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      The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) Framework for Understanding Musicality-Language Links Across the Lifespan

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          Abstract

          Using individual differences approaches, a growing body of literature finds positive associations between musicality and language-related abilities, complementing prior findings of links between musical training and language skills. Despite these associations, musicality has been often overlooked in mainstream models of individual differences in language acquisition and development. To better understand the biological basis of these individual differences, we propose the Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) framework. This novel integrative framework posits that musical and language-related abilities likely share some common genetic architecture (i.e., genetic pleiotropy) in addition to some degree of overlapping neural endophenotypes, and genetic influences on musically and linguistically enriched environments. Drawing upon recent advances in genomic methodologies for unraveling pleiotropy, we outline testable predictions for future research on language development and how its underlying neurobiological substrates may be supported by genetic pleiotropy with musicality. In support of the MAPLE framework, we review and discuss findings from over seventy behavioral and neural studies, highlighting that musicality is robustly associated with individual differences in a range of speech-language skills required for communication and development. These include speech perception-in-noise, prosodic perception, morphosyntactic skills, phonological skills, reading skills, and aspects of second/foreign language learning. Overall, the current work provides a clear agenda and framework for studying musicality-language links using individual differences approaches, with an emphasis on leveraging advances in the genomics of complex musicality and language traits.

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          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

              Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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                Author and article information

                Contributors
                Role: Role: Role: Role: Role: Role: Role: Role: Role:
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                Journal
                Neurobiol Lang (Camb)
                Neurobiol Lang (Camb)
                nol
                Neurobiology of Language
                MIT Press (One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA journals-info@mit.edu )
                2641-4368
                2022
                16 December 2022
                : 3
                : 4
                : 615-664
                Affiliations
                [1]Department of Otolaryngology – Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
                [2]Department of Psychology, Middle Tennessee State University, Murfreesboro, TN, USA
                [3]Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
                [4]Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
                [5]Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
                [6]Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
                [7]Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
                [8]PhD Program in Literacy Studies, Middle Tennessee State University, Murfreesboro, TN, USA
                [9]Curb Center for Art, Enterprise, and Public Policy, Vanderbilt University, Nashville, TN, USA
                [10]Vanderbilt Kennedy Center, Vanderbilt University Medical Center, TN, USA
                [11]Department of Linguistics, Potsdam University, Potsdam, Germany
                [12]Vanderbilt University School of Medicine, Vanderbilt University, TN, USA
                [13]Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Handling Editor: Robert Zatorre

                Author information
                https://orcid.org/0000-0001-6581-3510
                https://orcid.org/0000-0001-5388-6886
                https://orcid.org/0000-0003-2853-682X
                https://orcid.org/0000-0002-3331-1580
                https://orcid.org/0000-0002-1470-4928
                https://orcid.org/0000-0002-3132-1996
                https://orcid.org/0000-0003-4943-9244
                https://orcid.org/0000-0003-1643-6979
                Article
                nol_a_00079
                10.1162/nol_a_00079
                9893227
                36742012
                533ef59b-516d-4660-875e-c89cc60d8abc
                © 2022 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.

                History
                : 29 October 2021
                : 08 August 2022
                Page count
                Pages: 50
                Funding
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: 1926794
                Award Recipient :
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: 1926786
                Award Recipient :
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: R01DC016977
                Award Recipient :
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: DP2 HD098859
                Award Recipient :
                Funded by: Max Planck Society;
                Award Recipient :
                Categories
                Review Article
                Custom metadata
                Nayak, S., Coleman, P. L., Ladányi, E., Nitin, R., Gustavson, D. E., Fisher, S. E., Magne, C. L., & Gordon, R. L. (2022). The musical abilities, pleiotropy, language, and environment (MAPLE) framework for understanding musicality-language links across the lifespan. Neurobiology of Language, 3(4), 615–664. https://doi.org/10.1162/nol_a_00079

                complex trait genetics,musicality,speech and language development,pleiotropy,individual differences,neural endophenotypes

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