Daniel J. Benjamin

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

Selected publications

• Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

  Nature Genetics · 2022 · 673 citations

  We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other…

  DOI

• Problems with Using Polygenic Scores to Select Embryos

  New England Journal of Medicine · 2021 · 183 citations

  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 variet…

  PublisherOA PDFDOI

• Resource profile and user guide of the Polygenic Index Repository

  Nature Human Behaviour · 2021 · 178 citations

  Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. 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…

  DOI

• Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility

  The Hastings Center Report · 2023 · 66 citations

  In this consensus report by a diverse group of academics who conduct and/or are concerned about social and behavioral genomics (SBG) research, the authors recount the often-ugly history of scientific attempts to understand the genetic contributions to human behaviors and social outcomes. They then describe what the current science-including genomewide association studies and polygenic indexes-can and cannot tell us, as well as its risks and potential benefits. They conclude with a discussion of…

  PublisherOA PDFDOI

• Family-GWAS reveals effects of environment and mating on genetic associations

  medRxiv · 2024-10-04 · 35 citations

  preprintOpen access

  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 di…

  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

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

Similar researchers at University of California, Los Angeles

• Andrew G. Atkesonh-52
• Sushil Bikhchandanih-41
• Shlomo Benartzih-34