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AbstractWe reconcile competing theories of the role of phonological memory in reading development, by uncovering their dynamic relationship during the first 5 years of school. Phonological memory, reading and phoneme awareness were assessed in 780 phonics‐educated children at age 4, 5, 6 and 9. Confirmatory factor analyses demonstrated that phonological memory loaded onto two factors: verbal short‐term memory (verbal STM; phonological tasks that loaded primarily on serial order memory) and nonword repetition. Using longitudinal structural equation models, we found that verbal STM directly predicted early word‐level reading from age 4 to 6, reflecting the importance of serial‐order memory for letter‐by‐letter decoding. In contrast, reading had no reciprocal influence on the development of verbal STM. The relationship between nonword repetition and reading was bidirectional across the 5 years of study: nonword repetition and reading predicted each other both directly and indirectly (via phoneme awareness). Indirect effects from nonword repetition (and verbal STM) to reading support the view that phonological memory stimulates phonemically detailed representations through repeated encoding of complex verbal stimuli. Similarly, the indirect influence of reading on nonword repetition suggests that improved reading ability promotes the phoneme‐level specificity of phonological representations. Finally, the direct influence from reading to nonword repetition suggests that better readers use orthographic cues to help them remember and repeat new words accurately. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=70LZfTR0BjE.

This study tested the multi-society generalizability of an eight-syndrome assessment model derived from factor analyses of American adults' self-ratings of 120 behavioral, emotional, and social problems. The Adult Self-Report (ASR; Achenbach and Rescorla 2003) was completed by 17,152 18–59-year-olds in 29 societies. Confirmatory factor analyses tested the fit of self-ratings in each sample to the eight-syndrome model. The primary model fit index (Root Mean Square Error of Approximation) showed good model fit for all samples, while secondary indices showed acceptable to good fit. Only 5 (0.06%) of the 8,598 estimated parameters were outside the admissible parameter space. Confidence intervals indicated that sampling fluctuations could account for the deviant parameters. Results thus supported the tested model in societies differing widely in social, political, and economic systems, languages, ethnicities, religions, and geographical regions. Although other items, societies, and analytic methods might yield different results, the findings indicate that adults in very diverse societies were willing and able to rate themselves on the same standardized set of 120 problem items. Moreover, their self-ratings fit an eight-syndrome model previously derived from self-ratings by American adults. The support for the statistically derived syndrome model is consistent with previous findings for parent, teacher, and self-ratings of 1½–18-year-olds in many societies. The ASR and its parallel collateral-report instrument, the Adult Behavior Checklist (ABCL), may offer mental health professionals practical tools for the multi-informant assessment of clinical constructs of adult psychopathology that appear to be meaningful across diverse societies.

Funding: AG and TFMA were supported by the Munich Cluster for Systems Neurology (SyNergy). AG 535 was supported by Fondazione Umberto Veronesi. SP is a Royal Society University Research fellow. BMM, CF, BSP and SEF are supported by the Max Planck Society. AW, BM and HK were funded by the Fraunhofer Society and the Max Planck Society within the 'Pakt für Forschung und Innovation'. HK was also supported by LIFE – Leipzig Research Center for Civilization Diseases funded by means of the European Union; the European Regional Development Fund (ERDF); and the Free State of Saxony within the excellence initiative. FR is supported by Agence Nationale de la Recherche (ANR-06-NEURO-019-01, ANR-17-EURE-542 0017 IEC, ANR-10-IDEX-0001-02 PSL, ANR-11-BSV4-014-01), European Commission (LSHM-CT-2005-018696). TFMA was supported by the BMBF through the DIFUTURE consortium of the Medical Informatics Initiative Germany (grant 01ZZ1804A) and by the European Union's Horizon 2020 Research and Innovation Programme (grant MultipleMS, EU RIA 733161). ; 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. ; Publisher PDF ; Peer reviewed