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Understanding moral acceptability and willingness to use is crucial for informing policy

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

A goal of genome-wide association studies (GWASs) is to estimate the causal effects of alleles carried by an individual on that individual (‘direct genetic effects’). Typical GWAS designs, however, are susceptible to confounding due to gene-environment correlation and non-random mating (population stratification and assortative mating). Family-based GWAS, in contrast, is robust to such confounding since it uses random, within-family genetic variation. When both parents are genotyped, a regression controlling for parental genotype provides the most powerful approach. However, parental genotypes are often missing. We have previously shown that imputing the genotypes of missing parent(s) can increase power for estimation of direct genetic effects over using genetic differences between siblings. We extend the imputation method, which previously only applied to samples with at least one genotyped sibling or parent, to ‘singletons’ (individuals without any genotyped relatives). By including singletons, the effective sample size for estimation of direct effects can be increased by up to 50%. We apply this method to 408,254 ‘White British’ individuals from the UK Biobank, obtaining an effective sample size increase of between 25% and 43% (depending upon phenotype) by including 368,629 singletons. While this approach maximizes power, it can be biased when there is strong population structure. We therefore introduce an imputation based estimator that is robust to population structure and more powerful than other robust estimators. We implement our estimators in the software package snipar using an efficient linear-mixed model (LMM) specified by a sparse genetic relatedness matrix. We examine the bias and variance of different family-based and standard GWAS estimators theoretically and in simulations with differing levels of population structure, enabling researchers to choose the appropriate approach depending on their research goals.

How well do pre-school delay of gratification and life-course measures of self-regulation predict mid-life capital formation? We surveyed 113 participants of the 1967–1973 Bing pre-school studies on delay of gratification when they were in their late 40’s. They reported 11 mid-life capital formation outcomes, including net worth, permanent income, absence of high-interest debt, forward-looking behaviors, and educational attainment. To address multiple hypothesis testing and our small sample, we pre-registered an analysis plan of well-powered tests. As predicted, a newly constructed and pre-registered measure derived from preschool delay of gratification does not predict the 11 capital formation variables (i.e., the sign-adjusted average correlation was 0.02). A pre-registered composite self-regulation index, combining preschool delay of gratification with survey measures of self-regulation collected at ages 17, 27, and 37, does predict 10 of the 11 capital formation variables in the expected direction, with an average correlation of 0.19. The inclusion of the preschool delay of gratification measure in this composite index does not affect the index’s predictive power. We tested several hypothesized reasons that preschool delay of gratification does not have predictive power for our mid-life capital formation variables.

The compromise effect arises when being close to the "middle" of a choice set makes an option more appealing. The compromise effect poses conceptual and practical problems for economic research: by influencing choices, it can bias researchers' inferences about preference parameters. To study this bias, we conduct an experiment with 550 participants who made choices over lotteries from multiple price lists (MPLs). Following prior work, we manipulate the compromise effect to influence choices by varying the middle options of each MPL. We then estimate risk preferences using a discrete-choice model without a compromise effect embedded in the model. As anticipated, the resulting risk preference parameter estimates are not robust, changing as the compromise effect is manipulated. To disentangle risk preference parameters from the compromise effect and to measure the strength of the compromise effect, we augment our discrete-choice model with additional parameters that represent a rising penalty for expressing an indifference point further from the middle of the ordered MPL. Using this method, we estimate an economically significant magnitude for the compromise effect and generate robust estimates of risk preference parameters that are no longer sensitive to compromise-effect manipulations.

Companies have recently begun to sell a new service to patients considering in vitro fertilization: embryo selection based on polygenic scores (ESPS). These scores represent individualized predictions of health and other outcomes derived from genomewide association studies in adults to partially predict these outcomes. This article includes a discussion of many factors that lower the predictive power of polygenic scores in the context of embryo selection and quantifies these effects for a variety of clinical and nonclinical traits. Also discussed are potential unintended consequences of ESPS (including selecting for adverse traits, altering population demographics, exacerbating inequalities in society, and devaluing certain traits). Recommendations for the responsible communication about ESPS by practitioners are provided, and a call for a society-wide conversation about this technology is made. (Funded by the National Institute on Aging and others.).

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some of which are novel—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the “additive SNP factor.” Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

We present a new method, Multi-Ancestry Meta-Analysis (MAMA), which combines genome-wide association study (GWAS) summary statistics from multiple populations to produce new summary statistics for each population, identifying novel loci that would not have been discovered in either set of GWAS summary statistics alone. In simulations, MAMA increases power with less bias and generally lower type-1 error rate than other multi-ancestry meta-analysis approaches. We apply MAMA to 23 phenotypes in East-Asian- and European-ancestry populations and find substantial gains in power. In an independent sample, novel genetic discoveries from MAMA replicate strongly.

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer...

Abstract. Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects ( r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects ( r = −.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1–5 and has evolutionary consequences6–8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes. A century after being predicted by theory, the authors detect and quantify the genomic signature of assortative mating in ~400,000 contemporary human genomes, and report new genetic evidence for assortative mating on height and educational attainment.

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

What utility notion—e.g., flow/lifetime, self/family-centered—do self-reported well-being (SWB) questions measure? First, we clarify the theoretical assumptions underlying existing applications regarding the (i) life domains, (ii) time horizons, and (iii) other-regarding preferences captured by SWB data. Second, we document inconsistency in assumptions across papers, sometimes using the same SWB dataset. Third, we ask survey respondents what they had in mind regarding (i)–(iii) when answering commonly used—life satisfaction, happiness, ladder—and new SWB questions. We find that respondents’ self-reports differ from researchers’ assumptions, and differ across SWB questions and sociodemographic groups. We outline actionable suggestions for SWB researchers.

What utility notion—e.g. flow/lifetime, self/family-centered—do self-reported well-being (SWB) questions measure? Existing applications make different assumptions regarding the (i) life domains, (ii) time horizons, and (iii) other-regarding preferences captured by SWB data. To obtain relevant evidence, we ask survey respondents what they had in mind regarding (i)–(iii) when answering commonly used—life satisfaction, happiness, ladder—and new SWB questions. We find that respondents’ self-reports differ from researchers’ assumptions, and differ across SWB questions and sociodemographic groups. At the same time, simple SWB-question wording tweaks are effective in moving self-reports towards desired interpretations. We outline actionable suggestions for SWB researchers. JEL Classification: D69, D90, I31

Risk aversion is typically inferred from real or hypothetical choices over risky lotteries, but such “untutored” choices may reflect mistakes rather than preferences. We develop a procedure to disentangle preferences from mistakes: after eliciting untutored choices, we confront participants with their choices that are inconsistent with expected-utility axioms (broken down enough to be self-evident) and allow them to reconsider their choices. We demonstrate this procedure via a survey about hypothetical retirement investment choices administered to 596 Cornell students. We find that, on average, reconsidered choices are more consistent with almost all expected-utility axioms, with one exception related to regret.

Associations between genotype and phenotype derive from four sources: direct genetic effects, indirect genetic effects from relatives, population stratification, and correlations with other variants affecting the phenotype through assortative mating. Genome-wide association studies (GWAS) of unrelated individuals have limited ability to distinguish the different sources of genotype-phenotype association, confusing interpretation of results and potentially leading to bias when those results are applied – in genetic prediction of traits, for example. With genetic data on families, the randomisation of genetic material during meiosis can be used to distinguish direct genetic effects from other sources of genotype-phenotype association. Genetic data on siblings is the most common form of genetic data on close relatives. We develop a method that takes advantage of identity-by-descent sharing between siblings to impute missing parental genotypes. Compared to no imputation, this increases the effective sample size for estimation of direct genetic effects and indirect parental effects by up to one third and one half respectively. We develop a related method for imputing missing parental genotypes when a parent-offspring pair is observed. We provide the imputation methods in a software package, SNIPar (single nucleotide imputation of parents), that also estimates genome-wide direct and indirect effects of SNPs. We apply this to a sample of 45,826 White British individuals in the UK Biobank who have at least one genotyped first degree relative. We estimate direct and indirect genetic effects for ∼5 million genome-wide SNPs for five traits. We estimate the correlation between direct genetic effects and effects estimated by standard GWAS to be 0.61 (S.E. 0.09) for years of education, 0.68 (S.E. 0.10) for neuroticism, 0.72 (S.E. 0.09) for smoking initiation, 0.87 (S.E. 0.04) for BMI, and 0.96 (S.E. 0.01) for height. These results suggest that GWAS based on unrelated individuals provides an inaccurate picture of direct genetic effects for certain human traits.

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. This research was carried out under the auspices of the Social Science Genetic Association Consortium (SSGAC, https://www.thessgac.org/). The research has also been conducted using the UK Biobank Resource under Application Number 11425. The study was supported by funding from the Ragnar Söderberg Foundation (E9/11 and E42/15), the Swedish Research Council (421-2013-1061), The Jan Wallander and Tom Hedelius Foundation, an ERC Consolidator Grant to Philipp Koellinger (647648 EdGe), the Pershing Square Fund of the Foundations of Human Behavior, The Open Philanthropy Project (2016-152872, 010623-00001), and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 to NBER, and R01-AG042568-02 and R56-AG042568-04 to the University of Southern California. CCC was supported by the Intramural Research Program of the NIH/NIDDK and thanks Kevin Hall for informative discussions. PME was funded by Nestlé Nutrition. We thank the DietGen and CHARGE consortia for sharing diet-composition GWAS summary statistics, and we thank 23andMe, Inc., for sharing physical activity GWAS summary statistics. A full list of acknowledgements is provided in Supplementary Information 13.

Although technology-driven economic growth generates gains in consumption and employment opportunities, it may also negatively impact other dimensions of well-being, such as emotional well-being or sense of stability. We study 204 aspects of self-reported well-being among 1,576 US MTurk survey respondents, aggregated into seven themes: evaluative well-being, emotional well-being, positive perceptions of technology or economic growth, autonomy and flexibility, work environment, feelings of calmness and stability, and feelings of belonging and connection. Demographic associations with aspects of well-being vary somewhat across the themes. We highlight the value of a multidimensional approach when comparing well-being across different groups in the United States.

We join the call for governments to routinely collect survey-based measures of self-reported wellbeing and for researchers to study them. We list a number of challenges that have to be overcome in order for these measures to eventually achieve a status that is competitive with traditional economic indicators. We discuss in more detail one of the challenges, comprehensiveness: single-question wellbeing measures do not seem to fully capture what people care about. We briefly review the existing evidence, suggesting that survey respondents, when asked to make real or hypothetical trade-offs, would not always choose to maximize their predicted response to single-question wellbeing measures. The deviations appear systematic, and they persist under conditions where alternative explanations are less plausible. We also review an approach for combining single-question measures into a more comprehensive wellbeing index – an approach that itself is not free of ongoing theoretical and implementational challenges, but that we view as a promising direction.

Contains fulltext : 214941.pdf (Publisher’s version ) (Open Access) We present a consensus-based checklist to improve and document the transparency of research reports in social and behavioural research. An accompanying online application allows users to complete the form and generate a report that they can submit with their manuscript or post to a public repository. 3 p.

Author(s): Aczel, Balazs; Szaszi, Barnabas; Sarafoglou, Alexandra; Kekecs, Zoltan; Kucharský, Simon; Benjamin, Daniel; Chambers, Christopher D; Fisher, Agneta; Gelman, Andrew; Gernsbacher, Morton A; Ioannidis, John P; Johnson, Eric; Jonas, Kai; Kousta, Stavroula; Lilienfeld, Scott O; Lindsay, D Stephen; Morey, Candice C; Munafo, Marcus; Newell, Benjamin R; Pashler, Harold; Shanks, David R; Simons, Daniel J; Wicherts, Jelte M; Albarracin, Dolores; Anderson, Nicole D; Antonakis, John; Arkes, Hal R; Back, Mitja D; Banks, George C; Beevers, Christopher; Bennett, Andrew A; Bleidorn, Wiebke; Boyer, Ty W; Cacciari, Cristina; Carter, Alice S; Cesario, Joseph; Clifton, Charles; Conroy, Ronan M; Cortese, Mike; Cosci, Fiammetta; Cowan, Nelson; Crawford, Jarret; Crone, Eveline A; Curtin, John; Engle, Randall; Farrell, Simon; Fearon, Pasco; Fichman, Mark; Frankenhuis, Willem; Freund, Alexandra M; Gaskell, M Gareth; Giner-Sorolla, Roger; Green, Don P; Greene, Robert L; Harlow, Lisa L; de la Guardia, Fernando Hoces; Isaacowitz, Derek; Kolodner, Janet; Lieberman, Debra; Logan, Gordon D; Mendes, Wendy B; Moersdorf, Lea; Nyhan, Brendan; Pollack, Jeffrey; Sullivan, Christopher; Vazire, Simine; Wagenmakers, Eric-Jan | Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the “additive SNP factor.” Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Many in both government and academia are showing renewed interest in developing new measures of national well-being. A new measure that goes “beyond GDP” to comprehensively capture non-market goods could be a useful supplement to traditional economic indicators for guiding policy and more accurately tracking welfare. But how should national well-being be conceptualized in theory? How could it be measured in practice? How could it be constructed in a systematic and politically neutral way? These questions should be approached by economists with the same level of care that has been taken in the theoretical and practical development of GDP. In this short paper, we focus on one conceptual framework (Benjamin, Heffetz, Kimball, and Szembrot, 2014), which uses self-reported responses to subjective well-being (SWB) and stated preference (SP) survey questions to construct an index of well-being. We briefly review the framework and highlight challenges in the first two steps a government agency would need to take before conducting the SWB and SP surveys: (1) formulating a set of aspects of well-being that is theoretically valid and can be measured accurately via surveys; and (2) choosing and interpreting the surveys’ response scales.

We randomly vary religious identity salience in laboratory subjects to test how identity salience contributes to six hypothesized links from prior literature between religious identity and economic behavior. We find that religious identity salience makes Protestants increase contributions to public goods. Catholics decrease contributions to public goods, expect others to contribute less to public goods, and become less risk averse. Jews more strongly reciprocate as an employee in a bilateral labor market gift-exchange game. We find no evidence of religious identity salience effects on disutility of work effort, discount rates, or generosity in a dictator game.

I study a gift-exchange game, in which a profit-maximizing firm offers a wage to a fair-minded worker, who then chooses how much effort to exert. The worker judges a transaction fairer to the extent that his own gain is more nearly equal to the firm’s gain. The worker calculates both players’ gains relative to what they would have gained from the “reference transaction,” which is the transaction that the worker most recently personally experienced. The model explains several empirical regularities: rent sharing, persistence of a worker’s entry wage at a firm, insensitivity of an incumbent worker’s wage to market conditions, and—if the worker is loss averse and the reference wage is nominal—downward nominal wage rigidity. The model also makes a number of novel predictions. Whether the equilibrium is efficient depends on which notion of efficiency is used in the presence of the worker’s fairness concern, and which is appropriate to use partly depends on whether loss aversion is treated as legitimate for normative purposes.

We join the call for governments to routinely collect survey-based measures of self-reported wellbeing and for researchers to study them. We list a number of challenges that have to be overcome in order for these measures to eventually achieve a status competitive with traditional economic indicators. We discuss in more detail one of the challenges, comprehensiveness: single-question wellbeing measures do not seem to fully capture what people care about. We briefly review the existing evidence, suggesting that survey respondents, when asked to make real or hypothetical tradeoffs, would not always choose to maximize their predicted response to single-question wellbeing measures. The deviations appear systematic, and persist under conditions where alternative explanations are less plausible. We also review an approach for combining single-question measures into a more comprehensive wellbeing index—an approach that itself is not free of ongoing theoretical and implementational challenges, but that we view as a promising direction.

Many stockholders irrationally believe that high recent stock market returns predict high future stock market returns. The presence of these extrapolators can help resolve the equity premium puzzle if the elasticity of intertemporal substitution (EIS) is greater than one. In our model, extrapolators’ overreaction to dividend news generates countercyclical expected returns while attenuating their consumption response. The equity premium is high because extrapolators believe stocks are a bad hedge and rational investors’ limited risk-bearing capacity prevents them from fully compensating for extrapolators’ reluctance to hold stocks. We match the U.S. data with a relative risk aversion of 4 and an EIS of 2.

Errors in probabilistic reasoning have been the focus of much psychology research and are among the original topics of modern behavioral economics. This chapter reviews theory and evidence on this topic, with the goal of facilitating more systematic study of belief biases and their integration into economics. The chapter discusses biases in beliefs about random processes, biases in belief updating, the representativeness heuristic as a possible unifying theory, and interactions between biased belief updating and other features of the updating situation. Throughout, I aim to convey how much evidence there is for (and against) each putative bias, and I highlight when and how different biases may be related to each other. The chapter ends by drawing general lessons for when people update too much or too little, reflecting on modeling challenges, pointing to areas of economics to which the biases are relevant, and highlighting some possible directions for future work.

Much of science is (rightly or wrongly) driven by hypothesis testing. Even in situations where the hypothesis testing paradigm is correct, the common practice of basing inferences solely on p-values has been under intense criticism for over 50 years. We propose, as an alternative, the use of the odds of a correct rejection of the null hypothesis to incorrect rejection. Both pre-experimental versions (involving the power and Type I error) and post-experimental versions (depending on the actual data) are considered. Implementations are provided that range from depending only on the p-value to consideration of full Bayesian analysis. A surprise is that all implementations -- even the full Bayesian analysis -- have complete frequentist justification. Versions of our proposal can be implemented that require only minor modifications to existing practices yet overcome some of their most severe shortcomings., Comment: 62-00, 62-01, 62A01

We conducted genome-wide association study (GWAS) meta-analyses of relative caloric intake from fat, protein, carbohydrates and sugar in over 235,000 individuals. We identified 21 approximately independent lead SNPs. 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). Relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood poverty (|rg| ≈ 0.1 – 0.3). Overall, our results show that the relative intake of each macronutrient has a distinct genetic architecture and pattern of genetic correlations suggestive of health implications beyond caloric content.

Non-random mate-choice with respect to complex traits is widely observed in humans, but whether this reflects true phenotypic assortment, environment (social homogamy) or convergence after choosing a partner is not known. Understanding the causes of mate choice is important, because assortative mating (AM) if based upon heritable traits, has genetic and evolutionary consequences. AM is predicted under Fisher’s classical theory1to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify AM on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from SNPs on odd versus even chromosomes. We show by theory and simulation that the effect of AM can be distinguished from population stratification. We applied this approach to 32 complex traits and diseases using SNP data from ∼400,000 unrelated individuals of European ancestry. We found significant evidence of AM for height (θ=3.2%) and educational attainment (θ=2.7%), both consistent with theoretical predictions. Overall, our results imply that AM involves multiple traits, affects the genomic architecture of loci that are associated with these traits and that the consequence of mate choice can be detected from a random sample of genomes.

Humans vary substantially in their willingness to take risks. In a combined sample of over one 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. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated (to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Quadratic Voting and the Normalized Gradient Addition mechanism are both social choice mechanisms that confront individuals with quadratic budget constraints, but they are applicable in different contexts. Adapting one or both to apply to the same context, this paper explores the relationship between these two mechanisms in three contexts: marginal adjustments of continuous policies, simultaneous voting on many public choices, and voting on a single public choice accompanied by private monetary consequences. In the process, we provide some formal analysis of Quadratic Voting when (instead of money) votes are paid for with abstract tokens that are equally distributed by the mechanism designer.

This paper describes results of a pair of incentivized experiments on biases in judgments about random samples. Consistent with the Law of Small Numbers (LSN), participants exaggerated the likelihood that short sequences and random subsets of coin flips would be balanced between heads and tails. Consistent with the Non-Belief in the Law of Large Numbers (NBLLN), participants underestimated the likelihood that large samples would be close to 50% heads. However, we identify some shortcomings of existing models of LSN, and we find that NBLLN may not be as stable as previous studies suggest. We also find evidence for exact representativeness (ER), whereby people tend to exaggerate the likelihood that samples will (nearly) exactly mirror the underlying odds, as an additional bias beyond LSN. Our within-subject design of asking many different questions about the same data lets us disentangle the biases from possible rational alternative interpretations by showing that the biases lead to inconsistency in answers. Our design also centers on identifying and controlling for bin effects, whereby the probability assigned to outcomes systematically depends on the categories used to elicit beliefs in a way predicted by support theory. The bin effects are large and systematic and affect some results, but we find LSN, NBLLN, and ER even after controlling for them.

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this "non-belief in the Law of Large Numbers" by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a non-believer expects the distribution of signals will have fat tails. In inference, a non-believer remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.

I study a gift-exchange game, in which a profit-maximizing firm offers a wage to a fair-minded worker, who then chooses how much effort to exert. The worker judges a transaction fairer to the extent that his or her own gain is more nearly equal to the firm’s gain. The worker calculates both players’ gains relative to what they would have gained from the “reference transaction”, which is the transaction that the worker most recently personally experienced. The model explains several empirical regularities: rent sharing, persistence of a worker’s entry wage at a firm, insensitivity of an incumbent worker’s wage to market conditions, and, if the worker is loss averse and the reference wage is nominal, downward nominal wage rigidity. The model also makes a number of novel predictions. Whether the equilibrium is efficient depends on which notion of efficiency is used in the presence of the worker’s fairness concern, and which is appropriate to use partly depends on whether loss aversion is treated as legitimate for normative purposes.

Under what conditions do distributional preferences, such as altruism or a concern for fair outcomes, generate efficient trade? I analyze theoretically a simple bilateral exchange game: each player sequentially takes an action that reduces his own material payoff but increases the other player's. Each player's preferences may depend on both his/her own material payoff and the other player's. I identify two key properties of the second-mover's preferences: indifference curves kinked around “fair” material-payoff distributions, and materials payoffs entering preferences as “normal goods.” Either property can drive reciprocity-like behavior and generate a Pareto efficient outcome. (JEL C78, D63, D64)

We introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

The epigenome has been shown to be influenced by biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption, and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here, we provide evidence on the associations between epigenetic modifications—in our case, CpG methylation—and educational attainment (EA), a biologically distal environmental factor that is arguably among of the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10,767 individuals. While we find that 9 CpG probes are significantly associated with EA, only two remain associated when we restrict the sample to never-smokers. These two are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, their effect sizes on EA are far smaller than the known associations between CpG probes and biologically proximal environmental factors. Two analyses that combine the effects of many probes—polygenic methylation score and epigenetic-clock analyses—both suggest small associations with EA. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.

The American Psychiatric Association estimates that 3 to 7 per cent of all school aged children are diagnosed with attention deficit hyperactivity disorder (ADHD). Even after correcting for general cognitive ability, numerous studies report a negative association between ADHD and educational achievement. With polygenic scores we examined whether genetic variants that have a positive influence on educational attainment have a protective effect against ADHD. The effect sizes from a large GWA meta-analysis of educational attainment in adults were used to calculate polygenic scores in an independent sample of 12-year-old children from the Netherlands Twin Register. Linear mixed models showed that the polygenic scores significantly predicted educational achievement, school performance, ADHD symptoms and attention problems in children. These results confirm the genetic overlap between ADHD and educational achievement, indicating that one way to gain insight into genetic variants responsible for variation in ADHD is to include data on educational achievement, which are available at a larger scale. (C) 2014 Wiley Periodicals, Inc.

We survey 561 students from US medical schools shortly after they submit choice rankings over residencies to the National Resident Matching Program. We elicit (i) these choice rankings, (ii) anticipated subjective well-being (SWB) rankings, and (iii) expected features of the residencies (such as prestige). We find substantial differences between choice and anticipated-SWB rankings in the implied trade-offs between residency features. In our data, evaluative SWB measures (life satisfaction and Cantril's Ladder) imply trade-offs closer to choice than does affective happiness (even time-integrated), and as close as do multimeasure SWB indices. We discuss implications for using SWB data in applied work. (JEL D12, I31)

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total n deaths = 79,702) and ∼2.4% lower risk for fathers (total n deaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity. Marioni RE, Ritchie SJ, Joshi PK, Hagenaars SP, Okbay A, Fischer K, Adams MJ, Hill WD, Davies G, Social Science Genetic Association Consortium, Nagy R, Amador C, Läll K, Metspalu A, Liewald DC, Campbell A, Wilson JF, Hayward C, Esko T, Porteous DJ, Proceedings of the National Academy of Sciences of the United States of America, 2016, vol. 113, no. 47, pp. 13366-13371, 2016 Refereed/Peer-reviewed

In the version of the paper initially published, no competing interests were declared. The ‘Competing interests’ statement should have stated that B.M.N. is on the Scientific Advisory Board of Deep Genomics. The error has been corrected in the HTML and PDF versions of the article.