Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, United States; Department of Biomedical Informatics, Harvard Medical School, Boston, United States; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States; Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, United States
Andrea Ganna
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States; Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, United States; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
Alex Bloemendal
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States; Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, United States
Alicia R Martin
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States; Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, United States
Center for Computational Molecular Biology, Brown University, Providence, United States; Department of Ecology and Evolutionary Biology, Brown University, Providence, United States
Department of Preventive Medicine, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, United States
Joel Hirschhorn
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Departments of Pediatrics and Genetics, Harvard Medical School, Boston, United States; Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, United States
Mark J Daly
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States; Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, United States; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
Nick Patterson
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Department of Genetics, Harvard Medical School, Boston, United States
Benjamin Neale
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States; Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, United States
Iain Mathieson
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
David Reich
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Department of Genetics, Harvard Medical School, Boston, United States; Howard Hughes Medical Institute, Harvard Medical School, Boston, United States
Department of Biomedical Informatics, Harvard Medical School, Boston, United States; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States; Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, United States
Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
