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Article

Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis

1
Department of Psychiatry, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
2
Yonsei University College of Medicine, Seoul 03722, Korea
3
Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
4
College of Medicine, Gyeongsang National University, Jinju 52727, Korea
5
Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Korea
6
Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Korea
7
Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do 17035, Korea
8
Department of Nephrology, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
9
Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AB, UK
10
Mental Health Networking Biomedical Research Centre (CIBERSAM), 08036 Barcelona, Spain
11
Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institute, 11330 Stockholm, Sweden
12
Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
13
Department of Pediatrics, Luton & Dunstable University Hospital NHS Foundation Trust, Luton LU4ODZ, UK
14
CESP, Inserm UMR1178, Department of Psychiatry, Assistance Publique-Hôpitaux de Paris, Bicêtre University Hospital, 94275 Le Kremlin Bicêtre, France
15
Research and Development Unit, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Fundació Sant Joan de Déu, CIBERSAM, 08830 Barcelona, Spain
16
ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain
17
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, 28029 Madrid, Spain
18
Physiotherapy Department, South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
19
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
20
Department of Neurosciences, University of Padua, 90133 Padua, Italy
21
Neurosciences Center, University of Padua, 90133 Padua, Italy
22
Department of Psychiatry, University of Toledo Medical Center, Toledo, OH 43614, USA
23
Department of Internal Medicine IV, Medical University Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
24
Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, SE-581 85 Linköping, Sweden
25
Laboratory of Histological Analysis and Preparation (LAPHIS), Federal University of the Parnaiba Delta, Parnaiba 64202-020, Brazil
26
Department of Basic Sciences, Medicine Faculty of Tunis, Tunis El Manar University, 15 Rue Djebel Lakdar, Tunis 1007, Tunisia
27
University Hospital, University of São Paulo, São Paulo CEP 05508-000, Brazil
28
Service of Interdisciplinary Neuromodulation, Department and Institute of Psychiatry, University of São Paulo Medical School, São Paulo CEP 01246-903, Brazil
29
Laboratory of Neuroscience and National Institute of Biomarkers in Neuropsychiatry, Department and Institute of Psychiatry, University of São Paulo Medical School, São Paulo CEP 01246-903, Brazil
30
Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, 80336 Munich, Germany
31
Centre for Addiction & Mental Health, Toronto, ON M6J 1H4, Canada
32
Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
33
Department of Pharmaceutical Sciences and Interdepartmental Research Center of Pharmacogenetics and Pharmacogenomics (CRIFF), University of Piemonte Orientale, 28100 Novara, Italy
34
The Stockholm Center for Health and Social Change (SCOHOST), Södertörn University, 141 89 Huddinge, Sweden
35
Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashicho, Kodaira, Tokyo 187-8553, Japan
36
The Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, Cambridge CB1 1PT, UK
37
Department of Psychology, University of Greenwich, London SE10 9LS, UK
38
OASIS Service, South London and Maudsley NHS Foundation Trust, London SE8 5HA, UK
39
Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
*
Authors to whom correspondence should be addressed.
These authors contributed equally.
Brain Sci. 2020, 10(10), 692; https://doi.org/10.3390/brainsci10100692
Received: 19 August 2020 / Revised: 13 September 2020 / Accepted: 14 September 2020 / Published: 30 September 2020
(This article belongs to the Special Issue Advances in Autism Research)

Abstract

:
This study aimed to verify noteworthy findings between genetic risk factors and autism spectrum disorder (ASD) by employing the false positive report probability (FPRP) and the Bayesian false-discovery probability (BFDP). PubMed and the Genome-Wide Association Studies (GWAS) catalog were searched from inception to 1 August, 2019. We included meta-analyses on genetic factors of ASD of any study design. Overall, twenty-seven meta-analyses articles from literature searches, and four manually added articles from the GWAS catalog were re-analyzed. This showed that five of 31 comparisons for meta-analyses of observational studies, 40 out of 203 comparisons for the GWAS meta-analyses, and 18 out of 20 comparisons for the GWAS catalog, respectively, had noteworthy estimations under both Bayesian approaches. In this study, we found noteworthy genetic comparisons highly related to an increased risk of ASD. Multiple genetic comparisons were shown to be associated with ASD risk; however, genuine associations should be carefully verified and understood.

1. Introduction

Autism spectrum disorder (ASD) is a brain-based neurodevelopmental disorder characterized by pervasive impairments in reciprocal social communication, social interaction, and restricted and repetitive behaviors or interests, resulting in a substantial burden of individuals, families, and society [1,2]. The repeated reports of recent increase in the prevalence of ASD have raised substantial public concerns. For example, in large, nationwide population-based studies, the estimated ASD prevalence was reported to be 2.47% among U.S. children and adolescents in 2014–2016 [3,4,5].
Although the full range of etiologies underlying ASD remain largely unexplained, progress has been made in the past decade in identifying some neurobiological and genetic risk factors, and it has been well established that combination of genetic and environmental factors is involved in the etiopathogenesis of autism [1,6]. There is a strong genetic background of ASD, which was demonstrated by the fact that heritability is as high as 80–90% [7,8]. It is possible to estimate the heritability of ASD by taking into the account its covariance within twins, as twins are matched for many characteristics, including in utero and family environment, as well as other developmental aspects [7,9,10].
ASD is polygenic and genetic variants contribute to ASD risk and phenotypic variability. The results of previous studies showed genome-wide genetic links between ASD [11,12]. They indicated that typical variation in social behavior and adaptive functioning and multiple types of genetic risk for ASD influence a continuum of behavioral and developmental traits.
To the best of our knowledge, this is the comprehensive study to summarize the loci that are associated with ASD among the several known loci reported to be related with ASD. We have synthesized all available susceptibility loci for ASD retrieved from meta-analyses regarding the association between the individual polymorphisms and ASD. For the study, we reviewed observational studies, Genome-Wide Association Studies (GWAS) meta-analyses, the combined analysis of GWAS discovery and replication cohorts, the GWAS catalog and GWAS data from GWAS meta-analyses [13]. Furthermore, we applied a Bayesian approaches including false positive report probability (FPRP) and Bayesian false discovery probability (BFDP) to estimate the noteworthiness of the evidence [14,15]. Using these popular Bayesian statistics (i.e., FPRP and BFDP), our study shows that the results of genotype associations between the gene variant and disease were found to be noteworthy (genuine associations). Through these methods, we selected only statistically meaningful values excluding false-positive values and analyzed them again. We aimed to provide an overview to interpret the statistical significance of reported findings and discuss the identified associations in the suggested genetic risk factors for ASD.

2. Materials and Methods

This review was conducted following a registered protocol. The specified methods are available on the PROSPERO database with the registration number CRD42018091704. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines of this review are shown in Supplementary Table S1.

2.1. Experimental Section

2.1.1. Inclusion and Exclusion Criteria

Studies were included if they satisfied the following conditions: (1) estimated the risk of ASD in humans using meta-analyses in terms of odds ratio (OR) and 95% confidence interval (CI); (2) published in English. Articles were excluded if (1) they did not cover the subject of genetic polymorphism or ASD; (2) did not have individual results for ASD; (3) did not use statistical methods of meta-analysis.

2.1.2. Search Strategy

A PubMed search was performed to extract data from meta-analyses regarding the gene polymorphisms of ASD published until 1 August, 2019. Two of the authors (MJ Son and CY Son) used the search terms (autism AND meta OR meta-analysis) and obtained relevant articles, first, by scanning the titles and abstracts and, second, by reviewing the full-text (Figure 1). During the selection process, all genetic, gen*, and related terms were included in the relevant articles. Any disagreements were resolved by discussion and consensus. In the case of GWAS, the GWAS catalog was additionally used, as well as PubMed, for a more precise search.

2.1.3. Data Extraction

From each article, we extracted the first author, year of publication, the number of individual studies included, the number of cases and controls, and the number of families if a meta-analysis included family-based studies, the type of statistical model (fixed or random) and study design. We also recorded gene name, gene variants, genotypic comparison, OR with 95% CI, and the corresponding p-value. We retrieved all the main data (preferably adjusted), and, for comprehensiveness we additionally extracted subgroup analysis data if the main data were not statistically significant. When data were incomplete, we contacted the corresponding authors for additional information.
Reported association was considered statistically significant if p-value < 0.05 for meta-analyses of observational studies, and <5 × 10−8 for GWAS or meta-analyses of GWAS. Meanwhile, genetic associations with a 5 × 10−8 < p-value < 0.05 were defined as being of borderline significance in GWAS or meta-analyses of GWAS. In addition, we recorded genetic comparisons with p-value < 5 × 10−8 for our gene network, even when they were not re-analyzable due to insufficient raw data.

2.2. Statistical Analysis

Evaluations of the statistical significance of studies about genetic polymorphisms too often inferred false positives, when the evaluations were solely based on p-value [15]. Therefore, to clarify “noteworthy” association between re-analyzable genetic variants and ASD, we employed the two Bayesian approaches: FPRP and BFDP [15]. We used the Excel spreadsheets created by Wacholder et al. [15] and Wakefield [14] to calculate FPRP and BFDP, respectively. We computed FPRP at two prior probability levels of 10−3 and 10−6 and used statistical power to detect two OR levels, 1.2 and 1.5, so that readers can make their own judgment about the evidence for each genetic variant. BFDP is similar to FPRP but uses more information than FPRP [14]. Both prior probability levels were chosen as one of the low and very low values of levels, respectively. We computed BFDP at two prior probabilities levels, 10-3 and 10−6. We set the thresholds of noteworthiness of FPRP and BFDP to be <0.2 and <0.8, respectively, as recommended by the original papers and highlighted corresponding results in bold type [14,15]. Gene variants were determined to have a noteworthy association with ASD if they satisfied both thresholds.

2.3. Construction of Protein-Protein Interaction (PPI) Network

We collected genetic comparisons either with noteworthy results under both FPRP and BFDP or with p-value < 5 × 10−8 to establish a network of genes using STRING 9.1 (protein-protein interaction network, PPI network) related to ASD [16]. Genetic comparison results, which show genome-wide significance (p-value < 5 × 10−8) or borderline significance (p-value < 0.05) with a noteworthy association under both Bayesian approaches, were included. Any results with a p-value < 5 × 10−8 that were not re-analyzable were also added in the network analysis. PPI networks provide a critical assessment of protein function on ASD including direct (physical) as well as indirect (functional) associations.

3. Results

3.1. Study Characteristics

The initial PubMed literature search yielded 747 articles. Out these, 656 articles were excluded after screening the title and abstract, and 64 articles were omitted after reviewing the full-text. Twenty-seven studies were finally included for the re-analysis of observational studies, GWAS, and meta-analyses of GWAS (Figure 1).
Additionally, 25 articles were searched on the GWAS catalog, but 14 articles did not meet the criteria were excluded. Among the remaining 11 articles, five articles were not re-analyzable due to insufficient raw data. Moreover, five articles were already included in our dataset from the PubMed search. However, we retained three of the non-re-analyzable articles [17,18,19] since they satisfied the cut-off value of statistical significance for our PPI network (p-value < 5 × 10−8). Out of the remaining six articles, two were already in our dataset from the literature search from PubMed. Finally, four articles from the GWAS catalog were manually added to 27 articles previously screened from PubMed, leading to a total of 31 eligible articles [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47] being included in the systematic review (Figure 1).

3.2. Re-Analysis of Meta-Analyses

This paper is divided into two parts: (1) the observational studies part, and (2) the GWAS part. In the observational studies, all statistics were collected considering the overlapping, and results of gene variants with/without statistical significance (Table 1, Supplementary Table S2). Even though genetic variants examined in several studies, we excluded the studies if the data were not significant performed by FPRP or BFDP. In the GWAS part, data from previously published meta-analyses and newly added data from the GWAS catalog were re-analyzed.

3.2.1. Re-Analysis of Meta-Analyses of Observational Studies

Among the 31 eligible studies, 19 were meta-analyses of observational studies, which corresponded to 125 genetic comparisons. Thirty one out of 125 genotype comparisons were reported as being statistically significant using the criteria of p-value < 0.05 as listed in Table 1.
Out of the 31 genotype comparisons (Table 1), three (9.7%), and two (6.5%) were verified to be noteworthy (<0.2) using FPRP estimation, at a prior probability of 10−3 and 10−6 with a statistical power to detect an OR of 1.2; seven (22.6%) and two (6.5%) were verified to be noteworthy (<0.2) using FPRP estimation, at a prior probability of 10−3 and 10−6 with a statistical power to detect an OR of 1.5. In terms of BFDP, five (16.1%) and two (6.5%) comparisons had noteworthy findings (<0.8) at a prior probability of 10−3 and 10−6. Two single nucleotide polymorphisms (SNPs) were found to be noteworthy under FPRP estimation only, and not under BFDP (Comparison T vs. C, SLC25A12/rs2292813 [20]; C vs. T, SLC25A12/rs2292813 [24]). In contrast, none of the SNPs were identified to be noteworthy exclusively under BFDP. Consequently, five out of 31 SNPs were found noteworthy using both FPRP and BFDP (T vs. C, MTHFR C677T; T (minor), MTHFR C677T; Comparison G vs. A, DRD3/rs167771; C vs. G, RELN/rs362691; A (minor), OXTR/rs7632287).

3.2.2. Re-Analysis of Meta-Analyses of GWAS

Seven GWAS meta-analyses and one study with a combined analysis of GWAS discovery and replication added up to 203 genetic comparisons [30,31,32,33,34,46,47,48] with statistical or borderline significant results. Out of 277 comparisons, 44 had p-value ≥ 0.05 (Table S2), none of which showed noteworthy estimation of FPRP and BFDP with statistical or borderline significant results. From the 203 comparisons, only one (0.5%), MACROD2/rs4141463 A (minor allele), was statistically significant under the genome-wide significance threshold (p-value < 5 × 10−8), while the remaining 202 comparisons (99.5%) satisfied the criteria of borderline significance (5 × 10−8 < p-value < 0.05) previously defined.
We examined the 203 genetic comparisons with a genome-wide or borderline significance using both FPRP and BFDP estimation. With FPRP estimation, forty-one (20.2%) and four (2.0%) were assessed to be noteworthy at a prior probability of 10−3 and 10−6 with statistical power to detect an OR of 1.2. Moreover, fifty-four (26.6%) and eight (3.9%) were identified as noteworthy at a prior probability of 10−3 and 10−6 with statistical power to detect an OR of 1.5. Overall, forty genetic comparisons (19.7%) were found noteworthy under both Bayesian approaches, which included a single genetic comparison satisfying the conventional significance threshold of p-value < 0.05 (Table 2).

3.2.3. Re-Analysis of Results from the GWAS Catalog and GWAS Datasets Included in the GWAS Meta-Analyses

Genetic comparisons additionally extracted from the GWAS catalog were also re-analyzed (Table 3). Among the 20 included comparisons, two (10.0%) genotype comparisons, MACROD2/rs4141463 and LOCI105370358-LOCI107984602/rs4773054, extracted from the GWAS catalog were reported to be significant with a p-value < 5 × 10−8. The remaining 18 comparisons were of borderline statistical significance (p-value between 0.05 and 5 × 10−8).
While assessing noteworthiness, five (25.0%) and three (15.0%) were verified as being noteworthy using FPRP estimation, at a prior probability of 10−3 and 10−6, respectively, with the statistical power to detect a 1.2 OR. In addition, eighteen (90.0%) and four (25.0%) showed noteworthiness at a prior probability of 10−3 and 10−6 with the statistical power to detect a 1.5 OR, respectively. In the BFDP estimation, nineteen (95.0%) and two (10.0%) were assessed as being noteworthy at a prior probability of 10−3 and 10−6, respectively. Finally, 18 genetic associations (95%) of both significant and borderline statistically significant results were verified as being noteworthy under both the FPRP and BFDP approaches. The total number of associations included two comparisons with genome-wide significance (p-value < 5 × 10−8) and sixteen comparisons with borderline significance (p-value between 0.05 and 5 × 10−8).
In order to develop the analysis further, we extracted the GWAS data that was both statistically significant and noteworthy under both Bayesian approaches, from the GWAS meta-analysis and GWAS catalog. They were extracted from five articles [30,31,32,33,34], with 70 of the GWAS data being noteworthy under both FPRP and BFDP. Results with noteworthy association are summarized in Table 4.

3.3. Protein-Protein Interaction (PPI) Network

We established PPI networks related to the risk of ASD by filtering genes noteworthy under both FPRP and BFDP or genes with a p-value < 5 × 10−8. We included the results of both re-analyzed and non-re-analyzable genetic comparisons from meta-analyses of observational studies and GWAS, GWAS included in meta-analyses of GWAS, and the GWAS catalog. The statistically significant results of non-re-analyzable studies are presented in the Supplement Table S3.
The major genes that included a strong genetic connection were the myc-associated factor X (MAX) network transcriptional repressor (MNT), oxytocin receptor (OXTR), nucleolar and coiled-body phosphoprotein (NOLC1), peroxisome proliferator-activated receptor gamma related coactivator-related 1 (PPRC1), pyruvate carboxylase (PC), methylenetetrahydrofolate reductase (MTHFR), multiple epidermal growth factor like domains 10 (MEGF10), nuclear factor kappa B subunit 2 (NFKB2), histone deacetylase 4 (HDAC4), etc. (Figure 2 and Table 5).

4. Discussion

To our knowledge, this study is the first study of ASD genetic risk factors, which assessed the levels of evidence of the published meta-analyses showing the association between susceptible loci and ASD. Overall, genetic comparisons with noteworthy results were confirmed as risk factors for ASD. The genetic comparisons highly related to an increased risk of ASD might reflect the implication in neurodevelopment and specific synaptogenesis of ASD.
According to the PPI network, composed of noteworthy results obtained when using both Bayesian approaches, multiple genes were included as a risk factor for ASD. Investigating the lists genes as a risk factor, promising candidates encoded the protein associated with neural development and specification, and also with neurotransmitters and its receptors. These genes were RELN and DRD3 from observational studies, and PC, OPCML, ERBB4, OR2M4, MEGF10, OR2T33, NMB, and NOLC1, from GWAS. In line with our findings, previous reports have supported that the migration and proliferation of neuronal cells is essential to understanding neurodevelopmental disorders such as ASD or schizophrenia [49,50]. In addition, apart from anatomical approaches, genes correlated with neuropeptides and receptors, such as those in the brain or hippocampus, also explain the pathophysiology of the disease at a molecular level [51]. The list of genes included is presented in Table 5.
The present comprehensive re-analyses shows that, although a large number of studies have suggested numerous possible genetic risk factors for ASD, truly significant results are small and a partial part of whole results. For instance, we detected false positive results in 26 out of 31 (83.9%) meta-analyses of observational studies and 163 out of 203 (80.3%) in meta-analyses of GWAS, respectively. However, only a small portion of genetic comparisons with a p-value < 0.05 exhibited noteworthy associations with ASD under both Bayesian approaches (Table 1, Table 2, Table 3 and Table 4).
Moreover, we also detected that genetic comparisons with borderline statistical significance (5 × 10−8 < p-value < 0.05) accounted for 53 out of 126 (42%) noteworthy comparisons from GWAS or meta-analyses of GWAS. These genetic comparisons might have been neglected if the p-value alone was considered to determine noteworthiness. Using the two Bayesian approaches as we did, or relaxing the current GWAS threshold as Panagiotou et al. suggests, might enable better interpretation of GWAS results [48].
Based on the observational studies, out of 31 statistically significant genotype comparisons, five (16.1%) were found noteworthy under both FPRP and BFDP: T vs. C, MTHFR C677T; T (minor), MTHFR C677T; G vs. A, DRD3/rs167771; C vs. G, RELN/rs362691; A (minor), OXTR/rs7632287. From the meta-analyses of GWAS, we could confirm that 34 distinct genes are noteworthy under both Bayesian approaches with about 30 genetic connections. However, the fact that all three comparisons with a p-value < 5 × 10−8—rs1879532 (Table S3), rs4773054 (Table 2), rs4141463 (Table 2)—displayed noteworthiness may indicate that the stringent threshold of p < 5 × 10−8 is a good tool for verification of the true noteworthiness of genetic risk factors.
There are several limitations in our review. First, we did not include studies that have not been meta-analyzed, or meta-analyses that had insufficient data in our review. Secondly, we only included the single findings of a meta-analysis with the lowest p-value per genetic variant. Therefore, we could not consider potentially meaningful subgroup analyses for different ethnicity, location, gender, and type of genotype comparison (i.e., random or fixed) when selecting a certain outcome. We focused on whether the individual genotype variant was truly associated with ASD or not, regardless of the specific type of the genotype comparison or ethnicity.
Our study has several strengths and implications. For example, to our knowledge, this is the first study that simultaneously analyzed a sizeable amount of data about genetic factors including not only GWAS but also the GWAS catalog. Despite the known high heritability of ASD and abundant research in ASD that has focused on the underlying genetic causes, the literature on genetic risk factors for ASD has not fully reached a consensus. This comprehensive review of genetic associations linked to ASD may improve understanding of the strengths and limitations of each form of research, and advance better and novel approaches for examining ASD in the field of genetic research. The findings of this study could provide mechanisms that may be explored for the development of novel neurotherapeutic agents both for the prevention and treatment of ASD.

5. Conclusions

In conclusion, we synthesized published meta-analyses on risk factors of ASD to acquire noteworthy findings and false positive results by adopting two Bayesian approaches for genetic factors. We attempted to synthesize all meta-analyses on genetic polymorphisms linked to ASD and found noteworthy genetic factors highly related to an increased risk of ASD. We also investigated their validity by discovering false positive results under Bayesian methods. To verify results obtained from genetic analyses, both approaches may have advantages, especially for interpretation of results obtained from observational studies. We found noteworthy results from GWAS, not only with p-value ranging between 0.05 and 5 × 10−8, but also from genetic variants within borderline significance rage which were almost half of the genetic variants. This finding speculates that the genetic variants with borderline significance needs to be further analyzed to determine what associations are genuine.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-3425/10/10/692/s1, Supplementary Table S1. PRISMA 2009 Checklist; Supplementary Table S2. Gene variants without statistical significance (p-value ≥ 0.05) in meta-analyses of observational studies; Supplementary Table S3. Non-re-analyzable gene variants with genome wide statistical significance (p-value < 5 × 10−8) from the GWAS catalog, meta-analyses of GWAS and the GWAS datasets included in the GWAS meta-analysis.

Author Contributions

J.L., M.J.S., J.I.S. and P.F.-P. designed the study. J.L., M.J.S., C.Y.S., G.H.J., K.H.L., K.S.L. and J.I.S. collected the data and M.J.S., G.H.J., K.H.L. and Y.K. did the analysis. J.L., M.J.S., C.Y.S., G.H.J., K.H.L., K.S.L., Y.K., J.Y.K., J.Y.L., J.R., M.E., F.G., A.K. (Ai Koyanagi), B.S., M.S., T.B.R., A.K. (Andreas Kronbichler), E.D., D.F.P.V., F.R.P.d.S., K.T., A.R.B., A.F.C., S.C., S.T., A.S., L.S., T.T., J.I.S., and P.F.-P. wrote the first draft of the manuscript and gave critical comments on manuscript draft. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flow chart of literature search.
Figure 1. Flow chart of literature search.
Brainsci 10 00692 g001
Figure 2. Protein-protein interaction network of ASD. There were 34 distinct genes with about 30 genetic connections among them. The thickness of the line connecting genes represents the score of PPI interaction using STRING9.1 and the color of each gene represents the source of the data; orange, GWAS data: green, GWAS catalog: purple, meta-analysis of GWAS: light green, meta-analysis of observational studies.
Figure 2. Protein-protein interaction network of ASD. There were 34 distinct genes with about 30 genetic connections among them. The thickness of the line connecting genes represents the score of PPI interaction using STRING9.1 and the color of each gene represents the source of the data; orange, GWAS data: green, GWAS catalog: purple, meta-analysis of GWAS: light green, meta-analysis of observational studies.
Brainsci 10 00692 g002
Table 1. Re-analysis results of gene variants with statistical significance (p-value < 0.05) from observational studies.
Table 1. Re-analysis results of gene variants with statistical significance (p-value < 0.05) from observational studies.
Author, YearGene/VariantComparisonOR (95% CI)p-ValueModelNo. of StudiesPower
OR 1.2
Power
OR 1.5
FPRP Values at Prior ProbabilityBFDP
0.001
BFDP
0.000001
OR 1.2OR 1.5
0.0010.0000010.0010.000001
Gene variants with statistically significance (p-value < 0.05), FPRP < 0.2 and BFDP < 0.8 from observational studies
Rai 2016 [21]MTHFR C677TT vs. C1.37 (1.25, 1.50)<0.0001FixedOverall (13)0.0020.9750.0000.0050.0000.0000.0000.001
Mohammad et al., 2016 [23]MTHFR C677TT (minor)1.47 (1.31, 1.65)<0.0001FixedOverall (8)0.0000.6340.0000.1790.0000.0000.0000.009
Warrier et al., 2015 [24]DRD3/rs167771G vs. A1.822 (1.398, 2.375)9.08 × 10−6FixedOverall (2)0.0010.0750.9011.0000.1080.9920.6490.999
Warrier et al., 2015 [24]RELN/rs362691C vs. G0.832 (0.763, 0.908)3.93 × 10−5FixedOverall (6)0.4861.0000.0710.9870.0360.9740.5840.999
LoParo et al., 2015 [26]OXTR/rs7632287A (minor)1.43 (1.23, 1.68)0.000005RandomCaucasian (2)0.0160.7200.4510.9990.0180.9500.4320.999
Gene variants with statistically significance (p-value < 0.05), FPRP > 0.2 or BFDP > 0.8 from observational studies
Liu et al., 2015 [20]SLC25A12/rs2056202T vs. C0.809 (0.713, 0.917)0.001FixedOverall (8)0.3210.9990.7401.0000.4780.9990.9571.000
Liu et al., 2015 [20]SLC25A12/rs2292813T vs. C0.752 (0.649,0.871)<0.001FixedOverall (7)0.0850.9460.6260.9990.1310.9930.8311.000
Pu et al., 2013 [22]MTHFR C677TTT+CT vs. CC1.56 (1.12, 2.18)0.009RandomOverall (8)0.0620.4090.9931.0000.9571.0000.9951.000
Pu et al., 2013 [22]MTHFR A1298CCC vs. AA+AC0.73 (0.56, 0.97)0.03FixedOverall (5)0.1810.7340.9941.0000.9761.0000.9971.000
Warrier et al., 2015 [24]SLC25A12/rs2292813C vs. T1.372 (1.161, 1.621)1.97 × 10−4FixedOverall (6)0.0580.8530.7771.0000.1910.9960.8771.000
Warrier et al., 2015 [24]CNTNAP2/rs7794745A vs. T0.887 (0.828, 0.950)1.00 × 10−3FixedOverall (3)0.9631.0000.3890.9980.3800.9980.9521.000
Warrier et al., 2015 [24]SLC25A12/rs2056202T vs. C1.227 (1.079, 1.396)2.00 × 10−3FixedOverall (8)0.3680.9990.8371.0000.6540.9990.9761.000
Warrier et al., 2015 [24]OXTR/rs2268491T vs. C1.31 (1.092, 1.572)4.00 × 10−3FixedOverall (2)0.1730.9270.9551.0000.7991.0000.9871.000
Warrier et al., 2015 [24]EN2/rs1861972A vs. G1.125 (1.035, 1.224)6.00 × 10−3FixedOverall (8)0.9331.0000.8691.0000.8611.0000.9931.000
Warrier et al., 2015 [24]MTHFR/rs1801133T vs. C1.370 (1.079, 1.739)1.00 × 10−2RandomOverall (10)0.1380.7720.9861.0000.9261.0000.9941.000
Warrier et al., 2015 [24]ASMT/rs4446909G vs. A1.195 (1.038, 1.375)1.30 × 10−2FixedOverall (3)0.5230.9990.9611.0000.9281.0000.9951.000
Warrier et al., 2015 [24]MET/rs38845A vs. G1.322 (1.013, 1.724)1.60 × 10−2RandomOverall (3)0.2370.8240.9941.0000.9791.0000.9981.000
Warrier et al., 2015 [24]SLC6A4/rs2020936T vs. C1.244 (1.036, 1.492)1.90 × 10−2FixedOverall (4)0.3490.9780.9821.0000.9501.0000.9961.000
Warrier et al., 2015 [24]SLC6A4/STin2 VNTR12 vs. 9/101.492 (1.068, 2.083)1.90 × 10−2FixedCaucasian (4)0.1000.5130.9951.0000.9731.0000.9971.000
Warrier et al., 2015 [24]STX1A/rs4717806A vs. T0.851 (0.741, 0.978)2.30 × 10−2FixedOverall (4)0.6161.0000.9741.0000.9581.0000.9971.000
Warrier et al., 2015 [24]RELN/rs736707T vs. C1.269 (1.030, 1.563)2.50 × 10−2RandomOverall (7)0.2990.9420.9881.0000.9641.0000.9971.000
Warrier et al., 2015 [24]PON1/rs662A vs. G0.794 (0.642, 0.983)3.40 × 10−2FixedOverall (2)0.3290.9460.9901.0000.9731.0000.9971.000
Warrier et al., 2015 [24]OXTR/rs237887G vs. A1.163 (1.002, 1.349)4.70 × 10−2FixedOverall (2)0.6601.0000.9861.0000.9791.0000.9981.000
Warrier et al., 2015 [24]EN2/rs1861973T vs. C0.86 (0.791, 0.954)3.00 × 10−3FixedTDT (3)0.7241.0000.8581.0000.8141.0000.9891.000
Aoki et al., 2016 [25]SCL25A12/rs2292813G (risk allele)1.190 (1.052, 1.346)0.006RandomOverall (9)0.5531.0000.9111.0000.8491.0000.9901.000
Aoki et al., 2016 [25]SCL25A12/rs2056202G (risk allele)1.206 (1.035, 1.405)0.016RandomOverall (10)0.4740.9970.9721.0000.9421.0000.9961.000
LoParo et al., 2015 [26]OXTR/rs237887G (minor allele)0.89 (0.79, 0.98)0.0239RandomOverall (3)0.9101.0000.9511.0000.9471.0000.9971.000
LoParo et al., 2015 [26]OXTR/rs2268491T (minor allele)1.20 (1.05, 1.35)0.0075RandomOverall (3)0.5001.0000.8281.0000.7071.0000.9811.000
Wang et al., 2014 [27]RELN/rs362691R vs. NR0.69 (0.56, 0.86)0.001FixedOverall (7)0.0470.6200.9541.0000.6070.9990.9691.000
Torrico et al., 2015 [28]PTCHD1/rs7052177T (major allele)0.58 (0.45, 0.76)6.8 × 10−5FixedEuropean (4) 0.0040.1560.9481.0000.3330.9980.8901.000
Kranz et al., 2016 [29]OXTR/rs237889A vs. G1.12 (1.01, 1.24)0.0365RandomOverall (3)0.9081.0000.9701.0000.9671.0000.9981.000
Abbreviations: A, Adenine; C, Cytosine; G, Guanine; T, Thymine; R, Risk allele; NR, Non-risk allele; FPRP, false positive rate probability; BFDP, Bayesian false discovery probability; OR, odds ratio; CI, confidence interval; NA, not available; The bold in the table means significant results by FPRP and BFDP. This article reported only the number of datasets not the number of individual studies included in the meta-analysis. Thus, we wrote the number of datasets in the parenthesis.
Table 2. Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8p-value < 0.05) in GWAS meta-analyses.
Table 2. Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8p-value < 0.05) in GWAS meta-analyses.
Author, YearGeneVariantComparisonOR (95% CI)p-ValuePower
OR 1.2
Power
OR 1.5
FPRP Values at Prior ProbabilityBFDP
0.001
BFDP
0.000001
OR 1.2OR 1.5
0.0010.0000010.0010.000001
Gene variants with statistically significance (p-value < 5 × 10−8), FPRP < 0.2 and BFDP < 0.8 from meta-analysis of GWAS
Anney et al., 2010 [30]MACROD2rs4141463A (minor allele)0.73 (0.66–0.82)3.7 × 10−80.0130.9370.0090.8980.0000.1070.0080.891
Gene variants with statistically borderline significance (5 × 10−8 ≤ p-value < 0.05), FPRP < 0.2 and BFDP < 0.8 from meta-analyses of GWAS
Anney et al., 2017 [31]ALPK3 NMB SCAND2P SEC11A SLC28A1 WDR73 ZNF592rs4842996T vs. C1.08 (1.05–1.12)0.000010441.0001.0000.0320.9710.0320.9710.6881.000
EXOC4rs6467494T vs. C1.07 (1.04–1.09)0.00001721.0001.0000.0000.0000.0000.0000.0000.000
NArs13233145A vs. C1.07 (1.04–1.10)0.000029061.0001.0000.0020.6180.0020.6180.1360.994
NArs7684366T vs. C0.93 (0.90–0.96)0.000031371.0001.0000.0070.8820.0070.8820.3730.998
MEGF10rs73785549C vs. G1.15 (1.08–1.21)0.00013080.9501.0000.0000.0700.0000.0670.0050.835
ANO4rs2055471A vs. T1.07 (1.03–1.10)0.00013341.0001.0000.0020.6180.0020.6180.1360.994
BNC2rs7860276A vs. G1.10 (1.05–1.15)0.00031961.0001.0000.0260.9640.0260.9640.5980.999
NArs2293280C vs. G1.12 (1.06–1.18)0.00036060.9951.0000.0200.9540.0200.9540.5140.999
NArs16975940T vs. C1.07 (1.03–1.10)0.00047421.0001.0000.0020.6180.0020.6180.1360.994
NArs10169115C vs. G1.06 (1.02–1.09)0.0044651.0001.0000.0410.9770.0410.9770.7781.000
C10orf76 CUEDC2 ELOVL3 FBXL15 GBF1 HPS6 LDB1 MIR146B NFKB2 NOLC1 PITX3 PPRC1 PSDrs1409313T vs. C1.10 (1.06–1.14)1.467 × 10−61.0001.0000.0000.1450.0000.1450.0140.936
ESRRGrs12725407C vs. G1.10 (1.06–1.14)2.115 × 10−61.0001.0000.0000.1450.0000.1450.0140.936
HDAC4 MIR2467 MIR4269rs2931203A vs. T0.92 (0.88–0.95)4.243 × 10−61.0001.0000.0000.2610.0000.2610.0310.970
Ma et al., 2009 [32]NArs7704909C(minor)/T(major)1.30 (1.15–1.46)1.53 × 10−50.0880.9920.0960.9910.0090.9050.2950.998
NArs1896731C(minor)/T(major)0.76 (0.67–0.85)1.90 × 10−5 0.0530.9890.0280.9660.0020.6090.0760.988
NArs12518194G(minor)/A(major)1.31 (1.16–1.49)8.34 × 10−6 0.0910.9800.3020.9980.0390.9760.6050.999
NArs4307059C(minor)/T(major)1.31 (1.16–1.48)1.29 × 10−5 0.0790.9850.1530.9950.0140.9360.3830.998
NArs4327572T(minor)/C(major)1.32 (1.17–1.49)4.05 × 10−6 0.0620.9810.1030.9910.0070.8780.2490.997
Anney et al., 2010 [30]NArs4078417C (minor allele)1.19 (1.10–1.30)5.6 × 10−5 0.5741.0000.1670.9950.1030.9910.7951.000
PPP2R5Crs7142002G (minor allele)0.64 (0.53–0.78)2.9 × 10−6 0.0040.3430.6871.0000.0280.9660.4590.999
Kuo et al., 2015 [33]NAALADL2rs3914502A (minor allele)1.4 (1.2–1.6)3.5 × 10−6 0.0120.8440.0620.9850.0010.4820.0510.982
NAALADL2rs2222447A (minor allele)0.7 (0.6–0.8)5.3 × 10−5 0.0050.7630.0300.9690.0000.1780.0130.932
NArs12543592G (minor allele)0.7 (0.6–0.8)3.2 × 10−6 0.0050.7630.0300.9690.0000.1780.0130.932
NArs7026342C (minor allele)1.6 (1.2–2.0)1.8 × 10−4 0.0060.2850.8641.0000.1130.9920.7491.000
NArs7030851A (minor allele)1.6 (1.3–2.0)1.4 × 10−4 0.0060.2850.8641.0000.1130.9920.7491.000
Anney et al., 2012 [34]RASSF5rs11118968A0.44 (0.32–0.61)2.452 × 10−7 0.0000.0060.9301.0000.1170.9930.5040.999
DNERrs6752370G1.62 (1.33–1.96)8.526 × 10−7 0.0010.2140.4070.9990.0030.7640.0890.990
YEATS2rs263035G1.39 (1.22–1.57)2.258 × 10−7 0.0090.8900.0130.9280.0000.1150.0090.898
Noners29456A1.65 (1.37–1.99)1.226 × 10−7 0.0000.1590.2720.9970.0010.5040.0280.967
Noners1936295A1.69 (1.37–2.09)6.636 × 10−7 0.0010.1360.6200.9990.0090.9050.1790.995
Noners4761371A0.46 (0.34–0.63)3.914 × 10−7 0.0000.0100.9241.0000.1110.9920.5210.999
Noners288604G1.58 (1.32–1.88)2.975 × 10−7 0.0010.2790.2070.9960.0010.4730.0320.971
MACROD2rs6110458A1.46 (1.27–1.69)1.806 × 10−7 0.0040.6410.0840.9890.0010.3830.0330.971
MACROD2 NCRNA00186rs14135G1.49 (1.28–1.74)1.778 × 10−7 0.0030.5340.1300.9930.0010.4670.0420.977
NCRNA00186 MACROD2rs1475531C1.53 (1.30–1.79)2.011 × 10−7 0.0010.4020.0830.9890.0000.2130.0130.929
PARD3Brs4675502NA1.28 (1.16–1.41)4.34 × 10−7 0.0950.9990.0060.8560.0010.3620.0300.969
NArs7711337NA0.82 (0.76–0.89)8.25 × 10−7 0.3501.0000.0060.8540.0020.6720.0910.990
NArs7834018NA0.64 (0.53–0.77)7.54 × 10−7 0.0030.3330.4650.9990.0070.8710.1860.996
TAF1Crs4150167NA0.51 (0.39–0.66)2.91 × 10−7 0.0000.0210.7641.0000.0150.9370.1420.994
Gene variants with statistically borderline significance (5 × 10−8 ≤ p-value < 0.05), FPRP > 0.2 or BFDP > 0.2 from meta-analyses of GWAS
Waltes et al., 2014 [46]CYFIP1crs7170637G > A0.85 (0.75, 0.96)0.0070.6251.0000.9341.0000.8981.0000.9931.000
CAMK4crs25925C > G1.31 (1.04, 1.64)0.0210.2220.8810.9881.0000.9541.0000.9961.000
Anney et al., 2017 [31]NArs1436358T vs. C0.86 (0.79–0.93)0.000014730.7851.0000.1680.9950.1370.9940.8441.000
MACROD2 MACROD2-AS1rs6079556A vs. C0.94 (0.91–0.97)0.000017311.0001.0000.1020.9910.1020.9910.8871.000
LINC00535chr8_94389815_II vs. D0.92 (0.89–0.96)0.000021021.0001.0000.1090.9920.1090.9920.8671.000
LINCR-0001 PRSS55rs4840484T vs. C1.07 (1.04–1.11)0.000023071.0001.0000.2320.9970.2320.9970.9451.000
Anney et al., 2017 (continued)ADTRPrs10947543C vs. G0.94 (0.91–0.97)0.0000311.0001.0000.1020.9910.1020.9910.8871.000
LRRC4 MIR593 SND1 SND1-IT1chr7_127644308_DD vs. I0.93 (0.90–0.97)0.000032351.0001.0000.4220.9990.4220.9990.9721.000
CCDC93 DDX18 INSIG2chr2_118616767_DI vs. D0.85 (0.78–0.93)0.000035310.6671.0000.3740.9980.2850.9970.9211.000
NAchr14_99235398_II vs. D0.87 (0.81–0.94)0.000037650.8621.0000.3270.9980.2960.9980.9301.000
TTBK1rs2756174A vs. C0.94 (0.91–0.97)0.000052451.0001.0000.1020.9910.1020.9910.8871.000
HCG4B HLA-A HLA-Hrs115254791T vs. G0.94 (0.90–0.97)0.000053211.0001.0000.1020.9910.1020.9910.8871.000
MIR2113rs9482120A vs. C0.94 (0.91–0.97)0.000095131.0001.0000.1020.9910.1020.9910.8871.000
CRTAP SUSD5chr3_33191013_DI vs. D0.93 (0.89–0.97)0.00009571.0001.0000.4220.9990.4220.9990.9721.000
NArs9285005A vs. G0.91 (0.86–0.96)0.00011470.9991.0000.3540.9980.3540.9980.9561.000
LOC100505609rs73065342T vs. C0.89 (0.83–0.95)0.00011690.9761.0000.3220.9980.3170.9980.9411.000
DCAF4 DPF3 PAPLN PSEN1 RBM25 ZFYVE1rs1203311A vs. C0.86 (0.79–0.94)0.00013940.7561.0000.5400.9990.4700.9990.9601.000
MACROD2rs192259652A vs. T0.91 (0.85–0.96)0.00014380.9991.0000.3540.9980.3540.9980.9561.000
FOXP1rs76188283T vs. C1.09 (1.05–1.14)0.00020931.0001.0000.1420.9940.1420.9940.8921.000
CCDC38 NTN4 SNRPFchr12_96221819_DI vs. D0.94 (0.91–0.97)0.00021281.0001.0000.1020.9910.1020.9910.8871.000
NAchr3_182308608_ID vs. I0.94 (0.90–0.97)0.00027551.0001.0000.1020.9910.1020.9910.8871.000
ASTN2 PAPPA PAPPA-AS1rs7026354A vs. G1.05 (1.03–1.08)0.00030181.0001.0000.4070.9990.4070.9990.9791.000
NArs2368140A vs. G0.94 (0.91–0.98)0.00030491.0001.0000.7831.0000.7831.0000.9931.000
NArs13016472T vs. C0.94 (0.91–0.98)0.00036291.0001.0000.7831.0000.7831.0000.9931.000
DSCAMrs62235658T vs. C0.92 (0.87–0.97)0.00041321.0001.0000.6681.0000.6681.0000.9861.000
NArs3113169C vs. G0.93 (0.90–0.97)0.00042341.0001.0000.4220.9990.4220.9990.9721.000
CASKIN2 GGA3 GRB2 LOC100287042 MIF4GD MIR3678 MIR6785 MRPS7 NUP85 SLC25A19 TMEM94 TSEN54rs12950709A vs. G0.92 (0.87–0.97)0.00043871.0001.0000.6681.0000.6681.0000.9861.000
CAMP CDC25A CSPG5 DHX30 MAP4 MIR1226 MIR4443 SMARCC1 ZNF589rs7429990A vs. C0.94 (0.91–0.97)0.00045251.0001.0000.1020.9910.1020.9910.8871.000
NAchr8_84959513_DD vs. I0.89 (0.83–0.96)0.00046340.9561.0000.7281.0000.7181.0000.9851.000
ACTN2rs4659712A vs. G0.95 (0.92–0.98)0.00049761.0001.0000.5500.9990.5500.9990.9861.000
ASB4rs113706540T vs. C0.93 (0.88–0.97)0.00050061.0001.0000.4220.9990.4220.9990.9721.000
GJD4rs7897060C vs. G0.95 (0.91–0.98)0.00057891.0001.0000.5500.9990.5500.9990.9861.000
AK5 DNAJB4 FAM73A FUBP1 GIPC2
MGC27382 NEXN NEXN-AS1 USP33 ZZZ3
rs12126604T vs. C0.92 (0.87–0.97)0.00061611.0001.0000.6681.0000.6681.0000.9861.000
SEMA6Drs17387110T vs. G0.95 (0.92–0.98)0.00069961.0001.0000.5500.9990.5500.9990.9861.000
NAchr16_62649826_DD vs. I0.87 (0.80–0.95)0.00073690.8311.0000.6971.0000.6570.9990.9791.000
NArs4239875A vs. G1.06 (1.03–1.10)0.00080181.0001.0000.6721.0000.6721.0000.9901.000
CTNNA3 DNAJC12 HERC4 MYPN POU5F1P5 SIRT1chr10_69763783_DI vs. D0.91 (0.86–0.97)0.00084010.9971.0000.7921.0000.7911.0000.9911.000
CLIC5 ENPP4 ENPP5rs7762549A vs. G0.95 (0.92–0.98)0.000851.0001.0000.5500.9990.5500.9990.9861.000
NAchr18_76035713_DD vs. I0.93 (0.88–0.97)0.0008841.0001.0000.4220.9990.4220.9990.9721.000
BRICD5 CASKIN1 DNASE1L2 E4F1 MIR3180-5 MIR4516 MLST8 PGP PKD1 RAB26 SNHG19 SNORD60 TRAF7rs2078282A vs. G0.94 (0.91–0.98)0.00091871.0001.0000.7831.0000.7831.0000.9931.000
OPCMLrs7952100C vs. G1.06 (1.03–1.10)0.00093991.0001.0000.6721.0000.6721.0000.9901.000
LOC101927907 LRRTM4rs58500924A vs. G0.90 (0.84–0.96)0.00097210.9901.0000.5810.9990.5790.9990.9771.000
RNGTTrs35675874A vs. G0.94 (0.91–0.98)0.0010311.0001.0000.7831.0000.7831.0000.9931.000
LOC101928505 LOC101928539chr5_57079215_ID vs. I1.07 (1.03–1.11)0.0010761.0001.0000.2320.9970.2320.9970.9451.000
DPP4 SLC4A10rs2909451T vs. C0.94 (0.90–0.98)0.0010781.0001.0000.7831.0000.7831.0000.9931.000
ERAP2 LNPEPrs55767008T vs. C0.89 (0.82–0.96)0.0011820.9561.0000.7281.0000.7181.0000.9851.000
C2orf15 KIAA1211L LIPT1 LOC101927070 TSGA10rs10202643A vs. T0.95 (0.92–0.98)0.0012691.0001.0000.5500.9990.5500.9990.9861.000
AUTS2rs2293507T vs. G0.88 (0.81–0.96)0.0013370.8901.0000.8171.0000.7991.0000.9891.000
NArs138457704A vs. G1.07 (1.03–1.11)0.0013571.0001.0000.2320.9970.2320.9970.9451.000
GLDCrs13288399C vs. G0.95 (0.91–0.98)0.0013571.0001.0000.5500.9990.5500.9990.9861.000
MTFR1 PDE7Ars1513723C vs. G0.95 (0.92–0.98)0.0014471.0001.0000.5500.9990.5500.9990.9861.000
ASTN2 ASTN2-AS1 PAPPA TRIM32rs146737360T vs. G0.95 (0.92–0.98)0.0015341.0001.0000.5500.9990.5500.9990.9861.000
NAchr6_45726254_DD vs. I0.90 (0.83–0.96)0.0016060.9901.0000.5810.9990.5790.9990.9771.000
NArs6742513C vs. G1.07 (1.03–1.11)0.0016111.0001.0000.2320.9970.2320.9970.9451.000
NArs73204738A vs. C0.92 (0.88–0.97)0.0016171.0001.0000.6681.0000.6681.0000.9861.000
LINC01553rs11817353A vs. C0.95 (0.92–0.98)0.0016781.0001.0000.5500.9990.5500.9990.9861.000
Anney et al., 2017 (continued)RAD51Brs2842330A vs. C1.10 (1.04–1.16)0.0018450.9991.0000.3030.9980.3030.9980.9461.000
RBFOX1rs12930616C vs. G1.05 (1.02–1.09)0.0019851.0001.0000.9131.0000.9131.0000.9981.000
GRID2rs6811974T vs. C0.95 (0.93–0.98)0.0019951.0001.0000.5500.9990.5500.9990.9861.000
NArs7135621T vs. C0.96 (0.93–0.98)0.0020591.0001.0000.0940.9910.0940.9910.9151.000
GFER NOXO1 NPW RNF151 RPS2 SNHG9 SNORA78 SYNGR3 TBL3 ZNF598rs55742253T vs. C0.93 (0.88–0.98)0.0020751.0001.0000.8681.0000.8681.0000.9951.000
PTPRBrs10784860T vs. C0.95 (0.91–0.98)0.0022111.0001.0000.5500.9990.5500.9990.9861.000
LOC101927768rs9387201C vs. G1.09 (1.03–1.14)0.0024271.0001.0000.1420.9940.1420.9940.8921.000
BTBD11 LOC101929162 PRDM4 PWP1rs4964602T vs. G0.95 (0.91–0.98)0.002561.0001.0000.5500.9990.5500.9990.9861.000
NArs1376888T vs. C1.05 (1.02–1.08)0.0026681.0001.0000.4070.9990.4070.9990.9791.000
KLHL29rs10182178A vs. G1.05 (1.02–1.08)0.0035081.0001.0000.4070.9990.4070.9990.9791.000
UBE2Hrs78661858A vs. G0.91 (0.85–0.97)0.0036650.9971.0000.7921.0000.7911.0000.9911.000
VAPArs29063A vs. G1.04 (1.01–1.07)0.0040751.0001.0000.8731.0000.8731.0000.9971.000
NArs190401890A vs. T1.12 (1.04–1.20)0.0041140.9751.0000.5680.9990.5620.9990.9751.000
LOC102723427rs192668887T vs. C0.91 (0.84–0.97)0.0042050.9971.0000.7921.0000.7911.0000.9911.000
SLC12A7rs73031119A vs. C0.91 (0.84–0.97)0.0043990.9971.0000.7921.0000.7911.0000.9911.000
ADGRL2rs75695875A vs. G0.93 (0.87–0.98)0.0047151.0001.0000.8681.0000.8681.0000.9951.000
NArs1943999C vs. G0.96 (0.92–0.99)0.0049151.0001.0000.9031.0000.9031.0000.9981.000
DNAH6rs2222734A vs. G0.92 (0.87–0.98)0.0050580.9991.0000.9061.0000.9061.0000.9961.000
OR8A1 OR8B12rs2226753T vs. C0.96 (0.93–0.99)0.0050741.0001.0000.9031.0000.9031.0000.9981.000
TUSC5rs35713482A vs. G1.05 (1.01–1.08)0.0051541.0001.0000.4070.9990.4070.9990.9791.000
C5orf15 VDAC1rs67120295T vs. C1.06 (1.02–1.10)0.0057451.0001.0000.6721.0000.6721.0000.9901.000
NArs76010911A vs. G1.11 (1.04–1.19)0.0062550.9861.0000.7691.0000.7671.0000.9891.000
MTMR9 SLC35G5 TDHrs6601581T vs. C1.06 (1.02–1.11)0.0064631.0001.0000.9301.0000.9301.0000.9981.000
HSDL2 MIR3134 PTBP3 SUSD1rs7024761A vs. G1.05 (1.02–1.09)0.006481.0001.0000.9131.0000.9131.0000.9981.000
CRTC3 GABARAPL3 IQGAP1 ZNF774rs2601187A vs. G1.05 (1.01–1.08)0.0068591.0001.0000.4070.9990.4070.9990.9791.000
LOC101927189 LRRC1rs4715431A vs. G1.04 (1.01–1.08)0.0070071.0001.0000.9771.0000.9771.0000.9991.000
NArs646680A vs. G0.95 (0.92–0.99)0.007231.0001.0000.9371.0000.9371.0000.9981.000
CCNE1rs12609867A vs. G0.95 (0.91–0.99)0.007431.0001.0000.9371.0000.9371.0000.9981.000
NOS1AP OLFML2Brs75192393T vs. C1.07 (1.02–1.12)0.0076971.0001.0000.7871.0000.7871.0000.9931.000
KDM4A KDM4A-AS1 LOC101929592
MIR6079 PTPRF ST3GAL3
rs79857083T vs. C1.04 (1.01–1.08)0.0077581.0001.0000.9771.0000.9771.0000.9991.000
NArs142968358T vs. G1.04 (1.01–1.07)0.0077891.0001.0000.8731.0000.8731.0000.9971.000
C3orf30 IGSF11 IGSF11-AS1 UPK1Brs1102586A vs. G1.06 (1.02–1.10)0.0078441.0001.0000.6721.0000.6721.0000.9901.000
NAchr11_98107192_DD vs. I1.04 (1.01–1.08)0.007851.0001.0000.9771.0000.9771.0000.9991.000
C9orf135rs76014157A vs. G0.90 (0.82–0.98)0.0079460.9621.0000.9411.0000.9391.0000.9971.000
NArs6437449A vs. G1.07 (1.02–1.11)0.0087081.0001.0000.2320.9970.2320.9970.9451.000
MYO5Achr15_52811815_DI vs. D0.90 (0.81–0.98)0.0087990.9621.0000.9411.0000.9391.0000.9971.000
NArs9466619A vs. G0.95 (0.92–0.99)0.0090711.0001.0000.9371.0000.9371.0000.9981.000
NArs6117854A vs. G0.96 (0.93–0.99)0.010121.0001.0000.9031.0000.9031.0000.9981.000
C7orf33rs6955951A vs. T1.04 (1.01–1.07)0.010151.0001.0000.8731.0000.8731.0000.9971.000
LHX6rs72767788A vs. C0.95 (0.91–0.99)0.010931.0001.0000.9371.0000.9371.0000.9981.000
NArs2028664A vs. C1.04 (1.01–1.07)0.010951.0001.0000.8731.0000.8731.0000.9971.000
ELAVL2rs180861134A vs. T1.05 (1.01–1.09)0.011041.0001.0000.9131.0000.9131.0000.9981.000
RASGEF1Crs12659560T vs. C1.04 (1.01–1.07)0.01121.0001.0000.8731.0000.8731.0000.9971.000
MIR548AZ SYNE2rs2150291T vs. C1.05 (1.01–1.09)0.01131.0001.0000.9131.0000.9131.0000.9981.000
WDFY4rs118059975A vs. C0.95 (0.91–0.99)0.011461.0001.0000.9371.0000.9371.0000.9981.000
LINC01525 MAN1A2rs3820500A vs. G1.04 (1.01–1.07)0.01161.0001.0000.8731.0000.8731.0000.9971.000
GALNT10rs17629195T vs. C1.04 (1.01–1.07)0.0121.0001.0000.8731.0000.8731.0000.9971.000
MIR597 TNKSrs78853604T vs. C1.05 (1.01–1.08)0.012561.0001.0000.4070.9990.4070.9990.9791.000
EXT1rs7835763A vs. T1.04 (1.01–1.08)0.012831.0001.0000.9771.0000.9771.0000.9991.000
NArs4652928A vs. G0.96 (0.92–0.99)0.013841.0001.0000.9031.0000.9031.0000.9981.000
PDE1Crs11976985T vs. C0.95 (0.92–0.99)0.01411.0001.0000.9371.0000.9371.0000.9981.000
BAX FTL GYS1rs2230267T vs. C1.04 (1.01–1.07)0.014291.0001.0000.8731.0000.8731.0000.9971.000
Anney et al., 2017 (continued)GRID2rs6854329C vs. G0.92 (0.86–0.99)0.014860.9961.0000.9631.0000.9631.0000.9981.000
NArs1926229C vs. G1.05 (1.01–1.08)0.014961.0001.0000.4070.9990.4070.9990.9791.000
NArs261351T vs. C0.96 (0.93–0.99)0.014981.0001.0000.9031.0000.9031.0000.9981.000
RAPGEF2rs4440173A vs. G1.04 (1.01–1.07)0.015641.0001.0000.8731.0000.8731.0000.9971.000
MIR4650-1 MIR4650-2 POM121 SBDSP1 SPDYE7P TYW1Brs4392770T vs. C1.05 (1.01–1.09)0.015641.0001.0000.9131.0000.9131.0000.9981.000
NArs138493916C vs. G1.08 (1.02–1.14)0.017831.0001.0000.8401.0000.8401.0000.9941.000
NArs615512A vs. G1.08 (1.02–1.14)0.018111.0001.0000.8401.0000.8401.0000.9941.000
EP400 EP400NL PUS1 SNORA49rs11608890T vs. G0.94 (0.88–0.99)0.01871.0001.0000.9511.0000.9511.0000.9981.000
DIAPH3chr13_60161890_II vs. D1.05 (1.01–1.09)0.019841.0001.0000.9131.0000.9131.0000.9981.000
ADAM12rs1674923T vs. C0.96 (0.93–0.99)0.02031.0001.0000.9031.0000.9031.0000.9981.000
ATP2B2 GHRL GHRLOS IRAK2 LINC00852
MIR378B MIR885 SEC13 TATDN2
rs7619385A vs. G1.04 (1.01–1.07)0.021021.0001.0000.8731.0000.8731.0000.9971.000
UNC13Crs75099274A vs. G1.08 (1.01–1.14)0.021231.0001.0000.8401.0000.8401.0000.9941.000
ZSWIM6rs10053166A vs. G0.95 (0.90–0.99)0.022261.0001.0000.9371.0000.9371.0000.9981.000
HIVEP3rs2786484T vs. C0.93 (0.86–0.99)0.02371.0001.0000.9581.0000.9581.0000.9981.000
FJX1 TRIM44rs76847144T vs. C0.93 (0.86–0.99)0.026431.0001.0000.9581.0000.9581.0000.9981.000
WBSCR17rs148521358C vs. G0.94 (0.88–0.99)0.027311.0001.0000.9511.0000.9511.0000.9981.000
MIR3134 SUSD1rs2564899T vs. C0.97 (0.94–1.00)0.027351.0001.0000.9801.0000.9801.0000.9991.000
NAchr8_138837351_II vs. D1.05 (1.01–1.09)0.02841.0001.0000.9131.0000.9131.0000.9981.000
LINC01393 MDFICrs7799732A vs. G1.03 (1.00–1.06)0.031141.0001.0000.9781.0000.9781.0000.9991.000
TBX18 TBX18-AS1rs76397051A vs. G1.05 (1.01–1.10)0.0341.0001.0000.9751.0000.9751.0000.9991.000
NArs171794T vs. C1.06 (1.01–1.12)0.035871.0001.0000.9741.0000.9741.0000.9991.000
GDArs4327921A vs. G0.97 (0.94–1.00)0.039381.0001.0000.9801.0000.9801.0000.9991.000
NArs2167341T vs. G1.05 (1.00–1.10)0.042031.0001.0000.9751.0000.9751.0000.9991.000
EVA1Crs62216215A vs. C1.04 (1.00–1.08)0.045981.0001.0000.9771.0000.9771.0000.9991.000
LINC01036rs17589281T vs. C0.95 (0.89–1.00)0.047161.0001.0000.9801.0000.9801.0000.9991.000
LOC283585rs61979775T vs. C0.97 (0.93–1.00)0.048131.0001.0000.9801.0000.9801.0000.9991.000
CHMP4A GMPR2 MDP1 NEDD8
NEDD8-MDP1 TM9SF1 TSSK4
rs72694312T vs. G1.06 (1.00–1.11)0.048141.0001.0000.9301.0000.9301.0000.9981.000
Ma et al., 2009 [32]NArs10065041T(minor)/C(major)1.21 (1.08–1.36)3.24 × 10−4 0.4451.0000.7571.0000.5810.9990.9701.000
NArs10038113C(minor)/T(major)0.75 (0.70–0.90)3.40 × 10−6 0.1290.8970.9391.0000.6881.0000.9791.000
NArs6894838T(minor)/C(major)1.26 (1.12–1.42)8.00 × 10−5 0.2120.9980.4160.9990.1310.9930.8271.000
Anney et al., 2010 [30]HAT1rs6731562G (minor allele)1.25 (1.11–1.41)2.0 × 10−40.2530.9980.5270.9990.2200.9960.8911.000
POU6F2rs10258862G (minor allele)1.09 (1.00–1.18)4.6 × 10−20.9911.0000.9711.0000.9711.0000.9981.000
NArs6557675A (minor allele)0.84 (0.76–0.93)1.0 × 10−30.5611.0000.5830.9990.4400.9990.9531.000
MYH11rs17284809A (minor allele)0.63 (0.50–0.79)5.7 × 10−50.0080.3120.8911.0000.1680.9950.8211.000
GSG1Lrs205409G (minor allele)0.91 (0.84–0.99)2.8 × 10−20.9801.0000.9661.0000.9661.0000.9981.000
TAF1Crs4150167A (minor allele)0.54 (0.40–0.73)2.1 × 10−50.0020.0850.9631.0000.4200.9990.9051.000
Kuo et al., 2015 [33]GLIS1rs12082358C (minor allele)1.3 (1.1–1.5)2.2 × 10−40.1360.9750.7051.0000.2510.9970.9061.000
GLIS1rs12080993A (minor allele)1.3 (1.1–1.5)1.5 × 10−40.1360.9750.7051.0000.2510.9970.9061.000
GPD2rs3916984A (minor allele)1.3 (1.1–1.5)3.1 × 10−40.1360.9750.7051.0000.2510.9970.9061.000
LRP2/BBS5rs13014164C (minor allele)1.7 (1.3–2.3)8.6 × 10−50.0120.2090.9801.0000.7351.0000.9741.000
PDGFRArs7697680G (minor allele)1.5 (1.2–1.9)9.2 × 10−40.0320.5000.9601.0000.6070.9990.9671.000
FSTL4rs11741756A (minor allele)1.3 (1.1–1.5)1.2 × 10−20.1360.9750.7051.0000.2510.9970.9061.000
NArs13211684G (minor allele)1.3 (1.1–1.5)2.5 × 10−30.1360.9750.7051.0000.2510.9970.9061.000
NArs10966205T (minor allele)1.3 (1.2–1.5)2.9 × 10−50.1360.9750.7051.0000.2510.9970.9061.000
C10orf68rs10763893A (minor allele)1.6 (1.2–2.2)6.1 × 10−40.0380.3460.9901.0000.9171.0000.9921.000
NArs12366025A (minor allele)1.3 (1.1–1.6)3.8 × 10−30.2250.9120.9831.0000.9361.0000.9951.000
NArs11030597G (minor allele)1.3 (1.1–1.6)4.1 × 10−30.2250.9120.9831.0000.9361.0000.9951.000
NArs7933990A (minor allele)1.3 (1.1–1.6)2.5 × 10−30.2250.9120.9831.0000.9361.0000.9951.000
NArs11030606A (minor allele)1.3 (1.1–1.6)5.6 × 10−30.2250.9120.9831.0000.9361.0000.9951.000
MACROD2rs17263514A (minor allele)1.2 (1.0–1.4)1.4 × 10−20.5000.9980.9761.0000.9531.0000.9961.000
BCAS1/CYP24A1rs12479663C (minor allele)1.5 (1.3–1.9)4.0 × 10−50.0320.5000.9601.0000.6070.9990.9671.000
Abbreviations: A, Adenine; C, Cytosine; G, Guanine; T, Thymine; D, Deletion; I, Insertion; R, Risk allele; NR, Non-risk allele; FPRP, false positive rate probability; BFDP, Bayesian false discovery probability; OR, odds ratio; CI, confidence interval; NA, not available.
Table 3. Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8p-value < 0.05) in the genome-wide association studies (GWAS) catalog.
Table 3. Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8p-value < 0.05) in the genome-wide association studies (GWAS) catalog.
Author, YearGeneVariantComparisonOR (95% CI)p-ValuePower
OR 1.2
Power
OR 1.5
FPRP Values at Prior ProbabilityBFDP
0.001
BFDP
0.000001
OR 1.2OR 1.5
0.0010.0000010.0010.000001
Gene variants with statistically significance (p-value < 5 × 10−8), FPRP < 0.2 and BFDP < 0.8 from GWAS catalog
Anney et al., 2010 [30]MACROD2rs4141463NA1.37 (1.22–1.52)4.00 × 10−8 0.006 0.956 0.000 0.316 0.000 0.003 0.000 0.208
Chaste et al., 2014 [35]AL163541.1rs4773054NA2.66 (1.83–3.86)5.00 × 10−8 0.000 0.001 0.949 1.000 0.169 0.995 0.526 0.999
Gene variants with statistically borderline significance (5 × 10−8 ≤ p-value < 0.05), FPRP < 0.2 and BFDP < 0.8 from GWAS catalog
Anney et al., 2010 [30]PPP2R5Crs7142002NA1.56 (1.28–1.89)3.00 × 10−6 0.004 0.344 0.602 0.999 0.016 0.942 0.338 0.998
Anney et al., 2012 [34]TAF1Crs4150167NA1.96 (1.52–2.56)3.00 × 10−7 0.000 0.025 0.832 1.000 0.031 0.969 0.269 0.997
Anney et al., 2012 [34] PARD3Brs4675502NA1.28 (1.16–1.41)4.00 × 10−70.095 0.999 0.006 0.856 0.001 0.362 0.030 0.969
Anney et al., 2012 [34]AC113414.1rs7711337NA1.22 (1.12–1.32)8.00 × 10−70.340 1.000 0.002 0.689 0.001 0.429 0.038 0.975
Anney et al., 2012 [34]AC009446.1, EYA1rs7834018NA1.56 (1.3–1.89)8.00 × 10−70.004 0.344 0.602 0.999 0.016 0.942 0.338 0.998
Anney et al., 2017 [31]AL133270.1, AL139093.1rs142968358T (risk allele)1.1 (1.06–1.14)1.00 × 10−61.000 1.000 0.000 0.145 0.000 0.145 0.014 0.936
Anney et al., 2017 [31]EXT1rs7835763A (risk allele)1.1 (1.06–1.14)2.00 × 10−61.000 1.000 0.000 0.145 0.000 0.145 0.014 0.936
Chaste et al., 2014 [35]INHCAPrs1867503NA1.55 (1.30–1.84)4.00 × 10−70.002 0.354 0.241 0.997 0.002 0.608 0.058 0.984
Chaste et al., 2014 [35]CUEDC2rs1409313NA1.75 (1.40–2.18)4.00 × 10−70.000 0.085 0.610 0.999 0.007 0.876 0.121 0.993
Chaste et al., 2014 [35]CTU2rs11641365NA2.06 (1.54–2.76)3.00 × 10−70.0000.0170.8971.0000.0710.9870.4330.999
Chaste et al., 2014 [35]AC067752.1, AC024598.1, ZNF365rs93895NA1.91 (1.48–2.47)2.00 × 10−70.0000.0330.8041.0000.0240.9610.2410.997
Kuo et al., 2015 [33]LINC01151, AC108136.1rs12543592G (risk allele)1.43 (1.25–1.67)3.00 × 10−60.013 0.727 0.318 0.998 0.008 0.895 0.275 0.997
Kuo et al., 2015 [33]NAALADL2rs3914502A (risk allele)1.4 (1.20–1.60)4.00 × 10−60.012 0.844 0.062 0.985 0.001 0.482 0.051 0.982
Kuo et al., 2015 [33]OR2M4rs10888329NA1.82 (1.39–2.33)8.00 × 10−60.000 0.062 0.809 1.000 0.031 0.970 0.338 0.998
Kuo et al., 2015 [33]SGSM2rs2447097A (risk allele)1.53 (1.27–1.85)9.00 × 10−60.006 0.419 0.652 0.999 0.026 0.965 0.467 0.999
Ma et al., 2009 [32]Intergenic (RNU6-374P - MSNP1)rs10038113T (risk allele)1.33 (1.11–1.43]3.00 × 10−60.003 0.999 0.000 0.000 0.000 0.000 0.000 0.000
Gene variants with statistically borderline significance (5 × 10-8≤ p-value < 0.05), FPRP > 0.2 or BFDP > 0.8 from GWAS catalog
Chaste et al., 2014 [35]AL163541.1rs4773054NA2.9 (1.91–4.39)7.00 × 10−80.000 0.001 0.970 1.000 0.345 0.998 0.741 1.000
Anney et al., 2017 [31]HLA-A, AL671277.1rs115254791G (risk allele)1.0869565 (1.05–1.14)4.00 × 10−61.000 1.000 0.376 0.998 0.376 0.998 0.963 1.000
Abbreviations: A, Adenine; G; Guanine; T, Thymine; FPRP, false positive rate probability; BFDP, Bayesian false discovery probability; OR, odds ratio; CI, confidence interval; F, fixed effects model; R, random effects model; NA, not available; ASD, autism spectrum disorder.
Table 4. Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8p-value < 0.05) in the GWAS datasets included in GWAS meta-analyses (results of FPRP < 0.2 and BFDP < 0.8).
Table 4. Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8p-value < 0.05) in the GWAS datasets included in GWAS meta-analyses (results of FPRP < 0.2 and BFDP < 0.8).
Author, YearTraitGene(s)VariantComparisonOR (95% CI)p-ValuePower OR 1.2Power OR 1.5FPRP Values at Prior ProbabilityBFDP
0.001
BFDP
0.000001
OR 1.2OR 1.5
0.0010.0000010.0010.000001
Anney et al., 2012 [34]ASD (European)ERBB4rs1879532A2.02 (1.57–2.59)1.55 × 10−80.0000.0090.5950.9990.0030.7570.0260.964
Anney et al., 2012 [34]Autism (European)Noners289932A0.49 (0.38–0.64)5.04 × 10−80.0000.0120.7721.0000.0140.9320.1140.992
Anney et al., 2012 [34]ASDTMEM132Brs16919315A0.53 (0.42–0.67)5.12 × 10−80.0000.0280.5890.9990.0040.8000.0490.981
Anney et al., 2012 [34]Autism (European)ERBB4rs1879532A1.72 (1.39–2.11)1.66 × 10−7 0.0000.0950.4160.9990.0020.6760.0440.979
Anney et al., 2010 [30]AutismNArs6557675A (minor allele)0.61 (0.51–0.71)2.20 × 10−70.0000.1260.0060.8610.0000.0010.0000.048
Anney et al., 2012 [34]Autism (European)Noners289858A0.52 (0.40–0.67)2.81 × 10−70.0000.0270.7621.0000.0150.9400.1610.995
Anney et al., 2012 [34]ASDSYNE2rs2150291A1.72 (1.40–2.13)2.83 × 10−70.0000.1050.5790.9990.0060.8640.1190.993
Anney et al., 2012 [34]ASD (European)RPH3ALrs7207517A1.97 (1.51–2.57)3.05 × 10−70.0000.0220.8171.0000.0250.9630.2260.997
Anney et al., 2012 [34]Autism (European)Noners4761371A0.46 (0.34–0.63)3.91 × 10−70.0000.0100.9241.0000.1110.9920.5210.999
Anney et al., 2012 [34]ASD (European)PRAMEF12rs1812242A1.44 (1.25–1.66)4.29 × 10−70.0060.7130.0770.9880.0010.4110.0380.975
Anney et al., 2012 [34]ASDNoners10904487G0.63 (0.52–0.75)4.29 × 10−70.0010.2620.1980.9960.0010.4400.0280.966
Anney et al., 2012 [34]Autism (European)Noners289932A0.67 (0.57–0.79)5.42 × 10−70.0050.5240.2860.9980.0040.7840.1350.994
Anney et al., 2010 [30]AutismMACROD2rs4141463A (minor allele)0.62 (0.52–0.73)5.50 × 10−70.0000.1920.0470.9800.0000.0480.0020.655
Anney et al., 2012 [34]AutismNoners9608521A1.46 (1.25–1.69)7.62 × 10−70.0040.6410.0840.9890.0010.3830.0330.971
Anney et al., 2012 [34]ASDNoners1408744A0.65 (0.54–0.77)8.06 × 10−70.0020.3850.2350.9970.0020.6180.0620.985
Anney et al., 2017 [31]ASDLINC00535chr8_94389815_II vs. D1.14 (1.09–1.19)9.47 × 10−70.9901.0000.0000.0020.0000.0020.6861.000
Anney et al., 2012 [34]ASD (European)PCrs7122539A0.60 (0.49–0.74)9.64 × 10−70.0010.1620.6280.9990.0110.9170.2130.996
Anney et al., 2010 [30]AutismMACROD2rs4814324A (minor allele)1.58 (1.34–1.86)9.80 × 10−70.0000.2660.0760.9880.0000.1280.0060.859
Anney et al., 2010 [30]AutismMACROD2rs6079544A (minor allele)1.57 (1.33–1.84)1.20 × 10−60.0000.2870.0530.9820.0000.0810.0040.797
Anney et al., 2017 [31]ASDEXOC4rs6467494T vs. C1.12 (1.07–1.16)1.43 × 10−61.0001.0000.0000.0000.0000.0000.1970.996
Anney et al., 2010 [30]AutismMACROD2rs6079536A (minor allele)0.64 (0.54–0.75)1.60 × 10−60.0010.3070.0590.9840.0000.1020.0050.837
Anney et al., 2010 [30]ASDMYH11rs17284809A (minor allele)0.52 (0.39–0.69)1.70 × 10−60.0010.0430.9151.0000.1210.9930.6360.999
Anney et al., 2010 [30]AutismMACROD2rs6079553A (minor allele)1.55 (1.31–1.82)2.10 × 10−60.0010.3440.0900.9900.0000.2040.0110.920
Anney et al., 2010 [30]AutismMACROD2rs6074798A (minor allele)1.56 (1.32–1.84)2.10 × 10−60.0010.3210.1230.9930.0000.2870.0170.945
Anney et al., 2017 [31]ASDOPCMLrs7952100C vs.G1.14 (1.09–1.19)2.49 × 10−60.9901.0000.0000.0020.0000.0020.6861.000
Anney et al., 2010 [30]AutismMACROD2rs10446030G (minor allele)1.54 (1.30–1.81)3.20 × 10−60.0010.3750.1160.9920.0000.3010.0190.951
Kuo et al., 2015 [33]ASDSTYK1rs16922945C (minor allele)1.86 (1.43–2.43)3.43 × 10−60.0010.0570.8911.0000.0850.9890.5720.999
Anney et al., 2010 [30]ASDPOU5F2rs10258862G (minor allele)1.41 (1.23–1.61)3.70 × 10−60.0090.8200.0430.9780.0000.3190.0270.966
Anney et al., 2010 [30]AutismMACROD2rs6079540A (minor allele)0.65 (0.55–0.77)3.70 × 10−60.0020.3850.2350.9970.0020.6180.0620.985
Anney et al., 2010 [30]AutismMACROD2rs6074787A (minor allele)1.53 (1.30–1.80)4.10 × 10−60.0020.4060.1470.9940.0010.4180.0310.970
Anney et al., 2010 [30]ASDMACROD2rs6074798A (minor allele)1.38 (1.22–1.56)4.80 × 10−60.0130.9090.0200.9540.0000.2240.0180.948
Anney et al., 2010 [30]AutismMACROD2rs980319G (minor allele)1.52 (1.29–1.79)5.10 × 10−60.0020.4370.1840.9960.0010.5430.0500.981
Anney et al., 2010 [30]AutismMACROD2rs6079537G (minor allele)1.52 (1.29–1.79)6.00 × 10−60.0020.4370.1840.9960.0010.5430.0500.981
Kuo et al., 2015 [33]ASDNArs10966205A (minor allele)1.52 (1.27–1.83)6.25 × 10−60.0060.4440.6090.9990.0220.9570.4260.999
Kuo et al., 2015 [33]ASDOR2M4rs10888329T (minor allele)0.55 (0.43–0.72)8.05 × 10−60.0010.0810.9161.0000.1440.9940.7181.000
Anney et al., 2010 [30]ASDMACROD2rs6079536A (minor allele)0.73 (0.65–0.83)8.50 × 10−60.0220.9170.0670.9860.0020.6280.0840.989
Anney et al., 2010 [30]ASDNArs6557675A (minor allele)0.72 (0.63–0.82)8.70 × 10−60.0140.8770.0510.9820.0010.4570.0470.980
Kuo et al., 2015 [33]ASDNArs7933990A (minor allele)1.72 (1.35–2.19)9.40 × 10−60.0020.1330.8611.0000.0750.9880.6060.999
Kuo et al., 2015 [33]ASDMNTrs2447097A (minor allele)1.53 (1.27–1.85)9.45 × 10−60.0060.4190.6520.9990.0260.9650.4670.999
Anney et al., 2010 [30]ASDGSG1Lrs205409G (minor allele)0.72 (0.64–0.82)9.60 × 10−60.0140.8770.0510.9820.0010.4570.0470.980
Kuo et al., 2015 [33]ASDOR2M4rs6672981C (minor allele)0.55 (0.42–0.72)9.64 × 10−60.0010.0810.9161.0000.1440.9940.7181.000
Kuo et al., 2015 [33]ASDOR2M4rs4397683C (minor allele)0.55 (0.42–0.72)9.86 × 10−60.0010.0810.9161.0000.1440.9940.7181.000
Anney et al., 2010 [30]ASDMACROD2rs980319G (minor allele)1.36 (1.20–1.54)1.00 × 10−50.0240.9390.0490.9810.0010.5700.0680.987
Kuo et al., 2015 [33]ASDBCAS1/CYP24A1rs12479663G (minor allele)1.81 (1.38–2.36)1.08 × 10−50.0010.0830.9071.0000.1240.9930.6871.000
Anney et al., 2010 [30]ASDMACROD2rs4814324A (minor allele)1.36 (1.20–1.54)1.10 × 10−50.0240.9390.0490.9810.0010.5700.0680.987
Kuo et al., 2015 [33]ASDKRR1rs3741496C (minor allele)1.49 (1.24–1.78)1.15 × 10−50.0090.5290.5650.9990.0200.9540.4300.999
Kuo et al., 2015 [33]ASDOR2M4rs4642918C (minor allele)0.56 (0.43–0.73)1.24 × 10−50.0020.0990.9171.0000.1550.9950.7451.000
Anney et al., 2010 [30]ASDMACROD2rs6079544A (minor allele)1.35 (1.20–1.53)1.30 × 10−50.0330.9510.0740.9880.0030.7330.1240.993
Kuo et al., 2015 [33]ASDNArs13211684G (minor allele)1.56 (1.28–1.91)1.36 × 10−50.0060.3520.7501.0000.0450.9790.5720.999
Kuo et al., 2015 [33]ASDMNTrs2447095A (minor allele)1.52 (1.26–1.84)1.45 × 10−50.0080.4460.6951.0000.0380.9750.5520.999
Kuo et al., 2015 [33]ASDNArs12543592G (minor allele)0.67 (0.56–0.81)1.63 × 10−50.0120.5210.7441.0000.0630.9850.6781.000
Anney et al., 2010 [30]ASDMACROD2rs6079553A (minor allele)1.35 (1.19–1.52)1.70 × 10−50.0260.9590.0270.9650.0010.4240.0410.977
Kuo et al., 2015 [33]ASDKRR1rs1051446C (minor allele)1.47 (1.23–1.76)1.77 × 10−50.0140.5870.6691.0000.0450.9790.6140.999
Anney et al., 2010 [30]ASDNArs4078417C (minor allele)1.38 (1.21–1.57)1.90 × 10−50.0170.8970.0550.9830.0010.5240.0590.984
Anney et al., 2010 [30]ASDMACROD2rs10446030G (minor allele)1.34 (1.19–1.52)2.20 × 10−50.0430.9600.1100.9920.0060.8470.2100.996
Kuo et al., 2015 [33]ASDGPD2rs3916984T (minor allele)0.62 (0.49–0.77)2.25 × 10−50.0040.2560.8041.0000.0560.9840.5950.999
Kuo et al., 2015 [33]ASDNArs12366025T (minor allele)1.67 (1.31–2.11)2.49 × 10−50.0030.1840.8601.0000.0860.9890.6620.999
Ma et al., 2009 [32]AutismNArs10038113C(minor)/T(major)0.67 (0.56–0.81)2.75 × 10−50.0120.5210.7441.0000.0630.9850.6781.000
Anney et al., 2010 [30]ASDMACROD2rs6079540A (minor allele)0.75 (0.66–0.84)2.90 × 10−50.0340.9790.0190.9500.0010.3990.0370.975
Anney et al., 2010 [30]AutismHAT1rs6731562G (minor allele)1.51 (1.27–1.81)3.30 × 10−50.0060.4710.5620.9990.0170.9460.3830.998
Anney et al., 2010 [30]ASDMACROD2rs6074787A (minor allele)1.33 (1.18–1.50)3.40 × 10−50.0470.9750.0670.9860.0030.7760.1470.994
Kuo et al., 2015 [33]ASDGLIS1rs12080933A (minor allele)1.48 (1.23–1.78)3.57 × 10−50.0130.5570.7071.0000.0530.9830.6480.999
Kuo et al., 2015 [33]ASDFSTL4rs11741756T (minor allele)1.67 (1.31–2.13)3.64 × 10−50.0040.1940.9031.0000.1570.9950.7851.000
Kuo et al., 2015 [33]ASDSTYK1rs7953930G (minor allele)1.65 (1.30–2.09)3.83 × 10−50.0040.2150.8881.0000.1330.9940.7611.000
Anney et al., 2010 [30]AutismNArs4078417C (minor allele)1.50 (1.26–1.79)4.10 × 10−50.0070.5000.5090.9990.0140.9330.3390.998
Anney et al., 2010 [30]ASDMACROD2rs4141463A (minor allele)0.75 (0.66–0.85)4.30 × 10−50.0490.9670.1180.9930.0070.8730.2430.997
Kuo et al., 2015 [33]ASDOR2M3rs11204613G (minor allele)0.58 (0.45–0.75)4.60 × 10−50.0030.1440.9201.0000.1850.9960.7991.000
Anney et al., 2010 [30]ASDMACROD2rs6079537G (minor allele)1.32 (1.17–1.49)5.40 × 10−50.0620.9810.1030.9910.0070.8780.2490.997
Anney et al., 2010 [30]AutismGSG1Lrs205409G (minor allele)0.69 (0.58–0.81)1.10 × 10−40.0110.6630.3530.9980.0090.8960.2710.997
Anney et al., 2010 [30]AutismPOU5F2rs10258862G (minor allele)1.43 (1.21–1.71)1.80 × 10−40.0270.7000.7641.0000.1120.9920.7991.000
Abbreviations: ASD, Autism spectrum disorders; A, Adenine; C, Cytosine; G, Guanine; T, Thymine; D, Deletion; I, Insertion; FPRP, false positive rate probability; BFDP, Bayesian false discovery probability; OR, odds ratio; CI, confidence interval; GWAS, Genome-Wide Association Studies; NA, not available.
Table 5. Lists of genes involved in the PPI network.
Table 5. Lists of genes involved in the PPI network.
GeneFunction of the Encoding Proteins
OXTRReceptor for oxytocin associated with social recognition and emotion processing
MTHFRInfluences susceptibility to neural tube defect by changing folate metabolism
RELNControl cell positioning and neural migration during brain development
DRD3D3 subtype of the five dopamine receptors; localized to the limbic areas of the brain
MNTProtein member of the Myc/Max/Mad network; transcriptional repressor and an antagonist of Myc-dependent transcriptional activation and cell growth
OPCMLMember of the IgLON subfamily in the immunoglobulin protein superfamily of proteins; localized in the plasma membrane; accessory role in opioid receptor function
PCPyruvate carboxylase; gluconeogenesis, lipogenesis, insulin secretion and synthesis of neurotransmitter glutamate
ERBB4Tyr protein kinase family and the epidermal growth factor receptor subfamily; binds to and is activated by neuregulins, and induces mitogenesis and differentiation
OR2M4Members of a large family of GPCR; olfactory receptors initiating a neuronal response that triggers the perception of a smell
BCAS1Oncogene; highly expressed in three amplified breast cancer cell lines and in one breast tumor without amplification at 20q13.2.
CYP24A1Cytochrome P450 superfamily of enzymes; drug metabolism and synthesis of cholesterol, steroids and other lipids
TMEM132BThe function remains poorly understood despite their mutations associated with non-syndromic hearing loss, panic disorder, and cancer
KRR1Nucleolar protein; 18S rRNA synthesis and 40S ribosomal assembly
HAT1Type B histone acetyltransferase; rapid acetylation of newly synthesized cytoplasmic histones; replication-dependent chromatin assembly
SGSM2GTPase activator; regulators of membrane trafficking
EXT1Endoplasmic reticulum-resident type II transmembrane glycosyltransferase; involved in the chain elongation step of heparan sulfate biosynthesis
OR2T33Members of a large family of GPCR; share a 7-transmembrane domain structure with many neurotransmitter and hormone receptors
TAF1CBinds to the core promoter of ribosomal RNA genes to position the polymerase properly; acts as a channel for regulatory signals
HDAC4Class II of the histone deacetylase/acuc/apha family; represses transcription when tethered to a promoter
MEGF10Member of the multiple epidermal growth factor-like domains protein family; cell adhesion, motility and proliferation; critical mediator of apoptotic cell phagocytosis; amyloid-beta peptide uptake in brain
NFKB2Subunit of the transcription factor complex nuclear factor-kappa-B; central activator of genes involved in inflammation and immune function
BNC2Conserved zinc finger protein; skin color saturation
NMBMember of the bombesin-like family of neuropeptides; negatively regulate eating behavior; regulate colonic smooth muscle contraction
HPS6Organelle biogenesis associated with melanosomes, platelet dense granules, and lysosomes
ELOVL3GNS1/SUR4 family; elongation of long chain fatty acids to provide precursors for synthesis of sphingolipids and ceramides
PITX3Member of the RIEG/PITX homeobox family; transcription factors; lens formation during eye development
NAALADL2Not well-known, but diseases associated with NAALADL2 include Chromosome 6Pter-P24 Deletion Syndrome and Cornelia De Lange Syndrome.
MACROD2Deacetylase removing ADP-ribose from mono-ADP-ribosylated proteins; translocate from the nucleus to the cytoplasm upon DNA damage
CUEDC2CUE domain-containing protein; down-regulate ESR1 protein levels through progesterone-induced and degradation of receptors
FBXL15Substrate recognition component of SCF E3 ubiquitin-protein ligase complex; mediates the ubiquitination and subsequent proteasomal degradation of SMURF1
EXOC4Component of the exocyst complex; targeting exocytic vesicles to specific docking sites on the plasma membrane
NOLC1Nucleolar protein; act as a regulator of RNA polymerase I; neural crest specification; nucleologenesis
PPRC1Similar to PPAR-gamma coactivator 1; activate mitochondrial biogenesis through NRF1 in response to proliferative signals
SEC11AMember of the peptidase S26B family; subunit of the signal peptidase complex; cell migration and invasion, gastric cancer and lymph node metastasis
Abbreviations: OXTR, Oxytocin Receptor; MTHFR, Methylene tetrahydrofolate reductase; RELN, reelin, DRD3, Dopamine Receptor D3; MNT, Myc-associated factor X (MAX) Network Transcriptional Repressor; OPCML, opioid binding protein/cell adhesion molecule-like; PC, Pyruvate carboxylase; ERBB4, Erb-B2 Receptor Tyrosine Kinase 4; OR2M4, olfactory receptor family 2 subfamily M member 4; GPCR, G protein-coupled receptor; BCAS1, Breast Carcinoma Amplified Sequence 1; CYP24A1, Cytochrome P450 Family 24 Subfamily A Member 1; TMEM132B, transmembrane protein 132B; KRR1, KRR1 small subunit processome component homolog; HAT1, histone acetyltransferase 1; SGSM2, small G protein signaling modulator 2; EXT1, Exostosin-1; OR2T33, Olfactory receptor 2T33; TAF1C, TATA-Box Binding Protein Associated Factor, RNA Polymerase I Subunit C; HDAC4, Histone deacetylase 4; MEGF10, Multiple Epidermal Growth Factor Like Domains 10; NFKB2, Nuclear Factor Kappa B Subunit 2; BNC2, basonuclin-2; NMB, Neuromedin B; HPS6, Hermansky–Pudlak syndrome 6; ELOVL3, Elongation Of Very Long Chain Fatty Acids Protein 3, PITX3, Pituitary homeobox 3; NAALADL2, N-Acetylated Alpha-Linked Acidic Dipeptidase Like 2; MACROD2, Mono-ADP Ribosylhydrolase 2; CUEDC2, CUE domain containing 2; FBXL15, F-Box And Leucine Rich Repeat Protein 15; EXOC4, Exocyst Complex Component 4; NOLC1, Nucleolar And Coiled-Body Phosphoprotein 1; PPRC1, peroxisome proliferator-activated receptor gamma, coactivator-related 1; SEC11A, SEC11 Homolog A, Signal Peptidase Complex Subunit.

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MDPI and ACS Style

Lee, J.; Son, M.J.; Son, C.Y.; Jeong, G.H.; Lee, K.H.; Lee, K.S.; Ko, Y.; Kim, J.Y.; Lee, J.Y.; Radua, J.; Eisenhut, M.; Gressier, F.; Koyanagi, A.; Stubbs, B.; Solmi, M.; Rais, T.B.; Kronbichler, A.; Dragioti, E.; Vasconcelos, D.F.P.; Silva, F.R.P.d.; Tizaoui, K.; Brunoni, A.R.; Carvalho, A.F.; Cargnin, S.; Terrazzino, S.; Stickley, A.; Smith, L.; Thompson, T.; Shin, J.I.; Fusar-Poli, P. Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis. Brain Sci. 2020, 10, 692. https://doi.org/10.3390/brainsci10100692

AMA Style

Lee J, Son MJ, Son CY, Jeong GH, Lee KH, Lee KS, Ko Y, Kim JY, Lee JY, Radua J, Eisenhut M, Gressier F, Koyanagi A, Stubbs B, Solmi M, Rais TB, Kronbichler A, Dragioti E, Vasconcelos DFP, Silva FRPd, Tizaoui K, Brunoni AR, Carvalho AF, Cargnin S, Terrazzino S, Stickley A, Smith L, Thompson T, Shin JI, Fusar-Poli P. Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis. Brain Sciences. 2020; 10(10):692. https://doi.org/10.3390/brainsci10100692

Chicago/Turabian Style

Lee, Jinhee, Min Ji Son, Chei Yun Son, Gwang Hun Jeong, Keum Hwa Lee, Kwang Seob Lee, Younhee Ko, Jong Yeob Kim, Jun Young Lee, Joaquim Radua, Michael Eisenhut, Florence Gressier, Ai Koyanagi, Brendon Stubbs, Marco Solmi, Theodor B. Rais, Andreas Kronbichler, Elena Dragioti, Daniel Fernando Pereira Vasconcelos, Felipe Rodolfo Pereira da Silva, Kalthoum Tizaoui, André Russowsky Brunoni, Andre F. Carvalho, Sarah Cargnin, Salvatore Terrazzino, Andrew Stickley, Lee Smith, Trevor Thompson, Jae Il Shin, and Paolo Fusar-Poli. 2020. "Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis" Brain Sciences 10, no. 10: 692. https://doi.org/10.3390/brainsci10100692

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