Overcoming attenuation bias in regressions using polygenic indices.

Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.

Rank concordance of polygenic indices.

Polygenic indices (PGIs) are increasingly used to identify individuals at risk of developing disease and are advocated as screening tools for personalized medicine and education. Here we empirically assess rank concordance between PGIs created with different construction methods and discovery samples, focusing on cardiovascular disease and educational attainment. We find Spearman rank correlations between 0.17 and 0.93 for cardiovascular disease, and 0.40 and 0.83 for educational attainment, indicating highly unstable rankings across different PGIs for the same trait. Potential consequences for personalized medicine and gene-environment (G × E) interplay are illustrated using data from the UK Biobank. Simulations show how rank discordance mainly derives from a limited discovery sample size and reveal a tight link between the explained variance of a PGI and its ranking precision. We conclude that PGI-based ranking is highly dependent on PGI choice, such that current PGIs do not have the desired precision to be used routinely for personalized intervention.

The identification of mediating effects using genome-based restricted maximum likelihood estimation.

Mediation analysis is commonly used to identify mechanisms and intermediate factors between causes and outcomes. Studies drawing on polygenic scores (PGSs) can readily employ traditional regression-based procedures to assess whether trait M mediates the relationship between the genetic component of outcome Y and outcome Y itself. However, this approach suffers from attenuation bias, as PGSs capture only a (small) part of the genetic variance of a given trait. To overcome this limitation, we developed MA-GREML: a method for Mediation Analysis using Genome-based Restricted Maximum Likelihood (GREML) estimation. Using MA-GREML to assess mediation between genetic factors and traits comes with two main advantages. First, we circumvent the limited predictive accuracy of PGSs that regression-based mediation approaches suffer from. Second, compared to methods employing summary statistics from genome-wide association studies, the individual-level data approach of GREML allows to directly control for confounders of the association between M and Y. In addition to typical GREML parameters (e.g., the genetic correlation), MA-GREML estimates (i) the effect of M on Y, (ii) the direct effect (i.e., the genetic variance of Y that is not mediated by M), and (iii) the indirect effect (i.e., the genetic variance of Y that is mediated by M). MA-GREML also provides standard errors of these estimates and assesses the significance of the indirect effect. We use analytical derivations and simulations to show the validity of our approach under two main assumptions, viz., that M precedes Y and that environmental confounders of the association between M and Y are controlled for. We conclude that MA-GREML is an appropriate tool to assess the mediating role of trait M in the relationship between the genetic component of Y and outcome Y. Using data from the US Health and Retirement Study, we provide evidence that genetic effects on Body Mass Index (BMI), cognitive functioning and self-reported health in later life run partially through educational attainment. For mental health, we do not find significant evidence for an indirect effect through educational attainment. Further analyses show that the additive genetic factors of these four outcomes do partially (cognition and mental health) and fully (BMI and self-reported health) run through an earlier realization of these traits.

Right of association and new business entry: country-level evidence from the market sector.

Labor market institutions (LMIs) could enable new firm entry by lowering burdens to attracting and retaining human capital or restrict new firm entry by increasing concerns of additional demands on ventures facing liabilities of newness and smallness. In this study, we focus on the LMI of the right of association, and whether its relationship with new business entry depends on the vertical ordering of bargaining (represented in the centralization of collective bargaining) or the horizontal synchronization of wage-setting (represented in the coordination of wage-setting). In a panel of 44 countries covering the period 2005-2019, we find that the right of association in the market sector is positively associated with new business entry; however, with increasing centralization of collective bargaining, the association becomes negative. Coordination of wage-setting does not significantly affect the relationship between the right of association and new business entry. The results are robust to accounting for both serial correlation and cross-sectional correlation in the panel regressions and carry implications for policymakers regarding the effects of LMIs on new business creation.

The impact of entrepreneurship research on other academic fields.

The remarkable ascent of entrepreneurship witnessed as a scientific field over the last 4 decades has been made possible by entrepreneurship's ability to absorb theories, paradigms, and methods from other fields such as economics, psychology, sociology, geography, and even biology. The respectability of entrepreneurship as an academic discipline is now evidenced by many other fields starting to borrow from the entrepreneurship view. In the present paper, seven examples are given from this "pay back" development. These examples were first presented during a seminar at the Erasmus Entrepreneurship Event called what has the entrepreneurship view to offer to other academic fields? This article elaborates on the core ideas of these presentations and focuses on the overarching question of how entrepreneurship research impacts the development of other academic fields. We found that entrepreneurship research questions the core assumptions of other academic fields and provides new insights into the antecedents, mechanisms, and consequences of their respective core phenomena. Moreover, entrepreneurship research helps to legitimize other academic fields both practically and academically.

Entrepreneurship research questions the core assumptions of other academic fields and legitimizes them both practically and academically. Since the 1980s, entrepreneurship research has seen tremendous growth and development, establishing itself as an academic field. Entrepreneurship is also taught extensively in leading business schools around the world. Indeed, few business schools do not address entrepreneurship in their curriculum. This represents a sea change: although entrepreneurs and new ventures had a remarkable impact on society, academia barely noticed it in the 1980s. Simply put: economics and business students rarely, if ever, encountered any mention of entrepreneurship during their studies. While entrepreneurship research has now developed its own methodological toolbox, it has extensively borrowed perspectives, theories, and methods from other fields. In the 2020s, we now find that entrepreneurship scholars are sharing its toolbox with other academic fields, questioning the core assumptions of other academic fields and providing new insights into the antecedents, mechanisms, and consequences of their respective core phenomena. Moreover, entrepreneurship research helps to legitimize other academic fields both practically and academically. Hence, entrepreneurship research now plays not just an important role in entrepreneurship education, practice, and policy but also throughout many other research fields.

Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships.

BACKGROUND: Heritability and genetic correlation can be estimated from genome-wide single-nucleotide polymorphism (SNP) data using various methods. We recently developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) for statistically and computationally efficient estimation of SNP-based heritability ([Formula: see text]) and genetic correlation ([Formula: see text]) across many traits in large datasets. Here, we extend MGREML by allowing it to fit and perform tests on user-specified factor models, while preserving the low computational complexity. RESULTS: Using simulations, we show that MGREML yields consistent estimates and valid inferences for such factor models at low computational cost (e.g., for data on 50 traits and 20,000 individuals, a saturated model involving 50 [Formula: see text]'s, 1225 [Formula: see text]'s, and 50 fixed effects is estimated and compared to a restricted model in less than one hour on a single notebook with two 2.7 GHz cores and 16 GB of RAM). Using repeated measures of height and body mass index from the US Health and Retirement Study, we illustrate the ability of MGREML to estimate a factor model and test whether it fits the data better than a nested model. The MGREML tool, the simulation code, and an extensive tutorial are freely available at https://github.com/devlaming/mgreml/ . CONCLUSION: MGREML can now be used to estimate multivariate factor structures and perform inferences on such factor models at low computational cost. This new feature enables simple structural equation modeling using MGREML, allowing researchers to specify, estimate, and compare genetic factor models of their choosing using SNP data.

Genetic predispositions moderate the effectiveness of tobacco excise taxes.

BACKGROUND: Tobacco consumption is one of the leading causes of preventable death. In this study, we analyze whether someone's genetic predisposition to smoking moderates the response to tobacco excise taxes. METHODS: We interact polygenic scores for smoking behavior with state-level tobacco excise taxes in longitudinal data (1992-2016) from the US Health and Retirement Study (N = 12,058). RESULTS: Someone's genetic propensity to smoking moderates the effect of tobacco excise taxes on smoking behavior along the extensive margin (smoking vs. not smoking) and the intensive margin (the amount of tobacco consumed). In our analysis sample, we do not find a significant gene-environment interaction effect on smoking cessation. CONCLUSIONS: When tobacco excise taxes are relatively high, those with a high genetic predisposition to smoking are less likely (i) to smoke, and (ii) to smoke heavily. While tobacco excise taxes have been effective in reducing smoking, the gene-environment interaction effects we observe in our sample suggest that policy makers could benefit from taking into account the moderating role of genes in the design of future tobacco control policies.

Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior.

Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1-0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.

The Interplay between Maternal Smoking and Genes in Offspring Birth Weight.

It is well-established that both the child's genetic endowments as well as maternal smoking during pregnancy impact offspring birth weight. In this paper we move beyond the nature versus nurture debate by investigating the interaction between genetic endowments and this critical prenatal environmental exposure - maternal smoking - in determining birth weight. We draw on longitudinal data from the Avon Longitudinal Study of Parents and Children (ALSPAC) study and replicate our results using data from the UK Biobank. Genetic endowments of the children are proxied with a polygenic score that is constructed based on the results of the most recent genome-wide association study of birth weight. We instrument the maternal decision to smoke during pregnancy with a genetic variant (rs1051730) located in the nicotine receptor gene CHRNA3. This genetic variant is associated with the number of cigarettes consumed daily, and we present evidence that this is plausibly the only channel through which the maternal genetic variant affects the child's birth weight. Additionally, we deal with the misreporting of maternal smoking by using measures of cotinine, a biomarker of nicotine, collected from the mother's urine during their pregnancy. We confirm earlier findings that genetic endowments as well as maternal smoking during pregnancy significantly affects the child's birth weight. However, we do not find evidence of meaningful interactions between genetic endowments and an adverse fetal environment, suggesting that the child's genetic predisposition cannot cushion the damaging effects of maternal smoking.

Separation From the Life Partner and Exit From Self-Employment.

The survival of businesses in the market often hinges on contributions of the business owner's household members. Partners of the self-employed as well as their children may, for example, provide emotional support but also cheap and flexible labor. Although the household composition of self-employed individuals has been analyzed in many earlier studies, little is known about what happens to the self-employed individual and his or her business when one separates from a life partner. We argue that separation from a life partner has profound financial and social consequences for the business owner. Specifically, we propose that a decrease in household income and social functioning (which is the degree of interference with social activities due to mental and/or physical problems) after separation from the life partner may lead to an exit from self-employment. Our empirical analysis draws on data from the longitudinal HILDA (Household, Income and Labour Dynamics in Australia) survey, for the period 2002-2017. Based on information from 4,044 self-employed individuals aged 18-64 years (18,053 individual-year observations), we find that separating from the life partner in the past year significantly increases the probability of exit from self-employment in the next year. Furthermore, we find that the positive association between separation from the life partner and exit from self-employment can be explained for 29.7% by a reduction in social functioning and for 10.7% by a reduction in household income. We study five exit routes out of self-employment and find that separation from the life partner mainly increases the probabilities of becoming a wage worker and of re-entering self-employment after experiencing an exit. For exit to unemployment or to a position outside the labor force (voluntarily inactive/retirement or any other non-labor force position), we find insignificant relationships with separation from the life partner. Furthermore, for all exit routes except retirement, we find significant indirect effects implying that decreased household income and levels of social functioning are important mechanisms through which separation from the life partner is related to exit from self-employment.

The Relation Between Health and Earnings in Self-Employment.

Multiple studies have shown that, on average, the self-employed are healthier than wage workers. The link between the health of self-employed individuals and their financial performance in terms of earnings is, however, less understood. Based on human capital theory, we expect a positive link between health and earnings among the self-employed. For two reasons we expect the relationship between health and earnings to be stronger for the self-employed than for wage workers. First, the self-employed can more easily adapt their production activities such that they yield the highest returns to their human capital, including their health. Second, in the short term, the earnings of the self-employed are more dependent on the ability to work than the wages of wage workers. Our empirical analysis draws on data from the Household, Income and Labor Dynamics in Australia (HILDA) survey, a longitudinal dataset (2001-2017). Our outcome variable is an individual's total income derived from wage work and/or running a business. Health is measured using multi-item constructs for General health, Physical health, and Mental health from the Short Form Health Survey (SF-36). We distinguish between wage workers and self-employed individuals with and without employees. Fixed-effects regressions reveal a significant positive relationship between health and earnings in self-employment as well as in wage work. As expected, this relationship is significantly stronger in self-employment than in wage work (for General health and Physical health, but not for Mental health). The latter result holds particularly for self-employment without employees. We provide evidence that the higher returns can be partly explained by the fact that the earnings in self-employment are more dependent on the ability to work (as proxied by the number of working hours) than earnings in wage work. We also find a negative relationship between health and job termination. Again, this relationship is stronger for the self-employed (without employees) than for wage workers (for General health and Mental health, but not for Physical health).

Effect of Genetic Propensity for Obesity on Income and Wealth Through Educational Attainment.

OBJECTIVE: This study contributes to the literature on the income and wealth consequences of obesity by exploiting recent discoveries about the genetic basis of BMI. METHODS: The relation between a genetic risk score (GRS) for BMI, which reflects the genetic predisposition to have a higher body weight, and income and wealth was analyzed in a longitudinal data set comprising 5,962 individuals (22,490 individual-year observations) from the US Health and Retirement Study. RESULTS: Empirical analyses showed that the GRS for BMI lowers individual income and household wealth through the channel of lower educational attainment. Sex-stratified analyses showed that this effect is particularly significant among females. CONCLUSIONS: This study provides support for the negative effects of the GRS for BMI on individual income and household wealth through lower education for females. For males, the effects are estimated to be smaller and insignificant. The larger effects for females compared with males may be due to greater labor market taste-based discrimination faced by females.

ADHD and later-life labor market outcomes in the United States.

This study analyzes the relation between attention-deficit hyperactivity disorder (ADHD) and later-life labor market outcomes in the United States and whether these relationships are mediated by educational attainment. To overcome endogeneity concerns in the estimation of these relationships, we exploit the polygenic risk score (PRS) for ADHD in a cohort where the diagnosis of and treatment for ADHD were generally not available. We find that an increase in the PRS for ADHD reduces the likelihood of employment, individual income, and household wealth. Moreover, it increases the likelihood of receiving social security disability benefits, unemployment or worker compensation, and other governmental transfers. We provide evidence that educational attainment mediates these relationships to a considerable extent (14-58%).

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Pleiotropy-robust Mendelian randomization.

Background: The potential of Mendelian randomization studies is rapidly expanding due to: (i) the growing power of genome-wide association study (GWAS) meta-analyses to detect genetic variants associated with several exposures; and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian randomization (the exclusion restriction) can always be contested. Methods: We build upon and synthesize recent advances in the literature on instrumental variables (IVs) estimation that test and relax the exclusion restriction. Our pleiotropy-robust Mendelian randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If (i) a subsample exists for which the genetic variants do not affect the exposure; (ii) the selection into this subsample is not a joint consequence of the IV and the outcome; (iii) pleiotropic effects are homogeneous, PRMR obtains unbiased estimates of causal effects. Results: Simulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of: (i) tobacco exposure on body mass index (BMI); (ii) prostate cancer on self-reported health; and (iii) educational attainment on BMI in the UK Biobank data. Conclusions: PRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and it corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence.

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10-8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10-6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10-6). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10-29). These findings provide new insight into the genetic architecture of intelligence.

Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies.

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called 'missing heritability'. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51-62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36-38%). Hence, cross-study heterogeneity contributes to the missing heritability.