Resource profile and user guide of the Polygenic Index Repository

Becker, Joel; Burik, Casper A.P.; Goldman, Grant; Wang, Nancy; Jayashankar, Hariharan; Bennett, Michael H; Belsky, Daniel W.; Linnér, Richard Karlsson; Ahlskog, Rafael; Kleinman, Aaron; Hinds, David A.; Agee, Michelle; Alipanahi, Babak; Auton, Adam; Bell, Robert K.; Bryc, Katarzyna; Elson, Sarah L.; Fontanillas, Pierre; Furlotte, Nicholas A.; Huber, Karen E.; Litterman, Nadia K.; McCreight, Jennifer C.; McIntyre, Matthew H.; Mountain, Joanna L.; Northover, Carrie A. M.; Pitts, Steven J.; Sathirapongsasuti, J. Fah; Sazonova, Olga V.; Shelton, Janie F.; Shringarpure, Suyash; Tian, Chao; Tung, Joyce Y.; Vacic, Vladimir; Wilson, Catherine H.; Caspi, Avshalom; Corcoran, David L.; Moffitt, Terrie E.; Poulton, Richie; Sugden, Karen; Williams, Benjamin; Harris, Kathleen Mullan; Steptoe, Andrew; Ajnakina, Olesya; Milani, Lili; Esko, Tõnu; 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 I.; Beauchamp, Jonathan P.; Koellinger, Philipp; Oskarsson, Sven; Johannesson, Magnus; Visscher, Peter M.; Meyer, Michelle N.; Laibson, David; Cesarini, David; Benjamin, Daniel J.; Turley, Patrick; Okbay, Aysu

doi:10.1038/s41562-021-01119-3

Cited by 91 publications

(156 citation statements)

References 94 publications

Supporting

Mentioning

110

Contrasting

Order By: Relevance

“…sample shows that applying ORIV in a between-family design increases the estimated GWAS-heritability from 0.076 (s.e. Third, when comparing the ORIV estimator to the attenuation-corrected OLS estimator of Becker et al (29), we find that both estimators are consistent when the assumptions of the respective methods are satisfied. The relative performance of each therefore depends on the relative sizes of the ORIV standard error versus the standard error of the SNP-based heritability, which the Becker et al approach relies on.…”

Section: /35mentioning

confidence: 67%

“…Our paper also closely relates to the pioneering work by Becker et al (29). These authors not only launched a repository of PGSs, but additionally developed a Python tool to disattenuate between-family estimates of the PGS on a phenotype on basis of external information on the SNP-based heritability (see also (28)).…”

Section: /35mentioning

confidence: 79%

“…Although we do not directly implement the Becker et al (29) approach in our simulations, it can be thought of as an estimator with zero bias and an RMSE equal to the standard error of the square root of the SNP-based heritability estimate (see Methods section for details). Hence, when contrasting ORIV with the Becker et al approach, one should compare the RMSE of the ORIV estimate to the standard error of the square root of the SNP-based heritability estimate.…”

Section: /35mentioning

confidence: 99%

“…In a modest sample of 1,000 individuals, the correlation between the two independent PGSs should be at least 0.10. 8 It is harder to provide a concrete rule-of-thumb for when ORIV estimates outperform disattenuated OLS estimators such as the one derived by Becker et al (29) since there is typically a bias-variance trade-off. Using the RMSE as the relevant metric, ORIV outperforms the Becker et al approach when the RMSE of the ORIV estimates is below the standard error of the square root of the SNP-based heritability estimate.…”

Section: /35mentioning

confidence: 99%

“…In a recent paper independently developed around the same time as this study, Becker et al (29) exploit their equivalent of equation (15) to directly derive an estimator that corrects for measurement error. The authors suggest computing the attenuation…”

Section: Disattenuating the Ols Estimatormentioning

confidence: 99%

See 4 more Smart Citations

Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

Kippersluis

Biroli²,

Galama

et al. 2021

Preprint

View full text Add to dashboard Cite

Polygenic scores have become the workhorse for empirical analyses in social-science genetics. Because a polygenic score is constructed using the results of finite-sample Genome-Wide Association Studies (GWASs), it is a noisy approximation of the true latent genetic predisposition to a certain trait. The conventional way of boosting the predictive power of polygenic scores is to increase the GWAS sample size by meta-analyzing GWAS results of multiple cohorts. In this paper we challenge this convention. Through simulations, we show that Instrumental Variable (IV) regression using two polygenic scores from independent GWAS samples outperforms the typical Ordinary Least Squares (OLS) model employing a single meta-analysis based polygenic score in terms of bias, root mean squared error, and statistical power. We verify the empirical validity of these simulations by predicting educational attainment (EA) and height in a sample of siblings from the UK Biobank. We show that IV regression between-families approaches the SNP-based heritabilities, while compared to meta-analysis applying IV regression within-families provides a tighter lower bound on the direct genetic effect. IV estimation improves the predictive power of polygenic scores by 12% (height) to 22% (EA). Our findings suggest that measurement error is a key explanation for hidden heritability (i.e., the difference between SNP-based and GWAS-based heritability), and that it can be overcome using IV regression. We derive the practical rule of thumb that IV outperforms OLS when the correlation between the two polygenic scores used in IV regression is larger than √(10 / (N+10)), with N the sample size of the prediction sample.

show abstract

Section: /35mentioning

confidence: 67%

Section: /35mentioning

confidence: 79%

Section: /35mentioning

confidence: 99%

Section: /35mentioning

confidence: 99%

Section: Disattenuating the Ols Estimatormentioning

confidence: 99%

See 3 more Smart Citations

Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

Kippersluis

Biroli²,

Galama

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

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

Meyer¹,

Appelbaum²,

Benjamin³

et al. 2023

Hastings Center Report

Self Cite

View full text Add to dashboard Cite

Social and behavioral scientists are increasingly frequently collaborating with geneticists or adapting the methods of genetics research to investigate how genomic differences are associated with differences in a wide variety of behavioral and social phenotypes. The huge and varied range of phenotypes investigated in social and behavioral genomics (SBG) research, broadly construed, includes smoking and eating behavior, schizophrenia, attention deficit-hyperactivity disorder (ADHD), a sense of well-being, introversion, risk-taking preferences, income, intelligence, and educational attainment. Researchers study these phenotypes because they believe that doing so can, among other benefits, contribute to more rigorous social (and health) science, which can in turn contribute to more just social policies.Because, as we detail in part 1, there is such a long history of attempts to use claims about genetic differences in such phenotypes to advance unjust social policies-and because of the potential of such claims to undercut efforts at creating more just social policies-SBG research can be deeply controversial. In this report, we seek to convey what our working group, composed both of scientists who conduct SBG research and of scholars who think critically about it (see boxes 1 and 2), learned from three years of wrestling with the historical, social, and scientific facts relevant to the ethics of SBG research. More specifically, we seek to articulate where a majority of our working group did-and did not-achieve consensus about the issues with which we wrestled.To understand the risks and potential benefits of SBG research in more depth, our group first had to wrestle with the scientific question, what can genetics tell us about social outcomes as complex as, for example, educational attainment? Addressing that question required us to put SBG research in the context of genetics research more S3 SPECIAL REPORT: The Ethical Implications of Social and Behavioral Genomics

show abstract

Wrestling with Public Input on an Ethical Analysis of Scientific Research

Martschenko¹,

Callier²,

Garrison³

et al. 2023

Hastings Center Report

Self Cite

View full text Add to dashboard Cite

Bioethicists frequently call for empirical researchers to engage participants and community members in their research, but don't themselves typically engage community members in their normative research. In this article, we describe an effort to include members of the public in normative discussions about the risks, potential benefits, and ethical responsibilities of social and behavioral genomics (SBG) research. We reflect on what might—and might not— be gained from engaging the public in normative scholarship and on lessons learned about public perspectives on the risks and potential benefits of SBG research and the responsible conduct and communication of such research. We also provide procedural lessons for others in bioethics who are interested in engaging members of the public in their research.

show abstract

Resource profile and user guide of the Polygenic Index Repository

Cited by 91 publications

References 94 publications

Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

Overcoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of Approaches

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

Wrestling with Public Input on an Ethical Analysis of Scientific Research

Contact Info

Product

Resources

About