Background
Category fluency is a widely used task that relies on multiple
neurocognitive processes and is a sensitive assay of cortical dysfunction,
including in schizophrenia. The test requires naming of as many words
belonging to a certain category (e.g., animals) as possible within a short
period of time. The core metrics are the overall number of words produced
and the number of errors, namely non-members generated for a target
category. We combine a computational linguistic approach with a candidate
gene approach to examine the genetic architecture of this traditional
fluency measure.
Methods
In addition to the standard metric of overall word count, we applied
a computational approach to semantics, Latent Semantic Analysis (LSA), to
analyse the clustering pattern of the categories generated, as it likely
reflects the search in memory for meanings. Also, since fluency performance
probably also recruits verbal learning and recall processes, we included two
standard measures of this cognitive process: the Wechsler Memory Scale and
California Verbal Learning Test. To explore the genetic architecture of
traditional and LSA-derived fluency measures we employed a candidate gene
approach focused on SNPs with known function that were available from a
recent genome-wide association study (GWAS) of schizophrenia. The selected
candidate genes were associated with language and speech, verbal learning
and recall processes, and processing speed. A total of 39 coding SNPs were
included for analysis in 665 subjects.
Results and Discussion
Given the modest sample size, the results should be regarded as
exploratory and preliminary. Nevertheless, the data clearly illustrate how
extracting the meaning from participants’ responses, by analysing
the actual content of words, generates useful and neurocognitively viable
metrics. We discuss three replicated SNPs in the genes ZNF804A, DISC1 and
KIAA0319, as well as the potential for computational analyses of linguistic
and textual data in other genomics tasks.