Loos, M.J.H.M. van der

Molecular Genetics and Hormones: New Frontiers in Entrepreneurship Research (Doctoral Thesis)

2013-06-20T00:00:00Z

Recent studies suggest that entrepreneurship is partly heritable, but are unable to pinpoint the specific genes involved. This thesis presents results from novel research aiming to identify genes associated with entrepreneurship using genetic data on the molecular level. In addition, the relationship between testosterone and entrepreneurship is examined since genes may exert their influence through this hormone. The thesis starts by reviewing candidate gene studies that test a pre-specified set of genes for association, but which often fail to replicate. An example within the setting of entrepreneurship research is provided to illustrate this last point. Next, the genome-wide association study (GWAS) design is presented that scans the entire genome for associations. However, due to multiple testing, GWAS requires very large sample sizes to establish robust associations and we perform a simulation study to estimate the minimum sample size needed for a GWAS on entrepreneurship. The following part reports evidence that entrepreneurship is partly heritable and around half of the heritability is accounted for by actual molecular genetic data. However, a GWAS on entrepreneurship does not identify robustly associated genes and prediction exercises show that it is currently impossible to predict entrepreneurship solely from molecular genetic data. In the final part, we show that, in contrast to earlier findings, testosterone is not associated with entrepreneurship. Taken as a whole, the results suggest that entrepreneurship is likely to be influenced by hundreds if not thousands of genes with a very small effect size each, implying that very large sample sizes will be needed in future research to discover associated genes. Most importantly, this thesis may serve as a practical guide for studying the molecular genetics of other economic variables. In conclusion, this thesis helps to build the foundations for a novel research field that integrates molecular genetics into economics.

The Molecular Genetic Architecture of Self-Employment (Article)

2013-04-04T00:00:00Z

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable-entrepreneurship-that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σg2/σP2= 25%, h2= 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10-5were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases.

Most Reported Genetic Associations With General Intelligence Are Probably False Positives (Article)

2012-11-01T00:00:00Z

General intelligence (g) and virtually all other behavioral traits are heritable. Associations between g and specific single-nucleotide polymorphisms (SNPs) in several candidate genes involved in brain function have been reported. We sought to replicate published associations between g and 12 specific genetic variants (in the genes DTNBP1, CTSD, DRD2, ANKK1, CHRM2, SSADH, COMT, BDNF, CHRNA4, DISC1, APOE, and SNAP25) using data sets from three independent, well-characterized longitudinal studies with samples of 5,571, 1,759, and 2,441 individuals. Of 32 independent tests across all three data sets, only 1 was nominally significant. By contrast, power analyses showed that we should have expected 10 to 15 significant associations, given reasonable assumptions for genotype effect sizes. For positive controls, we confirmed accepted genetic associations for Alzheimer's disease and body mass index, and we used SNP-based calculations of genetic relatedness to replicate previous estimates that about half of the variance in g is accounted for by common genetic variation among individuals. We conclude that the molecular genetics of psychology and social science requires approaches that go beyond the examination of candidate genes.

The genetic architecture of economic and political preferences (Article)

2012-05-22T00:00:00Z

Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic-based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.

Candidate gene studies and the quest for the entrepreneurial gene (Article)

2011-10-01T00:00:00Z

Candidate gene studies of human behavior are gaining interest in economics and entrepreneurship research. Performing and interpreting these studies is not straightforward because the selection of candidates influences the interpretation of the results. As an example, Nicolaou et al. (Small Bus Econ 36:151-155, 2011) report a significant association between a common genetic variant in the DRD3 gene and the tendency to be an entrepreneur. We fail to replicate this finding using a much larger, independent dataset. In addition, we discuss the candidate gene approach and give suggestions to avoid the publication of false positives.

De genetica van ondernemerschap (Article)

2011-04-29T00:00:00Z

genetiGenoombreed associatieonderzoek is een moderne onderzoeksmethode die het mogelijk maakt genen te vinden die geassocieerd zijn met allerlei ziekten en menselijke eigenschappen. Een samenwerkingsverband tussen de Erasmus School of Economics en het Erasmus Medisch Centrum probeert deze veelbelovende methode toe te passen op de keuze voor ondernemerschap.

Genome-wide association studies in economics and entrepreneurship research: Promises and limitations (Article)

2010-05-13T00:00:00Z

The recently developed genome-wide association study (GWAS) design enables the identification of genes specifically associated with economic outcomes such as occupational and other choices. This is a promising new approach for economics research which we aim to apply to the choice for entrepreneurship. However, due to multiple testing issues, very large sample sizes are needed to differentiate between true and false positives. For a GWAS on entrepreneurship, we expect that a sample size of at least 30,000 observations is required.

Genome-wide Association Studies and the Genetics of Entrepreneurship (Research Paper)

2010-01-15T00:00:00Z

We are currently investigating genetic influences on self-employment in an international research consortium using genome-wide association studies (GWAS). By meta-analysing results from numerous independent samples we address identification issues arising from multiple testing. To our knowledge, this is the earliest attempt to apply GWAS to an economic outcome of a relatively general nature. Our study will reveal potentials and limitations of this approach for economic research.

Genome-wide association studies in economics and entrepreneurship research: promises and limitations (Article)

2010-01-01T00:00:00Z

Genome-wide association studies and the genetics of entrepreneurship (Article)

2010-01-01T00:00:00Z

Efficacy and safety of lamotrigine as add-on treatment to lithium in bipolar depression: A multicenter, double-blind, placebo-controlled trial (Article)

2009-02-01T00:00:00Z

Objective: Lamotrigine is one of the pharmacologic options for the treatment of bipolar depression but has only been studied as monotherapy. This study compared the acute effects of lamotrigine and placebo as add-on therapy to ongoing treatment with lithium in patients with bipolar depression. Method: Outpatients (N = 124) aged 18 years and older with a DSM-IV bipolar I or II disorder and a major depressive episode (Montgomery-Asberg Depression Rating Scale [MADRS] score ≥ 18 and Clinical Global Impressions-Bipolar Version [CGI-BP] severity of depression score ≥ 4) while receiving lithium treatment (0.6-1.2 mmol/L) were randomly assigned to 8 weeks of double-blind treatment with lamotrigine (titrated to 200 mg/d) or placebo. The primary outcome measure was mean change from baseline in total score on the MADRS at week 8. Secondary outcome measures were response (defined as a reduction of ≥ 50% on the MADRS and/or change of depression score on the CGI-BP of "much improved" or "very much improved" compared to baseline) and switch to mania or hypomania (defined as a CGI-BP severity of mania score of at least mildly ill at any visit). Patients were included in the study between August 2002 (Spain started in October 2003) and May 2005. Results: Endpoint mean change from baseline MADRS total score was -15.38 (SE = 1.32) points for lamotrigine and -11.03 (SE = 1.36) points for placebo (t = -2.29, df = 104, p = .024). Significantly more patients responded to lamotrigine than to placebo on the MADRS (51.6% vs. 31.7%, p = .030), but not on the CGI-BP change of depression (64.1% vs. 49.2%, p = .105). Switch to mania or hypomania occurred in 5 patients (7.8%) receiving lamotrigine and 2 patients (3.3%) receiving placebo (p = .441). Conclusion: Lamotrigine was found effective and safe as add-on treatment to lithium in the acute treatment of bipolar depression. Trial Registration: clinicaltrials.gov Identifier: NCT00224510.

Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering (Research Paper)

2008-08-27T00:00:00Z

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.