Defining the genetic architecture of human developmental language impairment

Abstract

Language is a uniquely human trait, which poses limitations on animal models for discovering biological substrates and pathways. Despite this challenge, rapidly developing biotechnology in the field of genomics has made human genetics studies a viable alternative route for defining the molecular neuroscience of human language. This is accomplished by studying families that transmit both normal and disordered language across generations. The language disorder reviewed here is specific language impairment (SLI), a developmental deficiency in language acquisition despite adequate opportunity, normal intelligence, and without any apparent neurological etiology. Here, we describe disease gene discovery paradigms as applied to SLI families and review the progress this field has made. After review the evidence that genetic factors influence SLI, we discuss methods and findings from scans of the human chromosomes, including the main replicated regions on chromosomes 13, 16 and 19 and two identified genes, ATP2C2 and CMIP that appear to account for the language variation on chromosome 16. Additional work has been done on candidate genes, i.e., genes chosen a priori and not through a genome scanning studies, including several studies of CNTNAP2 and some recent work implicating BDNF as a gene x gene interaction partner of genetic variation on chromosome 13 that influences language. These recent developments may allow for better use of post-mortem human brain samples functional studies and animal models for circumscribed language subcomponents. In the future, the identification of genetic variation associated with language phenotypes will provide the molecular pathways to understanding human language.

Figure 1

The human genetics paradigm for disease gene discovery. Linkage analysis (left) uses molecular genetic data (genotypes) from families and correlates the transmission of genotypes with the apparent transmission of language status or quantitative measures (phenotypes). Linkage peaks cover large genomic regions, often 20–30Mb, so alternative methods/design may be applied to narrow down the critical region. Association studies (middle) can be conducted using unrelated persons with the disease (cases) and without the disease (controls) where any allele frequencies differences indicate that allele is associated (correlated) with disease status. This method usually narrows down the critical region to one or few genes. Functional work (right) can be done to understand how, as in this example, gene expression is altered by an associated allele. Ultimately when functional work provides clear evidence that an allele can be used to separate cases from controls, or even subgroups of cases, use of that DNA biomarker information can be applied in the personalized medicine framework.