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The augmented multiple regression model for the analysis of data from selected
twin pairs was extended to facilitate analyses of data from twin pairs and
nontwin siblings. Fitting this extended model to data from both selected twin
pairs and siblings yields direct estimates of heritability (h2) and
the difference between environmental influences shared by members of twin pairs
and those of sib or twin–sib pairs (i.e., c2(t)
– c2 (s)). When this model was fitted to reading
performance data from 293 monozygotic and 436 dizygotic pairs selected for
reading difficulties, and 291 of their nontwin siblings, h2 = .48
± .22, p = .03, and c2 (t) –
c2 (s) = .22 ± .12, p = .06. Although the test
for differential shared environmental influences is only marginally significant,
the results of this analysis suggest that environmental influences on reading
performance that are shared by members of twin pairs (.36) may be substantially
greater than those for less contemporaneous twin–sibling pairs
(.14).

Because of recent concerns about the replication of published results in the behavioral and biomedical sciences (Ioannidis, PLoS Medicine, Vol. 2, 2005, p. e124; Open Science Collaboration, Science, Vol. 349, 2015, p. 943; Pashler & Wagenmakers, Perspectives on Psychological Science, Vol. 7, 2012, pp. 528–530), we have conducted a replication of our recently published analyses of longitudinal reading performance and attention deficit-hyperactivity disorder data from twin pairs selected for reading difficulties (Wadsworth et al., Twin Research and Human Genetics, Vol. 18, 2015, pp. 755–761). Results obtained from univariate and bivariate (DeFries & Fulker, Behavior Genetics, Vol. 15, 1985, pp. 467–473; Acta Geneticae Medicae et Gemellologiae: Twin Research, Vol. 37, 1988, pp. 205–216) analyses of data from a subset of twin pairs tested in the International Longitudinal Twin Study of Early Reading Development at post-4th grade, and its continuation into high school at post-9th grade, were compared to those from our previous report. Similar measures of reading performance, the same measures of inattention and hyperactivity/impulsivity, and similar selection criteria were used in the two studies. In general, the patterns of results obtained from these two independent studies were highly similar. Thus, these results clearly illustrate the principle that findings from studies in quantitative behavioral genetics often replicate (Plomin et al., Perspectives on Psychological Science, Vol. 11, 2016, pp. 3–23).

Approximately 60% of children with reading difficulties (RD) meet criteria for at least one co-occurring disorder. The most common of these, attention deficit-hyperactivity disorder (ADHD), occurs in 20–40% of individuals with RD. Recent studies have suggested that genetic influences are responsible. To assess the genetic etiologies of RD and the comorbidity of RD and two ADHD symptom dimensions –– inattention (IN) and hyperactivity/impulsivity (H/I) –– we are conducting the first longitudinal twin study of RD and ADHD. Data from twin pairs in which at least one member of the pair met criteria for proband status for RD at initial assessment, and were reassessed 5 years later, were subjected to DeFries-Fulker (DF) analysis. Analyses of reading composite data indicated that over 60% of the proband deficit at initial assessment was due to genetic influences, and that reading deficits at follow-up were due substantially to the same genetic influences. When a bivariate DF model was fitted to reading performance and IN data, genetic influences accounted for 60% of contemporaneous comorbidity and over 60% of the longitudinal relationship. In contrast, analysis of the comorbidity between reading performance and H/I indicated that common genetic influences accounted for only about 20% of the contemporaneous and about 10% of the longitudinal relationships. Results indicate that (1) genetic influences on RD are substantial and highly stable; (2) the comorbidity between RD and IN is due largely to genetic influences, both contemporaneously and longitudinally; and (3) genetic influences contribute significantly less to the comorbidity between RD and H/I.

AbstractSocioeconomic risks (SES risks) are robust risk factors influencing children's academic development. However, it is unclear whether the effects of SES on academic development operate universally in all children equally or whether they vary differentially in children with particular characteristics. The current study aimed to explore children's temperament as protective or risk factors that potentially moderate the associations between SES risks and academic development. Specifically, latent growth modeling (LGM) was used in two longitudinal datasets with a total of 2236 children to examine how family SES risks and children's temperament interactively predicted the development of reading and math from middle childhood to early adolescence. Results showed that low negative affect, high effortful control, and low surgency mitigated the negative associations between SES risks and both reading and math development in this developmental period. These findings underline the heterogeneous nature of the negative associations between SES risks and academic development and highlight the importance of the interplay between biological and social factors on individual differences in development.

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