<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Wesseldijk LW</submitter><funding>The Bank of Sweden Tercentenary Foundation</funding><funding>The National Institute on Deafness and Other Communication Disorders and the Office of the Director of the National Institutes of Health</funding><funding>NICHD NIH HHS</funding><funding>NIDCD NIH HHS</funding><funding>The Sven and Dagmar Salén Foundation and the Marcus and Amalia Wallenberg Foundation</funding><funding>Karolinska Institute</funding><pagination>14658</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9424203</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>12(1)</volume><pubmed_abstract>To further our understanding of the genetics of musicality, we explored associations between a polygenic score for self-reported beat synchronization ability (PGS&lt;sub>rhythm&lt;/sub>) and objectively measured rhythm discrimination, as well as other validated music skills and music-related traits. Using family data, we were able to further explore potential pathways of direct genetic, indirect genetic (through passive gene-environment correlation) and confounding effects (such as population structure and assortative mating). In 5648 Swedish twins, we found PGS&lt;sub>rhythm&lt;/sub> to predict not only rhythm discrimination, but also melody and pitch discrimination (betas between 0.11 and 0.16, p &lt; 0.001), as well as other music-related outcomes (p &lt; 0.05). In contrast, PGS&lt;sub>rhythm&lt;/sub> was not </pubmed_abstract><journal>Scientific reports</journal><pubmed_title>Using a polygenic score in a family design to understand genetic influences on musicality.</pubmed_title><pmcid>PMC9424203</pmcid><funding_grant_id>MAW 2018.0017</funding_grant_id><funding_grant_id>K18 DC017383</funding_grant_id><funding_grant_id>DP2 HD098859</funding_grant_id><funding_grant_id>R01DC016977</funding_grant_id><funding_grant_id>K18DC017383</funding_grant_id><funding_grant_id>M11-0451:1</funding_grant_id><funding_grant_id>R01 DC016977</funding_grant_id><funding_grant_id>DP2HD098859</funding_grant_id><pubmed_authors>Shringarpure S</pubmed_authors><pubmed_authors>Poznik GD</pubmed_authors><pubmed_authors>Gandhi PM</pubmed_authors><pubmed_authors>Freyman W</pubmed_authors><pubmed_authors>Tran V</pubmed_authors><pubmed_authors>Schumacher M</pubmed_authors><pubmed_authors>Weldon CH</pubmed_authors><pubmed_authors>Bell RK</pubmed_authors><pubmed_authors>23andMe Research Team</pubmed_authors><pubmed_authors>Kukar K</pubmed_authors><pubmed_authors>Faaborg A</pubmed_authors><pubmed_authors>Lowe M</pubmed_authors><pubmed_authors>Noblin ES</pubmed_authors><pubmed_authors>Wang W</pubmed_authors><pubmed_authors>Wang X</pubmed_authors><pubmed_authors>Hernandez A</pubmed_authors><pubmed_authors>Wilton P</pubmed_authors><pubmed_authors>Shastri AJ</pubmed_authors><pubmed_authors>Fletez-Brant K</pubmed_authors><pubmed_authors>McCreight JC</pubmed_authors><pubmed_authors>Wesseldijk LW</pubmed_authors><pubmed_authors>Fuller ST</pubmed_authors><pubmed_authors>Bielenberg J</pubmed_authors><pubmed_authors>Kim JS</pubmed_authors><pubmed_authors>Abdellaoui A</pubmed_authors><pubmed_authors>Gordon RL</pubmed_authors><pubmed_authors>Bullis E</pubmed_authors><pubmed_authors>O'Connell J</pubmed_authors><pubmed_authors>Aslibekyan S</pubmed_authors><pubmed_authors>Bryc K</pubmed_authors><pubmed_authors>Petrakovitz A</pubmed_authors><pubmed_authors>Partida GC</pubmed_authors><pubmed_authors>Fontanillas P</pubmed_authors><pubmed_authors>Heilbron K</pubmed_authors><pubmed_authors>Das S</pubmed_authors><pubmed_authors>Lin KH</pubmed_authors><pubmed_authors>Mosing MA</pubmed_authors><pubmed_authors>Coker D</pubmed_authors><pubmed_authors>Shelton JF</pubmed_authors><pubmed_authors>Wong C</pubmed_authors><pubmed_authors>Auton A</pubmed_authors><pubmed_authors>Moreno ME</pubmed_authors><pubmed_authors>Petrakovitz AA</pubmed_authors><pubmed_authors>Babalola E</pubmed_authors><pubmed_authors>McIntyre MH</pubmed_authors><pubmed_authors>Nandakumar P</pubmed_authors><pubmed_authors>Filshtein T</pubmed_authors><pubmed_authors>Dhamija D</pubmed_authors><pubmed_authors>Mountain JL</pubmed_authors><pubmed_authors>Jewett EM</pubmed_authors><pubmed_authors>Micheletti SJ</pubmed_authors><pubmed_authors>Elson SL</pubmed_authors><pubmed_authors>Huang Y</pubmed_authors><pubmed_authors>Lane V</pubmed_authors><pubmed_authors>Ullen F</pubmed_authors><pubmed_authors>Tung JY</pubmed_authors><pubmed_authors>Shi J</pubmed_authors><pubmed_authors>Hicks B</pubmed_authors><pubmed_authors>Tchakoute CT</pubmed_authors></additional><is_claimable>false</is_claimable><name>Using a polygenic score in a family design to understand genetic influences on musicality.</name><description>To further our understanding of the genetics of musicality, we explored associations between a polygenic score for self-reported beat synchronization ability (PGS&lt;sub>rhythm&lt;/sub>) and objectively measured rhythm discrimination, as well as other validated music skills and music-related traits. Using family data, we were able to further explore potential pathways of direct genetic, indirect genetic (through passive gene-environment correlation) and confounding effects (such as population structure and assortative mating). In 5648 Swedish twins, we found PGS&lt;sub>rhythm&lt;/sub> to predict not only rhythm discrimination, but also melody and pitch discrimination (betas between 0.11 and 0.16, p &lt; 0.001), as well as other music-related outcomes (p &lt; 0.05). In contrast, PGS&lt;sub>rhythm&lt;/sub> was not </description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Aug</publication><modification>2025-04-25T18:07:47.689Z</modification><creation>2024-12-04T01:59:02.456Z</creation></dates><accession>S-EPMC9424203</accession><cross_references><pubmed>36038631</pubmed><doi>10.1038/s41598-022-18703-w</doi></cross_references></HashMap>