Background
Over the past few years conflicting evidence has been obtained concerning the association between the gene encoding the regulator of G-protein signaling 4 (
RGS4) and schizophrenia. Initially evidence for linkage with schizophrenia was reported near
RGS4 at 1q21-22 [
1,
2] and several association studies also suggested modest associations for certain
RGS4 gene variants [
3‐
9]. However, a follow-up investigation of the original linkage study found no associations with RGS4 in this same sample [
10] and many other studies found also negative results. Three recent meta-analyses either show no association between
RGS4 and schizophrenia or suggest modest effects for SNP4 (rs951436) and for two common haplotypes [
11‐
13]. Updated evaluation of the relevant association data shows no significant association for any of the 4 most commonly studied polymorphisms [
14].
A plausible hypothesis to explain the inconsistencies in these studies is that
RGS4 variants may modulate
endophenotypes associated with schizophrenia rather than risk of disease itself [
15]. Endophenotypes are measurable components along the pathway between the genetic infrastructure and the presentation of a disorder. Candidate endophenotypes of schizophrenia with which the disorder presumably shares a degree of overlapping genetic liability include structural and morphometric brain alterations, neurocognitive deficits and schizotypal personality traits or symptoms. These are expressed as quantitative traits with different intensities across a broad phenotypic spectrum, ranging from patients to their unaffected relatives and extending to the (disease-free) general population. Recent evidence that RGS4 may indeed impact on such endophenotypes is provided by. Prasad et al [
16] who reported that left dorsolateral prefrontal cortex (DLPFC) volumes were significantly different across first episode schizophrenia patients and controls on SNPs 4 (rs951436) and 18 (rs2661319) after correcting for multiple comparisons, indicating that these
RGS4 polymorphisms may contribute to structural alterations in brain areas, previously associated with schizophrenia. Buckholtz et al [
17] provided further evidence that SNP 4 (rs951436) impacts frontoparietal and frontotemporal response during working memory (n-Back test) and regionally specific reductions in gray and white matter structural volume in healthy individuals carrying the common risk allele. These studies indicate that structural and functional integrity of the prefrontal cortex might be associated with
RGS4 polymorphic variation.
We have recently reported on the utility of adopting a population endophenotype approach to study the potential effect of candidate susceptibility genes for schizophrenia [
18]. We hypothesize that endophenotypes for schizophrenia such as schizotypal personality traits [
19], cognitive ability dependent on the integrity of prefrontal cortex such as sustained attention [
20], working memory [
21] and antisaccade eye-movements [
22], may serve as potential targets of schizophrenia susceptibility genes. In this study, we set out to evaluate in a large cohort of apparently healthy young males whether 4 single nucleotide polymorphic variability in the
RGS4 gene locus that has been previously associated with schizophrenia susceptibility, does modulate the expression of such schizophrenia related endophenotypes. We specifically hypothesized based on recent neuroimaging reports that RGS4 common "risk" alleles and haplotypes would be associated with a relative impairment of prefrontally mediated psychological and/or neuropsychological function.
Results
Descriptive data
In total, 2243 randomly selected young male conscripts (mean age, 20.7 +- 1.90) entered the study. As previously described in more detail [
18] the proportion of conscripts who gave eligible responses and measurements varied between 60% for SPQ and 90% for cognitive measurements.
Genotyping was successful for 81.4% on rs10917670, 88.2% on rs951436, 89.8% on rs951439, and 78.7% on rs2661319. Genotype availability was not significantly related to any of the instrument scores (p-values ranged between 0.64 and 1.00 for all analyses).
Genotype frequencies were as follows. For rs10917670: AA = 325, AG = 842, GG = 522; for rs951436 GG = 374, GT = 927, TT = 531; for rs951439: AA = 353, AG = 949, GG = 563; and for rs2661319: GG = 379, AG = 813, and AA = 441. No significant deviations from the HW law were seen for any of the 4 tested polymorphisms (p > 0.19 for all).
Strong linkage disequilibrium was observed between rs10917670 and rs951439 (r2 = 0.98). r2 values ranged between 0.51 and 0.81 for all other SNP pairs. In accordance with previous reported studies on individuals of Caucasian origin, the most common haplotypes were GGGG (44.0%), ATAA (40.8%), and GTGA (9.9%); all other haplotypes occurred in 3% or less of the population.
Associations with individual SNPs – model-free approach
In model-free analyses (analyses that compare outcome distributions across the three genotypes for each SNP) a single association between SNP4 (rs951436) and the negative dimension of schizotypal personality reached nominal statistical significance (p = 0.031).
Associations with individual SNPs – allele-load models
As shown in Table
1, there were three nominally statistically significant signals when analyses used an additive allele-load model. Specifically the T allele of SNP4 (rs951436) and the A allele of SNP18 were associated with an increase in negative schizotypy (p = 0.009 and p = 0.039, respectively). The latter was also associated with an increase in antisaccade error rate (p = 0.028). Effect sizes were modest for these nominally significant findings. The three nominally statistically significant associations (p = 0.009, 0.039 and 0.028) had Bayes factors of 0.15, 0.43, and 0.30, respectively, when the genetic effect under the alternative was assumed to have an average value in the negative direction of -0.01, -0.01, and -0.02 respectively (the magnitude of the effects estimates that were observed).
Table 1
Effect sizes (beta) per minor allele copy for RGS4 gene SNPs
IQ (RPM)
| 1470 | -0.39 | 0.235 (0.349) | 1595 | 0.17 | 0.603 (0.799) | 1411 | 0.10 | 0.773 (0.715) |
SPQ total score | 1043 | 0.49 | 0.363 (0.607) | 1134 | -0.65 | 0.215 (0.455) | 1004 | -0.64 | 0.246 (0.502) |
SPQ cognitive/perceptual factor | 1041 | 0.00 | 0.553 (0.839) | 1132 | -0.00 | 0.807 (0.966) | 1002 | -0.00 | 0.514 (0.795) |
SPQ negative factor
| 1041 | 0.01 | 0.184 (0.291) | 1132 | -0.01 | 0.009 (0.031) | 1002 | -0.01 | 0.039 (0.117) |
SPQ disorganization factor | 1041 | 0.01 | 0.256 (0.493) | 1132 | -0.01 | 0.266 (0.538) | 1002 | -0.01 | 0.262 (0.493) |
SPQ paranoid factor | 1041 | 0.00 | 0.980 (0.876) | 1132 | 0.00 | 0.908 (0.881) | 1002 | 0.00 | 0.896 (0.976) |
d'-S2B (spatial working memory) | 1434 | -0.06 | 0.153 (0.352) | 1558 | -0.02 | 0.514 (0.650) | 1384 | -0.02 | 0.682 (0.900) |
d'-V2B (verbal working memory) | 1372 | -0.05 | 0.124 (0.306) | 1494 | 0.02 | 0.556 (0.285) | 1324 | 0.01 | 0.755 (0.785) |
Antisaccade error rate
| 1585 | 0.01 | 0.286 (0.565) | 1731 | -0.01 | 0.069 (0.173) | 1532 | -0.02 | 0.028 (0.089) |
Haplotype-based analyses
Overall, haplotype analyses yielded consistent results with SNP-based analyses (Table
2 and Table
3). In main analyses, likelihood ratio tests suggested a non-significant relationship between
RGS4 haplotypes and the negative dimension of SPQ (p = 0.06; Table
2). The reference haplotype GGGG (that does not include the T allele of SNP4 and the A allele of SNP18) was associated with
lower values in the negative dimension of SPQ, compared to the common ATAA and GTGA haplotypes (p = 0.03; Table
2), or compared to all other haplotypes combined (p = 0.01; Table
3), albeit with modest effects. In addition, non-significant trends for a relationship of haplotypes with spatial working memory were also observed (likelihood ratio testing, p = 0.07; Table
2). Adjusted analyses were qualitatively similar for the aforementioned outcomes.
Table 2
Haplotypes based on best pairs (unweighted) UNADJUSTED
IQ (RPM)
| 1703 | -0.626 | (0.792) | -0.366 | (0.332) | 0.265 | (0.527) | 0.50 |
SPQ total score
| 1211 | 1.214 | (1.279) | 0.683 | (0.539) | 0.424 | (0.881) | 0.55 |
SPQ cognitive/perceptual factor
| 1211 | 0.003 | (0.012) | 0.003 | (0.005) | -0.001 | (0.008) | 0.91 |
SPQ negative factor
| 1211 | 0.012 | (0.013) | 0.011* | (0.005) | 0.020* | (0.009) | 0.059 |
SPQ disorganization factor
| 1211 | 0.026 | (0.021) | 0.012 | (0.009) | 0.004 | (0.014) | 0.42 |
SPQ paranoid factor
| 1211 | 0.006 | (0.019) | 0.001 | (0.008) | -0.007 | (0.013) | 0.92 |
d'-S2B (spatial working memory)
| 1656 | -0.047 | (0.091) | -0.015 | (0.039) | 0.146* | (0.063) | 0.073 |
d'-V2B (verbal working memory)
| 1583 | 0.017 | (0.077) | -0.028 | (0.034) | 0.015 | (0.053) | 0.77 |
Antisaccade error rate
| 1835 | -0.023 | (0.018) | 0.013 | (0.008) | 0.013 | (0.012) | 0.10 |
Table 3
Most Common haplotype vs all other haplotypes based on best pairs (unweighted)
IQ (RPM)
| 1703 | 0.262 | (0.310) | 0.40 | | | | |
SPQ total score
| 1211 | -0.677 | (0.503) | 0.18 | 1203 | -0.535 | (0.502) | 0.29 |
SPQ cognitive/perceptual factor
| 1211 | -0.002 | (0.005) | 0.63 | 1203 | -0.001 | (0.005) | 0.87 |
SPQ negative factor
| 1211 | -0.013* | (0.005) |
0.011
| 1203 | -0.011* | (0.005) |
0.024
|
SPQ disorganization factor
| 1211 | -0.012 | (0.008) | 0.16 | 1203 | -0.009 | (0.008) | 0.26 |
SPQ paranoid factor
| 1211 | 0.000 | (0.008) | 0.99 | 1203 | 0.001 | (0.008) | 0.87 |
d'-S2B (spatial working memory)
| 1656 | -0.012 | (0.036) | 0.74 | 1462 | -0.045 | (0.033) | 0.18 |
d'-V2B (verbal working memory)
| 1583 | 0.016 | (0.031) | 0.60 | 1394 | -0.004 | (0.029) | 0.88 |
Antisaccade error rate
| 1835 | -0.010 | (0.007) | 0.15 | 1622 | -0.004 | (0.007) | 0.60 |
Weighting for the probability of each haplotype (Table
4 and Table
5) suggested a trend for an association of haplotypes with spatial working memory (likelihood ratio tests, p = 0.048 without and p = 0.077 with adjustments). Specifically, each copy of the GTGA haplotype was associated with slightly superior spatial working memory performance (0.16 points) compared to individuals with the most common haplotype GGGG. Haplotype analyses limited to haplotypes where all SNPs were available versus including also those where some SNPs were missing, yielded similar results.
Table 4
Haplotypes weighting for the probability of each haplotype UNADJUSTED
IQ (RPM)
| 1703 | -0.643 | (0.794) | -0.351 | (0.335) | 0.314 | (0.539) | 0.49 |
SPQ total score
| 1211 | 1.322 | (1.281) | 0.657 | (0.545) | 0.421 | (0.900) | 0.54 |
SPQ cognitive/perceptual factor
| 1211 | 0.004 | (0.012) | 0.003 | (0.005) | -0.001 | (0.008) | 0.92 |
SPQ negative factor
| 1211 | 0.013 | (0.013) | 0.011* | (0.005) | 0.020* | (0.009) | 0.061 |
SPQ disorganization factor
| 1211 | 0.028 | (0.021) | 0.011 | (0.009) | 0.004 | (0.015) | 0.40 |
SPQ paranoid factor
| 1211 | 0.007 | (0.019) | 0.001 | (0.008) | -0.008 | (0.014) | 0.91 |
d'-S2B (spatial working memory)
| 1656 | -0.049 | (0.091) | -0.014 | (0.039) | 0.161* | (0.065) |
0.048
|
d'-V2B (verbal working memory)
| 1583 | 0.022 | (0.077) | -0.029 | (0.034) | 0.020 | (0.055) | 0.72 |
Antisaccade error rate
| 1835 | -0.021 | (0.018) | 0.013 | (0.008) | 0.013 | (0.012) | 0.12 |
Table 5
Haplotypes weighting for the probability of each haplotype ADJUSTED for age, IQ and interaction
SPQ total score
| 1203 | 0.741 | (1.290) | 0.510 | (0.543) | 0.517 | (0.895) | 0.77 |
SPQ cognitive/perceptual factor
| 1203 | -0.001 | (0.012) | 0.001 | (0.005) | -0.001 | (0.008) | 0.99 |
SPQ negative factor
| 1203 | 0.005 | (0.013) | 0.009 | (0.005) | 0.021* | (0.009) | 0.071 |
SPQ disorganization factor
| 1203 | 0.018 | (0.021) | 0.009 | (0.009) | 0.005 | (0.015) | 0.67 |
SPQ paranoid factor
| 1203 | 0.003 | (0.019) | -0.001 | (0.008) | -0.007 | (0.014) | 0.95 |
d'-S2B (spatial working memory)
| 1462 | 0.124 | (0.085) | 0.018 | (0.036) | 0.135* | (0.059) | 0.077 |
d'-V2B (verbal working memory)
| 1394 | 0.053 | (0.073) | -0.004 | (0.032) | 0.017 | (0.051) | 0.87 |
Antisaccade error rate
| 1622 | -0.025 | (0.019) | 0.008 | (0.008) | 0.002 | (0.013) | 0.35 |
Discussion
Our analysis has found that
RGS4 variants exhibit some tentative signals for association with endophenotypes that might be relevant to the pathogenesis of schizophrenia. Common 'risk' alleles of SNP4 and SNP18, the SNPs that have been previously related also to reduced prefrontal cortex volume and function, were associated in the current study with an increase in negative schizotypal personality traits amongst a large population of apparently healthy young male conscripts. Haplotype analysis generally supported this association. An isolated model-specific effect of risk allele A (SNP18) on antisaccade error rate was noted. The SNPs 1 and 7 that were not associated with reduced brain volumes in a previous study [
16] had no effect on cognitive or schizotypy endophenotypes.
Negative schizotypy personality traits in the general population similarly to their illness counterpart (negative symptoms in schizophrenia), are considered to be associated mostly with subtle deficits of prefrontal brain function. This is indicated by the modest but persistent correlation of this schizotypy factor to executive dysfunction and working memory deficits [
38‐
40]. If
RGS4 variants truly contribute to structural and functional alterations of the prefrontal cortex [
16,
17], the observed association of
RGS4 risk variants with negative schizotypy may reflect such an impact on prefrontal function. Furthermore, the dose effect of risk allele A of SNP18 on negative schizotypy and simultaneously on antisaccade error rate (arguably a good index of Dorsolateral Prefrontal Cortex integrity) provides some further support to the notion that
RGS4 risk variants are associated with subtle deficits at the information processing level surveyed by the prefrontal cortex. A similar pleiotropic effect of SNP4 on negative psychotic symptoms and tasks dependant on the integrity of prefrontal function (verbal fluency) was also noted in a recent association study [
9] although these associations did not survive multiple comparison testing. On the other hand, we observed no clear effect of
RGS4 polymorphisms on 2- Back working memory indices of performance as in a recent study [
17], but nevertheless haplotype analyses offered a soft signal of association with spatial working memory.
RGS4 variation was associated with negative rather than positive schizotypy. These two factors may have relatively independent neurocognitive [
40] neurochemical [
41] and genetic [
42,
43] underpinnings. Negative symptoms in schizophrenia lay in an etiological continuum with their personality counterpart [
44] and may have greater familiar, neurodevelopmental and possibly genetic basis than positive ones. Interestingly the first emerging association studies which include clinical and neurocognitive outcomes, also suggest associations of SNP4 or SNP18 with PANSS global psychopathology scores [
9,
45], negative PANSS symptoms and simultaneous association with tasks dependent of prefrontal brain function (verbal fluency) and impaired neurodevelopment (premorbid verbal IQ) [
9].
If RGS4 variation has only modest effects on negative schizotypy endophenotypes, as observed in our study, this may explain the inconsistency of the previous association studies and the lack of a demonstrable strong association with schizophrenia per se, when these studies have been combined in meta-analyses [
14]. Depending on the population mix and the importance that these endophenotypes may play in the disease process, some case-control studies of schizophrenia may show subtle effects with
RGS4 variants, while others may show none. Genetic effects on schizophrenia risk may be very small on average and may require thousands of several tens of thousands of subjects to document or refute convincingly [
46]. Conversely, quantitative traits such as these endophenotypes would be possible to study efficiently with relatively smaller sample sizes. Still, the importance of replicating our results in other cohorts cannot be over-emphasize [
47,
48]. Conclusive results are likely to require considerable sample sizes even for these endophenotypes, and in the presence of population or other heterogeneity, consistent replication may be difficult [
49].
Along these lines, we acknowledge that, several, if not all, of the identified signals in our study might be false positives [
50,
51]. With 4 SNPs and 10 outcome variables, there are 40 sets of analyses performed, even without consideration of haplotypes. However, 2 SNPs were in almost perfect linkage disequilibrium and many of the outcomes also show high correlation with each other. Therefore, the multiplicity of comparisons is far less than implied at first sight. Moreover, our approach was to target variants of a gene that already had some indirect or direct support for involvement in the pathogenesis of schizophrenia and therefore the pre-study probability of significant associations was not negligible as in a hypothesis-free, discovery-oriented approach. Regardless, the interpretation of the modestly significant associations should be conservative. In addition, the Bayes factors obtained for the nominally significant associations are not very strong, but they should not be disregarded, given the prior evidence on potential implication of this genes and variants in related phenotypes.
We should also acknowledge that observed associations may exist due to hitherto unidentified risk variants at RGS4 that are in strong linkage disequilibrium with these four SNPs. All four SNPs in the associated haplotype are non-coding SNPs, but SNPs 1, 4 and 7 are located in a 5' region of the gene that may play a role in transcription regulation.
Another limitation of our study was the considerable rate of non-responders for some items, especially the Schizotypal Personality Questionnaire and the loss of some information due to failure of genotyping in some mouthwash samples. These missing data may have eroded some of the power of the study to detect statistically significant genetic effects. However, there is no reason why genotypes would be missed preferentially in participants with specific phenotypes and indeed we found no hint of such association. Similarly, we found no evidence that phenotype data were missing based on phenotype. Therefore, missing data is unlikely to have generated false-positives.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
NS participated in the study protocol, overall design and execution of the study, first drafted this manuscript. TT organized and performed the bulk of statistical analyses as well as writing up part of this manuscript. DA was involved in data collection, and organizing the genetic arm of the study as well as reviewing this manuscript. NS and IE participated in the study protocol, data collection and dataset organization prior to statistical analyses, and result interpretation of this manuscript. ET contributed to snp, gene identification, background literature organization and preparation of this manuscript. AH participated in part of the statistical analysis and preparation of this manuscript. JI overviewed statistical analysis contributing also to writing part and reviewing this manuscript. CS is responsible for the overall conceptualization and supervision of the ASPIS study. He was involved in the interpretation of the results of this manuscript. All authors have read and approved the final manuscript.