their effects on rheumatoid arthritis and schizophrenia. We conclude that, while eQTLs are strongly enriched in GWAS signal, the enrichment is not specific to the tissue used in eQTL discovery. Therefore, working with relatively accessible tissues, such as whole blood, as proxy for eQTL discovery is sensible; and restricting lookups for GWAS hits to a specific tissue might not be advisable.

genomics↗