Genetic Polymorphism of GABRR2 Modulates Individuals’ General Cognitive Ability in Healthy Chinese Han People

Abstract

Previous studies have indicated that the cognitive impairment or deficit is associated with GABAergic signaling in central nervous system. Inspired by the finding that receptor GABRR2 modulates concentration of GABA and phasic inhibitory GABAergic transmission in brain. This study investigated to what extent a genetic variant (c.1423C>T, rs282129) of GABRR2 gene modulates individuals’ general cognitive ability in 987 Chinese Han people. Results showed a significant influence of GABRR2 gene polymorphism on individuals’ Raven’s Standard Progressive Matrices (RSPM) performance (F = 3.58, P = .028 by ANOVA and χ ² = 9.35, P = .009 by K–W test, respectively), even if non-genetic factors were partialed out (gender, major, types of birthplace, and socioeconomic index) (B = −.67, SE = .26, t = 2.63, P = .009). The finding provided a strong evidence, to our knowledge, for the view that genetic variant of GABRR2 gene may contribute to the difference of individuals’ general cognitive ability, independently.

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Introduction

Gamma-aminobutyric acid (GABA), the chief inhibitory neurotransmitter in vertebrate central nervous system (CNS), regulates a number of cognitive functions, including attention and working memory via its’ important role in proper cortical and synapse formation during development (Auger and Floresco 2014; Paine et al. 2011). The alterations in cortical GABA function and concentration in CNS (e.g., prefrontal cortex and hippocampus) are reported in a number of psychiatric disorders including, but not limited to, schizophrenia, autism spectrum, depression, and other cognitive disorders (Cellot and Cherubini 2014; Paine et al. 2011; Sanacora and Saricicek 2007). Moreover, experimental animal models with cognitive deficits also demonstrate the disruption of normal GABAeric signaling (Auger and Floresco 2014; Enomoto et al. 2011). Cognitive impairment or deficits is a common clinical feature of these psychiatric disorders. So, it has been speculated that changes related to or involved with GABA function underlie abnormal cognitive abilities observed in these disorders (Modrego et al. 2014). Then, the genetic variants, which are related to influence on GABA functional alterations, should have a close relationship with human cognitive dysfunction (Marenco et al. 2010), as lots of genetic and psychiatric researchers are devoting to investigate.

There are two classes of GABA receptors, GABA_A and GABA_B subunits, the former are ligand-gated ion channels and the latter are metabotropic receptors, which mediates the system’s fast and slow response to GABA, respectively. The previously so-called GABA_C receptors are thought to be homo- or heteropentamers composed of rho1, rho2, and rho3 subunits of GABA_A (Olsen and Sieghart 2008), and been detected in many tissues, including hippocampus and frontal cortex (Alakuijala et al. 2006) where the rho2 subunits has a dominant (a proportion of 85 % of all subunits) expression. It was reported that receptors contribute to functional ionotropic receptors that mediate a component of phasic inhibitory GABAergic transmission at interneurone–Purkinje cell synapses (Harvey et al. 2006), and are involved in human’s cognitive abilities and behaviors. GABRR2 gene encodes GABA receptor subunit rho2 (Zhang et al. 2001). Genetic variants of it were associated with several human psychiatric disorders, and effected on these patients’ cognitive dysfunction, because of its’ close relationship with cortical GABA function and concentration. Ota et al. (2014) discovered that GABRR2 decreasing mRNA level in blood might be an effect of change in GABA concentrations in a follow-up study, and supposed that the progression and treatment of psychotic disorders might be interfered by this gene’s expression (Ota et al. 2013). Sahun’s Down syndrome mouse models also indicated that Gabrr2 gene might contribute to cognitive defects in working and short-term recognition memory (Sahun et al. 2014), they believe that, Gabrr2 and other postsynaptic and component genes have undeniable encompassing dosage-sensitive genes or elements whose genetic variants directly affect learning and memory, synaptic function, and autistic-related behavior. Within a North Indian population, Kumari et al. (2011) reported the potential role of GABRR2 gene polymorphisms (C-1412T, rs2229944) in epilepsy susceptibility. Xuei et al. (2010) also reported the association between GABRR2 polymorphism (C-1423T, rs282129) and alcohol dependence (P = .002) defined by DSM-IV manual. The recent studies demonstrated a potential role for GABRR2 gene’s genetic variants, that is (1) they have an obvious association with human psychiatric disorders, and (2) may contribute to patients’ cognitive defects. However, knowledge about the influence of this gene on human general cognitive ability in random population is still limited, so far.

Here we demonstrated the impact of genetic variant GABRR2 polymorphism on human general cognitive ability with 987 under-graduated students.

Materials and Methods

Participants

Nine hundred and eighty-seven unrelated full-time undergraduate students (41.9 % male, mean age = 19.41 ± 1.03 years, rang 16.3–23.7, and all of them were fresh students) were recruited from Northwest University of China. Participants underwent a multiple-choice questionnaires requirement and had no distinctive clinical abnormalities. Basic demographic information and non-genetic elements, such as participants’ majors, types of birthplace where they grew up, parents’ highest education level, and parents’ occupations, were included in questionnaires. Written informed consents were obtained from each participant. This study was performed in accordance with Declaration of Helsinki and was approved by the Ethics Committee of the College of Life Science of Northwest University of China.

Sociological surveys have shown that socioeconomic status, parents’ highest levels of education, and parent’s occupation partly determine individuals' cognitive ability (Song et al. 2002). Different education style and content, such as Science and Arts chosen by individuals’ interests may impact their cognitive performance tests, as well as their academic performance (Wicherts and Vorst 2010). Here we collected data on socioeconomic status indexes (parents’ levels of education, parents’ occupation), types of birthplace, and majors of students to examine whether an effect of the GABRR2 polymorphism on individual general cognitive ability can be explained by these factors.

Socioeconomic Status Indexes

As Liu et al. (2014) described, the parents’ highest education levels and occupations were considered as one of the most important indicators, and collected with multiple-choice questionnaires, in this study. The choice for highest levels of education was composed of four options: (1) uneducated and primary school, (2) junior high school, (3) high school, (4) high level education, including bachelor’s degree, master’s degree or above. The choice for occupation was composed of six options, roughly corresponding to categories used in the Occupational Classification System of China (Security CLaS 2007): (1) unemployed, (2) farmer, (3) self-employed households, (4) blue-collar worker, (5) entrepreneurs or white-collar worker for private enterprises, (6) White-collar worker for government, public sector, or state-owned enterprises. The occupational prestige of the six aforementioned categories are in ascending order. Participants indicated the highest education level and occupation of their mother and father separately.

Speciality/Major

For the current pool of participants, the arts-comprehensive score and science-comprehensive score of National college Entrance Examination (NCEE) of China were used as an indicator in order to categorize participants into (0) science or (1) arts majors.

The Type of Birthplace

The places, where the participant grew up were classified into two following types: (1) city and (2) country.

Cognitive Test

Standard Progressive Matrices

Revised Raven’s Standard Progressive Matrices (RSPM) for adults in China (1985) were used in the current study to estimate individual’s general cognitive ability. The test time was limited within 30 min. The Raw score of RSPM was collected as individual’s general cognitive ability level, since all participants were in a rather narrow age hierarchy.

Genotyping

Genomic DNA was extracted from hair follicle cells and (or) oral mucosa exfoliative cells by Chelex-100 method. GABRR2 (C-1423T) was amplified by polymerase chain reaction (PCR). The upstream primer, 5′-AAGAAGAAAGGCAAGACAA-3′ and the downstream primer, 5′-AGTGCTGCTCAGGGTGGT-3′ were designed by Primer 3. The PCR reaction system contained 2.5 μl Taq Mix (2×), 0.2 μl primers, about 1 μl DNA sample, and up to 5 μl total system with ddH₂O. A product of 322 bp was amplified with an initial 5 min denaturation at 95 °C, followed by 35 cycles of 94 °C for 30 s, 58.5 °C for 30 s, 72 °C for 40 s, and a final extension at 72 °C for 5 min. Genotyping was performed by single-strand conformation polymorphism (SSCP) method with 14 % polyacrylamide gel electrophoresis in 300 V for 3 h and 180 V for 14 h at 4 °C, which was followed by silver staining. For genotyping, ten randomly selected samples were sequenced by Sanger method to confirm the alleles of genotyping results. And then, these samples, as standard marker, genotyping with other unknown samples by SSCP method. In the end, 816 participants whose genomic samples collected, were genotyped and had a valid Raven score.

Statistical Analysis

PASW statistics 18 (formerly SPSS Statistics; http://www.spss.com.hk/statistics) was used to analyze data. Raven score, as a measurement of individual general cognitive ability performance, were used and analyzed. Hardy–Weinberg equilibrium (HWE) was calculated (df = 1) by Finetti method (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). A one-way analysis of variance (ANOVA) with Bonferroni post hoc test was performed to examine the influence of polymorphisms of GABRR2 on individuals’ cognitive performance. The non-parameter test, Kruskal–Wallis (K–W) H test for scale measurements and median test for ordinal measurements was performed. Significant associations presented with both ANOVA and K–W tests will be considered, to eliminate interference from raw data’s distribution. To verify the effect of genetic polymorphism of GABRR2, even when we partialed out non-genetic factors (gender, speciality, birthplace types, and socioeconomic index), a multiple regression analysis was performed, in which genotype group served as one of the class variables. The procedure was summarized as follow: step 1, entering control variable(s), and step 2, entering both control variable(s) and C-1423T genotypes (0 = TT, 1 = CT and 2 = CC). The correlation of C-1423T and other non-genetic factors also performed. The multiple correction was performed with Benjamini & Hochberg (BH) method with a false discovery rate level of .05 for ANOVA, and Monte Carlo method (n = 1000) for K–W test, respectively. Statistical significance was referred to as P < .05 for two-tailed test. The statistic power analysis about this study was evaluated with (Gauderman and Morrison 2006) power analysis program Quanto (v1.2.4).

Result

Demographic Profile

Totally, 987 unrelated undergraduate students took part in this study (Supplementary Table S1), including 58.1 % female (573) and 2.2 % minority (22) students, with 19.41 ± 1.03 years as average age. Most of the participants (61.7 %) were born and grown up in country, and most of them are arts major (58.6 %). The multiple-choice questionnaires investigation showed that, the farmer and blue-collar worker were main occupations for participants’ parents (49.9 and 21.7 % for fathers, 52.8 and 21.6 % for mothers), high and junior school were the dominant highest education level (40.8 and 30.6 % for fathers, and 32.8 and 33.9 % for mothers). The demographic characters and their relationship with Raven score also were tested with ANOVA method. Most of the non-genetic factors, which included major, types of birthplaces, fathers’ occupations, mothers’ occupations, fathers’ education levels, and mothers’ education levels, had significant impact on individuals’ general cognitive ability (all Ps < .05), except nationality and gender (Supplementary Table S1).

Genotyping and Distribution

Table 1 showed that, 62.6 % of individuals with CC genotype, 32.1 % with CT, and 5.1 % with TT genotype. The allele C had a higher frequency (78.7 %) than that of allele T. No significant deviation from HWE (P > .05) was found for C-1423T polymorphisms. A similar distribution of alleles and genotypes between male and female groups was presented in Table 1, as well. Then, the subjects will be divided into three groups according to genotypes when we determine the effect of polymorphism of C-1423T on cognitive ability.

Table 1.

Genotyping of GABRR2 C-1423T, gender difference, and deviation from Hardy–Weinberg equilibrium (HWE)

	n a	Frequency of alleles		P b	Frequency of genotypes			P b	HWE testc
		T	C		TT	CT	CC
Total	816	348 (21.3)	1284 (78.7)		43 (5.1)	262 (32.1)	511 (62.6)		.21
Male	328	142 (21.6)	514 (78.4)	.79	17 (5.2)	108 (32.9)	203 (61.9)	.92
Female	488	206 (21.1)	770 (78.9)		26 (5.3)	154 (31.6)	308 (63.1)

^a 816 of all participants (987), where genomic DNA and Raven score were available

^b The gender difference test for alleles and genotypes

^c The genotype distribution deviation from HWE test

ANOVA Analysis Effect of Genotypes on Cognitive Measurements

In Table 2, ANOVA with Bonferroni post hoc test and K–W test were used to analyze the association between genetic variants of C-1423T and individuals’ Raven score. It showed that, genotypes of C-1423T were associated with individuals’ Raven score (F = 3.58, P = .028 by ANOVA and χ ² = 9.35, P = .009 by K–W test), significantly. Participants with CC genotype had a significant lower Raven score (51.02 ± 4.33) than T allele carriers (51.55 ± 4.08 for CT and 52.60 ± 3.90 for TT), and TT genotypes with the highest average Raven score. Due to the rare frequency of TT genotype (5.1 %) and because of the likelihood with extremely similar mean cognitive measurements between TT and CT genotype’s groups, the TT and CT genotypes were combined into one group. And then, the genotypes of C-1423T still showed similar effect on Raven score (data not shown).

Table 2.

The association between genotypes of GABRR2 C-1423T and cognitive performance

	n a	Average Raven score for different genotypes			ANOVA		K–W test (df = 2)		Monte Carlo Sig. (99 % CI) c
		TT	CT	CC	F	Sig.b	Chi square	Sig.b
Raven score	816	52.60	51.55	51.02	3.58	.028	9.35	.009	.01 (.01, .01)
Male	328	51.82	51.22	50.97	.341	.711	.77	.681	.68 (.67, .69)
Female	488	53.12	51.77	51.06	4.135	.017	7.06	.029	.03 (.03, .04)

ANOVA one-way analysis of variance; K–W test Kruskal–Wallis test

^a 816 of all participants (987), where genomic DNA and Raven score were available

^b Bold font indicated significantly associated statistic (P < .05)

^c Monte Carlo method to estimate the results of K–W test based on 10,000 sampled tables

Furthermore, hierarchical analysis confirmed the association between polymorphism of C-1423T and individuals’ Raven score (F = 4.14, P = .017 for ANOVA test; χ ² = 7.06, P = .029 for K–W test) in female subjects, rather than in males (Table 2). All positive association still showed a significant correlation with Raven score (all FDR values < .05), after the multiple correction.

Genotype Effect After Controlling for Non-Genetic Factors

Multiple linear regression demonstrated that C-1423T polymorphism still showed a significant association with Raven score (B = −.67, SE = .26, t = −2.63, P = .009), even after we controlled other variables. Combining with genetic polymorphism of C-1423T factor, the final model may contribute to 4 % variation of individual’s Raven score difference, significantly. Additionally, participants’ major also had a significant effect predictive feature for individual’s Raven score (B = 1.03, SE = .32, t = 3.18, P = .002). Predicted individuals’ Raven score by multiple linear regression model which included both genotype of C-1423T and control variables as predictors, also demonstrated significant difference among the three genotype individual’s groups (Supplementary Fig. S1) (F _2/769 = 96.34, P < .001). Correlation analysis also showed that, no significant correlation was found among the C-1423T genotype distribution and nationality, gender, and other non-genetic factors (all Ps > .05) (Supplementary Table S2).

Using the power analysis program Quanto (Gauderman and Morrison 2006), we calculated that we had sufficient power (>90 %) to detect the effect of genotype that accounts for as little as 3 % of the variation in the neurocognitive domains examined at a significance level of .05.

Discussion

Over the past decade, the view that recognizes the importance of nature (genetics) as well as nurture (environment) has been increasingly accepted, and believed that genetic variation makes an important role in differences among individuals in the normal/abnormal range of behavior. Disorders with cognitive ability deficits, may represent variation at the extremes of the same genetic and environmental factors that are responsible for variants within the normal range (Plomin 1999), although most of these genes are difficult to be identified as their small effects (Plomin et al. 1994). Hence, considering the scientific and social implications to investigate the variation difference of human cognitive abilities, with random participants who have normal cognitive abilities, seems more plausible to elaborate the natural influence of human intelligence.

Recent studies revealed that, genetic variants of GABRR2 showed an important role on human cognitive abilities and behaviors (Kumari et al. 2011; Ota et al. 2014; Sahun et al. 2014; Wang et al. 2010; Xuei et al. 2010), within certain special psychiatric patient cohorts, and some in vitro experiments. It was suspected that, alteration of GABRR2 polymorphisms may be contributed to functional ionotropic receptors which can mediate a component of phasic inhibitory GABAergic transmission at interneuron–Purkinje cell synapses in hippocampus and frontal cortex (Liu et al. 2004; Rozzo et al. 2002) which are involved in human’s cognitive abilities and behaviors. In line with these view, our observation that genetic variant of GABRR2 gene accounted for more than 4 % of the variance of individuals’ general intelligence, also suggested that genetic variants of GABRR2 gene made a polymorphism influence on human cognitive ability, not only within patients with cognitive deficit, but also within healthy individuals, as an independent factor.

The polymorphism of GABRR2 (C-1423T) was a nonsynonymous coding SNP that leads to either a methionine or a threonine at amino acid 430 of GABRR2. However, the functional background of it is still insufficient, so far. Our additional in silico analyses and literature tracking indicated that polymorphism of GABRR2 gene (C-1423T) may contribute certain functional change of GABRR2. Firstly, GABRR2 harbors a neurotransmitter-gated ion-channel transmembrane region which included the four transmembrane helices that form the ion-channel complexes that open transiently upon binding of specific ligands, allowing rapid transmission of signals at chemical synapses (Kofuji et al. 1991; Wagner et al. 1991). Secondly, C-1423T was located in a cytoplasmic Topological domain between the third and fourth helix domain of GABRR2 protein predicted by software Phobius (http://phobius.sbc.su.se/index.html), and the alteration of SNP changed a hydrophilic acid (Thr for G allele) to hydrophobic acid (Met for A allele), and may affect the solubility of protein [predicted by SNPeffect 2.0 (Reumers et al. 2005)]. Furthermore, the splicing regulation analysis by ESEfinder 3.0 (Cartegni et al. 2003) and ESRSearch (Fairbrother et al. 2002) also estimated that an exonic splicing enhancer (SF2/ASF) motif was changed by C-1423T. Which may regulate constitutive and alternative splicing, influence the removal of introns, and the joining of exons from mRNA precursors (Goren et al. 2006). Together, as Ota et al. (2014) and Xuei et al. (2010) suspect that, the change of genetic variants of C-1423T may influence the expression status of GABRR2 gene (e.g., mRNA level) and disturb the current cortical GABAergic network, and contribute to the difference variance among individuals' intelligence which had been observed in the current study.

General cognitive ability often referred to as ‘general intelligence’, is one of the most heritable behavioral traits (Plomin and Deary 2014), which typically accounts for 40–50 % of the between-individual variance (Deary 2012). Difference in cognitive abilities among males and females has long been a topic among researchers and scholars, and remains controversial. Mackintosh et al. (2011) also reported that, the sex differences in cognitive abilities are to be found in more narrow domains, that is, the outperform may be observed in different cognitive tasks (e.g., spatial tasks, verbal tasks, and the test score distribution) for males and females. Interesting, the present study also found out that, the genetic variants of GABRR2 gene showed a significant influence on the general cognitive ability in females (F = 4.14, P = .017 for ANOVA test; χ ² = 7.06, P = .029 for K–W test) rather than in males. But one should be cautioned that: (1) the gender-dependent expression of GABA receptor subunits should be considered, which can be altered by individuals’ physiological status (e.g., pregnancy, mental health, etc.) (Bhandage et al. 2015). So, for female, the expression of GABA receptor subunits, including GABRR2, were more sensitive than that of male; (2) brain dimorphisms and the difference cognitive strategies for male and female were another potential influence factors, that females have larger hippocampal formation significantly compared to male (Gonzalez-Gomez et al. 2014), and rely on large-scale integration of neural circuitry during solving the complex non-verbal reasoning tests of the RSPM (Bodizs et al. 2014). Consequently, the gender difference influence of GABRR2 polymorphism on human cognitive abilities will be another interesting topical issue, and help us to understand the basic genetic mechanism of human intelligence.

RSPM, as a reliable measurement of cognitive abilities, because of its independence of language and reading and writing skills, and its simplicity, was widely adopted in numerous research studies (Vanderpool and Catano 2008). It was surprising to find that, the majority of participants also showed a significant influence on participants’ RSPM performance (B = −.67, SE = .26, t = −2.63, P = .009) in our cohort (Table 3), and the Science group has a higher Raven score than that of Arts group (t = −2.44, df = 927, P = .015) (Supplementary Table S1), while no interaction between GABRR2 polymorphisms and individuals’ educational experience (major) was found . At the same time, we also found that, females took a dominant proportion in Arts group in our sample (67.4 % in arts vs. 46.0 % in science, χ ² = 34.6, df = 1, P < .001), as a common phenomenon (Wang and Shi 2012) that more females tend to choose the major of Arts in China. Hence, the influence of major seemed mainly to result from the inexact gender match design in the current sample, and a fine and strict independent sample should be designed, considering participants’ major, gender, and age, in order to clarify this issue, further.

Table 3.

Results of linear regression analysis examining the impacts of the polymorphism and control variables on individuals’ cognitive performance

Predictor	Unstandardized coefficients		Standardized coefficients	t	Sig.
	B	Std. error	Beta
Constant	51.39	1.19		43.08	<.001
Gender	.58	.32	.07	1.81	.071
Major	1.03	.32	.12	3.18	.002
Type of birthplace	−.59	.47	−.07	−1.26	.208
Father’s occupation	.15	.17	.05	.92	.359
Mother’s occupation	.18	.18	.05	.97	.332
Father’s education level	.06	.25	.01	.24	.811
Mother’s education level	−.04	.24	−.01	−.18	.854
rs218190	−.67	.26	−.09	−2.63	.009

Bold values indicate significantly associated statistic (P < .05)

R ² = .042 and adjusted R ² = .032. The assumptions of the linear regression model were met: the cases meeting the selection criterion, Durbin–Watson = 2.02, collinearity diagnostics of predictors, all VIFs <2.44

Our study also has limitations. First, participants included in present study is a rather special population composed of healthy undergraduate students in college, and are of narrow interval age (mean = 19.41 ± 1.03 years, ranged from 16.3 to 23.7), rather than a more representative cohort. Genetic influence on the individual difference in intelligence can thus be revealed for this special population; second, genetic influence of GABRR2 on individual intelligence difference still remained significant, after controlling demographic, sexual, and other non-genetic factors. While the differences were not great among three genotype groups, for instance, the maximum difference of their average Raven score only achieved 2.06 (53.12 for TT and 51.06 for CC in female subjects). Which means that, the ‘ceiling effect’ resulting from single RSPM adoption in one study, should be considered in the future; thirdly, only less than 5 % of the variance in individuals’ intelligence could be explained by the C-1423T polymorphism, alone. Consequently, the larger population sample, reliable measurements, and more effective analysis methods were warranted, although the Quanto program indicated that we have a sufficient power in this study. In addition, further complex interactions between genetic, cognitive ability, and non-genetic factors are still need to established.

Nevertheless, our results demonstrated the importance of GABRR2 gene C-1423T in individual differences on general cognitive ability, thereby providing evidence for the genetic contribution to human complex intelligence and behavior in certain contexts.

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