A genome-wide sib-pair scan for quantitative language traits reveals linkage to chromosomes 10 and 13

The landscape of neuroscience research in Africa: current state, progress, and challenges; a perspective

The field of neuroscience research in Africa has witnessed significant advancements in recent years, contributing to understanding the brain and neurological disorders. This paper provides an overview of the current state of neuroscience research in Africa, highlighting the progress made, ongoing efforts, and the challenges researchers face. Despite limited resources and funding constraints, African scientists have made remarkable strides in various areas of neuroscience. Collaborative networks and international partnerships have been crucial in advancing education, research infrastructure, and capacity building in the field. Institutions in countries such as Egypt, Ghana, South Africa, Nigeria, Tunisia, and Morocco have emerged as key hubs for neuroscience research, fostering a growing community of researchers dedicated to unravelling the complexities of the brain. Efforts in neuroscience research have encompassed diverse domains, including neurogenomics, neuroimaging, neurophysiology, neurorehabilitation, and neuroepidemiology. Studies have focused on investigating genetic factors underlying neurological conditions, exploring the neural mechanisms of cognition and behaviour, and developing innovative therapeutic approaches for neurological disorders. However, challenges persist in the African neuroscience research landscape. Limited funding remains a significant barrier, hindering the establishment of well-equipped laboratories, access to advanced technologies, and support for research projects. Addressing these concerns is crucial to ensure research outcomes' integrity, validity, and relevance. Looking ahead, strategic interventions are required to address these challenges and further advance neuroscience research in Africa.

The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) Framework for Understanding Musicality-Language Links Across the Lifespan

Using individual differences approaches, a growing body of literature finds positive associations between musicality and language-related abilities, complementing prior findings of links between musical training and language skills. Despite these associations, musicality has been often overlooked in mainstream models of individual differences in language acquisition and development. To better understand the biological basis of these individual differences, we propose the Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) framework. This novel integrative framework posits that musical and language-related abilities likely share some common genetic architecture (i.e., genetic pleiotropy) in addition to some degree of overlapping neural endophenotypes, and genetic influences on musically and linguistically enriched environments. Drawing upon recent advances in genomic methodologies for unraveling pleiotropy, we outline testable predictions for future research on language development and how its underlying neurobiological substrates may be supported by genetic pleiotropy with musicality. In support of the MAPLE framework, we review and discuss findings from over seventy behavioral and neural studies, highlighting that musicality is robustly associated with individual differences in a range of speech-language skills required for communication and development. These include speech perception-in-noise, prosodic perception, morphosyntactic skills, phonological skills, reading skills, and aspects of second/foreign language learning. Overall, the current work provides a clear agenda and framework for studying musicality-language links using individual differences approaches, with an emphasis on leveraging advances in the genomics of complex musicality and language traits.

The Genetic and Molecular Basis of Developmental Language Disorder: A Review

Language disorders are highly heritable and are influenced by complex interactions between genetic and environmental factors. Despite more than twenty years of research, we still lack critical understanding of the biological underpinnings of language. This review provides an overview of the genetic landscape of developmental language disorders (DLD), with an emphasis on the importance of defining the specific features (the phenotype) of DLD to inform gene discovery. We review the specific phenotype of DLD in the genetic literature, and the influence of historic variation in diagnostic inclusion criteria on researchers' ability to compare and replicate genotype-phenotype studies. This review provides an overview of the recently identified gene pathways in populations with DLD and explores current state-of-the-art approaches to genetic analysis based on the hypothesised architecture of DLD. We will show how recent global efforts to unify diagnostic criteria have vastly increased sample size and allow for large multi-cohort metanalyses, leading the identification of a growing number of contributory loci. We emphasise the important role of estimating the genetic architecture of DLD to decipher underlying genetic associations. Finally, we explore the potential for epigenetics and environmental interactions to further unravel the biological basis of language disorders.

Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems

Purpose Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders. Supplemental Material https://doi.org/10.23641/asha.12753578.

Quantitative genome-wide association analyses of receptive language in the Danish High Risk and Resilience Study

BACKGROUND

One of the most basic human traits is language. Linguistic ability, and disability, have been shown to have a strong genetic component in family and twin studies, but molecular genetic studies of language phenotypes are scarce, relative to studies of other cognitive traits and neurodevelopmental phenotypes. Moreover, most genetic studies examining such phenotypes do not incorporate parent-of-origin effects, which could account for some of the heritability of the investigated trait. We performed a genome-wide association study of receptive language, examining both child genetic effects and parent-of-origin effects.

RESULTS

Using a family-based cohort with 400 children with receptive language scores, we found a genome-wide significant paternal parent-of-origin effect with a SNP, rs11787922, on chromosome 9q21.31, whereby the T allele reduced the mean receptive language score by ~ 23, constituting a reduction of more than 1.5 times the population SD (P = 1.04 × 10-8). We further confirmed that this association was not driven by broader neurodevelopmental diagnoses in the child or a family history of psychiatric diagnoses by incorporating covariates for the above and repeating the analysis.

CONCLUSIONS

Our study reports a genome-wide significant association for receptive language skills; to our knowledge, this is the first documented genome-wide significant association for this phenotype. Furthermore, our study illustrates the importance of considering parent-of-origin effects in association studies, particularly in the case of cognitive or neurodevelopmental traits, in which parental genetic data are not always incorporated.

Next-generation DNA sequencing identifies novel gene variants and pathways involved in specific language impairment

A significant proportion of children have unexplained problems acquiring proficient linguistic skills despite adequate intelligence and opportunity. Developmental language disorders are highly heritable with substantial societal impact. Molecular studies have begun to identify candidate loci, but much of the underlying genetic architecture remains undetermined. We performed whole-exome sequencing of 43 unrelated probands affected by severe specific language impairment, followed by independent validations with Sanger sequencing, and analyses of segregation patterns in parents and siblings, to shed new light on aetiology. By first focusing on a pre-defined set of known candidates from the literature, we identified potentially pathogenic variants in genes already implicated in diverse language-related syndromes, including ERC1, GRIN2A, and SRPX2. Complementary analyses suggested novel putative candidates carrying validated variants which were predicted to have functional effects, such as OXR1, SCN9A and KMT2D. We also searched for potential "multiple-hit" cases; one proband carried a rare AUTS2 variant in combination with a rare inherited haplotype affecting STARD9, while another carried a novel nonsynonymous variant in SEMA6D together with a rare stop-gain in SYNPR. On broadening scope to all rare and novel variants throughout the exomes, we identified biological themes that were enriched for such variants, including microtubule transport and cytoskeletal regulation.