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We aimed to elucidate the associations between polygenic scores for educational attainment and clinical criterion counts for three SUDs (alcohol, nicotine and cannabis).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Polygenic association and sibling comparison methods. The latter strengthens inferences in observational research by controlling for confounding factors that differ between families.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Six sites in the United States.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>European ancestry participants aged 25\u00a0years and older from the Collaborative Study on the Genetics of Alcoholism (COGA). Polygenic association analyses included 5582 (54% female) participants. Sibling comparisons included 3098 (52% female) participants from 1226 sibling groups nested within the overall sample.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements<\/jats:title>\n                    <jats:p>\n                      Outcomes included criterion counts for DSM\u20105 alcohol use disorder (AUDSX), Fagerstr\u00f6m nicotine dependence (NDSX) and DSM\u20105 cannabis use disorder (CUDSX). We derived polygenic scores for educational attainment (\n                      <jats:italic>EduYears\u2010GPS<\/jats:italic>\n                      ) using summary statistics from a large (&gt;\u00a01 million) genome\u2010wide association study of educational attainment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>\n                      In polygenic association analyses, higher\n                      <jats:italic>EduYears\u2010GPS<\/jats:italic>\n                      predicted lower AUDSX, NDSX and CUDSX [\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.01, effect sizes (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      ) ranging from 0.30 to 1.84%]. These effects were robust in sibling comparisons, where sibling differences in\n                      <jats:italic>EduYears\u2010GPS<\/jats:italic>\n                      predicted all three SUDs (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.05,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      0.13\u20130.20%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Individuals who carry more alleles associated with educational attainment tend to meet fewer clinical criteria for alcohol, nicotine and cannabis use disorders, and these effects are robust to rigorous controls for potentially confounding factors that differ between families (e.g. socio\u2010economic status, urban\u2013rural residency and parental education).<\/jats:p>\n         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