To compare the direct effect of the PGI on various phenotypes to its population effect, we used data on siblings and trios from UKB3 (link), GS7 (link), and STR38 (link). In both UKB and GS, first-degree relatives were identified using KING with the “--related --degree 1” option72 . For parent-offspring relations, the parent was identified as the older individual in the pair. We removed 621 individuals from GS that had been previously identified by GS as being also present in UKB (Supplementary Note section 7.3). We analyzed PGIs for EA and cognitive performance in all three samples, and height and BMI only in UKB and GS. PGIs were made using GWAS results that exclude GS, STR and all related individuals of up to third degree from UKB (Supplementary Note section 7.3), following the LDpred PGI pipeline described in Supplementary Note section 5.1. We selected 23 phenotypes related to education, cognition, income, and health (Supplementary Table 9) available in at least one of the datasets. For each phenotype in each dataset, we first regressed the phenotype onto sex and age, age2 (link), and age3 (link), and their interactions with sex. In addition, for UKB, we included as covariates the top 40 genetic PCs provided by UKB and the genotyping array dummies3 (link). For GS and STR, we included the top 20 genetic PCs (see Supplementary Note section 5.3 for how the PCs were created). We then took the residuals from the regression of the phenotype on the covariates and normalized the residuals’ variance within each sex separately, so that the phenotypic residual variance was 1 in each sex in the combined sample of siblings and individuals with both parents genotyped. The PGIs of the phenotyped individuals were also normalized to have variance 1 in the same sample. Thus, effect estimates correspond to (partial) correlations, and their squares to proportions of phenotypic variance explained. We give an overview of the statistical analyses performed here, with details in Supplementary Note section 7.4. In the siblings, we regressed individuals’ phenotypes onto the difference between the individual’s PGI and the mean PGI among the siblings in that individual’s family, and the mean PGI among siblings in that family. In trios, we regressed phenotypes onto the individual’s PGI and the individual’s father’s and mother’s PGIs. In both the siblings and trios, we used a linear mixed model to account for relatedness in the samples. We meta-analyzed the results from the siblings and trios, accounting for covariance between the estimates from the sibling and trio samples from the same datasets. We applied a transformation to the meta-analysis that accounts for assortative mating to estimate the population effect of the PGI and the difference between the direct and population effects.
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Okbay A., Wu Y., Wang N., Jayashankar H., Bennett M., Nehzati S.M., Sidorenko J., Kweon H., Goldman G., Gjorgjieva T., Jiang Y., Hicks B., Tian C., Hinds D.A., Ahlskog R., Magnusson P.K., Oskarsson S., Hayward C., Campbell A., Porteous D.J., Freese J., Herd P., Watson C., Jala J., Conley D., Koellinger P.D., Johannesson M., Laibson D., Meyer M.N., Lee J.J., Kong A., Yengo L., Cesarini D., Turley P., Visscher P.M., Beauchamp J.P., Benjamin D.J, & Young A.I. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437-449.
Publication 2022
Cognition Genetic Gwas Mother Parents Phenotype sex Phenotypes Sex variance Trio
Other organizations :
University of Queensland, National Bureau of Economic Research, Anderson University - South Carolina, 23andMe (United States), Uppsala University, Karolinska Institutet, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Stanford University, Georgetown University, University of Southern California, Novo Nordisk Foundation, University of Copenhagen, Erasmus University Rotterdam, Icelandic Heart Association, New York Genome Center, New York University, Helmholtz Zentrum München, deCODE Genetics (Iceland), Institute of Genetic and Biomedical Research, National Research Council, University of Illinois Urbana-Champaign, Radboud University Nijmegen, University of Trieste, University of Helsinki, University of Leicester, Medical University of Graz, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Harokopio University of Athens, QIMR Berghofer Medical Research Institute, Max Planck Institute for Human Development, King's College London, University of Tartu, Twin Cities Orthopedics, University of Minnesota, University Medical Center Groningen, University of Groningen, Hunter Medical Research Institute, University of Manchester, GGZ inGeest, National Institute on Aging, University of Turku, University of Lausanne, University of Milan, Institut für Medizinische Informatik, Biometrie und Epidemiologie, National Cancer Institute, MRC Epidemiology Unit, University of Bristol, Universitätsmedizin Greifswald, Brigham and Women's Hospital, Harvard University, Rush University Medical Center, University of Michigan–Ann Arbor, Erasmus MC, Copenhagen Prospective Studies on Asthma in Childhood, Gentofte Hospital, University of Warwick, Queen Mary University of London, William Harvey Research Institute, Charité - Universitätsmedizin Berlin, German Institute for Economic Research, Finnish Institute for Health and Welfare, Washington University in St. Louis, University of Bonn, National Institutes of Health, University of Aberdeen, Broad Institute, Norwegian Institute of Public Health, Genomics England, Imperial College London, Tampere University, Tampere University Hospital, University of Split, University of Amsterdam, IRCCS Materno Infantile Burlo Garofolo, NorthShore University HealthSystem, Institute for Molecular Medicine Finland, University of Newcastle Australia, University of Dundee, KU Leuven, Florida State University, Guy's and St Thomas' NHS Foundation Trust, Steno Diabetes Center, Institute for Integrative and Experimental Genomics, University of Lübeck, Autism & Developmental Medicine Institute, Geisinger Health System, Folkhälsans Forskningscentrum, University of Iceland, University of South Australia, Fimlab (Finland), Queen's University, Salford Royal Hospital, South Australian Health and Medical Research Institute, Glenfield Hospital, NIHR Leicester Cardiovascular Biomedical Research Unit, Montpellier Business School, Princeton University, University of Wisconsin–Madison, Stockholm School of Economics, George Mason University, University of California, Los Angeles
Polygenic Indices (PGIs) for education attainment (EA) and cognitive performance
PGIs for height and BMI
dependent variables
23 phenotypes related to education, cognition, income, and health
control variables
Age^2
Age^3
Interactions of sex with age, age^2, and age^3
Top 40 genetic principal components (PCs) in UK Biobank (UKB)
Genotyping array dummies in UKB
Top 20 genetic PCs in Generation Scotland (GS) and Swedish Twin Registry (STR)
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