About

Daniel J. Benjamin is a Professor of Behavioral Economics and Genoeconomics at UCLA Anderson. His research integrates ideas and methods from psychology into economic analysis, focusing on understanding errors in statistical reasoning, utilizing survey measures of subjective well-being to track national well-being and evaluate policies, and identifying genetic variants associated with outcomes such as educational attainment and subjective well-being. His work in genoeconomics develops tools for incorporating genomic data into the social sciences, contributing to the understanding of how genetic factors influence various social and economic outcomes.

Research topics

• Biology
• Computer Science
• Sociology
• Genetics
• Psychology
• Artificial Intelligence
• Information Retrieval
• Demography
• Social psychology
• Communication
• World Wide Web
• Evolutionary biology
• Data science

Selected publications

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

  UNC Libraries · 2026-02-10

  articleOpen access
  PublisherDOI

• Genomic-Relatedness Matching Expands Population Coverage, Improves Power, and Reduces Bias in Genetic Association Analyses

  medRxiv · 2026-05-18

  articleOpen access

  ABSTRACT We introduce a novel approach, Genomic-Relatedness-Matched Association (GRMA) studies, as an alternative to genome-wide association studies (GWAS). GWAS are typically restricted to samples of mostly unrelated individuals with a single, shared continental ancestry and nevertheless can still be biased by gene-environment correlation and assortative mating. In contrast, GRMA can be implemented in ancestrally diverse samples—retaining individuals of mixed or underrepresented ancestries and eliminating the need to assign labels to ancestry groups—and can reduce bias relative to standard GWAS. GRMA matches each individual to a group of controls whose pairwise relatedness with the individual exceeds a user-specified threshold. It generates SNP-level summary statistics based on within-group associations. In applications using the UK Biobank and All of Us data, we find that GRMA compares favorably to GWAS methods in terms of bias, precision, and population coverage. GRMA enables several novel findings; for example, we find that “genetic nurture” is unlikely to be an important source of genome-wide bias in population GWAS of body mass index, height, and educational attainment. The method is computationally efficient and supported by open-source software, facilitating its application in large-scale scientific and health-related studies.

  PublisherDOI

• An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-18 · 4 citations

  preprintOpen access

  Polygenic indexes (PGIs) - DNA-based predictors of individual phenotypes - have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI's predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository's scope, and introduce novel resources that reduce confounding bias and improve interpretability.

  PublisherOA PDFDOI

• An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

  Research Square · 2025-10-13

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Redefine statistical significance

  Artefactual Field Experiments · 2025-01-10 · 21 citations

  articleOpen access
  PublisherOA PDFDOI

• Author response for "Overconfidence Persists Despite Years of Accurate, Precise, Public, and Continuous Feedback: Two Studies of Tournament Chess Players"

  2025-06-24

  peer-review
  PublisherDOI

• Dissecting the Predictive Accuracy of Polygenic Indexes for Behavioral Phenotypes Across Genetic Ancestries

  Research Square · 2025-10-03

  preprintOpen access
  PublisherOA PDFDOI

• Authors’ response to Unjournal (interim) evaluations of "Adjusting for Scale-Use Heterogeneity in Self-Reported Well-Being"

  2025-12-09

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Family-based genome-wide association study designs for increased power and robustness

  Nature Genetics · 2025-03-10 · 9 citations

  articleOpen access

  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.

  PublisherOA PDFDOI

• Associations between common genetic variants and income provide insights about the socio-economic health gradient

  Nature Human Behaviour · 2025-01-28 · 24 citations

  articleOpen access

  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.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21AG037741

  NIH · $355k · 2015

• NIH Grant R56AG042568

  NIH · $803k · 2020

• Genome-Wide Analyses of Health and Well-Being Phenotypes

  NIH · $2.8M · 2015–2023

• NIH Grant R01AG051903

  NIH · $3.1M · 2022

• EAGER Proposal: Workshop for the Formation of a Social Science Genetic Association Consortium

  NSF · $99k · 2011–2014

Frequent coauthors

• David Cesarini

  220 shared

• David Laibson

  Harvard University Press

  212 shared

• Miles Kimball

  University of Colorado Boulder

  203 shared

• Ori Heffetz

  187 shared

• Patrick Turley

  University of Southern California

  170 shared

• Peter M. Visscher

  University of Cambridge

  152 shared

• Michelle N. Meyer

  123 shared

• Benjamin M. Neale

  Massachusetts General Hospital

  118 shared