Abstract
Background
Autism is a severe neurodevelopmental disorder characterized by social interaction deficits, impairments in communication, and restricted and repetitive stereotyped behavior and activities. Family and twin studies suggested an essential role of genetic factors in the etiology of autism spectrum disorder (ASD). Also, other studies found SORCS3 and GSDME (DFNA5) might be involved in brain development and susceptible to ASD.
Methods
In this study, 17 genome-wide significant SNPs reported in previous ASD genome-wide association studies (GWAS) and 7 SNPs in strong linkage disequilibrium with known ASD GWAS hits were selected to investigate the association between these SNPs and autism in the Han Chinese population. Then, 10 tagSNPs in SORCS3 and 11 tagSNPs in GSDME were selected to analyze the association between these SNPs and autism. The selected 24 SNPs and tagSNPs were genotyped using the Agena MassARRAY SNP genotyping assay in 757 Han Chinese autism trios.
Results
Rs1484144 in NAA11 was significantly associated with autism; significance remained after the Bonferroni correction (P < 0.0022). Also, rs79879286, rs12154597, and rs12540919 near GSDME, as well as rs9787523 and rs3750261 in SORCS3, were nominally associated with autism.
Conclusion
Our study suggests that rs1484144 in NAA11 is a significant SNP for autism in the Han Chinese population, while SORCS3 and GSDME might be the susceptibility genes for autism in this population.
Keywords: Autism, Family-based association, Genome-wide significant SNPs, GSDME, SORCS3, NAA11
1. Introduction
Autism is the most severe neurodevelopmental phenotype of autism spectrum disorder (ASD) [1,2], characterized by impaired social interaction, communication disorder, and repetitive and restrictive behavioral activities [3]. The global prevalence of ASD was about 1 %, and the boy-to-girl ratio is 4:1 [3,4]. Family and twin studies indicated an important role of genetic factors in the etiology of ASD, whose heritability is 80%–93 % [4,5]. Common genetic variants, particularly single-nucleotide polymorphisms (SNPs), contribute to the majority of genetic risk of ASD, and among common variants the genetic risk of single-nucleotide polymorphisms (SNPs) was to almost 50 % [5,6].
Genome-wide association studies (i.e., GWAS) help scientists identify genes associated with a particular disease by exploring the set of DNA of a large group of people and searching for SNPs [7]. Thus far, several candidate genes, including RELN, MECP2, OXTR, CNTNAP2, EN2, and GABA receptor subunits (GABRB3, GABRA5, and GABRG3) have been identified in patients with autism [[8], [9], [10], [11], [12], [13]]. These genes have been repeatedly verified in several independent studies with large sample sizes [13], while some have been tested using GWAS. In European populations, GWAS found that SNPs in CDH10, MSNP1AS, and MACROD2 are significantly associated with ASD and rs4307059 near MSNP1AS has been associated with autism in Han Chinese people [14]. In addition, studies have suggested that some genetic loci might be involved in the pathogenesis of autism in different populations [14,15]. Furthermore, four autism-related GWAS with large sample sizes recently found some new genome-wide positive loci significantly associated with ASD [[16], [17], [18], [19]]. Grove et al. compared 18,381 ASD patients with 27,969 controls and found that rs71190156 in MACROD2, rs111931861 in KMT2E, rs910805 near XRN2, rs10099100 near SOX7, and rs201910565 near PTBP2 were significantly associated with ASD [16]. Furthermore, they used Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWAS of different traits, revealed 4 loci, including rs1620977 in NEGR1 in patients with ASD, compared to those with major depression (111,902 patients with major depression and 312,113 controls) [16]. Another MTAG on ASD and educational attainment (328,917 persons on average) found that rs11787216 near MROH5 and other 2 loci were associated with ASD and educational attainment of the European population at the genome level [16]. A previous study analyzed the GWAS data of Psychiatric Genomics Consortium (PGC) ASD and schizophrenia and found that 12 new loci, including rs880446 near EEF1A2, had genome-wide significance [17]. Another GWAS analyzed six psychiatric disorders of Integrative Psychiatric Research (iPSYCH), revealing that 4 independent loci, including rs4322805 near PDE1A, are significant at the genome level [18]. In addition, PGC analyzed eight psychiatric disorders and found that 23 novel loci, including rs9787523 in SORCS3 and rs79879286 near GSDME (DFNA5), are significantly associated with at least four psychiatric disorders (including ASD) [19]. Yet, the association between these variants and autism in the Han Chinese population remains unclear.
Herein, we performed a family-based association study on 757 Han Chinese autism trios to further explore whether these ASD-related positive SNPs in European and North American populations contributed to the etiology of autism in the Han Chinese population. We found SNPs in NAA11, SORCS3, and near GSDME associated with autism in the Han Chinese population. SORCS3 is a vacuolar protein sorting 10 receptor family member with an important role in neuronal signal transduction and synaptic inhibition regulation [[20], [21], [22]]. The expression level of SORCS3 in the brain increases during early embryonic development and remains high after birth suggested on Human Brain Transcriptome (HBT) database (http://hbatlas.org/). Also, some studies suggested that SORCS3 might be involved in brain development [[20], [21], [22], [23]]. Genetic studies showed that some SNPs in SORCS3 were associated with neuropsychiatric disorders or related characteristics such as autism, schizophrenia, attention-deficit/hyperactivity disorder, Alzheimer's disease, and intelligence [[24], [25], [26], [27], [28]]. GSDME is a pore-forming protein that mediates pyroptosis [29,30]. Previous studies suggested that GSDME has an important role in immunity and neural development by mediating pyroptosis [[29], [30], [31], [32], [33], [34], [35]]. Similar to SORCS3, the expression level of GSDME in the brain increases during early human embryonic development and remains high after birth, suggesting that this gene is also involved in brain development on HBT databese. GSDME is a GSDMD-related family member that shares approximately 28 % identity with the GSDMD N-terminal domain corresponding to pyroptosis. These two proteins could be specifically cleaved by caspase-3, inducing pyroptosis [36]. The central nervous system cell pruning decreases in the brains of Gsdmd-deficient mice, which suggests that pyroptosis is involved in sculpting the brain [37]. Genetic studies have also suggested that a few SNPs in GSDME were associated with several neuropsychiatric disorders, such as schizophrenia, major depression, and bipolar disorder [23,[38], [39], [40], [41], [42], [43], [44]].
Replication helps ensure that a genotype-phenotype association observed in a GWA study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. Our replication results and these previous mass researches indicated that SORCS3 and GSDME might be associated with autism, which needs further evidence. Furthermore, we selected some tagSNPs to investigate the association between SORCS3, GSDME, and autism in 757 autism trios of Han Chinese ancestry to explore the association between autism and these two genes.
2. Material and methods
2.1. Participants
A total of 757 Han Chinese autism trios (757 autistic children and their biological parents) were recruited from Peking University Sixth Hospital (Beijing, China) in this study. The sex ratio of autistic children was about 7.2:1, including 665 boys (87.8 %) and 92 girls (12.2 %). The median age of diagnosis was 4.83 (upper quartile 3.50 to lower quartile 6.43) years. In addition, these 757 trios included the 640 Han Chinese autism trios, which were assessed in a previous GWAS [14]. This study found that the reported rs4307059 near MSNP1AS in the European population is also a susceptibility variant for autism in the Han Chinese population.
Only individuals with typical symptoms of autism were selected to reduce heterogeneity. The inclusion criteria were: (1) children who met the Diagnostic and Statistical Manual of Mental Disorders (fourth edition) criteria and were independently diagnosed by two senior child psychiatrists [2]; (2) autistic children and their biological parents of Han Chinese ethnicity; (3) children affected with autism <18 years old with no family history of neuropsychiatric and other genetic diseases; (4) Autism Behavior Checklist score ≥53 [45]; (5) Childhood Autism Rating Scale score ≥36 [46]. The exclusion criteria were: (1) children with Asperger syndrome or pervasive developmental disorder not otherwise specified; (2) children with phenylketonuria, fragile X syndrome, or other neurological diseases; (3) children with severe physical disease; (4) children carrying abnormal karyotypes.
2.2. Selection of replication SNPs
PubMed (https://www.ncbi.nlm.nih.gov/pubmed) and GWAS catalog (http://www.ebi.ac.uk/gwas/) were used to search GWAS on autism using the keywords “genome-wide association study” OR “GWAS” AND “autism” OR “psychiatric disorder”. Then, the autism-GWAS hits not studied in the Han Chinese population were selected.
The genotype data of SNPs in the Han Chinese general population referred to the Ensembl GRCh37 Release 93 (http://grch37.ensembl.org/index.html), the dbSNP in the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/snp), and the Han Chinese Genomes Database (https://www.biosino.org/pgghan2/index). The SNPs with minor allele frequency (MAF) in Han Chinese Beijing (CHB) > 0.05 were included in the study. The SNPs with no association (P < 0.05) with ASD in PGC (http://www.med.unc.edu/pgc/results-and-downloads) were excluded from this study.
2.3. Selection of tagSNPs in SORCS3 and GSDME
Priority selection was performed as follows: (1) SNPs located in the functional regions of genes [e.g., promoter region, 5′ untranslated region (UTR), exon, and 3′ UTR region]; (2) SNPs found to be associated with autism or autism-related phenotypes in previous studies; (3) SNPs with MAF in CHB being >0.05; (4) considering the physical location of SNPs in genes and then selecting and optimizing SNPs using HaploView 4.2. As a result, 10 tagSNPs in SORCS3 (Table S5 and Fig. 1) and 11 tagSNPs in GSDME (Table S5 and Fig. 2) were selected for the present study.
Fig. 1.
A diagram of the positions of 10 tagSNPs in SORCS3. SNP in bold indicates nominal association with autism in the Chinese Han population; red blocks indicate exons in SORCS3. The distribution diagram was drawn according to information from SORCS3 (human transcript: NM_014,978) and each tagSNP from Ensembl GRCh37 Release 93 and dbSNP in NCBI databases. Abbreviations: SNP, single nucleotide polymorphism.
Fig. 2.
A diagram of the positions of 11 tagSNPs in GSDME. SNP in bold indicates nominal association with autism in the Chinese Han population; red blocks indicate exons in GSDME. The distribution diagram was drawn according to information of GSDME (human transcript: NM_004403) and each tagSNP from Ensembl GRCh37 Release 93 and dbSNP in NCBI databases. Abbreviations: SNP, single nucleotide polymorphism.
2.4. DNA extraction and genotyping
The peripheral blood samples were obtained from children with autism and their biological parents. Genomic DNA was extracted using the Qiagen QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). The extracted DNA was quality controlled using a NanoDrop spectrophotometer (Thermo Fisher, MA, USA) to ensure that the samples met the following criteria: (1) DNA concentration >40 ng/μL; (2) the OD260/OD280 ratio between 1.8 and 2.0; (3) the OD260/OD230 ratio between 2.0 and 2.2.
SNP genotyping was performed using Agena MassARRAY SNP typing through the Agena Bioscience platform (Agena Bioscience, CA, USA) [47]. All primers were designed based on the sequence of forward strands provided by the NCBI human reference genome GRCh37 (hg19) (primers of 24 ASD-related genome-wide SNPs shown in Table S1; primers of 21 tagSNPs shown in Table S6). The DNA products were obtained using the following three steps: (1) polymerase chain reaction (PCR); (2) use of shrimp alkaline phosphatase to remove unincorporated nucleotides and primer extension reaction; (3) transfer of the products to SpectroCHIP for analysis through the mass spectrometer to acquire SNP genotypes.
2.5. Data analysis
The chi-square goodness-of-fit test was used to analyze whether the genotype frequency distribution deviated from Hardy–Weinberg equilibrium (HWE), and SNPs were excluded from the subsequent analysis when the P values were less than 0.05.
The statistical power of SNPs was calculated using the Quanto version1.2.4 [48]. The population risk was set as 0.01 [4], and the genetic effect was estimated to range from 1.3 to 1.5, according to previous studies [7,49,50]. The type I error rate was 0.05 (two-sided) under the log-additive model [51].
Family-based association tests were performed using family-based association test (FBAT) 2.0.3 software , which could detect Mendelian errors automatically and reset their genotypes to zero [51]. The processed data were used to analyze the association between SNPs and autism under additive and recessive genetic models separately with the FBAT program. Then, Bonferroni correction was performed to reduce the type I error rate with the significance level of P < α/n (α = 0.05, n is the number of SNPs). The significance level for all statistical tests was two-tailed. The ratio of transmission to untransmission (T:U) of alleles of individual SNPs was calculated using HaploView version 4.2 (http://www.broad.mit.edu/mpg/haploview/) [52].
3. Results
3.1. Quality control
A few genome-wide SNPs were detected in seven GWAS studies with large ASD sample size. Seven autism genome-wide significant SNPs reported in four GWAS were replicated in the Han Chinese population [14]. Four GWAS reported 24 autism-GWAS hits associated with ASD in PGC (P < 0.05); their MAF in the Han Chinese population was more than 0.05 [[16], [17], [18], [19]]. The details of all SNPs are shown in Table 1. Seven of these 24 SNPs were incompatible with other SNPs when designing primers. Therefore, SNPs in strong linkage disequilibrium with these 7 SNPs (Table S2) and compatible with other 17 autism-GWAS SNPs (Table S3) were selected for further association analysis.
Table 1.
The details of 24 ASD-related genome-wide SNPs.
| GWAS | ASD sample size |
other sample size |
main |
positive SNPs | related mental disorders |
|---|---|---|---|---|---|
| (cases/controls) | (cases/controls) | Ancestry | |||
| PGC-ASD group, 2017 | 7387/8567 | 36989/113075 (SCZ) | European | 12 | ASD, SCZ |
| Grove et al., 2019 | 18381/27969 | N = 328917 (Edu) | European | 12 | 5/12 ASD |
| 111902/111902 (MD) | 4/12 ASD & MD | ||||
| 34129/45512 (SCZ) | 3/12 ASD & Edu | ||||
| Schork et al., 2019 | 12371/19526 | 46008/19526 (ASD, ADHD, SCZ, BIP, AFF, ANO) | European | 4 | six mental disorders |
| Cross-disorder group of PGC, 2019 | 18381/27969 | 232964/494162 (ASD, ADHD, SCZ, BIP, MD, OCD, TS, AN) | European | 23 | more than four mental disorders |
Abbreviations: GWAS, genome-wide association study; SNP, single nucleotide polymorphism; ASD, autism spectrum disorder; SCZ, schizophrenia; Edu, educational attainment; MD, major depression; ADHD, attention-deficit/hyperactivity disorder; BIP, bipolar disorder; AFF, affective disorder; ANO, anorexia; OCD, obsessive-compulsive disorder; TS, Tourette syndrome; AN, anorexia nervosa.
All 24 ASD-related genome-wide SNPs clustered clearly on genotyping except rs2332700 (Fig. S1). The call rate of these well-clustered SNPs was better than 0.90, and the genotype distribution did not deviate from HWE in unaffected parents (Table S4). The statistical power calculated using Quanto under the log-additive model was 37%–99 %. All 21 tagSNPs SNPs clustered clearly on genotyping except rs6584621 and rs11192147 in SORCS3 and rs2237306 in GSDME (Fig. S2). The call rate of these well-clustered SNPs was better than 0.90, and all SNPs were not deviated from HWE in unaffected parents except for rs3757652, rs17149888, and rs754554 (Table S7). Therefore, six SNPs that failed genotyping or deviated from HWE were not further analyzed. The statistical power calculated using Quanto under the log-additive model was 39%–99 %. During the FBAT, the data from families with Mendelian errors would reset ‘0' and were not calculated in the further analysis.
3.2. Association analyses of ASD-related genome-wide SNPs
Single-SNP association analyses demonstrated that the T allele of rs1484144 was over-transmitted from unaffected parents to their autistic children under the additive model (T > C, Z = 3.087, P = 0.0020) and recessive model (T > C, Z = 2.314, P = 0.0207) (Table 2). The statistical significance of the additive model remained after the Bonferroni correction (P < α/n = 0.05/23 = 0.0022). The rs79879286 near GSDME was nominally associated with autism under the additive model (G > C, Z = 2.010, P = 0.0445) and recessive model (G > C, Z = 2.194, P = 0.0283) (Table 2). The rs9787523 of SORCS3 was nominally associated with autism under the recessive model (C > T, Z = −1.963, P = 0.0496) (Table 2). However, the significance of rs79879286 and rs9787523 did not remain after Bonferroni correction. The remaining 20 SNPs showed no association with autism under the additive or recessive model.
Table 2.
Results of association analyses of 23 ASD-related genome-wide SNPs in 757 Han Chinese autism trios by FBAT.
| SNP | Location | Gene | Allele | AFreq | T:Ua | Addictive model |
Recessive model |
||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fam | Z | Pb | Fam | Z | Pb | ||||||
| rs910805 | 20:21248116 | near XRN2 | A | 0.791 | 249:237 | 404 | 0.544 | 0.5862 | 380 | 0.580 | 0.5618 |
| G | 0.209 | 237:249 | 404 | −0.544 | 0.5862 | 106 | −0.108 | 0.9139 | |||
| rs1452075 | 3:62481063 | CADPS | C | 0.296 | 304:310 | 487 | −0.242 | 0.8087 | 173 | 0.547 | 0.5844 |
| T | 0.704 | 310:304 | 487 | 0.242 | 0.8087 | 441 | 0.618 | 0.5366 | |||
| rs325506 | 5:104012303 | near NUDT12 | C | 0.576 | 358:359 | 552 | −0.037 | 0.9702 | 415 | 0.388 | 0.6981 |
| G | 0.424 | 359:358 | 552 | 0.037 | 0.9702 | 302 | 0.526 | 0.5986 | |||
| rs1620977 | 1:72729142 | NEGR1 | A | 0.163 | 200:186 | 328 | 0.661 | 0.5087 | 74 | 0.715 | 0.4749 |
| G | 0.837 | 186:200 | 328 | −0.661 | 0.5087 | 313 | −0.434 | 0.6641 | |||
| rs10149470 | 14:104017953 | near COA8 | A | 0.584 | 346:353 | 519 | −0.265 | 0.7912 | 410 | 0.105 | 0.9166 |
| G | 0.416 | 353:346 | 519 | 0.265 | 0.7912 | 289 | 0.576 | 0.5645 | |||
| rs880446 | 20:62133177 | near EEF1A2 | A | 0.463 | 368:353 | 560 | 0.559 | 0.5764 | 335 | −0.612 | 0.5408 |
| G | 0.537 | 353:368 | 560 | −0.559 | 0.5764 | 386 | −1.371 | 0.1703 | |||
| rs4904167 | 14:84700744 | - | C | 0.281 | 271:301 | 468 | −1.254 | 0.2097 | 156 | −1.228 | 0.2195 |
| T | 0.719 | 301:271 | 468 | 1.254 | 0.2097 | 416 | 0.810 | 0.4178 | |||
| rs4322805 | 2:183535884 | near PDE1A | A | 0.506 | 359:353 | 540 | 0.225 | 0.8221 | 357 | −0.395 | 0.6928 |
| G | 0.494 | 353:359 | 540 | −0.225 | 0.8221 | 355 | −0.736 | 0.4617 | |||
| rs6780942 | 3:117828678 | near IGSF11 | C | 0.644 | 313:316 | 492 | −0.120 | 0.9048 | 403 | −0.234 | 0.8147 |
| T | 0.356 | 316:313 | 492 | 0.120 | 0.9048 | 226 | −0.108 | 0.9137 | |||
| rs9360557 | 6:73132745 | near KCNQ5 | C | 0.621 | 309:279 | 457 | 1.237 | 0.2160 | 369 | 0.900 | 0.3682 |
| G | 0.379 | 279:309 | 457 | −1.237 | 0.2160 | 219 | −0.989 | 0.3226 | |||
| rs6125656 | 20:48090779 | KCNB1 | A | 0.069 | 96:97 | 185 | −0.072 | 0.9426 | 9 | – | – |
| G | 0.931 | 97:96 | 185 | 0.072 | 0.9426 | 184 | 0.000 | 1.0000 | |||
| rs11570190 | 11:57560452 | CTNND1 | A | 0.846 | 176:194 | 316 | −0.936 | 0.3494 | 309 | −0.698 | 0.4851 |
| C | 0.154 | 194:176 | 316 | 0.936 | 0.3494 | 61 | 0.871 | 0.3840 | |||
| rs1484144 | 4:80217597 | NAA11 | C | 0.244 | 202:269 | 377 | −3.087 | 0.0020 | 118 | −2.572 | 0.0101 |
| T | 0.756 | 269:202 | 377 | 3.087 | 0.0020 | 353 | 2.314 | 0.0207 | |||
| rs9787523 | 10:106460460 | SORCS3 | C | 0.679 | 330:293 | 483 | 1.441 | 0.1495 | 420 | 0.535 | 0.5924 |
| T | 0.321 | 293:330 | 483 | −1.441 | 0.1495 | 204 | −1.963 | 0.0496 | |||
| rs7531118 | 1:72837239 | near NEGR1 | C | 0.283 | 319:281 | 477 | 1.509 | 0.1312 | 161 | 0.965 | 0.3347 |
| T | 0.717 | 281:319 | 477 | −1.509 | 0.1312 | 440 | −1.286 | 0.1986 | |||
| rs79879286 | 7:24826589 | near GSDME | C | 0.048 | 54:77 | 126 | −2.010 | 0.0445 | 5 | – | - |
| G | 0.952 | 77:54 | 126 | 2.010 | 0.0445 | 126 | 2.194 | 0.0283 | |||
| rs9375225 | 6:98588754 | near MMS22L | G | 0.594 | 323:312 | 465 | 0.437 | 0.6625 | 374 | −0.384 | 0.7006 |
| T | 0.406 | 312:323 | 465 | −0.437 | 0.6625 | 261 | −1.218 | 0.2233 | |||
| rs746839 | 8:142617261 | near MROH5 | C | 0.831 | 175:194 | 325 | −0.880 | 0.3787 | 315 | −0.402 | 0.6876 |
| G | 0.169 | 194:175 | 325 | 0.880 | 0.3787 | 58 | 1.474 | 0.1403 | |||
| rs11242522 | 5:103904914 | – | C | 0.406 | 370:344 | 542 | 0.973 | 0.3305 | 292 | 1.521 | 0.1283 |
| T | 0.594 | 344:370 | 542 | −0.973 | 0.3305 | 422 | −0.103 | 0.9182 | |||
| rs7499750 | 16:13749265 | – | A | 0.107 | 125:126 | 220 | −0.063 | 0.9497 | 31 | −0.311 | 0.7557 |
| C | 0.893 | 126:125 | 220 | 0.063 | 0.9497 | 220 | −0.034 | 0.9726 | |||
| rs13236223 | 7:140666965 | near MRPS33 | C | 0.103 | 142:131 | 247 | 0.666 | 0.5056 | 29 | −0.843 | 0.3991 |
| T | 0.897 | 131:142 | 247 | −0.666 | 0.5056 | 244 | −0.973 | 0.3304 | |||
| rs6961430 | 7:110058448 | – | C | 0.197 | 217:224 | 377 | −0.333 | 0.7389 | 86 | −0.478 | 0.6326 |
| G | 0.803 | 224:217 | 377 | 0.333 | 0.7389 | 355 | 0.163 | 0.8706 | |||
| rs7188257 | 16:6314935 | near RBFOX1 | C | 0.219 | 215:240 | 375 | −1.024 | 0.3061 | 113 | −0.995 | 0.3200 |
| T | 0.781 | 240:215 | 375 | 1.024 | 0.3061 | 349 | 0.691 | 0.4896 | |||
Abbreviations: SNP, single nucleotide polymorphism; Position referenced the NCBI human reference genome GRCh37/hg19; AFreq, allele frequency; Fam, number of informative families; S, test statistics for the observed number of transmitted alleles; E(S), the expected value of S under the null hypothesis (i.e., no linkage and no association).
The ratio of transmission to untransmission (T:U) for each selected SNP was calculated by the Haploview version 4.2.
P value with bold character means nominally associated with autism (P < 0.05).
3.3. Association analyses of tagSNPs
The C allele of rs12154597 in GSDME was over-transmitted from unaffected parents to their autistic children under both additive (C > G, Z = 2.008, P = 0.0446) and recessive models (C > G, Z = 2.168, P = 0.0301) (Table 3). The C allele of rs12540919 in GSDME was nominally associated with autism under the additive model (C > T, Z = 2.045, P = 0.0409) (Table 3). The C allele of rs3750261 in SORCS3 was over-transmitted from unaffected parents to their autistic children, and the T allele was a protective factor under the recessive model (C > T, Z = −2.302, P = 0.0214) (Table 3). All the above statistical significance was not corrected by Bonferroni correction P < α/n = 0.0033 (α = 0.05, n = 15). The remaining tagSNPs showed no association with autism under additive or recessive models.
Table 3.
Results of association analyses of 15 tagSNPs in 757 Han Chnese autism trios by FBAT.
| SNP | Location | Gene location | Allele | AFreq | T:Ua | Addictive model |
Recessive model |
||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Fam | Z | Pb | Fam | Z | Pb | ||||||
| rs902305 | 10:106532224 | SORCS3 | A | 0.751 | 265:239 | 403 | 1.331 | 0.1832 | 376 | 1.417 | 0.1565 |
| G | 0.249 | 239:265 | 403 | −1.331 | 0.1832 | 132 | −0.340 | 0.7336 | |||
| rs10786831 | 10:106614571 | SORCS3 | A | 0.226 | 231:211 | 373 | 0.949 | 0.3425 | 93 | −0.403 | 0.6866 |
| G | 0.774 | 211:231 | 373 | −0.949 | 0.3425 | 351 | −1.287 | 0.1980 | |||
| rs1961639 | 10:106635997 | SORCS3 | A | 0.903 | 122:125 | 228 | −0.254 | 0.7995 | 225 | −0.337 | 0.7360 |
| G | 0.097 | 125:122 | 228 | 0.254 | 0.7995 | 23 | −0.236 | 0.8137 | |||
| rs790647 | 10:106776484 | SORCS3 | A | 0.224 | 233:235 | 393 | 0.000 | 1.0000 | 110 | −0.581 | 0.5615 |
| C | 0.776 | 235:233 | 393 | 0.000 | 1.0000 | 364 | −0.297 | 0.7667 | |||
| rs791125 | 10:106907440 | SORCS3 | C | 0.71 | 278:275 | 440 | 0.127 | 0.8989 | 397 | −0.235 | 0.8145 |
| T | 0.29 | 275:278 | 440 | −0.127 | 0.8989 | 160 | −0.656 | 0.5119 | |||
| rs1947988 | 10:106947006 | SORCS3 | C | 0.837 | 180:155 | 291 | 1.253 | 0.2102 | 290 | 0.899 | 0.3687 |
| T | 0.163 | 155:180 | 291 | −1.253 | 0.2102 | 47 | −1.343 | 0.1794 | |||
| rs1484246 | 10:106961404 | SORCS3 | A | 0.166 | 189:193 | 337 | −0.102 | 0.9191 | 53 | 0.079 | 0.9372 |
| G | 0.834 | 193:189 | 337 | 0.102 | 0.9191 | 335 | 0.139 | 0.8892 | |||
| rs3750261 | 10:107023390 | SORCS3 | C | 0.785 | 239:204 | 366 | 1.614 | 0.1066 | 349 | 0.771 | 0.4406 |
| T | 0.215 | 204:239 | 366 | −1.614 | 0.1066 | 95 | −2.302 | 0.0214 | |||
| rs12154597 | 7:24785882 | GSDME | C | 0.949 | 68:49:00 | 114 | 2.008 | 0.0446 | 114 | 2.168 | 0.0301 |
| G | 0.051 | 49:68 | 114 | −2.008 | 0.0446 | 6 | – | – | |||
| rs2237318 | 7:24769278 | GSDME | C | 0.173 | 202:197 | 357 | −0.350 | 0.7267 | 59 | −0.433 | 0.6650 |
| T | 0.827 | 197:202 | 357 | 0.350 | 0.7267 | 342 | 0.220 | 0.8260 | |||
| rs12540919 | 7:24756951 | GSDME | C | 0.853 | 163:129 | 252 | 2.045 | 0.0409 | 248 | 1.719 | 0.0857 |
| T | 0.147 | 129:163 | 252 | −2.045 | 0.0409 | 45 | −1.442 | 0.1493 | |||
| rs2299098 | 7:24756377 | GSDME | C | 0.308 | 269:287 | 439 | −0.762 | 0.4461 | 162 | −0.043 | 0.9653 |
| G | 0.692 | 287:269 | 439 | 0.762 | 0.4461 | 396 | 0.914 | 0.3605 | |||
| rs17149943 | 7:24749199 | GSDME | G | 0.848 | 147:132 | 248 | 0.898 | 0.3692 | 239 | 0.690 | 0.4899 |
| T | 0.152 | 132:147 | 248 | −0.898 | 0.3692 | 40 | −0.792 | 0.4281 | |||
| rs2240005 | 7:24742553 | GSDME | A | 0.213 | 185:195 | 317 | −0.512 | 0.6089 | 81 | −0.311 | 0.7560 |
| G | 0.787 | 195:185 | 317 | 0.512 | 0.6089 | 301 | 0.444 | 0.6567 | |||
| rs2257061 | 7:24738372 | GSDME | C | 0.802 | 221:201 | 371 | 1.116 | 0.2646 | 350 | 1.363 | 0.1729 |
| T | 0.198 | 201:221 | 371 | −1.116 | 0.2646 | 75 | 0.255 | 0.7987 | |||
Abbreviations: SNP, single nucleotide polymorphism; Position referenced the NCBI human reference genome GRCh37/hg19; AFreq, allele frequency; Fam, number of informative families; S, test statistics for the observed number of transmitted alleles; E(S), the expected value of S under the null hypothesis (i.e., no linkage and no association).
The ratio of transmission to untransmission (T:U) for each selected SNP was calculated by the Haploview version 4.2.
P value with bold character means nominally associated with autism (P < 0.05).
4. Discussion
This family-based association study investigated the association between autism and 16 significantly ASD-related genome-wide SNPs and 7 SNPs in strong linkage disequilibrium with autism-GWAS hits in the 757 Han Chinese patients. Our data suggested that rs1484144 in NAA11 was significantly associated with autism under the additive model and nominally associated with autism under the recessive model. Besides, rs79879286 near GSDME and rs9787523 in SORCS3 were nominally associated with autism. The above replication results and mass findings suggest that GSDME and SORCS3 might be autism susceptibility genes. We also explored the association of these two genes with autism, which showed that rs12154597 and rs12540919 in GSDME and rs3750261 in SORCS3 were nominally associated with autism.
The GWAS of PGC meta-analyses on eight psychiatric disorders showed that rs1484144, rs9787523 and rs79879286 were risk gene variants shared by cross-psychiatric disorders, including ASD [19]. GWAS meta-analysis of the mood disorder spectrum (including bipolar disorder and major depressive disorder) found that rs79879286 was a positive locus [39]. Also, rs12154597 in GSDME was found to be associated with schizophrenia [41].
The genes containing autism-related SNPs found in this study were also associated with psychiatric disorders or related traits in other studies. GWAS showed that rs12642606 in NAA11 was associated with depressive disorders [53]. In SORCS3, we found 2 nominally positive SNPs. A GWAS on six psychiatric disorders found that rs12265655 in SORCS3 was a significant genome-wide positive locus [18]. Another study found that rs3896224 in SORCS3 was associated with intelligence [28]. A few studies showed that some SNPs in SORCS3 are associated with major depressive disorder, bipolar depression disorder, and Alzheimer's disease [[24], [25], [26], [27]]. The SORCS3 region, located at 10q25.1, was specifically shared by the three adult-onset disorders (schizophrenia, bipolar disorder, and major depressive disorder), two childhood psychiatric disorders (attention deficit hyperactivity disorder, and autism spectrum disorder) and shared by all the above five disorders; this region has also been reported to contain copy number variants (CNVs) associated with ASD [54]. A study using whole-exome sequencing and autozygome analysis found a homozygous missense variant in SORCS3 (c.3110C > G) on two brothers with neurological phenotypes including intellectual disability, global developmental delay, and delayed myelination [55]. In addition, a study found a pair of adjacent de novo copy number variants (sizes 242 Kb and 318 Kb) in 10q25.1 duplicated regions overlapping the SORCS3 and SORCS1 genes in an ADHD proband [56]. This study showed that a nearby SNP and 2 SNPs in GSDME were associated with autism. Several studies suggested that some SNPs in GSDME were associated with schizophrenia, bipolar depression disorder, major depressive disorder, educational attainment and math ability [[40], [41], [42], [43], [44],57]. The GSDME (DFNA5) region was shared by the three adult-onset disorders (schizophrenia, bipolar disorder, and major depressive disorder) and shared by five dioders (the above three disorders, ADHD and ASD); this region has also been reported with CNVs associated with ASD [54]. Another study found about 4.36 Mb heterozygous deletion at 7p15.3-p15.1, including GSDME gene deletion, in a child with developmental delay [38]. In addition, some SNPs in the SORCS3 and GSDME regions are eQTLs in human tissues and affect the expression of SORCS3 and GSDME [54].
Several studies suggested the involvement of SORCS3 in the pathogenesis of autism and other psychiatric disorders. Wu et al. used knowledge-based algorithms to show that SORCS3 correlates with five psychiatric disorders, including ASD, schizophrenia, ADHD, bipolar disorder and depressive disorder, through eight proteins (NGF, APP, DLG4, PICK1, INS, BDNF, AGRP and NTRK2) and a small molecule of glutamate [23]. Other studies suggested that SORCS3 is bound with nerve growth factor on the plasma membrane and plays an important role in the postsynaptic protein network [20,21]. Another study found altered synaptic plasticity, impaired learning and fear memory in SORCS3-deficient mice [22]. These studies suggested that SORCS3 might be a susceptibility gene for ASD and participated in the pathogenesis of autism.
However, no association was found between other ASD-related positive SNPs and autism in the Han Chinese population. The results of this study were not completely consistent with the findings of GWAS in the European population, which may be due to the following factors: first, the positive loci suggested in previous studies were not exactly pathogenic, implying that the variants in strong linkage disequilibrium with the positive SNPs might be the exact pathogenic loci; second, different ethnic genetic backgrounds should be considered that the allele frequencies of some SNPs were quite different in European and Han Chinese populations; third ASD has high genetic heterogeneity. Most foreign studies recruited patients with ASD, whereas, in this study, only patients with typical autism were selected to reduce heterogeneity.
This study had several limitations. First, previous studies showed that the copy number variants of SORCS3 and GSDME and the mutations in SORCS3 were associated with mental disorders. Thus, an association between other genetic variants of these two genes and autism should be further investigated. Second, autism is a complex polygenic disorder with high heterogeneity. Future studies should evaluate the intelligence, disease severity, and comorbidities in autism to investigate the association between autism-related SNPs in this study and autism phenotypes. Third, two autism-positive loci in the Han Chinese population were located in functional regions, while their function needs further investigation. Rs3750261, located in the 3′-UTR of SORCS3, was a binding target of hsa-miR-5197-5p. The C allele of rs3750261 might promote the binding of SORCS3 and hsa-miR-5197-5p, while the T allele of rs3750261 did not promote the binding of mature miRNA to SORCS3 [58]. Whether this variation affects the expression and function of SORCS3 still needs further investigation. Rs12540919 (C.619 gG> A, Val207Met), located in the exon of GSDME, is a missense variant. The impact of this SNP and the activity of GSDME also needs exploration. Fourth, how these SNPs and genes are involved in the pathology of autism requires further integration of multi-omics data, such as transcriptomics, proteomics and epigenetics, to further elucidate the specific pathogenic mechanisms of these autism susceptibility SNPs or genes.
In summary, we found that rs1484144 in NAA11 was significantly associated with autism in the Han Chinese population, and NAA11 was a susceptibility gene for autism. In addition, rs9787523 and rs3750261 in SORCS3 and rs12154597, rs12540919, and rs79879286 near GSDME were associated with autism; these two genes might be susceptibility genes for autism. The function and possible pathogenic mechanisms of these genes need further studies.
Ethics statement
The ethics committee of Peking University Sixth Hospital, China, approved this study (approval number: 2021-2-23-7). The ethics committee approving these experiments included the following authors: Hongqiang Sun, Hongyan Zhang, Lin Lu, Yufeng Wang, Jing Liu, Weihua Yue, Guizhong Yao, Huali Wang, Xueqin Wang, Qingjiu Cao, Xuehua Liu, Yali Cong, Jian Yang, Li Xu, and Yiman Ye.
Before beginning the study, the investigators informed the parents and guardians of the purpose, content, principles of participation, the risks of blood sampling and the benefits of participating in this study. Written informed consents was obtained from all the children's parents or guardians.
Data availability statement
Data associated with this study was not deposited into a publicly available repository and will be made available on request.
CRediT authorship contribution statement
Fen Lin: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Jun Li: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. Ziqi Wang: Formal analysis, Data curation. Tian Zhang: Formal analysis, Data curation. Tianlan Lu: Formal analysis, Data curation. Miaomiao Jiang: Writing – review & editing, Formal analysis, Data curation. Kang Yang: Writing – review & editing, Formal analysis, Data curation. Meixiang Jia: Formal analysis, Data curation. Dai Zhang: Writing – review & editing, Funding acquisition, Conceptualization. Lifang Wang: Writing – review & editing, Funding acquisition, Formal analysis, Conceptualization.
Declaration of competing interest
The authors declare no competing interests.
Acknowledgements
This work was supported by the Key-Area Research and Development Program of Guangdong Province (2019B030335001), the National Natural Science Foundation of China (grant numbers 81971283, 82171537, 82071541, and 82271576), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2023-PT320-08) and the Young Elite Scientists Sponsorship Program by CAST (YESS20160068). We would like to thank the staff for assisting with carrying out this study. Finally, we would also like to thank all the participants and their families.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e23677.
Appendix A. Supplementary data
The following is the supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data associated with this study was not deposited into a publicly available repository and will be made available on request.