Daniel J. Benjamin’s research while affiliated with University of California, Los Angeles and other places

Family-based genome-wide association studies (FGWASs) use random, within-family genetic variation to remove confounding from estimates of direct genetic effects (DGEs). Here we introduce a ‘unified estimator’ that includes individuals without genotyped relatives, unifying standard and FGWAS while increasing power for DGE estimation. We also introduce a ‘robust estimator’ that is not biased in structured and/or admixed populations. In an analysis of 19 phenotypes in the UK Biobank, the unified estimator in the White British subsample and the robust estimator (applied without ancestry restrictions) increased the effective sample size for DGEs by 46.9% to 106.5% and 10.3% to 21.0%, respectively, compared to using genetic differences between siblings. Polygenic predictors derived from the unified estimator demonstrated superior out-of-sample prediction ability compared to other family-based methods. We implemented the methods in the software package snipar in an efficient linear mixed model that accounts for sample relatedness and sibling shared environment.

We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1–5% of income variance, with only one fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma and back pain. The Income Factor had a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment. Accounting for the genetic overlap of educational attainment with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health.

There is recognition among human complex-trait geneticists that not only are many common assumptions made for the sake of statistical tractability (e.g., random mating, independence of parent/offspring environments) unlikely to apply in many contexts, but that methods reliant on such assumptions can yield misleading results, even in large samples. Investigations of the consequences of violating these assumptions so far have focused on individual perturbations operating in isolation. Here, we analyze widely used estimators of genetic architectural parameters, including LD-score regression and both population-based and within-family GWAS, across a broad array of perturbations to classical assumptions, such as multivariate assortative mating and vertical transmission (parental effects on offspring phenotypes not mediated by genetic inheritance). We find that widely-used statistical approaches are unreliable across a broad range of perturbations, and that structural sources of confounding often operate synergistically to distort conclusions. For example, mild multivariate assortative mating and vertical transmission together can dramatically inflate heritability estimates and GWAS false positive rates. Further, GWAS will become progressively more polluted by off-target associations as sample sizes increase. Given these challenges, we introduce xftsim, a forward time simulation library capable of modeling a wide range of genetic architectures, mating regimes, and transmission dynamics, to facilitate the systematic comparison of existing approaches and the development of robust methods. Together, our findings illustrate the importance of comprehensive sensitivity analysis and present a valuable tool for future research.

Linkage studies have successfully mapped loci underlying monogenic disorders, but mostly failed when applied to common diseases. Conversely, genome-wide association studies (GWASs) have identified replicable associations between thousands of SNPs and complex traits, yet capture less than half of the total heritability. In the present study we reconcile these two approaches by showing that linkage signals of height and body mass index (BMI) from 119,000 sibling pairs colocalize with GWAS-identified loci. Concordant with polygenicity, we observed the following: a genome-wide inflation of linkage test statistics; that GWAS results predict linkage signals; and that adjusting phenotypes for polygenic scores reduces linkage signals. Finally, we developed a method using recombination rate-stratified, identity-by-descent sharing between siblings to unbiasedly estimate heritability of height (0.76 ± 0.05) and BMI (0.55 ± 0.07). Our results imply that substantial heritability remains unaccounted for by GWAS-identified loci and this residual genetic variation is polygenic and enriched near these loci.

Genome–wide association studies (GWAS) have discovered thousands of replicable genetic associations, guiding drug target discovery and powering genetic prediction of human phenotypes and diseases. However, genetic associations can be affected by gene–environment correlations and non–random mating, which can lead to biased inferences in downstream analyses. Family–based GWAS (FGWAS) uses the natural experiment of random assignment of genotype within families to separate out the contribution of direct genetic effects (DGEs) — causal effects of alleles in an individual on an individual — from other factors contributing to genetic associations. Here, we report results from an FGWAS meta–analysis of 34 phenotypes from 17 cohorts. We found evidence that factors uncorrelated with DGEs make substantial contributions to genetic associations for 27 phenotypes, with population stratification confounding — a form of gene–environment correlation — likely the major cause. By estimating SNP heritability and genetic correlations using DGEs, we found evidence that assortative mating has led to overestimation of SNP heritability for 5 phenotypes and overestimation of the degree of shared genetic effects (pleiotropy) between 22 pairs of phenotypes. Polygenic predictors constructed from DGEs are particularly useful for studying natural selection, assortative mating, and indirect genetic effects (effects of relatives′ genes mediated through the family environment). We validate our meta–analysis results by predicting phenotypes in hold–out samples using polygenic predictors constructed from DGEs, achieving statistically significant out–of–sample prediction for 24 phenotypes with little attenuation of predictive power within–families. We provide FGWAS summary statistics for 34 phenotypes that can be used for downstream analyses. Our study provides both a template for performing FGWAS and an argument for its value for debiasing inferences and understanding the impact of environment and mating patterns.

Happiness data—survey respondents’ self-reported well-being (SWB)—have become increasingly common in economics research, with recent calls to use them in policymaking. Researchers have used SWB data in novel ways—for example, to learn about welfare or preferences when choice data are unavailable or difficult to interpret. Focusing on leading examples of this pioneering research, the first part of this review uses a simple theoretical framework to reverse-engineer some of the crucial assumptions that underlie existing applications. The second part discusses evidence bearing on these assumptions and provides practical advice to the agencies and institutions that generate SWB data, the researchers who use them, and the policymakers who may use the resulting research. While we advocate creative uses of SWB data in economics, we caution that their use in policy will likely require both additional data collection and further research to better understand the data.

While digital tools, such as the Internet, smartphones, and social media, are an important part of modern society, little is known about the specific role they play in the healthcare management of individuals and caregivers affected by rare disease. Collectively, rare diseases directly affect up to 10% of the global population, suggesting that a significant number of individuals might benefit from the use of digital tools. The purpose of this qualitative interview‐based study was to explore: (a) the ways in which digital tools help the rare disease community; (b) the healthcare gaps not addressed by current digital tools; and (c) recommended digital tool features. Individuals and caregivers affected by rare disease who were comfortable using a smartphone and at least 18 years old were eligible to participate. We recruited from rare disease organizations using purposive sampling in order to achieve a diverse and information rich sample. Interviews took place over Zoom and reflexive thematic analysis was utilized to conceptualize themes. Eight semistructured interviews took place with four individuals and four caregivers. Three themes were conceptualized which elucidated key aspects of how digital tools were utilized in disease management: (1) digital tools should lessen the burden of managing a rare disease condition; (2) digital tools should foster community building and promote trust; and (3) digital tools should provide trusted and personalized information to understand the condition and what the future may hold. These results suggest that digital tools play a central role in the lives of individuals with rare disease and their caregivers. Digital tools that centralize trustworthy information, and that bring the relevant community together to interact and promote trust are needed. Genetic counselors can consider these ideal attributes of digital tools when providing resources to individuals and caretakers of rare disease.

Overconfidence is thought to be a fundamental cognitive bias, but it is typically studied in environments where people lack accurate information about their abilities. Tournament chess players receive objective, precise, and public feedback, so we conducted a preregistered survey experiment and replication to learn whether overconfidence persists in an environment that should diminish or eradicate it. Our combined sample comprised 3388 rated players aged 5–88 years, from 22 countries, with M=18.8 years of tournament experience. On average, participants asserted their ability was 89 Elo rating points higher than their observed ratings indicated—expecting to outscore an equally-rated opponent by 2:1. One year later, only 11.3% of overconfident players achieved their asserted ability rating. Low-rated players overestimated their skill the most and top-rated players were calibrated. These patterns emerged in every sociodemographic subgroup we considered. We conclude that overconfidence persists even in real-world information environments that should be inhospitable to it.

We conducted a genome-wide association study (GWAS) on income among individuals of European descent and leveraged the results to investigate the socio-economic health gradient (N=668,288). We found 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes. Our GWAS-derived polygenic index captures 1 - 4% of income variance, with only one-fourth attributed to direct genetic effects. A phenome-wide association study using this polygenic index showed reduced risks for a broad spectrum of diseases, including hypertension, obesity, type 2 diabetes, coronary atherosclerosis, depression, asthma, and back pain. The income factor showed a substantial genetic correlation (0.92, s.e. = .006) with educational attainment (EA). Accounting for EA's genetic overlap with income revealed that the remaining genetic signal for higher income related to better mental health but reduced physical health benefits and increased participation in risky behaviours such as drinking and smoking.