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Ahluwalia, T. S., Prins, B. P., Abdollahi, M., Armstrong, N. J., Aslibekyan, S., Bain, L., Jefferis, B., Baumert, J., Beekman, M., Ben-Shlomo, Y., Bis, J. C., Mitchell, B. D., De Geus, E., Delgado, G. E., Marek, D., Eriksson, J., Kajantie, E., Kanoni, S., Kemp, J. P., Lu, C. and 106 moreAhluwalia, T. S., Prins, B. P., Abdollahi, M., Armstrong, N. J., Aslibekyan, S., Bain, L., Jefferis, B., Baumert, J., Beekman, M., Ben-Shlomo, Y., Bis, J. C., Mitchell, B. D., De Geus, E., Delgado, G. E., Marek, D., Eriksson, J., Kajantie, E., Kanoni, S., Kemp, J. P., Lu, C., Marioni, R. E., McLachlan, S., Milaneschi, Y., Nolte, I. M., Petrelis, A. M., Porcu, E., Sabater-Lleal, M., Naderi, E., Seppälä, I., Shah, T., Singhal, G., Standl, M., Teumer, A., Thalamuthu, A., Thiering, E., Trompet, S., Ballantyne, C. M., Benjamin, E. J., Casas, J. P., Toben, C., Dedoussis, G., Deelen, J., Durda, P., Engmann, J., Feitosa, M. F., Grallert, H., Hammarstedt, A., Harris, S. E., Homuth, G., Hottenga, J.-J., Jalkanen, S., Jamshidi, Y., Jawahar, M. C., Jess, T., Kivimaki, M., Kleber, M. E., Lahti, J., Liu, Y., Marques-Vidal, P., Mellström, D., Mooijaart, S. P., Müller-Nurasyid, M., Penninx, B., Revez, J. A., Rossing, P., Räikkönen, K., Sattar, N., Scharnagl, H., Sennblad, B., Silveira, A., St Pourcain, B., Timpson, N. J., Trollor, J., CHARGE Inflammation Working Group, Van Dongen, J., Van Heemst, D., Visvikis-Siest, S., Vollenweider, P., Völker, U., Waldenberger, M., Willemsen, G., Zabaneh, D., Morris, R. W., Arnett, D. K., Baune, B. T., Boomsma, D. I., Chang, Y.-P.-C., Deary, I. J., Deloukas, P., Eriksson, J. G., Evans, D. M., Ferreira, M. A., Gaunt, T., Gudnason, V., Hamsten, A., Heinrich, J., Hingorani, A., Humphries, S. E., Jukema, J. W., Koenig, W., Kumari, M., Kutalik, Z., Lawlor, D. A., Lehtimäki, T., März, W., Mather, K. A., Naitza, S., Nauck, M., Ohlsson, C., Price, J. F., Raitakari, O., Rice, K., Sachdev, P. S., Slagboom, E., Sørensen, T. I. A., Spector, T., Stacey, D., Stathopoulou, M. G., Tanaka, T., Wannamethee, S. G., Whincup, P., Rotter, J. I., Dehghan, A., Boerwinkle, E., Psaty, B. M., Snieder, H., & Alizadeh, B. Z. (2021). Genome-wide association study of circulating interleukin 6 levels identifies novel loci. Human Molecular Genetics, 5(1), 393-409. doi:10.1093/hmg/ddab023.
Abstract
Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (Pcombined = 1.8 × 10−11), HLA-DRB1/DRB5 rs660895 on Chr6p21 (Pcombined = 1.5 × 10−10) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (Pcombined = 1.2 × 10−122). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology. -
Cuellar-Partida, G., Tung, J. Y., Eriksson, N., Albrecht, E., Aliev, F., Andreassen, O. A., Barroso, I., Beckmann, J. S., Boks, M. P., Boomsma, D. I., Boyd, H. A., Breteler, M. M. B., Campbell, H., Chasman, D. I., Cherkas, L. F., Davies, G., De Geus, E. J. C., Deary, I. J., Deloukas, P., Dick, D. M. and 98 moreCuellar-Partida, G., Tung, J. Y., Eriksson, N., Albrecht, E., Aliev, F., Andreassen, O. A., Barroso, I., Beckmann, J. S., Boks, M. P., Boomsma, D. I., Boyd, H. A., Breteler, M. M. B., Campbell, H., Chasman, D. I., Cherkas, L. F., Davies, G., De Geus, E. J. C., Deary, I. J., Deloukas, P., Dick, D. M., Duffy, D. L., Eriksson, J. G., Esko, T., Feenstra, B., Geller, F., Gieger, C., Giegling, I., Gordon, S. D., Han, J., Hansen, T. F., Hartmann, A. M., Hayward, C., Heikkilä, K., Hicks, A. A., Hirschhorn, J. N., Hottenga, J.-J., Huffman, J. E., Hwang, L.-D., Ikram, M. A., Kaprio, J., Kemp, J. P., Khaw, K.-T., Klopp, N., Konte, B., Kutalik, Z., Lahti, J., Li, X., Loos, R. J. F., Luciano, M., Magnusson, S. H., Mangino, M., Marques-Vidal, P., Martin, N. G., McArdle, W. L., McCarthy, M. I., Medina-Gomez, C., Melbye, M., Melville, S. A., Metspalu, A., Milani, L., Mooser, V., Nelis, M., Nyholt, D. R., O'Connell, K. S., Ophoff, R. A., Palmer, C., Palotie, A., Palviainen, T., Pare, G., Paternoster, L., Peltonen, L., Penninx, B. W. J. H., Polasek, O., Pramstaller, P. P., Prokopenko, I., Raikkonen, K., Ripatti, S., Rivadeneira, F., Rudan, I., Rujescu, D., Smit, J. H., Smith, G. D., Smoller, J. W., Soranzo, N., Spector, T. D., St Pourcain, B., Starr, J. M., Stefánsson, H., Steinberg, S., Teder-Laving, M., Thorleifsson, G., Stefansson, K., Timpson, N. J., Uitterlinden, A. G., Van Duijn, C. M., Van Rooij, F. J. A., Vink, J. M., Vollenweider, P., Vuoksimaa, E., Waeber, G., Wareham, N. J., Warrington, N., Waterworth, D., Werge, T., Wichmann, H.-E., Widen, E., Willemsen, G., Wright, A. F., Wright, M. J., Xu, M., Zhao, J. H., Kraft, P., Hinds, D. A., Lindgren, C. M., Magi, R., Neale, B. M., Evans, D. M., & Medland, S. E. (2021). Genome-wide association study identifies 48 common genetic variants associated with handedness. Nature Human Behaviour, 5, 59-70. doi:10.1038/s41562-020-00956-y.
Abstract
Handedness has been extensively studied because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and the International Handedness Consortium, we conducted a genome-wide association meta-analysis of handedness (N = 1,766,671). We found 41 loci associated (P < 5 × 10−8) with left-handedness and 7 associated with ambidexterity. Tissue-enrichment analysis implicated the CNS in the aetiology of handedness. Pathways including regulation of microtubules and brain morphology were also highlighted. We found suggestive positive genetic correlations between left-handedness and neuropsychiatric traits, including schizophrenia and bipolar disorder. Furthermore, the genetic correlation between left-handedness and ambidexterity is low (rG = 0.26), which implies that these traits are largely influenced by different genetic mechanisms. Our findings suggest that handedness is highly polygenic and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders.Additional information
supplementary tables -
Gialluisi, A., Andlauer, T. F. M., Mirza-Schreiber, N., Moll, K., Becker, J., Hoffmann, P., Ludwig, K. U., Czamara, D., St Pourcain, B., Honbolygó, F., Tóth, D., Csépe, V., Huguet, H., Chaix, Y., Iannuzzi, S., Demonet, J.-F., Morris, A. P., Hulslander, J., Willcutt, E. G., DeFries, J. C. and 29 moreGialluisi, A., Andlauer, T. F. M., Mirza-Schreiber, N., Moll, K., Becker, J., Hoffmann, P., Ludwig, K. U., Czamara, D., St Pourcain, B., Honbolygó, F., Tóth, D., Csépe, V., Huguet, H., Chaix, Y., Iannuzzi, S., Demonet, J.-F., Morris, A. P., Hulslander, J., Willcutt, E. G., DeFries, J. C., Olson, R. K., Smith, S. D., Pennington, B. F., Vaessen, A., Maurer, U., Lyytinen, H., Peyrard-Janvid, M., Leppänen, P. H. T., Brandeis, D., Bonte, M., Stein, J. F., Talcott, J. B., Fauchereau, F., Wilcke, A., Kirsten, H., Müller, B., Francks, C., Bourgeron, T., Monaco, A. P., Ramus, F., Landerl, K., Kere, J., Scerri, T. S., Paracchini, S., Fisher, S. E., Schumacher, J., Nöthen, M. M., Müller-Myhsok, B., & Schulte-Körne, G. (2021). Genome-wide association study reveals new insights into the heritability and genetic correlates of developmental dyslexia. Molecular Psychiatry, 26, 3004-3017. doi:10.1038/s41380-020-00898-x.
Abstract
Developmental dyslexia (DD) is a learning disorder affecting the ability to read, with a heritability of 40–60%. A notable part of this heritability remains unexplained, and large genetic studies are warranted to identify new susceptibility genes and clarify the genetic bases of dyslexia. We carried out a genome-wide association study (GWAS) on 2274 dyslexia cases and 6272 controls, testing associations at the single variant, gene, and pathway level, and estimating heritability using single-nucleotide polymorphism (SNP) data. We also calculated polygenic scores (PGSs) based on large-scale GWAS data for different neuropsychiatric disorders and cortical brain measures, educational attainment, and fluid intelligence, testing them for association with dyslexia status in our sample. We observed statistically significant (p < 2.8 × 10−6) enrichment of associations at the gene level, for LOC388780 (20p13; uncharacterized gene), and for VEPH1 (3q25), a gene implicated in brain development. We estimated an SNP-based heritability of 20–25% for DD, and observed significant associations of dyslexia risk with PGSs for attention deficit hyperactivity disorder (at pT = 0.05 in the training GWAS: OR = 1.23[1.16; 1.30] per standard deviation increase; p = 8 × 10−13), bipolar disorder (1.53[1.44; 1.63]; p = 1 × 10−43), schizophrenia (1.36[1.28; 1.45]; p = 4 × 10−22), psychiatric cross-disorder susceptibility (1.23[1.16; 1.30]; p = 3 × 10−12), cortical thickness of the transverse temporal gyrus (0.90[0.86; 0.96]; p = 5 × 10−4), educational attainment (0.86[0.82; 0.91]; p = 2 × 10−7), and intelligence (0.72[0.68; 0.76]; p = 9 × 10−29). This study suggests an important contribution of common genetic variants to dyslexia risk, and novel genomic overlaps with psychiatric conditions like bipolar disorder, schizophrenia, and cross-disorder susceptibility. Moreover, it revealed the presence of shared genetic foundations with a neural correlate previously implicated in dyslexia by neuroimaging evidence.Additional information
Supplementary File S1 Supplementary File S2 Supplementary File S3 Supplementary File S4 Acknowledgements -
Shapland, C. Y., Verhoef, E., Smith, G. D., Fisher, S. E., Verhulst, B., Dale, P. S., & St Pourcain, B. (2021). Multivariate genome-wide covariance analyses of literacy, language and working memory skills reveal distinct etiologies. npj Science of Learning, 6: 23. doi:10.1038/s41539-021-00101-y.
Abstract
Several abilities outside literacy proper are associated with reading and spelling, both phenotypically and genetically, though our knowledge of multivariate genomic covariance structures is incomplete. Here, we introduce structural models describing genetic and residual influences between traits to study multivariate links across measures of literacy, phonological awareness, oral language, and phonological working memory (PWM) in unrelated UK youth (8-13 years, N=6,453). We find that all phenotypes share a large proportion of underlying genetic variation, although especially oral language and PWM reveal substantial differences in their genetic variance composition with substantial trait-specific genetic influences. Multivariate genetic and residual trait covariance showed concordant patterns, except for marked differences between oral language and literacy/phonological awareness, where strong genetic links contrasted near-zero residual overlap. These findings suggest differences in etiological mechanisms, acting beyond a pleiotropic set of genetic variants, and implicate variation in trait modifiability even among phenotypes that have high genetic correlations.Additional information
supplementary information -
Ip, H. F., Van der Laan, C. M., Krapohl, E. M. L., Brikell, I., Sánchez-Mora, C., Nolte, I. M., St Pourcain, B., Bolhuis, K., Palviainen, T., Zafarmand, H., Colodro-Conde, L., Gordon, S., Zayats, T., Aliev, F., Jiang, C., Wang, C. A., Saunders, G., Karhunen, V., Hammerschlag, A. R., Adkins, D. E. and 129 moreIp, H. F., Van der Laan, C. M., Krapohl, E. M. L., Brikell, I., Sánchez-Mora, C., Nolte, I. M., St Pourcain, B., Bolhuis, K., Palviainen, T., Zafarmand, H., Colodro-Conde, L., Gordon, S., Zayats, T., Aliev, F., Jiang, C., Wang, C. A., Saunders, G., Karhunen, V., Hammerschlag, A. R., Adkins, D. E., Border, R., Peterson, R. E., Prinz, J. A., Thiering, E., Seppälä, I., Vilor-Tejedor, N., Ahluwalia, T. S., Day, F. R., Hottenga, J.-J., Allegrini, A. G., Rimfeld, K., Chen, Q., Lu, Y., Martin, J., Soler Artigas, M., Rovira, P., Bosch, R., Español, G., Ramos Quiroga, J. A., Neumann, A., Ensink, J., Grasby, K., Morosoli, J. J., Tong, X., Marrington, S., Middeldorp, C., Scott, J. G., Vinkhuyzen, A., Shabalin, A. A., Corley, R., Evans, L. M., Sugden, K., Alemany, S., Sass, L., Vinding, R., Ruth, K., Tyrrell, J., Davies, G. E., Ehli, E. A., Hagenbeek, F. A., De Zeeuw, E., Van Beijsterveldt, T. C., Larsson, H., Snieder, H., Verhulst, F. C., Amin, N., Whipp, A. M., Korhonen, T., Vuoksimaa, E., Rose, R. J., Uitterlinden, A. G., Heath, A. C., Madden, P., Haavik, J., Harris, J. R., Helgeland, Ø., Johansson, S., Knudsen, G. P. S., Njolstad, P. R., Lu, Q., Rodriguez, A., Henders, A. K., Mamun, A., Najman, J. M., Brown, S., Hopfer, C., Krauter, K., Reynolds, C., Smolen, A., Stallings, M., Wadsworth, S., Wall, T. L., Silberg, J. L., Miller, A., Keltikangas-Järvinen, L., Hakulinen, C., Pulkki-Råback, L., Havdahl, A., Magnus, P., Raitakari, O. T., Perry, J. R. B., Llop, S., Lopez-Espinosa, M.-J., Bønnelykke, K., Bisgaard, H., Sunyer, J., Lehtimäki, T., Arseneault, L., Standl, M., Heinrich, J., Boden, J., Pearson, J., Horwood, L. J., Kennedy, M., Poulton, R., Eaves, L. J., Maes, H. H., Hewitt, J., Copeland, W. E., Costello, E. J., Williams, G. M., Wray, N., Järvelin, M.-R., McGue, M., Iacono, W., Caspi, A., Moffitt, T. E., Whitehouse, A., Pennell, C. E., Klump, K. L., Burt, S. A., Dick, D. M., Reichborn-Kjennerud, T., Martin, N. G., Medland, S. E., Vrijkotte, T., Kaprio, J., Tiemeier, H., Davey Smith, G., Hartman, C. A., Oldehinkel, A. J., Casas, M., Ribasés, M., Lichtenstein, P., Lundström, S., Plomin, R., Bartels, M., Nivard, M. G., & Boomsma, D. I. (2021). Genetic association study of childhood aggression across raters, instruments, and age. Translational Psychiatry, 11: 413. doi:10.1038/s41398-021-01480-x.
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Verhoef, E., Grove, J., Shapland, C. Y., Demontis, D., Burgess, S., Rai, D., Børglum, A. D., & St Pourcain, B. (2021). Discordant associations of educational attainment with ASD and ADHD implicate a polygenic form of pleiotropy. Nature Communications, 12: 6534. doi:10.1038/s41467-021-26755-1.
Abstract
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are complex co-occurring neurodevelopmental conditions. Their genetic architectures reveal striking similarities but also differences, including strong, discordant polygenic associations with educational attainment (EA). To study genetic mechanisms that present as ASD-related positive and ADHD-related negative genetic correlations with EA, we carry out multivariable regression analyses using genome-wide summary statistics (N = 10,610–766,345). Our results show that EA-related genetic variation is shared across ASD and ADHD architectures, involving identical marker alleles. However, the polygenic association profile with EA, across shared marker alleles, is discordant for ASD versus ADHD risk, indicating independent effects. At the single-variant level, our results suggest either biological pleiotropy or co-localisation of different risk variants, implicating MIR19A/19B microRNA mechanisms. At the polygenic level, they point to a polygenic form of pleiotropy that contributes to the detectable genome-wide correlation between ASD and ADHD and is consistent with effect cancellation across EA-related regions.Additional information
supplementary information -
Verhoef, E., Shapland, C. Y., Fisher, S. E., Dale, P. S., & St Pourcain, B. (2021). The developmental origins of genetic factors influencing language and literacy: Associations with early-childhood vocabulary. Journal of Child Psychology and Psychiatry, 62(6), 728-738. doi:10.1111/jcpp.13327.
Abstract
Background
The heritability of language and literacy skills increases from early‐childhood to adolescence. The underlying mechanisms are little understood and may involve (a) the amplification of genetic influences contributing to early language abilities, and/or (b) the emergence of novel genetic factors (innovation). Here, we investigate the developmental origins of genetic factors influencing mid‐childhood/early‐adolescent language and literacy. We evaluate evidence for the amplification of early‐childhood genetic factors for vocabulary, in addition to genetic innovation processes.
Methods
Expressive and receptive vocabulary scores at 38 months, thirteen language‐ and literacy‐related abilities and nonverbal cognition (7–13 years) were assessed in unrelated children from the Avon Longitudinal Study of Parents and Children (ALSPAC, Nindividuals ≤ 6,092). We investigated the multivariate genetic architecture underlying early‐childhood expressive and receptive vocabulary, and each of 14 mid‐childhood/early‐adolescent language, literacy or cognitive skills with trivariate structural equation (Cholesky) models as captured by genome‐wide genetic relationship matrices. The individual path coefficients of the resulting structural models were finally meta‐analysed to evaluate evidence for overarching patterns.
Results
We observed little support for the emergence of novel genetic sources for language, literacy or cognitive abilities during mid‐childhood or early adolescence. Instead, genetic factors of early‐childhood vocabulary, especially those unique to receptive skills, were amplified and represented the majority of genetic variance underlying many of these later complex skills (≤99%). The most predictive early genetic factor accounted for 29.4%(SE = 12.9%) to 45.1%(SE = 7.6%) of the phenotypic variation in verbal intelligence and literacy skills, but also for 25.7%(SE = 6.4%) in performance intelligence, while explaining only a fraction of the phenotypic variation in receptive vocabulary (3.9%(SE = 1.8%)).
Conclusions
Genetic factors contributing to many complex skills during mid‐childhood and early adolescence, including literacy, verbal cognition and nonverbal cognition, originate developmentally in early‐childhood and are captured by receptive vocabulary. This suggests developmental genetic stability and overarching aetiological mechanisms.
Additional information
supporting information -
Verhoef, E., Shapland, C. Y., Fisher, S. E., Dale, P. S., & St Pourcain, B. (2021). The developmental genetic architecture of vocabulary skills during the first three years of life: Capturing emerging associations with later-life reading and cognition. PLoS Genetics, 17(2): e1009144. doi:10.1371/journal.pgen.1009144.
Abstract
Individual differences in early-life vocabulary measures are heritable and associated with subsequent reading and cognitive abilities, although the underlying mechanisms are little understood. Here, we (i) investigate the developmental genetic architecture of expressive and receptive vocabulary in early-life and (ii) assess timing of emerging genetic associations with mid-childhood verbal and non-verbal skills. We studied longitudinally assessed early-life vocabulary measures (15–38 months) and later-life verbal and non-verbal skills (7–8 years) in up to 6,524 unrelated children from the population-based Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We dissected the phenotypic variance of rank-transformed scores into genetic and residual components by fitting multivariate structural equation models to genome-wide genetic-relationship matrices. Our findings show that the genetic architecture of early-life vocabulary involves multiple distinct genetic factors. Two of these genetic factors are developmentally stable and also contribute to genetic variation in mid-childhood skills: One genetic factor emerging with expressive vocabulary at 24 months (path coefficient: 0.32(SE = 0.06)) was also related to later-life reading (path coefficient: 0.25(SE = 0.12)) and verbal intelligence (path coefficient: 0.42(SE = 0.13)), explaining up to 17.9% of the phenotypic variation. A second, independent genetic factor emerging with receptive vocabulary at 38 months (path coefficient: 0.15(SE = 0.07)), was more generally linked to verbal and non-verbal cognitive abilities in mid-childhood (reading path coefficient: 0.57(SE = 0.07); verbal intelligence path coefficient: 0.60(0.10); performance intelligence path coefficient: 0.50(SE = 0.08)), accounting for up to 36.1% of the phenotypic variation and the majority of genetic variance in these later-life traits (≥66.4%). Thus, the genetic foundations of mid-childhood reading and cognitive abilities are diverse. They involve at least two independent genetic factors that emerge at different developmental stages during early language development and may implicate differences in cognitive processes that are already detectable during toddlerhood.Additional information
supporting information -
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. 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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. -
Hofer, E., Roshchupkin, G. V., Adams, H. H. H., Knol, M. J., Lin, H., Li, S., Zare, H., Ahmad, S., Armstrong, N. J., Satizabal, C. L., Bernard, M., Bis, J. C., Gillespie, N. A., Luciano, M., Mishra, A., Scholz, M., Teumer, A., Xia, R., Jian, X., Mosley, T. H. and 79 moreHofer, E., Roshchupkin, G. V., Adams, H. H. H., Knol, M. J., Lin, H., Li, S., Zare, H., Ahmad, S., Armstrong, N. J., Satizabal, C. L., Bernard, M., Bis, J. C., Gillespie, N. A., Luciano, M., Mishra, A., Scholz, M., Teumer, A., Xia, R., Jian, X., Mosley, T. H., Saba, Y., Pirpamer, L., Seiler, S., Becker, J. T., Carmichael, O., Rotter, J. I., Psaty, B. M., Lopez, O. L., Amin, N., Van der Lee, S. J., Yang, Q., Himali, J. J., Maillard, P., Beiser, A. S., DeCarli, C., Karama, S., Lewis, L., Harris, M., Bastin, M. E., Deary, I. J., Witte, A. V., Beyer, F., Loeffler, M., Mather, K. A., Schofield, P. R., Thalamuthu, A., Kwok, J. B., Wright, M. J., Ames, D., Trollor, J., Jiang, J., Brodaty, H., Wen, W., Vernooij, M. W., Hofman, A., Uitterlinden, A. G., Niessen, W. J., Wittfeld, K., Bülow, R., Völker, U., Pausova, Z., Pike, G. B., Maingault, S., Crivello, F., Tzourio, C., Amouyel, P., Mazoyer, B., Neale, M. C., Franz, C. E., Lyons, M. J., Panizzon, M. S., Andreassen, O. A., Dale, A. M., Logue, M., Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Stein, J. L., Thompson, P. M., Medland, S. E., ENIGMA-consortium, Sachdev, P. S., Kremen, W. S., Wardlaw, J. M., Villringer, A., Van Duijn, C. M., Grabe, H. J., Longstreth, W. T., Fornage, M., Paus, T., Debette, S., Ikram, M. A., Schmidt, H., Schmidt, R., & Seshadri, S. (2020). Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nature Communications, 11: 4796. doi:10.1038/s41467-020-18367-y.
Additional information
supplementary information -
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
Supporting information -
Nivard, M. G., Gage, S. H., Hottenga, J. J., van Beijsterveldt, C. E. M., Abdellaoui, A., Bartels, M., Baselmans, B. M. L., Ligthart, L., St Pourcain, B., Boomsma, D. I., Munafò, M. R., & Middeldorp, C. M. (2017). Genetic overlap between schizophrenia and developmental psychopathology: Longitudinal and multivariate polygenic risk prediction of common psychiatric traits during development. Schizophrenia Bulletin, 43(6), 1197-1207. doi:10.1093/schbul/sbx031.
Abstract
Background: Several nonpsychotic psychiatric disorders in childhood and adolescence can precede the onset of schizophrenia, but the etiology of this relationship remains unclear. We investigated to what extent the association between schizophrenia and psychiatric disorders in childhood is explained by correlated genetic risk factors. Methods: Polygenic risk scores (PRS), reflecting an individual’s genetic risk for schizophrenia, were constructed for 2588 children from the Netherlands Twin Register (NTR) and 6127 from the Avon Longitudinal Study of Parents And Children (ALSPAC). The associations between schizophrenia PRS and measures of anxiety, depression, attention deficit hyperactivity disorder (ADHD), and oppositional defiant disorder/conduct disorder (ODD/CD) were estimated at age 7, 10, 12/13, and 15 years in the 2 cohorts. Results were then meta-analyzed, and a meta-regression analysis was performed to test differences in effects sizes over, age and disorders. Results: Schizophrenia PRS were associated with childhood and adolescent psychopathology. Meta-regression analysis showed differences in the associations over disorders, with the strongest association with childhood and adolescent depression and a weaker association for ODD/CD at age 7. The associations increased with age and this increase was steepest for ADHD and ODD/CD. Genetic correlations varied between 0.10 and 0.25. Conclusion: By optimally using longitudinal data across diagnoses in a multivariate meta-analysis this study sheds light on the development of childhood disorders into severe adult psychiatric disorders. The results are consistent with a common genetic etiology of schizophrenia and developmental psychopathology as well as with a stronger shared genetic etiology between schizophrenia and adolescent onset psychopathology. -
Nivard, M. G., Lubke, G. H., Dolan, C. V., Evans, D. M., St Pourcain, B., Munafo, M. R., & Middeldorp, C. M. (2017). Joint developmental trajectories of internalizing and externalizing disorders between childhood and adolescence. Development and Psychopathology, 29(3), 919-928. doi:10.1017/S0954579416000572.
Abstract
This study sought to identify trajectories of DSM-IV based internalizing (INT) and externalizing (EXT) problem scores across childhood and adolescence and to provide insight into the comorbidity by modeling the co-occurrence of INT and EXT trajectories. INT and EXT were measured repeatedly between age 7 and age 15 years in over 7,000 children and analyzed using growth mixture models. Five trajectories were identified for both INT and EXT, including very low, low, decreasing, and increasing trajectories. In addition, an adolescent onset trajectory was identified for INT and a stable high trajectory was identified for EXT. Multinomial regression showed that similar EXT and INT trajectories were associated. However, the adolescent onset INT trajectory was independent of high EXT trajectories, and persisting EXT was mainly associated with decreasing INT. Sex and early life environmental risk factors predicted EXT and, to a lesser extent, INT trajectories. The association between trajectories indicates the need to consider comorbidity when a child presents with INT or EXT disorders, particularly when symptoms start early. This is less necessary when INT symptoms start at adolescence. Future studies should investigate the etiology of co-occurring INT and EXT and the specific treatment needs of these severely affected children. -
Stergiakouli, E., Martin, J., Hamshere, M. L., Heron, J., St Pourcain, B., Timpson, N. J., Thapar, A., & Smith, G. D. (2017). Association between polygenic risk scores for attention-deficit hyperactivity disorder and educational and cognitive outcomes in the general population. International Journal of Epidemiology, 46(2), 421-428. doi:10.1093/ije/dyw216.
Abstract
Background: Children with a diagnosis of attention-deficit hyperactivity disorder (ADHD) have lower cognitive ability and are at risk of adverse educational outcomes; ADHD genetic risks have been found to predict childhood cognitive ability and other neurodevelopmental traits in the general population; thus genetic risks might plausibly also contribute to cognitive ability later in development and to educational underachievement.
Methods: We generated ADHD polygenic risk scores in the Avon Longitudinal Study of Parents and Children participants (maximum N: 6928 children and 7280 mothers) based on the results of a discovery clinical sample, a genome-wide association study of 727 cases with ADHD diagnosis and 5081 controls. We tested if ADHD polygenic risk scores were associated with educational outcomes and IQ in adolescents and their mothers.
Results: High ADHD polygenic scores in adolescents were associated with worse educational outcomes at Key Stage 3 [national tests conducted at age 13–14 years; β = −1.4 (−2.0 to −0.8), P = 2.3 × 10−6), at General Certificate of Secondary Education exams at age 15–16 years (β = −4.0 (−6.1 to −1.9), P = 1.8 × 10−4], reduced odds of sitting Key Stage 5 examinations at age 16–18 years [odds ratio (OR) = 0.90 (0.88 to 0.97), P = 0.001] and lower IQ scores at age 15.5 [β = −0.8 (−1.2 to −0.4), P = 2.4 × 10−4]. Moreover, maternal ADHD polygenic scores were associated with lower maternal educational achievement [β = −0.09 (−0.10 to −0.06), P = 0.005] and lower maternal IQ [β = −0.6 (−1.2 to −0.1), P = 0.03].
Conclusions: ADHD diagnosis risk alleles impact on functional outcomes in two generations (mother and child) and likely have intergenerational environmental effects. -
Stergiakouli, E., Smith, G. D., Martin, J., Skuse, D. H., Viechtbauer, W., Ring, S. M., Ronald, A., Evans, D. E., Fisher, S. E., Thapar, A., & St Pourcain, B. (2017). Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development. Molecular Autism, 8: 18. doi:10.1186/s13229-017-0131-2.
Abstract
Background: Shared genetic influences between attention-deficit/hyperactivity disorder (ADHD) symptoms and
autism spectrum disorder (ASD) symptoms have been reported. Cross-trait genetic relationships are, however,
subject to dynamic changes during development. We investigated the continuity of genetic overlap between ASD
and ADHD symptoms in a general population sample during childhood and adolescence. We also studied uni- and
cross-dimensional trait-disorder links with respect to genetic ADHD and ASD risk.
Methods: Social-communication difficulties (N ≤ 5551, Social and Communication Disorders Checklist, SCDC) and
combined hyperactive-impulsive/inattentive ADHD symptoms (N ≤ 5678, Strengths and Difficulties Questionnaire,
SDQ-ADHD) were repeatedly measured in a UK birth cohort (ALSPAC, age 7 to 17 years). Genome-wide summary
statistics on clinical ASD (5305 cases; 5305 pseudo-controls) and ADHD (4163 cases; 12,040 controls/pseudo-controls)
were available from the Psychiatric Genomics Consortium. Genetic trait variances and genetic overlap between
phenotypes were estimated using genome-wide data.
Results: In the general population, genetic influences for SCDC and SDQ-ADHD scores were shared throughout
development. Genetic correlations across traits reached a similar strength and magnitude (cross-trait rg ≤ 1,
pmin = 3 × 10−4) as those between repeated measures of the same trait (within-trait rg ≤ 0.94, pmin = 7 × 10−4).
Shared genetic influences between traits, especially during later adolescence, may implicate variants in K-RAS signalling
upregulated genes (p-meta = 6.4 × 10−4).
Uni-dimensionally, each population-based trait mapped to the expected behavioural continuum: risk-increasing alleles
for clinical ADHD were persistently associated with SDQ-ADHD scores throughout development (marginal regression
R2 = 0.084%). An age-specific genetic overlap between clinical ASD and social-communication difficulties during
childhood was also shown, as per previous reports. Cross-dimensionally, however, neither SCDC nor SDQ-ADHD scores
were linked to genetic risk for disorder.
Conclusions: In the general population, genetic aetiologies between social-communication difficulties and ADHD
symptoms are shared throughout child and adolescent development and may implicate similar biological pathways
that co-vary during development. Within both the ASD and the ADHD dimension, population-based traits are also linked
to clinical disorder, although much larger clinical discovery samples are required to reliably detect cross-dimensional
trait-disorder relationships. -
Tachmazidou, I., Süveges, D., Min, J. L., Ritchie, G. R. S., Steinberg, J., Walter, K., Iotchkova, V., Schwartzentruber, J., Huang, J., Memari, Y., McCarthy, S., Crawford, A. A., Bombieri, C., Cocca, M., Farmaki, A.-E., Gaunt, T. R., Jousilahti, P., Kooijman, M. N., Lehne, B., Malerba, G. and 83 moreTachmazidou, I., Süveges, D., Min, J. L., Ritchie, G. R. S., Steinberg, J., Walter, K., Iotchkova, V., Schwartzentruber, J., Huang, J., Memari, Y., McCarthy, S., Crawford, A. A., Bombieri, C., Cocca, M., Farmaki, A.-E., Gaunt, T. R., Jousilahti, P., Kooijman, M. N., Lehne, B., Malerba, G., Männistö, S., Matchan, A., Medina-Gomez, C., Metrustry, S. J., Nag, A., Ntalla, I., Paternoster, L., Rayner, N. W., Sala, C., Scott, W. R., Shihab, H. A., Southam, L., St Pourcain, B., Traglia, M., Trajanoska, K., Zaza, G., Zhang, W., Artigas, M. S., Bansal, N., Benn, M., Chen, Z., Danecek, P., Lin, W.-Y., Locke, A., Luan, J., Manning, A. K., Mulas, A., Sidore, C., Tybjaerg-Hansen, A., Varbo, A., Zoledziewska, M., Finan, C., Hatzikotoulas, K., Hendricks, A. E., Kemp, J. P., Moayyeri, A., Panoutsopoulou, K., Szpak, M., Wilson, S. G., Boehnke, M., Cucca, F., Di Angelantonio, E., Langenberg, C., Lindgren, C., McCarthy, M. I., Morris, A. P., Nordestgaard, B. G., Scott, R. A., Tobin, M. D., Wareham, N. J., Burton, P., Chambers, J. C., Smith, G. D., Dedoussis, G., Felix, J. F., Franco, O. H., Gambaro, G., Gasparini, P., Hammond, C. J., Hofman, A., Jaddoe, V. W. V., Kleber, M., Kooner, J. S., Perola, M., Relton, C., Ring, S. M., Rivadeneira, F., Salomaa, V., Spector, T. D., Stegle, O., Toniolo, D., Uitterlinden, A. G., Barroso, I., Greenwood, C. M. T., Perry, J. R. B., Walker, B. R., Butterworth, A. S., Xue, Y., Durbin, R., Small, K. S., Soranzo, N., Timpson, N. J., & Zeggini, E. (2017). Whole-Genome Sequencing coupled to imputation discovers genetic signals for anthropometric traits. The American Journal of Human Genetics, 100(6), 865-884. doi:10.1016/j.ajhg.2017.04.014.
Abstract
Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.Additional information
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