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An Informatics Approach to Integrating Genetic and Neurological Data in Speech and Language Neuroscience

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Abstract

A number of heritable disorders impair the normal development of speech and language processes and occur in large numbers within the general population. While candidate genes and loci have been identified, the gap between genotype and phenotype is vast, limiting current understanding of the biology of normal and disordered processes. This gap exists not only in our scientific knowledge, but also in our research communities, where genetics researchers and speech, language, and cognitive scientists tend to operate independently. Here we describe a web-based, domain-specific, curated database that represents information about genotype-phenotype relations specific to speech and language disorders, as well as neuroimaging results demonstrating focal brain differences in relevant patients versus controls. Bringing these two distinct data types into a common database (http://neurospeech.org/sldb) is a first step toward bringing molecular level information into cognitive and computational theories of speech and language function. One bridge between these data types is provided by densely sampled profiles of gene expression in the brain, such as those provided by the Allen Brain Atlases. Here we present results from exploratory analyses of human brain gene expression profiles for genes implicated in speech and language disorders, which are annotated in our database. We then discuss how such datasets can be useful in the development of computational models that bridge levels of analysis, necessary to provide a mechanistic understanding of heritable language disorders. We further describe our general approach to information integration, discuss important caveats and considerations, and offer a specific but speculative example based on genes implicated in stuttering and basal ganglia function in speech motor control.

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Notes

  1. NIDCD Statistics on Voice, Speech, and Language, http://www.nidcd.nih.gov/health/statistics/vsl/Pages/Default.aspx

  2. The microarray-based data we analyze provide measures of messenger RNA levels, not protein levels.

  3. Note, however, that other follow-up studies have found relevant associations with DYX1C1 (e.g., Bates et al. 2010; Brkanac et al. 2007; Lim et al. 2011; Wigg et al. 2004; Zhang et al. 2012).

  4. These data are the same data upon which the gene expression analyses in this paper are based (although note that cross-brain normalization procedures slightly alter expression values as new data are added); it should be noted, however, that these download links will provide data from all donors and for all probes available on the array for a given gene, while we select one probe per gene for analysis (see below).

  5. Correspondingly, the URL http://neurospeech.org/sldb/api/genePhenotype/?format=json provides the same output formatted as JSON.

  6. We include results P < 0.05, uncorrected, for studies with small numbers of comparisons (e.g., a targeted association study), but – where possible – annotate those that pass corrections for multiple comparisons as described by the authors. Any results from genome wide association studies will be subjected to multiple comparisons-based thresholds of significance.

  7. Note that Pubmed will automatically explode search terms with synonymous gene symbols as well as synonyms from Medical Subject Headings (MeSH) and Unified Medical Language System (UMLS) ontologies.

  8. http://help.brain-map.org/display/humanbrain/Documentation

  9. GNPTAB encodes the alpha and beta subunits of GlcNAc-phosphotransferase, while GNPTG encodes the gamma subunit. NAGPA encodes the uncovering enzyme, a catalyst acting in the same biological pathway.

  10. Focal expression in the striatum is readily observed in the ABA profiles for both human (http://human.brain-map.org/microarray/search/show?search_term=DRD2&search_type=gene) and mouse (http://mouse.brain-map.org/search/show?search_term=DRD2&search_type=gene).

  11. It should be noted that brain imaging results that are correlated with genotypic variation need not be equivalent to those that are revealed in group studies of patients vs. controls. This could be due to individual variability at the genetic or neural information processing levels within the group.

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Acknowledgments

Support for EM and EK was provided through the National Science Foundation (NSF) Center of Excellence for Learning in Education, Science, and Technology (CELEST; NSF SMA 0835976; PI Barbara Shinn-Cunningham). The authors are grateful to Drs. Michael Hawrylycz and Changkyu Lee from the Allen Institute for Brain Science for their support with gene expression datasets and to Prof. Michael Arbib for organizing the Workshop on Action, Language, and Neuroinformatics in July 2011, during which early versions of this work were presented and discussed. We also thank the two anonymous reviewers, whose critical feedback and recommendations greatly improved this manuscript and database.

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Authors and Affiliations

  1. Departments of Health Sciences and Speech, Language, and Hearing Sciences, Boston University, 635 Commonwealth Ave, Room 403, Boston, MA, 02215, USA

    Jason W. Bohland

  2. Graduate Program for Neuroscience, Boston University, Boston, MA, 02215, USA

    Emma M. Myers

  3. Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, 21250, USA

    Esther Kim

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  1. Jason W. Bohland
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Correspondence to Jason W. Bohland.

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Supplementary Figure 1

Expanded view of the information displayed to the web application user for a single GenePhenotype record for the gene GNPTG. (PDF 243 kb)

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Bohland, J.W., Myers, E.M. & Kim, E. An Informatics Approach to Integrating Genetic and Neurological Data in Speech and Language Neuroscience. Neuroinform 12, 39–62 (2014). https://doi.org/10.1007/s12021-013-9201-6

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