Displaying 1 - 9 of 9
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Doust, C., Fontanillas, P., Eising, E., Gordon, S. D., Wang, Z., Alagöz, G., Molz, B., 23andMe Research Team, Quantitative Trait Working Group of the GenLang Consortium, St Pourcain, B., Francks, C., Marioni, R. E., Zhao, J., Paracchini, S., Talcott, J. B., Monaco, A. P., Stein, J. F., Gruen, J. R., Olson, R. K., Willcutt, E. G., DeFries, J. C., Pennington, B. F. and 7 moreDoust, C., Fontanillas, P., Eising, E., Gordon, S. D., Wang, Z., Alagöz, G., Molz, B., 23andMe Research Team, Quantitative Trait Working Group of the GenLang Consortium, St Pourcain, B., Francks, C., Marioni, R. E., Zhao, J., Paracchini, S., Talcott, J. B., Monaco, A. P., Stein, J. F., Gruen, J. R., Olson, R. K., Willcutt, E. G., DeFries, J. C., Pennington, B. F., Smith, S. D., Wright, M. J., Martin, N. G., Auton, A., Bates, T. C., Fisher, S. E., & Luciano, M. (2022). Discovery of 42 genome-wide significant loci associated with dyslexia. Nature Genetics. doi:10.1038/s41588-022-01192-y.
Abstract
Reading and writing are crucial life skills but roughly one in ten children are affected by dyslexia, which can persist into adulthood. Family studies of dyslexia suggest heritability up to 70%, yet few convincing genetic markers have been found. Here we performed a genome-wide association study of 51,800 adults self-reporting a dyslexia diagnosis and 1,087,070 controls and identified 42 independent genome-wide significant loci: 15 in genes linked to cognitive ability/educational attainment, and 27 new and potentially more specific to dyslexia. We validated 23 loci (13 new) in independent cohorts of Chinese and European ancestry. Genetic etiology of dyslexia was similar between sexes, and genetic covariance with many traits was found, including ambidexterity, but not neuroanatomical measures of language-related circuitry. Dyslexia polygenic scores explained up to 6% of variance in reading traits, and might in future contribute to earlier identification and remediation of dyslexia. -
Eising, E., Mirza-Schreiber, N., De Zeeuw, E. L., Wang, C. A., Truong, D. T., Allegrini, A. G., Shapland, C. Y., Zhu, G., Wigg, K. G., Gerritse, M., Molz, B., Alagöz, G., Gialluisi, A., Abbondanza, F., Rimfeld, K., Van Donkelaar, M. M. J., Liao, Z., Jansen, P. R., Andlauer, T. F. M., Bates, T. C. and 70 moreEising, E., Mirza-Schreiber, N., De Zeeuw, E. L., Wang, C. A., Truong, D. T., Allegrini, A. G., Shapland, C. Y., Zhu, G., Wigg, K. G., Gerritse, M., Molz, B., Alagöz, G., Gialluisi, A., Abbondanza, F., Rimfeld, K., Van Donkelaar, M. M. J., Liao, Z., Jansen, P. R., Andlauer, T. F. M., Bates, T. C., Bernard, M., Blokland, K., Børglum, A. D., Bourgeron, T., Brandeis, D., Ceroni, F., Dale, P. S., Landerl, K., Lyytinen, H., De Jong, P. F., DeFries, J. C., Demontis, D., Feng, Y., Gordon, S. D., Guger, S. L., Hayiou-Thomas, M. E., Hernández-Cabrera, J. A., Hottenga, J.-J., Hulme, C., Kerr, E. N., Koomar, T., Lovett, M. W., Martin, N. G., Martinelli, A., Maurer, U., Michaelson, J. J., Moll, K., Monaco, A. P., Morgan, A. T., Nöthen, M. M., Pausova, Z., Pennell, C. E., Pennington, B. F., Price, K. M., Rajagopal, V. M., Ramus, F., Richer, L., Simpson, N. H., Smith, S., Snowling, M. J., Stein, J., Strug, L. J., Talcott, J. B., Tiemeier, H., Van de Schroeff, M. M. P., Verhoef, E., Watkins, K. E., Wilkinson, M., Wright, M. J., Barr, C. L., Boomsma, D. I., Carreiras, M., Franken, M.-C.-J., Gruen, J. R., Luciano, M., Müller-Myhsok, B., Newbury, D. F., Olson, R. K., Paracchini, S., Paus, T., Plomin, R., Schulte-Körne, G., Reilly, S., Tomblin, J. B., Van Bergen, E., Whitehouse, A. J., Willcutt, E. G., St Pourcain, B., Francks, C., & Fisher, S. E. (2022). Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people. Proceedings of the National Academy of Sciences of the United States of America, 119(35): e2202764119. doi:10.1073/pnas.2202764119.
Abstract
The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 × 10−8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits. -
Laureys, F., De Waelle, S., Barendse, M. T., Lenoir, M., & Deconinck, F. J. (2022). The factor structure of executive function in childhood and adolescence. Intelligence, 90: 101600. doi:10.1016/j.intell.2021.101600.
Abstract
Executive functioning (EF) plays a major role in many domains of human behaviour, including self-regulation, academic achievement, and even sports expertise. While a significant proportion of cross-sectional research has focused on the developmental pathways of EF, the existing literature is fractionated due to a wide range of methodologies applied to narrow age ranges, impeding comparison across a broad range of age groups. The current study used a cross-sectional design to investigate the factor structure of EF within late childhood and adolescence. A total of 2166 Flemish children and adolescents completed seven tasks of the Cambridge Brain Sciences test battery. Based on the existing literature, a Confirmatory Factor Analysis was performed, which indicated that a unitary factor model provides the best fit for the youngest age group (7–12 years). For the adolescents (12–18 years), the factor structure consists of four different components, including working memory, shifting, inhibition and planning. With regard to differences between early (12–15 years) and late (15–18 years) adolescents, working memory, inhibition and planning show higher scores for the late adolescents, while there was no difference on shifting. The current study is one of the first to administer the same seven EF tests in a considerably large sample of children and adolescents, and as such contributes to the understanding of the developmental trends in EF. Future studies, especially with longitudinal designs, are encouraged to further increase the knowledge concerning the factor structure of EF, and the development of the different EF components. -
Neumann, A., Nolte, I. M., Pappa, I., Ahluwalia, T. S., Pettersson, E., Rodriguez, A., Whitehouse, A., Van Beijsterveldt, C. E. M., Benyamin, B., Hammerschlag, A. R., Helmer, Q., Karhunen, V., Krapohl, E., Lu, Y., Van der Most, P. J., Palviainen, T., St Pourcain, B., Seppälä, I., Suarez, A., Vilor-Tejedor, N. and 41 moreNeumann, A., Nolte, I. M., Pappa, I., Ahluwalia, T. S., Pettersson, E., Rodriguez, A., Whitehouse, A., Van Beijsterveldt, C. E. M., Benyamin, B., Hammerschlag, A. R., Helmer, Q., Karhunen, V., Krapohl, E., Lu, Y., Van der Most, P. J., Palviainen, T., St Pourcain, B., Seppälä, I., Suarez, A., Vilor-Tejedor, N., Tiesler, C. M. T., Wang, C., Wills, A., Zhou, A., Alemany, S., Bisgaard, H., Bønnelykke, K., Davies, G. E., Hakulinen, C., Henders, A. K., Hyppönen, E., Stokholm, J., Bartels, M., Hottenga, J.-J., Heinrich, J., Hewitt, J., Keltikangas-Järvinen, L., Korhonen, T., Kaprio, J., Lahti, J., Lahti-Pulkkinen, M., Lehtimäki, T., Middeldorp, C. M., Najman, J. M., Pennell, C., Power, C., Oldehinkel, A. J., Plomin, R., Räikkönen, K., Raitakari, O. T., Rimfeld, K., Sass, L., Snieder, H., Standl, M., Sunyer, J., Williams, G. M., Bakermans-Kranenburg, M. J., Boomsma, D. I., Van IJzendoorn, M. H., Hartman, C. A., & Tiemeier, H. (2022). A genome-wide association study of total child psychiatric problems scores. PLOS ONE, 17(8): e0273116. doi:10.1371/journal.pone.0273116.
Abstract
Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.Additional information
Full summary results -
Postema, A., Van Mierlo, H., Bakker, A. B., & Barendse, M. T. (2022). Study-to-sports spillover among competitive athletes: A field study. International Journal of Sport and Exercise Psychology. Advance online publication. doi:10.1080/1612197X.2022.2058054.
Abstract
Combining academics and athletics is challenging but important for the psychological and psychosocial development of those involved. However, little is known about how experiences in academics spill over and relate to athletics. Drawing on the enrichment mechanisms proposed by the Work-Home Resources model, we posit that study crafting behaviours are positively related to volatile personal resources, which, in turn, are related to higher athletic achievement. Via structural equation modelling, we examine a path model among 243 student-athletes, incorporating study crafting behaviours and personal resources (i.e., positive affect and study engagement), and self- and coach-rated athletic achievement measured two weeks later. Results show that optimising the academic environment by crafting challenging study demands relates positively to positive affect and study engagement. In turn, positive affect related positively to self-rated athletic achievement, whereas – unexpectedly – study engagement related negatively to coach-rated athletic achievement. Optimising the academic environment through cognitive crafting and crafting social study resources did not relate to athletic outcomes. We discuss how these findings offer new insights into the interplay between academics and athletics. -
Schlag, F., Allegrini, A. G., Buitelaar, J., Verhoef, E., Van Donkelaar, M. M. J., Plomin, R., Rimfeld, K., Fisher, S. E., & St Pourcain, B. (2022). Polygenic risk for mental disorder reveals distinct association profiles across social behaviour in the general population. Molecular Psychiatry, 27, 1588-1598. doi:10.1038/s41380-021-01419-0.
Abstract
Many mental health conditions present a spectrum of social difficulties that overlaps with social behaviour in the general population including shared but little characterised genetic links. Here, we systematically investigate heterogeneity in shared genetic liabilities with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), bipolar disorder (BP), major depression (MD) and schizophrenia across a spectrum of different social symptoms. Longitudinally assessed low-prosociality and peer-problem scores in two UK population-based cohorts (4–17 years; parent- and teacher-reports; Avon Longitudinal Study of Parents and Children(ALSPAC): N ≤ 6,174; Twins Early Development Study(TEDS): N ≤ 7,112) were regressed on polygenic risk scores for disorder, as informed by genome-wide summary statistics from large consortia, using negative binomial regression models. Across ALSPAC and TEDS, we replicated univariate polygenic associations between social behaviour and risk for ADHD, MD and schizophrenia. Modelling variation in univariate genetic effects jointly using random-effect meta-regression revealed evidence for polygenic links between social behaviour and ADHD, ASD, MD, and schizophrenia risk, but not BP. Differences in age, reporter and social trait captured 45–88% in univariate effect variation. Cross-disorder adjusted analyses demonstrated that age-related heterogeneity in univariate effects is shared across mental health conditions, while reporter- and social trait-specific heterogeneity captures disorder-specific profiles. In particular, ADHD, MD, and ASD polygenic risk were more strongly linked to peer problems than low prosociality, while schizophrenia was associated with low prosociality only. The identified association profiles suggest differences in the social genetic architecture across mental disorders when investigating polygenic overlap with population-based social symptoms spanning 13 years of child and adolescent development. -
Vogelezang, S., Bradfield, J. P., the Early Growth Genetics Consortium, Grant, S. F. A., Felix, J. F., & Jaddoe, V. W. V. (2022). Genetics of early-life head circumference and genetic correlations with neurological, psychiatric and cognitive outcomes. BMC Medical Genomics, 15: 124. doi:10.1186/s12920-022-01281-1.
Abstract
Background
Head circumference is associated with intelligence and tracks from childhood into adulthood.
Methods
We performed a genome-wide association study meta-analysis and follow-up of head circumference in a total of 29,192 participants between 6 and 30 months of age.
Results
Seven loci reached genome-wide significance in the combined discovery and replication analysis of which three loci near ARFGEF2, MYCL1, and TOP1, were novel. We observed positive genetic correlations for early-life head circumference with adult intracranial volume, years of schooling, childhood and adult intelligence, but not with adult psychiatric, neurological, or personality-related phenotypes.
Conclusions
The results of this study indicate that the biological processes underlying early-life head circumference overlap largely with those of adult head circumference. The associations of early-life head circumference with cognitive outcomes across the life course are partly explained by genetics. -
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., Shatokhina, N., Zsembik, L. C. P., Thomopoulos, S. I., Zhu, A. H., Strike, L. T., Agartz, I., Alhusaini, S., Almeida, M. A. A., Alnæs, D., Amlien, I. K. and 341 moreGrasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., Shatokhina, N., Zsembik, L. C. P., Thomopoulos, S. I., Zhu, A. H., Strike, L. T., Agartz, I., Alhusaini, S., Almeida, M. A. A., Alnæs, D., Amlien, I. K., Andersson, M., Ard, T., Armstrong, N. J., Ashley-Koch, A., Atkins, J. R., Bernard, M., Brouwer, R. M., Buimer, E. E. L., Bülow, R., Bürger, C., Cannon, D. M., Chakravarty, M., Chen, Q., Cheung, J. W., Couvy-Duchesne, B., Dale, A. M., Dalvie, S., De Araujo, T. K., De Zubicaray, G. I., De Zwarte, S. M. C., Den Braber, A., Doan, N. T., Dohm, K., Ehrlich, S., Engelbrecht, H.-R., Erk, S., Fan, C. C., Fedko, I. O., Foley, S. F., Ford, J. M., Fukunaga, M., Garrett, M. E., Ge, T., Giddaluru, S., Goldman, A. L., Green, M. J., Groenewold, N. A., Grotegerd, D., Gurholt, T. P., Gutman, B. A., Hansell, N. K., Harris, M. A., Harrison, M. B., Haswell, C. C., Hauser, M., Herms, S., Heslenfeld, D. J., Ho, N. F., Hoehn, D., Hoffmann, P., Holleran, L., Hoogman, M., Hottenga, J.-J., Ikeda, M., Janowitz, D., Jansen, I. E., Jia, T., Jockwitz, C., Kanai, R., Karama, S., Kasperaviciute, D., Kaufmann, T., Kelly, S., Kikuchi, M., Klein, M., Knapp, M., Knodt, A. R., Krämer, B., Lam, M., Lancaster, T. M., Lee, P. H., Lett, T. A., Lewis, L. B., Lopes-Cendes, I., Luciano, M., Macciardi, F., Marquand, A. F., Mathias, S. R., Melzer, T. R., Milaneschi, Y., Mirza-Schreiber, N., Moreira, J. C. V., Mühleisen, T. W., Müller-Myhsok, B., Najt, P., Nakahara, S., Nho, K., Olde Loohuis, L. M., Orfanos, D. P., Pearson, J. F., Pitcher, T. L., Pütz, B., Quidé, Y., Ragothaman, A., Rashid, F. M., Reay, W. R., Redlich, R., Reinbold, C. S., Repple, J., Richard, G., Riedel, B. C., Risacher, S. L., Rocha, C. S., Mota, N. R., Salminen, L., Saremi, A., Saykin, A. J., Schlag, F., Schmaal, L., Schofield, P. R., Secolin, R., Shapland, C. Y., Shen, L., Shin, J., Shumskaya, E., Sønderby, I. E., Sprooten, E., Tansey, K. E., Teumer, A., Thalamuthu, A., Tordesillas-Gutiérrez, D., Turner, J. A., Uhlmann, A., Vallerga, C. L., Van der Meer, D., Van Donkelaar, M. M. J., Van Eijk, L., Van Erp, T. G. M., Van Haren, N. E. M., Van Rooij, D., Van Tol, M.-J., Veldink, J. H., Verhoef, E., Walton, E., Wang, M., Wang, Y., Wardlaw, J. M., Wen, W., Westlye, L. T., Whelan, C. D., Witt, S. H., Wittfeld, K., Wolf, C., Wolfers, T., Wu, J. Q., Yasuda, C. L., Zaremba, D., Zhang, Z., Zwiers, M. P., Artiges, E., Assareh, A. A., Ayesa-Arriola, R., Belger, A., Brandt, C. L., Brown, G. G., Cichon, S., Curran, J. E., Davies, G. E., Degenhardt, F., Dennis, M. F., Dietsche, B., Djurovic, S., Doherty, C. P., Espiritu, R., Garijo, D., Gil, Y., Gowland, P. A., Green, R. C., Häusler, A. N., Heindel, W., Ho, B.-C., Hoffmann, W. U., Holsboer, F., Homuth, G., Hosten, N., Jack Jr., C. R., Jang, M., Jansen, A., Kimbrel, N. A., Kolskår, K., Koops, S., Krug, A., Lim, K. O., Luykx, J. J., Mathalon, D. H., Mather, K. A., Mattay, V. S., Matthews, S., Mayoral Van Son, J., McEwen, S. C., Melle, I., Morris, D. W., Mueller, B. A., Nauck, M., Nordvik, J. E., Nöthen, M. M., O’Leary, D. S., Opel, N., Paillère Martinot, M.-L., Pike, G. B., Preda, A., Quinlan, E. B., Rasser, P. E., Ratnakar, V., Reppermund, S., Steen, V. M., Tooney, P. A., Torres, F. R., Veltman, D. J., Voyvodic, J. T., Whelan, R., White, T., Yamamori, H., Adams, H. H. H., Bis, J. C., Debette, S., Decarli, C., Fornage, M., Gudnason, V., Hofer, E., Ikram, M. A., Launer, L., Longstreth, W. T., Lopez, O. L., Mazoyer, B., Mosley, T. H., Roshchupkin, G. V., Satizabal, C. L., Schmidt, R., Seshadri, S., Yang, Q., Alzheimer’s Disease Neuroimaging Initiative, CHARGE Consortium, EPIGEN Consortium, IMAGEN Consortium, SYS Consortium, Parkinson’s Progression Markers Initiative, Alvim, M. K. M., Ames, D., Anderson, T. J., Andreassen, O. A., Arias-Vasquez, A., Bastin, M. E., Baune, B. T., Beckham, J. C., Blangero, J., Boomsma, D. I., Brodaty, H., Brunner, H. G., Buckner, R. L., Buitelaar, J. K., Bustillo, J. R., Cahn, W., Cairns, M. J., Calhoun, V., Carr, V. J., Caseras, X., Caspers, S., Cavalleri, G. L., Cendes, F., Corvin, A., Crespo-Facorro, B., Dalrymple-Alford, J. C., Dannlowski, U., De Geus, E. J. C., Deary, I. J., Delanty, N., Depondt, C., Desrivières, S., Donohoe, G., Espeseth, T., Fernández, G., Fisher, S. E., Flor, H., Forstner, A. J., Francks, C., Franke, B., Glahn, D. C., Gollub, R. L., Grabe, H. J., Gruber, O., Håberg, A. K., Hariri, A. R., Hartman, C. A., Hashimoto, R., Heinz, A., Henskens, F. A., Hillegers, M. H. J., Hoekstra, P. J., Holmes, A. J., Hong, L. E., Hopkins, W. D., Hulshoff Pol, H. E., Jernigan, T. L., Jönsson, E. G., Kahn, R. S., Kennedy, M. A., Kircher, T. T. J., Kochunov, P., Kwok, J. B. J., Le Hellard, S., Loughland, C. M., Martin, N. G., Martinot, J.-L., McDonald, C., McMahon, K. L., Meyer-Lindenberg, A., Michie, P. T., Morey, R. A., Mowry, B., Nyberg, L., Oosterlaan, J., Ophoff, R. A., Pantelis, C., Paus, T., Pausova, Z., Penninx, B. W. J. H., Polderman, T. J. C., Posthuma, D., Rietschel, M., Roffman, J. L., Rowland, L. M., Sachdev, P. S., Sämann, P. G., Schall, U., Schumann, G., Scott, R. J., Sim, K., Sisodiya, S. M., Smoller, J. W., Sommer, I. E., St Pourcain, B., Stein, D. J., Toga, A. W., Trollor, J. N., Van der Wee, N. J. A., van 't Ent, D., Völzke, H., Walter, H., Weber, B., Weinberger, D. R., Wright, M. J., Zhou, J., Stein, J. L., Thompson, P. M., & Medland, S. E. (2020). The genetic architecture of the human cerebral cortex. Science, 367(6484): eaay6690. doi:10.1126/science.aay6690.
Abstract
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder. -
Howe, L. J., Hemani, G., Lesseur, C., Gaborieau, V., Ludwig, K. U., Mangold, E., Brennan, P., Ness, A. R., St Pourcain, B., Smith, G. D., & Lewis, S. J. (2020). Evaluating shared genetic influences on nonsyndromic cleft lip/palate and oropharyngeal neoplasms. Genetic Epidemiology, 44(8), 924-933. doi:10.1002/gepi.22343.
Abstract
It has been hypothesised that nonsyndromic cleft lip/palate (nsCL/P) and cancer may share aetiological risk factors. Population studies have found inconsistent evidence for increased incidence of cancer in nsCL/P cases, but several genes (e.g.,CDH1,AXIN2) have been implicated in the aetiologies of both phenotypes. We aimed to evaluate shared genetic aetiology between nsCL/P and oral cavity/oropharyngeal cancers (OC/OPC), which affect similar anatomical regions. Using a primary sample of 5,048 OC/OPC cases and 5,450 controls of European ancestry and a replication sample of 750 cases and 336,319 controls from UK Biobank, we estimate genetic overlap using nsCL/P polygenic risk scores (PRS) with Mendelian randomization analyses performed to evaluate potential causal mechanisms. In the primary sample, we found strong evidence for an association between a nsCL/P PRS and increased odds of OC/OPC (per standard deviation increase in score, odds ratio [OR]: 1.09; 95% confidence interval [CI]: 1.04, 1.13;p = .000053). Although confidence intervals overlapped with the primary estimate, we did not find confirmatory evidence of an association between the PRS and OC/OPC in UK Biobank (OR 1.02; 95% CI: 0.95, 1.10;p = .55). Mendelian randomization analyses provided evidence that major nsCL/P risk variants are unlikely to influence OC/OPC. Our findings suggest possible shared genetic influences on nsCL/P and OC/OPC.Additional information
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