Keinath 1
Christiana Keinath
Dr. Ely
BIOL 303
1 November 2014
One Gene, Many Approaches: Investigating the Role of KIAA0319 in Dyslexia
Dyslexia, also known as reading disability (RD), is a neurologically-based learning disability characterized by difficulty reading and processing language, as in spelling, writing, and listening to or producing speech (“About Dyslexia,” 2014). Signs of the disorder may manifest as soon as a child begins school. Despite possessing normal intelligence, affected individuals often struggle in educational and social environments that increasingly demand proficiency in text-based communication (“What is Dyslexia,” 2014). According Dyslexia International[1], Dyslexia, affects 5 – 15% of the global population, or roughly 700 million individuals worldwide (“About Dyslexia,” 2014). Both its prevalence and the challenges it presents to affected individuals have inspired researchers around the world to subject dyslexia to detailed investigations into its etiology and symptoms.
Though once it was believed that dyslexia was due to environmental factors such as poor education, current research shows it to have a strong genetic basis (“What is Dyslexia,” 2014). Four genes have been named as candidates for association with the disorder, including the most closely associated, KIAA0319 (Centanni et al. 2014). Examining the plethora of studies including or centered around KIAA0319 reveals the wide range of methods used in genetic research to illuminate the intricate relationships between genes and physical and behavioral phenotypes both typical and pathological.
In one such method, researchers take existing data about a population and analyze it for associations between genetic markers and other characteristics. Scerri et al. did so in 2011, when they performed quantitative and case-control analyses to look for association between three language related genes (including KIAA0319) and various measures used to assess language proficiency and general cognitive traits in children. The data came from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, a project in which children born in southwest England in the early 1990s were followed over time, and detailed phenotypic, environmental, genetic, and epigenetic records were kept about the children and their parents (Boyd et al. 2012, Scerri et al. 2011).
Scerri et al. (2011) chose a large sample size (n=3725) to allow analysis of different subgroups within the sample, which enabled testing for comorbidity between dyslexia and specific language impairment (SLI), a childhood disorder characterized by trouble acquiring oral language. Individuals were sorted into groups of RD, SLI, attention-deficit/hyperactivity disorder, any of the four comorbid combinations, or unaffected (Scerri et al. 2011). For the first step of the quantitative analysis, the researchers chose 19 single nucleotide polymorphisms (SNPs) to analyze for all individuals in the sample with respect to READ, a core measure for dyslexia that tests single-word reading accuracy, and NW_REPT, a phonological short-term memory test and core measure of SLI. From this initial analysis, the SNPs showing p values less than 0.05 were chosen for analysis with other reading and language-related measures. For all three genes, the strongest associations at this step were with SPELL, a measure of single-word spelling accuracy. The researchers then tested associations with READ and SPELL on different subsets of the original sample, as diagramed in Figure 1 (Scerri et al. 2011).
Figure 1. Diagram illustrating how phenotypic subgroups were identified. The subgroups above the black horizontal lines were used for quantitative analysis while the ones below were used for case-control analysis. The extent of co-morbidity (hence the non-mutually exclusive definition of cases), can be seen in Figure 2. ADHD, attention-deficit/hyperactivity disorder; CCC_SUM7, sum of first seven scales from the Children’s Communication Checklist; PERF_IQ, performance IQ; RD, reading disability; SLI, specific language impairment.
Analysis by subset revealed underlying patterns not observable in the original data. For example, the SNP of KIAA0319 rs2143340 showed the most significant association in the initial analysis. Unlike some of the other SNPs investigated, rs2143340 was found to be significantly associated with reading skills for all subgroups, even the one only containing typical individuals, leading the authors suggest that variations in KIAA0319 are related to variations in reading ability for all individuals, not just those with language disorders (Table 1). In contrast, two other SNPs of KIAA0319, rs6935076 and rs9461045, showed modest association that decreased as more individuals were excluded from the sample. For these SNPs, no association was detectable when analyzing only unaffected individuals (Table 1) (Scerri et al. 2011).
Table 1. Summary of Results Showing Association (p<.05) with Quantitative Measures
The second method employed by Scerri et al. is known as a case-control study, in which the genotypes of a specific number of unaffected individuals (controls) are compared with a specific number of affected individuals (cases) (Cordell and Clayton, 2005). In this study, a control group was chosen from the unaffected individuals and compared to four subgroups: SLI only, RD only, SLI with comorbidity, and RD with comorbidity (Scerri et al. 2011). For KIAA0319, the same polymorphism that showed a strong signal across all subgroups of the quantitative analysis was similarly not associated specifically with dyslexia in case-control. Two other SNPs of this gene did show association with dyslexia in the case-control analysis. One possible drawback to the case-control method is the possibility of other variables confounding the results (Cordell and Clayton, 2005). In this study in particular, all individuals were born within the same geographic location (Scerri et al. 2011). Future studies should investigate whether these results remain constant in other populations. Based on the results of both the quantitative and case-control studies, Scerri et al. proposed that some variants of KIAA0319 are related to the reading process in general, while others may be directly related to dyslexia. It is also important to note that associations were found between genes and reading measures for single-word reading and spelling, not any of the other general measures tested such as memory and performance IQ. The latter lack of association supports the idea that dyslexia is not a sign of generally poor cognition (Scerri et al. 2011).
While some researchers like Scerri et al. choose to relate genetic markers to performance measurements, others seek associations between genetic and physical traits, as Darki et al. did in their 2012 study of the effects of three dyslexia susceptibility genes (including KIAA0319) on variability of white matter in the brain. White matter is a type of tissue found in the brain and spinal cord that consists of the axons of neurons, which are covered by a white, insulating substance known as myelin (White Matter 2014). Previous studies have indicated that dyslexic patients have abnormally structured white matter in certain brain regions, and that white matter variability is also related to variations in reading ability within the normal range (Klingberg et al. 2000). The goal of the Darki et al. (2012) study was to search for a potential genetic cause behind such white matter variability.
The randomly selected subjects were 76 healthy 6 to 25-year-olds (Darki et al. 2012). According to parent reports, one subject had dyslexia and two others were being tested for the disorder. Along with a reading comprehension test, participants were also given a more basic test in which they read as many words correctly as possible in two minutes. Performance on this second test is not as easily influenced by traits other than reading ability, such as attention and working memory. Genetic material was collected from each participant’s blood or saliva, and genotypes were determined of 13 SNPs in or near the three genes of interest. For each participant, the researchers measured the volume and microstructure, or connectivity, of white matter and performed statistical analyses to see if any of the chosen polymorphisms appeared to affect white matter volume. Once a cluster of white matter was determined to be significant, connectivity data was used to look for bundles of axons called neural tracts that passed through the cluster (Darki et al. 2012).
One SNP from KIA0319, rs6935076, did show a significant association with white matter volume which, like the two other significant associations found in this study, remained consistent when measurements from the same subjects were retaken two years later. This polymorphism was found to be associated with a specific cluster of white matter in the brain’s left temporo-parietal region. The researchers found connectivity to differ noticeably from one individual to another but were still able to find consistent connections between the middle temporal gyrus/superior temporal sulcus and the supramarginal and angular gyri in the parietal lobe. According to several previous studies, these areas have lower activation levels in dyslexic patients (Darki et al. 2014).
Finally, the researchers found the mean white matter volume for each significant cluster, associated with each SNP, for each individual, and compared these means to reading scores using a linear model (Darki et al. 2014). The model results confirmed the initial correlations between SNPs and white matter volumes. The volumes were also significantly correlated with reading scores on the first test (all p < .00009) and accuracy on the second reading test. However, no significant, direct correlation was observable between the SNPs and performance on either test, as depicted in Figure 3 (E). The authors believe that the genes affect neural connectivity between specific temporal and parietal brain regions, leading to variation in reading ability. However, they cite the relatively small sample size in this study as necessitating repetition in an independent population before firm conclusions are drawn (Darki et al. 2014). Nevertheless, this study showed that seeking information about the normal range of phenotypes associated with a gene can lead to an understanding of disorders related to the same gene.
Figure 2. White matter connections and correlation to behavior. (A) Example of tract tracing results from one individual (red, green, and blue fibers show left–right, anterior–posterior, and inferior–superior directions, respectively). (B) Overlay of tract tracing from 30 individuals. The color bar shows the number of individuals with overlapping connections. (C) The cortical regions most consistently connected are the middle temporal gyrus/superior temporal sulcus inferiorly and supramarginal and angular gyrus superiorly. (D) Regions of interest drown by the radius of 5 mm at the centers of 60, 56, 0 (reported by Paulesu et al. [5] for middle temporal) and 38, 48, 40 (reported by Richlan et al. [6] for inferior parietal lobule). (E) Correlations between genes (SNPs), white matter volume, and reading (reading comprehension test). DYX1C1_L and DYX1C1_R denote the clusters found for rs3743204, in the left and the right hemispheres, respectively.
Because the complex genetic basis and phenotypic results of dyslexia vary widely between individuals, researchers sometimes choose to investigate the disorder in a simpler system. A homologous gene to KIAA0319 exists in rats, and experimental manipulation of it can elicit dyslexia-like symptoms in the rodents (Centanni et al. 2014). Since rats can also distinguish speech sounds in non-ideal conditions with a similar accuracy as humans, they are suitable model organisms for studying dyslexia. Centanni et al. made use of this similarity in their 2014 study investigating the speech sound processing abilities of rats with the KIA0319 homolog knocked down by in utero RNA interference. Importantly for those seeking treatment for dyslexia, the researchers also examined the effectiveness and neuronal consequences of different types of training meant to improve language abilities. Phonemes, the smallest units of sound that distinguish one word from another, were a particular focus because dyslexic individuals often have trouble with tasks requiring phoneme processing (Centanni et al. 2014).
RNA interference (RNAi) is a technique of genetic modification by which introduced DNA expresses short hairpin RNA (shRNA) which has been programed to silence target genes (Pratt and MacRae 2009). The silencing mechanism in this instance was repression of translation by RNA-induced silencing complexes (RISCs). Experimental rats (KIA-) were given shRNA targeting the KIAA0319 homolog, causing the gene to be expressed in only some of the neurons of any one rat. Control rats were given an inactive sequence. The rats were trained to respond to a target sound and to ignore distracting sounds, and were tested under a variety of conditions (Centanni et al. 2014).
The researchers’ first goal was to verify that knockdown of the KIAA0319 homolog truly impaired the rats’ ability to discriminate sounds. During the first five days of training, the KIA- rats responded to the incorrect sounds significantly more frequently than the control rats (Figure 3). The KIA- rats required five more days of experience with the task to reach the accuracy achieved by the controls, as shown in part A of Figure 4 (Centanni et al. 2014).
Figure 3. Rats with in utero RNAi of Kiaa0319 are impaired at speech discrimination tasks.
A. Performance of KIA- and control rats on the first 5 days of full length speech training. KIA- rats were significantly worse than control rats on the full speech discrimination task on 4 of the days (* = p,0.01). B. On day 5 of testing, KIA- rats hit to the target sound dad at the same rate as control rats (unpaired t-test; p = 0.33), but false alarmed to the distractor sounds significantly more than control rats (* = p = 0.04). C. Break down of lever press rates on day 5 of testing to each of the distractor sounds. KIA- rats responded to every sound significantly more than control rats (unpaired t-tests, * = p,0.01).
Three other tasks were developed for the rats based on skills that challenge human individuals with dyslexia. The KIA- rats performed significantly worse than the controls when distinguishing speech sounds in four levels of background noise and when required to distinguish sounds after only being exposed to the first 40 milliseconds, as shown in parts B and D of Figure 4, respectively. However, there was no significant difference between the groups’ performance when required to distinguish target sounds from a rapid string of distractor speech sounds, as shown in part C of Figure 4 (Centanni et al. 2014).
Figure 4. Extensive speech discrimination training can improve on clear speech tasks. Horizontal lines in each panel represent chance performance for that task. A. Timeline of performance on the full length speech task. After an additional week of training, 8 KIA- rats were able to perform the full speech task at the same level as 5 control rats (unpaired t-test, p = 0.24). B. Timeline of performance on speech in noise task. KIA- rats remained significantly below control levels at the end of training (*=p,0.05). C. Timeline of performance on sequence task. There were no significant differences between control and KIA- rats during this 40 day training period. Symbols correspond to the first day of training at each new stage (see panel F for symbol key). D. Timeline of performance on truncated speech task. KIA- rats were significantly impaired at this task compared to controls until the final day of training (*=p,0.01). E. Last day performance of rats on the speech in noise task. (*=p,0.01). F. Last day performance of rats on the sequence task. There were no significant differences between control and KIA- rats at any presentation rate tested (2 sps, p=0.45; 4 sps, p=0.68; 5 sps, p=0.27; 6.67 sps, p=0.65; 10 sps, p=0.99; 20 sps, p=0.74).