Resource profile and user guide of the Polygenic Index Repository.
paper
Cited
Public
- Authors
- Becker, Joel; Burik, Casper A P; Goldman, Grant; Wang, Nancy; Jayashankar, Hariharan; Bennett, Michael; Belsky, Daniel W; Karlsson LinnΓ©r, Richard; Ahlskog, Rafael; Kleinman, Aaron; Hinds, David A; 23andMe Research Group; Caspi, Avshalom; Corcoran, David L; Moffitt, Terrie E; Poulton, Richie; Sugden, Karen; Williams, Benjamin S; Harris, Kathleen Mullan; Steptoe, Andrew; Ajnakina, Olesya; Milani, Lili; Esko, TΓ΅nu; Iacono, William G; McGue, Matt; Magnusson, Patrik K E; Mallard, Travis T; Harden, K Paige; Tucker-Drob, Elliot M; Herd, Pamela; Freese, Jeremy; Young, Alexander; Beauchamp, Jonathan P; Koellinger, Philipp D; Oskarsson, Sven; Johannesson, Magnus; Visscher, Peter M; Meyer, Michelle N; Laibson, David; Cesarini, David; Benjamin, Daniel J; Turley, Patrick; Okbay, Aysu
- Year
- 2021
- Journal
- Nature human behaviour
- PMID
- 34140656
- DOI
- 10.1038/s41562-021-01119-3
- PMCID
- PMC8678380
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 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 this 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.
Type of study in presentations at Behavior Genetics Association Annual Meetings.Notes: For a description of the data underlying this figure, see Methods. Out of 1,993 presentations in total (over the 2009β2019 period), the percentages that are in exactly 0, 1, 2, or 3 categories are 26.6%, 67.6%, 5.5%, and 0.2%, respectively.
LLM interpretation
This line graph shows the percentage of presentations by study type at Behavior Genetics Association Annual Meetings from 2009 to 2019. The y-axis represents the percentage of presentations, and the x-axis represents the year. "Twin, Family, and Adoption Studies" (green) consistently represent the highest percentage, fluctuating between approximately 52% and 71%, while "PGI Studies" (red) show a general upward trend and "Candidate-Gene Studies" (blue) show an overall decline over the period.
Algorithm determining which single-trait and multi-trait PGIs were generated for the Repository.Notes: See Table 1 for the 36 single-trait PGIs and 35 multi-trait PGIs included in the Repository.
LLM interpretation
This figure is a flow diagram illustrating the algorithmic process used to generate single-trait and multi-trait Polygenic Indices (PGIs) from summary statistics of 53 phenotypes. The workflow branches into two paths: a single-trait path involving meta-analysis of available summary statistics and a multi-trait path using joint analysis (MTAG) of target and supplementary phenotypes. Both paths culminate in a calculation of $\mathbb{E}(R^2)$, where a threshold of $\geq 0.01$ determines the final inclusion of 36 single-trait and 35 multi-trait PGIs in the repository.
Predictive power of Repository single-trait PGIs.Notes: Error bars show 95% confidence intervals from bootstrapping with 1,000 repetitions. Panel (A): Incremental R2 from adding Repositoryβs single-trait PGI to a regression of the phenotype on 10 principal components of the genetic relatedness matrix for HRS, WLS, Dunedin and E-Risk, and on 20 principal components and 106 genotyping batch dummies for UKB. Prior to the regression, phenotypes are residualized on a second-degree polynomial for age or birth year, sex, and their interactions (see Supplementary Tables 5 and 12). For the sample sizes of the GWAS that the PGIs are based on, see Supplementary Table 8. Panel (B): Difference in incremental R2 between Repository single-trait PGI and PGI constructed from publicly available summary statistics using our Repository pipeline. (Note that the latter do not include PGI directly available from datasets, such as the ones accessible from the HRS website.) If no publicly available summary statistics are available for a phenotype, then the difference in incremental R2 is equal to the incremental R2 of the single-trait PGI and is represented by an open circle. βCigarettes per Dayβ in Dunedin was omitted from the Figure because the confidence interval (β5.99% to 0.94%) around the point estimate (β2.38%) required extending the y-axis substantially, making the figure hard to read. For the GWAS sample sizes of the PGIs based on publicly available summary statistics, see Supplementary Table 13.
LLM interpretation
This figure consists of two sets of bar and point plots (Panels A and B) evaluating the predictive power of single-trait Polygenic Indices (PGIs) across various phenotypes and five datasets (HRS, WLS, Dunedin, E-Risk, and UKB). Panel A uses grouped bar charts to show the incremental $R^2$ for phenotypes categorized by Anthropometric, Cognition and Education, Fertility and Sexual Development, Health and Health Behaviors, and Personality and Well-Being, with error bars representing 95% confidence intervals. Panel B uses a point plot to show the difference in incremental $R^2$ ($\Delta$ Incremental $R^2$) between Repository PGIs and those constructed from public summary statistics, with open circles indicating cases where no public statistics were available.
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In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics. | 2023 | 37713023 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Assessing Orthogonality in Gene-Environment Interaction Studies Using Polygenic Indices. | Slob EAW et al. | β | 2026 | β |
| Challenges in clinical translation of polygenic risk score analyses: A systematic review. | MartΓnez-Minguet D et al. | β | 2026 | β |
| Clinical use of polygenic risk scores: current status, barriers and future directions. | Kullo IJ | β | 2026 | β |
| Genetic associations with education have increased and are patterned by socioeconomic context: Evidence from 3 studies born 1946-1970. | Morris TT et al. | β | 2026 | β |
| Genetic disparities in sleep traits and human capital development: A 25-year study in Finnish population-based cohorts. | Hazak A et al. | β | 2026 | β |
| Heterogeneous associations of polygenic indices of 35 traits with mortality: a register-linked population-based follow-up study. | Lahtinen H et al. | β | 2026 | β |
| Polygenic Risks for Mood Disorders and Economic Well-being: Study of Finnish Cohorts. | Hazak A et al. | β | 2026 | β |
| Scanning the Horizon of Sociogenomics: an Assessment of the Development and Growth of Polygenic Indices for Social and Behavioral Traits. | Canter C et al. | β | 2026 | β |
| The Economics and Econometrics of Gene-Environment Interplay. | Biroli P et al. | β | 2026 | β |
| Association Between Polygenic Risk Scores and Treatment Response to Antidepressants, Benzodiazepines, and Antihistamines in Anxiety and Depression. | Markant A et al. | β | 2025 | β |
| Correcting for volunteer bias in GWAS increases SNP effect sizes and heritability estimates. | van Alten S et al. | β | 2025 | β |
| Genetic influences, lifestyle and psychosocial aspects in relation to metabolically healthy obesity and conversion to a metabolically unhealthy state. | Ojalehto Lindfors E et al. | β | 2025 | β |
| Genetic predisposition for morningness-eveningness and economic disadvantage: Evidence from Finland over 25 years. | Hazak A et al. | β | 2025 | β |
| Genetics and Socioeconomic Status: Some Preliminary Evidence on Mechanisms. | Carvalho LS | β | 2025 | β |
| Genotypic and socioeconomic risks for depressive symptoms in two U.S. cohorts spanning early to older adulthood. | Sbarra DA et al. | β | 2025 | β |
| Heritability and polygenic load for comorbid anxiety and depression. | Tabrizi F et al. | β | 2025 | β |
| Large language models predict cognition and education close to or better than genomics or expert assessment. | Wolfram T | β | 2025 | β |
| Lessons in adjusting for genetic confounding in population research on education and health. | Zacher M et al. | β | 2025 | β |
| Neighborhood context, genetic influences, and life satisfaction: Evidence from the German twin family panel. | Harerimana NV et al. | β | 2025 | β |
| Neuropsychiatric polygenic scores are weak predictors of professional categories. | Voloudakis G et al. | β | 2025 | β |
| PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits. | Miao J et al. | β | 2025 | β |
| Raising the Floor? Genetic Influences on Educational Attainment Through the Lens of the Evolving Swedish Welfare State. | Pettersson O | β | 2025 | β |
| Schizophrenia genetic risk and labour market outcomes in the Finnish general population: Are schizophrenia-related traits penalised or rewarded? | Hazak A et al. | β | 2025 | β |
| Social and Behavioral Genomics: On the Ethics of the Research and Its Downstream Applications. | Martschenko DO et al. | β | 2025 | β |
| The effect of peers' genetic predisposition to depression on own mental health. | Jeong Y | β | 2025 | β |
| Using genomic structural equation modeling to examine the genetic architecture of PTSD and life satisfaction phenotypes. | Cusack SE et al. | β | 2025 | β |
| A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology. | Gupta P et al. | β | 2024 | β |
| An analysis of the accuracy of retrospective birth location recall using sibling data. | von Hinke S et al. | β | 2024 | β |
| Causal interpretations of family GWAS in the presence of heterogeneous effects. | Veller C et al. | β | 2024 | β |
| Childhood cognitive ability and self-harm and suicide in later life. | Iveson MH et al. | β | 2024 | β |
| Exposure to sugar rationing in the first 1000 days of life protected against chronic disease. | Gracner T et al. | β | 2024 | β |
| Gene-environment interaction in expertise acquisition: Practice effects on musical expertise vary by polygenic scores for cognitive performance. | Wesseldijk LW et al. | β | 2024 | β |
| Genetic analysis of selection bias in a natural experiment: Investigating in-utero famine effects on elevated body mass index in the Dutch Hunger Winter Families Study. | Zhou J et al. | β | 2024 | β |
| Genetic influences on depression and selection into adverse life experiences. | Rauf T et al. | β | 2024 | β |
| Genetic links between atopy, allergy, and alopecia areata: insights from a Mendelian randomization study. | Xu W et al. | β | 2024 | β |
| Implications of the genomic revolution for education research and policy. | Morris TT et al. | β | 2024 | β |
| Individual differences in adolescent self-control: The role of gene-environment interplay. | Willems YE et al. | β | 2024 | β |
| Infrastructuring Educational Genomics: Associations, Architectures, and Apparatuses. | Williamson B et al. | β | 2024 | β |
| Metabolic-dysfunction associated steatotic liver disease-related diseases, cognition and dementia: A two-sample mendelian randomization study. | Li YS et al. | β | 2024 | β |
| Natural Selection Across Three Generations of Americans. | Hugh-Jones D et al. | β | 2024 | β |
| Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation. | Burt CH | β | 2024 | β |
| Polygenic Scores Clarify the Relationship Between Mental Health and Gender Diversity. | Thomas TR et al. | β | 2024 | β |
| Predicting political beliefs with polygenic scores for cognitive performance and educational attainment. | Edwards T et al. | β | 2024 | β |
| The Add Health Parent Study: A Biosocial Resource for the Study of Multigenerational Racial/Ethnic Disparities in Alzheimer's Disease and Alzheimer's Disease-Related Dementias. | Perreira KM et al. | β | 2024 | β |
| The Legal Uncertainties of Sociogenomic Polygenic Scores. | Callier S et al. | β | 2024 | β |
| Using the phenotype differences model to identify genetic effects in samples of partially genotyped sibling pairs. | Trejo S et al. | β | 2024 | β |
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| Causal interpretations of family GWAS in the presence of heterogeneous effects | Veller C et al. | β | 2023 | β |
| Challenges in studying the interplay of genes and environment. A study of childhood financial distress moderating genetic predisposition for peak smoking. | Bierut L et al. | β | 2023 | β |
| Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G). | Koellinger PD et al. | β | 2023 | β |
| Genetic associations between alcohol phenotypes and life satisfaction: a genomic structural equation modelling approach. | Bountress KE et al. | β | 2023 | β |
| Genetic confounding in bullying research: Causal claims revisited. | Vrijen C et al. | β | 2023 | β |
| Genetic propensity, socioeconomic status, and trajectories of depression over a course of 14 years in older adults. | Kosciuszko M et al. | β | 2023 | β |
| Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics. | Williams CM et al. | β | 2023 | β |
| Implementing Reporting Standards for Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease. | Smith JL et al. | β | 2023 | β |
| Maternal genetic risk for depression and child human capital. | Menta G et al. | β | 2023 | β |
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| Overcoming attenuation bias in regressions using polygenic indices. | van Kippersluis H et al. | β | 2023 | β |
| Partners in Health: Investigating Social Genetic Effects Among Married and Cohabiting Couples. | Otten K et al. | β | 2023 | β |
| Polygenic Prediction of Education and Its Role in the Intergenerational Transmission of Education: Cohort Changes Among Finnish Men and Women Born in 1925-1989. | Lahtinen H et al. | β | 2023 | β |
| Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics. | Miao J et al. | β | 2023 | β |
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| The Dunedin study after half a century: reflections on the past, and course for the future. | Poulton R et al. | β | 2023 | β |
| The ELSI Virtual Forum, 30 Years of the Genome: Integrating and Applying ELSI Research. | Moore CB et al. | β | 2023 | β |
| The identification of mediating effects using genome-based restricted maximum likelihood estimation. | Rietveld CA et al. | β | 2023 | β |
| The impact of entrepreneurship research on other academic fields. | Thurik AR et al. | β | 2023 | β |
| Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019. | Wright L et al. | β | 2023 | β |
| Wrestling with Public Input on an Ethical Analysis of Scientific Research. | Martschenko DO et al. | β | 2023 | β |
| Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility. | Meyer MN et al. | β | 2023 | β |
| Assessing the contribution of genetic nurture to refractive error. | Guggenheim JA et al. | β | 2022 | β |
| Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists. | Collister JA et al. | β | 2022 | β |
| Clinical autism subscales have common genetic liabilities that are heritable, pleiotropic, and generalizable to the general population. | Thomas TR et al. | β | 2022 | β |
| High polygenic predisposition for ADHD and a greater risk of all-cause mortality: a large population-based longitudinal study. | Ajnakina O et al. | β | 2022 | β |
| Luck, lottery, or legacy? The problem of confounding. A reply to Harden. | Coop G et al. | β | 2022 | β |
| Reply to Qiu etΒ al.: Hunting for leadership "causal" genes: Mission possible? | Song Z et al. | β | 2022 | β |
| Research Review: A guide to computing and implementing polygenic scores in developmental research. | Allegrini AG et al. | β | 2022 | β |
| Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics. | Judd N et al. | β | 2022 | β |
| Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research. | Lopez-Pineda A et al. | β | 2022 | β |
| A polygenic score for educational attainment partially predicts voter turnout. | Dawes CT et al. | β | 2021 | β |
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