Gender Differences in Cognitive Ability Associated ... - Karger Publishers

Original Paper Received: August 28, 2009 Accepted after revision: March 16, 2010 Published online: August 14, 2010

Neuropsychobiology 2010;62:221–228 DOI: 10.1159/000319948

Gender Differences in Cognitive Ability Associated with Genetic Variants of NLGN4 Kejin Zhang a, b Xiaocai Gao a, b Hongbin Qi a, b Jing Li c Zijian Zheng c Fuchang Zhang a, b

a

Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, b Institute of Population and Health, and c Institute of Applied Psychology, Northwest University, Xi’an, China

Key Words Neurocognition ⴢ Genetics ⴢ Analysis of variance ⴢ Haplotype ⴢ Intelligence factors

cognitive ability and other intelligence factors. Future research will involve determining the relationship between NLGN4 and personal cognitive ability. Copyright © 2010 S. Karger AG, Basel

Abstract Neuroligin-4 (NL4), encoded by the NLGN4 gene on the X chromosome, is a neuronal-specific brain membrane protein which plays an important role in the formation of functional presynaptic elements and axon specialization. The genetic variants of NLGN4 affect the biological function of NL4, resulting in the manifestation of different psychiatric disorders. The present study investigates the influence of these genetic variants on cognitive performance. The cognitive abilities of 351 subjects were evaluated using the Chinese Wechsler Intelligence Scale Children. The haplotypes were assigned with the PHASE program. The ANOVA method was applied to investigate the relationship between single SNP, the identified target haplotypes and cognitive performance in a random sample. We observed that the XC allele of rs5916271 and XA allele of the re6638575 carriers had significantly higher cognitive ability performances than the noncarrier boys (p ! 0.05). The target haplotype composed of 2 allele (XCA+) carriers also displayed a higher cognitive performance than that of the noncarriers boys. The genetic polymorphism of NLGN4 also had a significant effect on the boys’

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Introduction

Intelligence and cognitive ability are characteristic multifactorial traits, which are influenced by genetic factors. They are highly heritable and are affected by environmental factors [1]. Cognitive ability according to Spearman [2, 3] is composed of a general (g) and several specific factors (s). The ‘g’ factor is involved in all aspects of cognitive ability and can be assessed by using the appropriate psychometric test batteries and follows a normal distribution. Human cognitive ability has a strong genetic base. Its heritability varies from 40 to 80% [1, 4] and increases linearly from childhood to young adulthood [5]. A large number of genes can influence human cognitive ability [6], and a number of candidate genes have been identified. Some of them have been demonstrated to play an important role in the survival and development of neurons, and in the establishment and maintenance of synapse functions during development. Fuchang Zhang Taibai Road, No. 229 Xi’an 710069 (China) Tel./Fax +86 29 8830 3328 E-Mail zhfch @ nwu.edu.cn

Neurexins and neuroligins (NLGNs) are arguably the best-characterized synaptic cell adhesion molecules and they are the only ones for which a specifically synaptic function has been established [7, 8]. They probably function by binding to each other and by interacting with intracellular proteins, although the precise mechanisms involved and their relationship to synaptic transmission remain unclear [9]. Mammals express 4 genes encoding NLGNs, with NLGN3 and NLGN4 in humans localized to the X chromosome. Recent studies have identified mutations in the genes encoding NLGN4 as a cause of autism spectrum disorders, Tourette’s syndrome, learning disability and/ or schizophrenia [10–15]. A positive association between the genetic variants of NLGN4 and nonspecific mental retardation children of the Qinba region in China was also demonstrated in our previous study [16]. The description of the various mutations in NLGN4 seems to provide overwhelming evidence for a role of this gene in cognitive diseases. However, few reports discussed whether these genetic variants of NLGN4 have an effect on the cognitive ability of the individuals and whether they influence gender differences in cognitive abilities in the normal population. In the present study, we assess the effects of genetic variants in the NLGN4 gene on the childrens’ cognitive ability. We used the analysis of variance (ANOVA) method on a random population in the Qinba region in China. Materials and Methods Participants All subjects were recruited from the Qinba region of the Shaanxi province (Western China). The region is closed and isolated because of its difficult living conditions (average elevation 750–1,500 m). According to the random principle, 1 600 unrelated children (6–14 years) without clinical disorders/disabilities or history of genetic diseases were chosen from 7,487 children based on household registration cards of several villages in the Qinba region. From the 600 unrelated children initially chosen, we only got 351 whose DNA samples and cognitive ability performances were both available. All subjects were informed and consents were obtained from their parents or guardians. The protocol was reviewed and approved by the ethics committee of the National Human Genome Center. All subjects were of Han Chinese origin. Cognitive Testing All subjects in our study received a battery of standardized cognitive measures as indicated by the Chinese Wechsler Intelligence Scale Children (C-WISC). The C-WISC is a revised edition of the Wechsler Intelligence Scale for Children-Revised (WISCR) by Gong and Cai in 1993 [17]. It is the most widely used indi-

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Neuropsychobiology 2010;62:221–228

vidually administered measure of intelligence in children and adolescents among the Chinese people. The C-WISC contains 11 subtests, which comprise information, similarities, arithmetic, vocabulary, comprehension, digit span, picture completion, picture arrangement, block design, object assembly, and coding/ maze. Previous analytical studies applying the C-WISC also involved tests using demonstrative schemes such as verbal comprehension (VC), perceptual organization (PO) and memory/concentration ability (MC). The individual’s general cognitive ability (g) and factors (including VC, PO and MC) can be assessed by using the full scale of C-WISC and evaluated by certain subtests [18]. The assessments are administered by a trained tester assigned to an individual subject. Variant Identification and Genotyping Genomic DNA was extracted from peripheral blood mononuclear cells using a modified standard phenol/chloroform extraction method and stored at –20 ° C for genotyping. NLGN4 harbors 19 single-nucleotide polymorphisms (SNPs) (mean distance = 826 bp) in X chromosomal positions 5685–5667 kb. There were 5 targeted SNPs (rs5916271, rs7049300, rs6638575, rs3810686 and rs1882260) with minor allele frequency 15% in the CHB + JPT population (JPT: Japanese in Tokyo, Japan; CHB: Han Chinese in Beijing, China) identified by using data from the Hap Map Genome Browser (phase 3) (http://www.hapmap.org/ cgi-perl/gbrowse ) and Haploview 4.0 software [19]. The 5 SNPs were identified in the same block by high intermarker LD using Haploview software with the default (Dⴕ 1 0.80) set to the solid spine method. This insured a higher heterozygosity and strong LD among the 5 SNPs. The rs5916271 SNP was located in intron 4, rs7049300 in exon 5, rs5538575 in intron 5, rs3810686 in the 3ⴕ UTR region and rs1882260 in the 3ⴕ UTR region of exon 6 (fig. 1). The rs5916271, rs7049300 and rs1882260 SNPs were genotyped using the restriction fragment length polymorphism (RFLP) method. Polymerase chain reaction single strand conformation polymorphism (SSCP) and sequence analysis methods were used to genotype the rs6638575 and rs3810686 SNPs. Polymerase chain reactions (PCR) were carried out in 96-well microliter plates with each well containing a 5-l reaction mixture [20 m M (NH4)2SO4, 75 m M Tris-HCl (pH 8.8 at 25 ° C), 0.01% Tween 20, 0.2 m M dNTPs, 2.5 mM Mg Cl2, 0.3 M of primer, 20 ng DNA and 1 U Taq DNA polymerase) (MBI, USA)]. The PCR protocols included an initial 2-min denaturation at 95 ° C, 29 cycles at 94 ° C for 30 s, annealing temperature for 30 s (the annealing temperatures needed for each pair of primers are listed in table 1), 72 ° C for 30 s and a final extension period of 2 min at 72 ° C using the Mastercycler gradient 5531 PCR System (Eppendorf, Germany). PCR products were digested with the appropriate restriction enzymes for the RFLP method or denatured by using the following buffer (95% formamide, 0.025% xylene-cyanole, 0.025% bromophenol blue, 10 mM EDTA, pH 8.0) at 97 ° C for 5 min and snapchilled on ice for at least 10 min. The samples were then loaded onto 11% nondenaturing polyacrylamide gels (29: 1 acrylamide to bisacrylamide) containing 0.5! TBE (0.045 M Tris-borate, 0.001 M EDTA, pH 8.0). Electrophoresis was kept at a constant temperature (4 ° C) with a cooling unit and was carried out in a vertical unit at 11 V/cm for 15 h. The individual’s genotype was confirmed by silver staining and gel imaging with a Bio-Rad imaging instrument. Randomly selected PCR products that exhib

Zhang /Gao /Qi /Li /Zheng /Zhang

E6

E5

E4

E3

E2

E1 NLGN4

rs3810686

rs3810687

rs3810688

rs6638575

rs5961884

rs5961379

rs4463569

rs4358933

rs12837799

rs5915620

rs17315232

rs7049300

rs11094874

rs6529896

rs5916271

Block 1 (17 kb) 2 3 4

5

6

7

8

9

10

11

12

13

15

17

18

20

21

23

rs1882260

rs5916269

1

rs5915618

rs5961378

18 kb

92

95

58 58

92 95

48 58

92 58 92 56 65 65

Fig.1. Schematic picture of the haploblock structure of NLGN4 and flanking regions of its fifth and sixth exons in the CHB + JPT sample (chromosomal region spanning from positions 5685 kb to 5667 kb, 19 SNPs) in Haploview. The haploblock structure was generated using solid spine of linkage disequilibrium (LD), with the default setting of Dⴕ >0.80. Five of the SNPs were chosen within the region (rs5916271, rs7049300, rs6638575, rs3810686, and rs1882260) indicated by a frame around the SNP.

ited different migration patterns on gels were selected for sequencing to confirm their polymorphisms and genotypes. To check for genotyping errors, 10 DNA samples were randomly selected from each SNP result and regenotyped by the sequencing method. Additionally, genotypic samples which failed to sequence by RFLP or SSCP methods were regenotyped. After performing these methods, we observed that all genotypes were identical to those obtained from the first round of genotyping. Procedure According to the double-blind trial principle, a research group made up of geneticists was blinded to the subjects’ characteristics (the 5 SNP polymorphisms of the NLGN4 gene identified using PCR-SSCP, PCR-RFLP and sequencing methods). Another research group, consisting of experienced psychologists who were

The Genetic Effect of NLGN4 on Cognitive Ability

blinded to the subjects’ genotypes, evaluated the cognitive abilities of each child. Our study consisted of 351 children, including 175 girls (9.29 8 2.26 years) and 176 boys (9.29 8 2.25 years), whose complete cognitive scores and the genotyping information were readily available at the time of the study. Statistical Analysis Microsoft Visual Foxpro 9.0 and SPSS 16.0 (SPSS Inc., Chicago, Ill., USA) was used for data analysis and for performing statistical analysis. The Kolmogorov-Smirnov method was appplied to calculate the deviation from normal distribution of the children’s cognitive performance. The F test was used to compare the boy and girl groups. PHASE (2.1) was utilized to estimate haplotype frequencies, using an algorithm method to calculate maximum likelihood estimates of haplotype frequencies given a geno-


223

Table 1. The related information of 5 SNP genotypings

SNPs

Primers

Method

Enzymes

Allele

Size, bp

rs5916271

forward: reverse:

AACCATCTACTGCTTAGGCGTC TAGGGAGTGTCCAGAGTTTGCT

RFLP

MspI

A/C

315

rs7049300

forward: reverse:

CTCCTTGTAGTTCTTGTTCCGCA GCATTTCTGTCCTGTGGGTTTT

RFLP

BstXI

A/G

271

rs6638575

forward: reverse:

TGAAGGTAAAAAGATGAAAGCA GGGAAGTGTTATCCTAAAAGAGTC

SSCP

–

A/G

295

rs3810686

forward: reverse:

AAGTGTCCTTGGCTGAGTTTC GACCCTTATCGTTGGTGTTTT

SSCP

–

C/T

211

rs1882260

forward: reverse:

TAAATCTCTCCGCTAAAGTGG CTGTAGGAAAGAAATGTTGC

RFLP

BSP-1407I

C/T

289

Table 2. Characteristics of subjects and their average scores of cognitive ability

n

Boys Girls Total K-S Z test (sig.) p value

176 175 351

Age, years

9.2982.25 9.2982.26 9.2982.25

Average scores of cognitive ability g

VC

PO

M/C

85.11819.67 81.87819.58 83.50819.67 1.045 (0.225)

89.02819.39 86.40818.58 87.72819.02 0.986 (0.285)

89.62814.99 85.71815.14 87.68815.17 1.269 (0.080)

76.72823.67 75.82823.45 76.27823.54 1.309 (0.065)

0.085

0.152

0.007

0.693

Fig ures are means 8 SD. The deviations from normal distribution were tested by the Kolmogorov-Smirnov (K-S) method in the boy and girl groups. p value: the F test for cognitive performance score differences between boys and girls.

type which does not specify the phase [20]. The subjects’ haplotypes with a confidence of 695% were included. For each polymorphism, the Hardy-Weinberg equilibrium was tested according to the method of Finniti [21]. The differences in allele distribution between different genders were assessed by the Monte Carlo method using CLUMP 2.3 with 10,000 simulations [22]. The variation in the quantitative traits of psychometric scores was compared according to genotypes and haplotype combination using ANOVA yielding an F test. Statistical significance was referred to as p ! 0.05. The multiple test correction was performed in haplotype analysis using the Bonferrioni method.

available at the time of the study. The KolmogorovSmirnov test indicated that the cognitive performance ability conformed to a normal distribution among boys and girls. In addition, the F test showed that the boys’ average PO performance was significantly higher than that of the girls (table 2). This may be attributed to the gender difference in WISC scores for children [23] and the educational disadvantage condition for girls in this specific region [24]. It was reported that boys have a better PO performance than girls and more educational advantage in this Qinba region.

Results

Subject Characteristics There were 351 children (175 girls: 9.29 8 2.26 years; 176 boys: 9.29 8 2.25 years) who had the integrated genotyping data and the cognitive ability performance 224


The Allelic Distribution of Target SNPs The allelic distribution of target SNPs is described in table 3. Because NLGN4 is located on the X chromosome, boys have only 1 copy of each SNP allele and therefore the Hardy-Weinberg equilibrium is only valid in girls. Five Zhang /Gao /Qi /Li /Zheng /Zhang

Table 3. The alleles and frequencies of single SNPs

SNPs

n

The frequencies of alleles and genotypes of SNPs allele

HWE p value

genotype

145 153

XA 235 (0.81) 132 (0.86)

XC 55 (0.19) 21 (0.14)

XAXA 97 (0.67)

XAXC 41 (0.28)

XCXC 7 (0.05)

0.335

rs7049300 Girls Boys

149 144

XA 28 (0.09) 19 (0.13)

XG 270 (0.91) 125 (0.87)

XAXA 2 (0.01)

XAXG 24 (0.16)

XGXG 123 (0.82)

0.618


159 160

XA 74 (0.23) 22 (0.14)

XG 244 (0.77) 138 (0.86)

XGXG 6 (0.04)

XGXA 62 (0.39)

XAXA 91 (0.57)

0.246


154 149

XT 202 (0.66) 104 (0.70)

XC 106 (0.34) 45 (0.30)

XTXT 70 (0.45)

XTXC 62 (0.40)

XCXC 22 (0.14)

0.180


168 162

XT 259 (0.77) 138 (0.85)

XC 77 (0.23) 24 (0.15)

XTXT 97 (0.58)

XTXC 65 (0.39)

XCXC 6 (0.04)

0.218


The Hardy-Weinberg tests were performed only in girls for the location of SNPs in the X chromosome.

SNPs were consistent with the Hardy-Weinberg equilibrium (p 1 0.05). Genotypic Effects on Cognitive Performance In table 4, we discuss the effects of the SNPs presented in NLGN4 on the scores of the C-WISC. One-way ANOVA was performed to determine whether the genotypes and alleles of single SNPs affected the cognitive abilities (table 4). We observed that 2 of the SNPs had significant effects on the performance of boys. For rs5916271, the average VC performance of the XC carriers boys were significantly higher than that of non-carrier boys (102.43 8 16.5 and 93.73 8 16.78, p ! 0.05). Furthermore, for rs6638575, the X A allele carriers had a higher g average performance (97.64 8 14.37 and 90.21 8 16.22, p ! 0.05) and their average VC performance was significantly higher than that of the noncarriers in the boy group (101.36 8 15.7 and 93.48 8 16.64, p ! 0.05). However, no other significant differences were found in the 5 SNPs among the girls (data not displayed). Target Haplotype Effects on Cognitive Performance Five SNPs were located in 1 identified block with strong linkage disequilibrium (fig. 1). Therefore, the haplotypes in combination with rs5916271 and rs6638575 The Genetic Effect of NLGN4 on Cognitive Ability

were estimated by the PHASE program. The subjects’ genotypes were categorized into the haplotype XCA carrier (XCA+) and noncarrier (XCA–) groups (table 5). The effects of haplotypes on cognitive performance were evaluated using an ANOVA. The boys with XCA+ had a higher cognitive ability (including the g, VC, PO and MC) than the noncarrier ones (p ! 0.05). Also, no significant results were found among the girls.

Discussion

In the present study, 5 SNPs were chosen to assess the effects of the gene on cognitive ability in children from the Qinba region. The 2 target SNPs, rs5916271 and rs6638575, were detected using single-SNP genotype analysis. The boys, harboring the XC alleles of rs5916271 or the X A alleles of rs6638575, showed significantly higher cognitive performances as indicated by their g and VC scores (p ! 0.05) when compared to the others. Most of the SNPs are bialleles. So their polymorphism information content is very limited. The haplotypes combined by 62 SNPs have a higher power to capture underlying causes than single SNPs [25], but the multiple haplotypes may reduce the statistical power beNeuropsychobiology 2010;62:221–228

225

Table 4. The quantitative effects of the single SNP on cognitive ability in boys

Polymorphisms and genotypes

g

VC

PO

mean 8 SD

F

Rs5916271 Boys XAY XCY

90.41816.25 97.62815.63

3.60 0.06

93.73816.78 4.90 0.03 102.43816.5

93.31812.81 2.38 0.13 98.00813.80

82.62820.74 1.45 0.23 88.33816.03

Rs7049300 Boys XGY X AY

91.20815.71 92.53817.31

0.11 0.74

94.70815.75 0.01 0.91 94.21821.00

94.13813.12 0.01 0.94 94.37810.77

83.37817.95 1.20 0.28 88.42823.43

Rs6638575 Boys XAY X GY

97.64814.37 90.21816.22

4.10 0.04

101.36815.7 4.32 0.04 93.48816.64

97.73814.98 2.41 0.12 93.16812.47

89.55812.16 2.08 0.15 83.32819.65

Rs3810686 Boys XTY XCY

91.22816.05 91.33815.91

0.00 0.97

94.38817.70 0.04 0.85 94.93815.88

93.38813.17 0.14 0.71 94.25812.92

84.00820.68 0.09 0.77 83.00818.44

Rs1882260 Boys XTY XCY

94.83814.10 90.35816.54

1.56 0.21

99.00815.40 2.19 0.14 93.50817.00

94.46813.09 0.10 0.75 93.54812.97

89.08811.91 2.13 0.15 83.07819.53

p

mean 8 SD

F

p

mean 8 SD

M/C F

mean 8 SD

p

F

p

Significant statistical associations are indicated in italics. XGY = The boys’ genotype, with single allele XG.

Table 5. Impact of target haplotypes on cognitive ability and oth-

er intelligence factors in boys Average scores of each haplotype (mean 8 SD)

g VC PO MC

XCA+

XCA–

F

p

102.2815.80 105.3817.47 102.5812.50 94.42811.86

89.97814.43 93.44816.34 92.82812.48 82.62818.63

6.78 5.81 6.67 4.63

0.01 0.02 0.01 0.03

Significant statistical associations are indicated in italics. XCA+ = XC-A haplotype carrier; XCA– = XC-A haplotype noncarrier.

cause a large number of haplotypes need to be tested, thus increasing the number of degrees of freedom. Therefore, we only analyzed the effects of the target haplotype (XCA) on cognitive ability. Our analysis suggested that the XCA+ carrier boys had significantly higher general cognitive ability g, VC, PO and M/C performances than the noncarriers. These results indicated that the genetic variants of NLGN4 may influence the boys’ cognitive abilities.

226


Neurexins and NLGNs are thought to form proteins. By database analysis, Jamain et al. [26] indentified human NLGN4, which encodes an 873-amino acid protein containing a signal peptide, esterase domain, transmembrane domain and PDZ domain-binding motif. Massive reports indicated that, the mutations of NLGN4 can cause mental retardation, autism, Tourette’s syndrome and attention deficit-hyperactivity disorder with or without deficient cognitive abilities by changing synaptic function [10–12, 27]. As a result, we reasoned that the genetic variants of NLGN4 may affect cognitive ability in humans. Although the two target SNPs, rs5916271 and rs6638575, were synonymous SNPs and had no specific function, our results may indicate the underlying cause between the genetic variants of functional region of NLGN4 and personal cognitive ability. Moreover, we also analyzed whether or not the two SNPs were located near an mRNA splicing site through an in silico analysis using GeneSplicer Web Interface (developed by the Institute for Genomic Research (TIGR) http://www.cbcb.umd.edu/ software/GeneSplicer/gene_spl.shtml) [28]. We observed that they were not. Another possibility is that the target haplotype in combination with rs5916271 and rs6638575 may be in linkage disequilibrium and possess a functionZhang /Gao /Qi /Li /Zheng /Zhang

al polymorphism located elsewhere in NLGN4. These polymorphisms can influence the biological function of NLGN4 or the expression of NLGN4. The effect of these possibilities mentioned above can account for the differences in individual cognitive ability, but further investigation is warranted. The effects of NLGN4 on individual cognitive abilities showed an obvious gender difference. It had a significant effect on the boys’ cognitive performance when compared to the girls’. This may be due to two factors which should be taken into consideration. First, the NLGN4 gene is located on the X chromosome and is presented as a haploid allele in males. As a result, both genders may display the same allelic effect associated with the variants. Lawson-Yuen et al. [12] also observed this similar phenomenon, that is, a female who was a carrier of risk allele of NLGN4 had milder symptoms than that of a male. Second, population stratification is also an important element in this study. For instance, a significant difference in allelic distribution was found between both genders. Because all subjects were recruited from a closed and isolated region, we also compared SNPs alleles’ frequencies between this study and the CHB sample. We did

not observe a significant difference in the rs5916271 and rs6638575 SNPs. Therefore, this may suggest that gender differences may not result from the influences of population stratification. There are several limitations to our study. The sample population was small for a genetic association study. In particular, our small sample population may not be sufficient for effectively analyzing every individual haplotype in our study. Additionally, the imbalance of allelic frequencies in the different SNPs between the genders may be a limitation. This may indicate that heterogeneity may influence the interpretation of our results in our sample. For future studies, a larger sample size and a targeted stratagem are needed. This may lead to the analysis of additional SNPs which can be used to further investigate the functional regions of NLGN4. In conclusion, our results indicate that the genetic variants located in NLGN4 can affect the cognitive abilities of boys’ in the Qinba region of China. We further suggest that NLGN4 may be a candidate gene that plays a role in cognitive ability. Further work is required to investigate the mechanisms by which NLGN4 may affect individual cognitive ability.

References 1 McClearn GE, Johansson B, Berg S, Pedersen NL, Ahern F, Petrill SA, Plomin R: Substantial genetic influence on cognitive abilities in twins 80 or more years old. Science 1997;276: 1560–1563. 2 Spearman C: ‘General intelligence’ objectively determined and measured. Am J Psychol 1904;15:93. 3 Spearman C: The Abilities of Man. New York, Macmillan, 1927. 4 De Geus EJ, Wright MJ, Martin NG, Boomsma DI: Genetics of brain function and cognition. Behav Genet 2001;31:489–495. 5 Haworth CM, Wright MJ, Luciano M, Martin NG, de Geus EJ, van Beijsterveldt CE, Bartels M, Posthuma D, Boomsma DI, Davis OS, Kovas Y, Corley RP, Defries JC, Hewitt JK, Olson RK, Rhea SA, Wadsworth SJ, Iacono WG, McGue M, Thompson LA, Hart SA, Petrill SA, Lubinski D, Plomin R: The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry 2009. Epub ahead of print. 6 Morley KI, Montgomery GW: The genetics of cognitive processes: candidate genes in humans and animals. Behav Genet 2001; 31: 511–531. 7 Craig AM, Kang Y: Neurexin-neuroligin signaling in synapse development. Curr Opin Neurobiol 2007;17:43–52.

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8 Dean C, Dresbach T: Neuroligins and neurexins: linking cell adhesion, synapse formation and cognitive function. Trends Neurosci 2006;29:21–29. 9 Sudhof TC: Neuroligins and neurexins link synaptic function to cognitive disease. Nature 2008;455:903–911. 10 Jamain S, Quach H, Betancur C, Rastam M, Colineaux C, Gillberg IC, Soderstrom H, Giros B, Leboyer M, Gillberg C, Bourgeron T: Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat Genet 2003;34:27–29. 11 Laumonnier F, Bonnet-Brilhault F, Gomot M, Blanc R, David A, Moizard MP, Raynaud M, Ronce N, Lemonnier E, Calvas P, Laudier B, Chelly J, Fryns JP, Ropers HH, Hamel BC, Andres C, Barthelemy C, Moraine C, Briault S: X-linked mental retardation and autism are associated with a mutation in the NLGN4 gene, a member of the neuroligin family. Am J Hum Genet 2004;74:552–557. 12 Lawson-Yuen A, Saldivar JS, Sommer S, Picker J: Familial deletion within NLGN4 associated with autism and Tourette syndrome. Eur J Hum Genet 2008;16:614–618.

13 Macarov M, Zeigler M, Newman JP, Strich D, Sury V, Tennenbaum A, Meiner V: Deletions of VCX-A and NLGN4: a variable phenotype including normal intellect. J Intellect Disabil Res 2007;51:329–333. 14 Talebizadeh Z, Lam DY, Theodoro MF, Bittel DC, Lushington GH, Butler MG: Novel splice isoforms for NLGN3 and NLGN4 with possible implications in autism. J Med Genet 2006;43:e21. 15 Yan J, Oliveira G, Coutinho A, Yang C, Feng J, Katz C, Sram J, Bockholt A, Jones IR, Craddock N, Cook EH Jr, Vicente A, Sommer SS: Analysis of the neuroligin 3 and 4 genes in autism and other neuropsychiatric patients. Mol Psychiatry 2005;10:329–332. 16 Qi H, Xing L, Zhang K, Gao X, Zheng Z, Huang S, Guo Y, Zhang F: Positive association of neuroligin-4 gene with nonspecific mental retardation in the Qinba mountains region of China. Psychiatr Genet 2009; 19: 1–5. 17 Gong YX, Cai TS: Wechsler Intelligence Scale for Children, Chinese Revision (CWISC). Changsha, Hunan Map Press, 1993. 18 Tang CS, Lau BH, Chang SS: Factor structure of the Chinese version of the WAIS-R for Chinese adults in the lowest percentiles of IQ. J Clin Psychol 1996;52:345–355.


227

19 Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 263–265. 20 Stephens M, Scheet P: Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am J Hum Genet 2005;76:449–462. 21 Sasieni PD: From genotypes to genes: doubling the sample size. Biometrics 1997; 53: 1253–1261. 22 Stephens M, Smith NJ, Donnelly P: A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 2001;68:978–989.

228

23 Allen LM, Nelson CJ, Rouhbakhsh P, Scifres SL, Greene RL, Kordinak ST, Davis LJ Jr, Morse RM: Gender differences in factor structure of the self-administered alcoholism screening test. J Clin Psychol 1998; 54: 439–445. 24 Zheng Z, Shong HE, Zhang FC, Li J, Li TB: The applied analysis of combined Raven’s test in the research of the children’ IQ in Qinba mountain area. J Northwest Univ (Nat Sci Ed) 1999;29:3. 25 Knight J, Curtis D, Sham PC: Clumphap: a simple tool for performing haplotype-based association analysis. Genet Epidemiol 2008; 32:539–545.


26 Jamain S, Radyushkin K, Hammerschmidt K, Granon S, Boretius S, Varoqueaux F, Ramanantsoa N, Gallego J, Ronnenberg A, Winter D, Frahm J, Fischer J, Bourgeron T, Ehrenreich H, Brose N: Reduced social interaction and ultrasonic communication in a mouse model of monogenic heritable autism. Proc Natl Acad Sci USA 2008;105:1710–1715. 27 Chih B, Afridi SK, Clark L, Scheiffele P: Disorder-associated mutations lead to functional inactivation of neuroligins. Hum Mol Genet 2004;13:1471–1477. 28 Pertea M, Lin X, Salzberg SL: Genesplicer: a new computational method for splice site prediction. Nucleic Acids Res 2001;29:1185– 1190.

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