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Reading Disability (RD) is often characterized by difficulties in the phonology of the language. While the molecular mechanisms underlying it are largely undetermined, loci are being revealed by genome-wide association studies (GWAS). In a previous GWAS for word reading (Price, 2020), we observed that top single-nucleotide polymorphisms (SNPs) were located near to or in genes involved in neuronal migration/axon guidance (NM/AG) or loci implicated in autism spectrum disorder (ASD). A prominent theory of RD etiology posits that it involves disturbed neuronal migration, while potential links between RD-ASD have not been extensively investigated. To improve power to identify associated loci, we up-weighted variants involved in NM/AG or ASD, separately, and performed a new Hypothesis-Driven (HD)–GWAS. The approach was applied to a Toronto RD sample and a meta-analysis of the GenLang Consortium. For the Toronto sample (n = 624), no SNPs reached significance; however, by gene-set analysis, the joint contribution of ASD-related genes passed the threshold (p~1.45 × 10–2, threshold = 2.5 × 10–2). For the GenLang Cohort (n = 26,558), SNPs in DOCK7 and CDH4 showed significant association for the NM/AG hypothesis (sFDR q = 1.02 × 10–2). To make the GenLang dataset more similar to Toronto, we repeated the analysis restricting to samples selected for reading/language deficits (n = 4152). In this GenLang selected subset, we found significant association for a locus intergenic between BTG3-C21orf91 for both hypotheses (sFDR q < 9.00 × 10–4). This study contributes candidate loci to the genetics of word reading. Data also suggest that, although different variants may be involved, alleles implicated in ASD risk may be found in the same genes as those implicated in word reading. This finding is limited to the Toronto sample suggesting that ascertainment influences genetic associations., Translational Psychiatry, 12 (1), ISSN:2158-3188

Published August 23, 2022 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 × 1028) 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. We thank all the children, twins, families, and participants who took part and are taking part in the 22 cohorts whose data contributed to these GWAS meta-analyses; the staff working on the different cohorts, including volunteers, study coordinators, interviewers, teachers, nurses, research scientists, general practitioners, midwives, psychologists, psychometrists, computer and laboratory technicians, and colleagues who assisted in the quality control and preparation of the imputed GWAS data; and the pharmacies and hospitals that were involved. B.M., B.M.-M., B.S.P., C.F., E.E., E.V., G.A., M.v.D., and S.E.F. are supported by the Max Planck Society. A.G. and T.F.M.A. were supported by the Munich Cluster for Systems Neurology (SyNergy), and A.G. was supported by Fondazione Umberto Veronesi. A.T.M. is supported by National Health and Medical Research Council of Australia (NHMRC) Grants 1105008 and 1195955 and Centre of Research Excellence Grant 1116976. A.J.O.W. is supported by NHMRC Grant 1173896. B.S.P. is supported by Simons Foundation Autism Research Initiative Grant 514787. C.Y.S. works in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/3). D.I.B. acknowledges Royal Netherlands Academy of Science Professor Award PAH/6635. E.E. is supported by NIH Grant R01DC016977. E.G.W. and J.R.G. are supported by National Institute of Child Health and Human Development (NICHD) Grant P50 HD 27802. F.R. is supported by Agence Nationale de la Recherche Grants ANR-06-NEURO-019-01, ANR-17-EURE-0017 IEC, ANR-10-IDEX-0001-02 PSL, and ANR-11-BSV4-014-01 and European Commission Grant LSHM-CT-2005-018696. H.T. is supported by the Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (ZonMW) Grant VICI 016.VICI.170.200. J.C.D. was supported by NICHD Grant P50 HD 27802. J.J.M., J.B.To., and T.K. were supported by NIH Grant R01 DC014489. K.M.P. was supported by the Hospital for Sick Children Research Training Program (Restracomp). K.R. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (213514/Z/18/Z). M.J.S. is supported by Wellcome Trust Grant WT082032MA. S.P. and F.A. are supported by Royal Society Grants UF150663 and RGF\EA\180141. T.B. is supported by Institut Pasteur, the Bettencourt-Schueller Foundation, and Université de Paris. The Adolescent Brain Cognitive Development Study is supported by the NIH and additional federal partners (NIH Grants U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147). The Aston Cohort was supported by funding from European Union (EU) Horizon 2020 Programme 641652 and Waterloo Foundation Grant 797/17290. The St. Andrews Bioinformatics Unit is funded by Wellcome Trust Grants 105621/Z/14/Z and 204821/Z/16/Z. ALSPAC is supported by UK Medical Research Council and Wellcome Grant 217065/Z/19/Z and the University of Bristol. A comprehensive list of grant funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). The Basque Center on Cognition, Brain and Language (BCBL) cohort was supported by the Basque Government through the Basic Excellence Research Centre program and the Agencia Estatal de Investigación through BCBL Severo Ochoa excellence accreditation. The Brisbane Adolescent Twin Sample was supported by Australian Research Council Grants A7960034, A79906588, A79801419, DP0212016, and DP0343921, with genotyping funded by the NHMRC Grant 389891. The Colorado Learning Disabilities Research Center cohort was supported by NICHD Grant P50 HD 27802. The Early Language in Victoria Study was supported by NHMRC Grant 436958. The Familial Influences on Literacy Abilities cohort is supported by the University of Amsterdam, the Max Planck Institue Nijmegen, and NWO Grants Rubicon 446-12-005 and VENI 451-15-017. The GRaD study was funded by the Manton Foundation, NIH Grants P50-HD027802 and K99-HD094902, and the Lambert Family. NeuroDys was funded by an EU Sixth Framework Program grant to the NeuroDys Consortium, Swiss National Science Foundation Grant 32-108130, and Austrian Science Fund Grant 18351-B02. The Netherlands Twin Register is funded by NWO Grants 480-04-004, 481-08-011, 056-32-010, 024.001.003, 480-15-001/674, 184.021.007, 184.033.111, and 56-464-14192; ZonMW Grants 911-09-032 and 912-10-020; the Amsterdam Public Health and Amsterdam Reproduction and Development Research Institutes; European Science Council Grant ERC Advanced 230374; EU Seventh Framework Program (FP7) Grant FP7/2007-2013: 602768; National Institute of Mental Health (NIMH) Grants U24 MH068457-06, R01 MH58799-03, and 1RC2 MH089995; and the Avera Institute for Human Genetics. The Pediatric Imaging, Neurocognition, and Genetics cohort is funded by NIH Grant RC2DA029475, the National Institute on Drug Abuse, and the Eunice Kennedy Shriver NICHD. The Philadelphia Neurodevelopmental Cohort is funded by NIH Grants RC2MH089983 and RC2MH089924, an institutional development award to the Center for Applied Genomics from The Children’s Hospital of Philadelphia, and a donation from Adele and Daniel Kubert and thanks the NIH data repository. The Raine study was supported by long-term funding from NHMRC Grants 572613, 403981, and 1059711 and Canadian Institutes of Health Research (CIHR) Grant MOP-82893. Funding was also provided by the University of Western Australia, Curtin University, the Women and Infants Research Foundation, the Telethon Kids Institute, Edith Cowan University, Murdoch University, the University of Notre Dame Australia, and the Raine Medical Research Foundation. The Raine study analyses were supported by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. The Saguenay Youth Study is supported by the CIHR, the Heart and Stroke Foundation of Quebec, and the Canadian Foundation for Innovation. The SLI Consortium was funded by Wellcome Trust Grant 076566 and UK Medical Research Council Grant G1000569. The Twins Early Development Study is supported by UK Medical Research Council Grants MR/V012878/1 and MR/M021475/1, NIH Grant AG046938, and the EU FP7 grant FP7/2007-2013/: 602768. Toronto was supported by CIHR Grant MOP-133440. UK Dyslexia was supported by Wellcome Trust Grants 076566/Z/05/Z and 075491/Z/04, Waterloo Foundation Grant 797–1720, EU Grant 018696, and Royal Society Grant UF100463. The York cohort was funded by Wellcome Trust Grant 082036/B/07/Z. We acknowledge iPSYCH for sharing their summary statistics. The iPSYCH team was supported by Lundbeck Foundation Grants R102-A9118, R155-2014-1724, and R248-2017-2003; NIMH Grant 1U01MH109514-01; and the Universities and University Hospitals of Aarhus and Copenhagen. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. High-performance computer capacity was provided by the Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing, Aarhus University, Denmark.

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Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

The research in this thesis was aimed at identifying genes and molecular pathways involved in migraine. To this end, two gene expression analyses were performed in brain tissue obtained from transgenic mouse models for familial hemiplegic migraine (FHM), a monogenic subtype of migraine with aura. Next, a study was conducted aimed at analyzing epigenetic marks at migraine candidate genes in FHM mice. In addition to the studies in migraine-relevant mouse models, the genetic architecture of migraine was explored by performing various gene-based pathway analyses to mine the extensive data sets resulting from genome wide association studies (GWAS) for the common forms of migraine. With these data, the overlap in genetic architecture of migraine with aura (MA) and migraine without aura (MO) was explored, as well as the cell types and brain regions that are likely involved in the pathophysiology of migraine and the two migraine subtypes. Finally, a gene expression profiling study was performed in blood of patients suffering from primary headache, in this case cluster headache. Combining the various approaches let to the identification of migraine-relevant genes, pathways and cell types that helps to unravel pathophysiological mechanisms of migraine and other primary headache disorders.

Apolipoprotein E (APOE) is a protein that plays an important role in lipid metabolism (including cholesterols), and has been implicated in synaptogenesis, repair of injured nerve tissue and the modulation of beta-amyloid plaques and neurofibrillary tangles that characterise Alzheimer’s Disease (AD) (for review, see Rocchi, Pellegrini, Siciliano, & Murri, 2003; Belloy, Napolioni, & Greicius, 2019). The gene coding for APOE is located on chromosome 19 and is polymorphic in the general population. The three most common alleles are ε2, ε3, and ε4, with approximate allele frequencies of 6%, 78% and 15% in healthy Caucasian Europeans (Eisenberg et al., 2010). Possession of the ε4 allele has been associated with poorer cognitive abilities and more rapid longitudinal decline in healthy older people, particularly in episodic memory (e.g., Schiepers et al., 2012; Jack et al., 2015; Jochemsen, Muller, van der Graaf, & Geerlings, 2012; Jorm et al., 2007; Marioni et al., 2016; Mondadori et al., 2007; Schultz et al., 2008; Shin et al., 2014; Lyall et al., 2019; see Wisdom, Callahan & Hawkins, 2011, for a meta-analysis). It has also been associated with a three-to-fourfold increase in the risk of late onset AD in a gene dose-dependent manner (Farrer et al., 1997) and with an earlier age at onset by nearly 6 years on average for ε4 carriers (Blacker et al., 1997). Indeed, some have argued that APOE has no influence on cognition in mid- or late-life beyond increasing risk for AD, such that effects found on cognition reflect the decades of a pre-symptomatic period of AD pathology (e.g., Vemuri et al., 2010; see also O’Donoghue, Murphy, Zamboni, Nobre, & Mackay, 2018).