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. 2020 Sep 30;10(10):692. doi: 10.3390/brainsci10100692

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

Jinhee Lee 1,†, Min Ji Son 2,†, Chei Yun Son 3,†, Gwang Hun Jeong 4,†, Keum Hwa Lee 5,†, Kwang Seob Lee 6, Younhee Ko 7, Jong Yeob Kim 2, Jun Young Lee 8, Joaquim Radua 9,10,11,12, Michael Eisenhut 13, Florence Gressier 14, Ai Koyanagi 15,16,17, Brendon Stubbs 18,19, Marco Solmi 9,20,21, Theodor B Rais 22, Andreas Kronbichler 23, Elena Dragioti 24, Daniel Fernando Pereira Vasconcelos 25, Felipe Rodolfo Pereira da Silva 25, Kalthoum Tizaoui 26, André Russowsky Brunoni 27,28,29,30, Andre F Carvalho 31,32, Sarah Cargnin 33, Salvatore Terrazzino 33, Andrew Stickley 34,35, Lee Smith 36, Trevor Thompson 37, Jae Il Shin 5,*, Paolo Fusar-Poli 9,38,39,*
PMCID: PMC7600188  PMID: 33007889

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.

Keywords: autism spectrum disorder, false positive report probability (FPRP), Bayesian false-discovery probability (BFDP), meta-analysis, Genome-Wide Association Studies (GWAS)

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.

Figure 1.

Figure 1

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Flow chart of literature 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.

Table 1.

Re-analysis results of gene variants with statistical significance (p-value < 0.05) from observational studies.

Author, Year Gene/Variant Comparison OR (95% CI) p-Value Model No. of Studies Power
OR 1.2		 Power
OR 1.5		 FPRP Values at Prior Probability BFDP
0.001		 BFDP
0.000001
OR 1.2 OR 1.5
0.001 0.000001 0.001 0.000001
Gene variants with statistically significance (p-value < 0.05), FPRP < 0.2 and BFDP < 0.8 from observational studies
Rai 2016 [21] MTHFR C677T T vs. C 1.37 (1.25, 1.50) <0.0001 Fixed Overall (13) 0.002 0.975 0.000 0.005 0.000 0.000 0.000 0.001
Mohammad et al., 2016 [23] MTHFR C677T T (minor) 1.47 (1.31, 1.65) <0.0001 Fixed Overall (8) 0.000 0.634 0.000 0.179 0.000 0.000 0.000 0.009
Warrier et al., 2015 [24] DRD3/rs167771 G vs. A 1.822 (1.398, 2.375) 9.08 × 10−6 Fixed Overall (2) 0.001 0.075 0.901 1.000 0.108 0.992 0.649 0.999
Warrier et al., 2015 [24] RELN/rs362691 C vs. G 0.832 (0.763, 0.908) 3.93 × 10−5 Fixed Overall (6) 0.486 1.000 0.071 0.987 0.036 0.974 0.584 0.999
LoParo et al., 2015 [26] OXTR/rs7632287 A (minor) 1.43 (1.23, 1.68) 0.000005 Random Caucasian (2) 0.016 0.720 0.451 0.999 0.018 0.950 0.432 0.999
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/rs2056202 T vs. C 0.809 (0.713, 0.917) 0.001 Fixed Overall (8) 0.321 0.999 0.740 1.000 0.478 0.999 0.957 1.000
Liu et al., 2015 [20] SLC25A12/rs2292813 T vs. C 0.752 (0.649,0.871) <0.001 Fixed Overall (7) 0.085 0.946 0.626 0.999 0.131 0.993 0.831 1.000
Pu et al., 2013 [22] MTHFR C677T TT+CT vs. CC 1.56 (1.12, 2.18) 0.009 Random Overall (8) 0.062 0.409 0.993 1.000 0.957 1.000 0.995 1.000
Pu et al., 2013 [22] MTHFR A1298C CC vs. AA+AC 0.73 (0.56, 0.97) 0.03 Fixed Overall (5) 0.181 0.734 0.994 1.000 0.976 1.000 0.997 1.000
Warrier et al., 2015 [24] SLC25A12/rs2292813 C vs. T 1.372 (1.161, 1.621) 1.97 × 10−4 Fixed Overall (6) 0.058 0.853 0.777 1.000 0.191 0.996 0.877 1.000
Warrier et al., 2015 [24] CNTNAP2/rs7794745 A vs. T 0.887 (0.828, 0.950) 1.00 × 10−3 Fixed Overall (3) 0.963 1.000 0.389 0.998 0.380 0.998 0.952 1.000
Warrier et al., 2015 [24] SLC25A12/rs2056202 T vs. C 1.227 (1.079, 1.396) 2.00 × 10−3 Fixed Overall (8) 0.368 0.999 0.837 1.000 0.654 0.999 0.976 1.000
Warrier et al., 2015 [24] OXTR/rs2268491 T vs. C 1.31 (1.092, 1.572) 4.00 × 10−3 Fixed Overall (2) 0.173 0.927 0.955 1.000 0.799 1.000 0.987 1.000
Warrier et al., 2015 [24] EN2/rs1861972 A vs. G 1.125 (1.035, 1.224) 6.00 × 10−3 Fixed Overall (8) 0.933 1.000 0.869 1.000 0.861 1.000 0.993 1.000
Warrier et al., 2015 [24] MTHFR/rs1801133 T vs. C 1.370 (1.079, 1.739) 1.00 × 10−2 Random Overall (10) 0.138 0.772 0.986 1.000 0.926 1.000 0.994 1.000
Warrier et al., 2015 [24] ASMT/rs4446909 G vs. A 1.195 (1.038, 1.375) 1.30 × 10−2 Fixed Overall (3) 0.523 0.999 0.961 1.000 0.928 1.000 0.995 1.000
Warrier et al., 2015 [24] MET/rs38845 A vs. G 1.322 (1.013, 1.724) 1.60 × 10−2 Random Overall (3) 0.237 0.824 0.994 1.000 0.979 1.000 0.998 1.000
Warrier et al., 2015 [24] SLC6A4/rs2020936 T vs. C 1.244 (1.036, 1.492) 1.90 × 10−2 Fixed Overall (4) 0.349 0.978 0.982 1.000 0.950 1.000 0.996 1.000
Warrier et al., 2015 [24] SLC6A4/STin2 VNTR 12 vs. 9/10 1.492 (1.068, 2.083) 1.90 × 10−2 Fixed Caucasian (4) 0.100 0.513 0.995 1.000 0.973 1.000 0.997 1.000
Warrier et al., 2015 [24] STX1A/rs4717806 A vs. T 0.851 (0.741, 0.978) 2.30 × 10−2 Fixed Overall (4) 0.616 1.000 0.974 1.000 0.958 1.000 0.997 1.000
Warrier et al., 2015 [24] RELN/rs736707 T vs. C 1.269 (1.030, 1.563) 2.50 × 10−2 Random Overall (7) 0.299 0.942 0.988 1.000 0.964 1.000 0.997 1.000
Warrier et al., 2015 [24] PON1/rs662 A vs. G 0.794 (0.642, 0.983) 3.40 × 10−2 Fixed Overall (2) 0.329 0.946 0.990 1.000 0.973 1.000 0.997 1.000
Warrier et al., 2015 [24] OXTR/rs237887 G vs. A 1.163 (1.002, 1.349) 4.70 × 10−2 Fixed Overall (2) 0.660 1.000 0.986 1.000 0.979 1.000 0.998 1.000
Warrier et al., 2015 [24] EN2/rs1861973 T vs. C 0.86 (0.791, 0.954) 3.00 × 10−3 Fixed TDT (3) 0.724 1.000 0.858 1.000 0.814 1.000 0.989 1.000
Aoki et al., 2016 [25] SCL25A12/rs2292813 G (risk allele) 1.190 (1.052, 1.346) 0.006 Random Overall (9) 0.553 1.000 0.911 1.000 0.849 1.000 0.990 1.000
Aoki et al., 2016 [25] SCL25A12/rs2056202 G (risk allele) 1.206 (1.035, 1.405) 0.016 Random Overall (10) 0.474 0.997 0.972 1.000 0.942 1.000 0.996 1.000
LoParo et al., 2015 [26] OXTR/rs237887 G (minor allele) 0.89 (0.79, 0.98) 0.0239 Random Overall (3) 0.910 1.000 0.951 1.000 0.947 1.000 0.997 1.000
LoParo et al., 2015 [26] OXTR/rs2268491 T (minor allele) 1.20 (1.05, 1.35) 0.0075 Random Overall (3) 0.500 1.000 0.828 1.000 0.707 1.000 0.981 1.000
Wang et al., 2014 [27] RELN/rs362691 R vs. NR 0.69 (0.56, 0.86) 0.001 Fixed Overall (7) 0.047 0.620 0.954 1.000 0.607 0.999 0.969 1.000
Torrico et al., 2015 [28] PTCHD1/rs7052177 T (major allele) 0.58 (0.45, 0.76) 6.8 × 10−5 Fixed European (4) † 0.004 0.156 0.948 1.000 0.333 0.998 0.890 1.000
Kranz et al., 2016 [29] OXTR/rs237889 A vs. G 1.12 (1.01, 1.24) 0.0365 Random Overall (3) 0.908 1.000 0.970 1.000 0.967 1.000 0.998 1.000
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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.

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).

Table 2.

Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8 ≤ p-value < 0.05) in GWAS meta-analyses.

Author, Year Gene Variant Comparison OR (95% CI) p-Value Power
OR 1.2		 Power
OR 1.5		 FPRP Values at Prior Probability BFDP
0.001		 BFDP
0.000001
OR 1.2 OR 1.5
0.001 0.000001 0.001 0.000001
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] MACROD2 rs4141463 A (minor allele) 0.73 (0.66–0.82) 3.7 × 10−8 0.013 0.937 0.009 0.898 0.000 0.107 0.008 0.891
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 ZNF592 rs4842996 T vs. C 1.08 (1.05–1.12) 0.00001044 1.000 1.000 0.032 0.971 0.032 0.971 0.688 1.000
EXOC4 rs6467494 T vs. C 1.07 (1.04–1.09) 0.0000172 1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000
NA rs13233145 A vs. C 1.07 (1.04–1.10) 0.00002906 1.000 1.000 0.002 0.618 0.002 0.618 0.136 0.994
NA rs7684366 T vs. C 0.93 (0.90–0.96) 0.00003137 1.000 1.000 0.007 0.882 0.007 0.882 0.373 0.998
MEGF10 rs73785549 C vs. G 1.15 (1.08–1.21) 0.0001308 0.950 1.000 0.000 0.070 0.000 0.067 0.005 0.835
ANO4 rs2055471 A vs. T 1.07 (1.03–1.10) 0.0001334 1.000 1.000 0.002 0.618 0.002 0.618 0.136 0.994
BNC2 rs7860276 A vs. G 1.10 (1.05–1.15) 0.0003196 1.000 1.000 0.026 0.964 0.026 0.964 0.598 0.999
NA rs2293280 C vs. G 1.12 (1.06–1.18) 0.0003606 0.995 1.000 0.020 0.954 0.020 0.954 0.514 0.999
NA rs16975940 T vs. C 1.07 (1.03–1.10) 0.0004742 1.000 1.000 0.002 0.618 0.002 0.618 0.136 0.994
NA rs10169115 C vs. G 1.06 (1.02–1.09) 0.004465 1.000 1.000 0.041 0.977 0.041 0.977 0.778 1.000
C10orf76 CUEDC2 ELOVL3 FBXL15 GBF1 HPS6 LDB1 MIR146B NFKB2 NOLC1 PITX3 PPRC1 PSD rs1409313 T vs. C 1.10 (1.06–1.14) 1.467 × 10−6 1.000 1.000 0.000 0.145 0.000 0.145 0.014 0.936
ESRRG rs12725407 C vs. G 1.10 (1.06–1.14) 2.115 × 10−6 1.000 1.000 0.000 0.145 0.000 0.145 0.014 0.936
HDAC4 MIR2467 MIR4269 rs2931203 A vs. T 0.92 (0.88–0.95) 4.243 × 10−6 1.000 1.000 0.000 0.261 0.000 0.261 0.031 0.970
Ma et al., 2009 [32] NA rs7704909 C(minor)/T(major) 1.30 (1.15–1.46) 1.53 × 10−5 0.088 0.992 0.096 0.991 0.009 0.905 0.295 0.998
NA rs1896731 C(minor)/T(major) 0.76 (0.67–0.85) 1.90 × 10−5 0.053 0.989 0.028 0.966 0.002 0.609 0.076 0.988
NA rs12518194 G(minor)/A(major) 1.31 (1.16–1.49) 8.34 × 10−6 0.091 0.980 0.302 0.998 0.039 0.976 0.605 0.999
NA rs4307059 C(minor)/T(major) 1.31 (1.16–1.48) 1.29 × 10−5 0.079 0.985 0.153 0.995 0.014 0.936 0.383 0.998
NA rs4327572 T(minor)/C(major) 1.32 (1.17–1.49) 4.05 × 10−6 0.062 0.981 0.103 0.991 0.007 0.878 0.249 0.997
Anney et al., 2010 [30] NA rs4078417 C (minor allele) 1.19 (1.10–1.30) 5.6 × 10−5 0.574 1.000 0.167 0.995 0.103 0.991 0.795 1.000
PPP2R5C rs7142002 G (minor allele) 0.64 (0.53–0.78) 2.9 × 10−6 0.004 0.343 0.687 1.000 0.028 0.966 0.459 0.999
Kuo et al., 2015 [33] NAALADL2 rs3914502 A (minor allele) 1.4 (1.2–1.6) 3.5 × 10−6 0.012 0.844 0.062 0.985 0.001 0.482 0.051 0.982
NAALADL2 rs2222447 A (minor allele) 0.7 (0.6–0.8) 5.3 × 10−5 0.005 0.763 0.030 0.969 0.000 0.178 0.013 0.932
NA rs12543592 G (minor allele) 0.7 (0.6–0.8) 3.2 × 10−6 0.005 0.763 0.030 0.969 0.000 0.178 0.013 0.932
NA rs7026342 C (minor allele) 1.6 (1.2–2.0) 1.8 × 10−4 0.006 0.285 0.864 1.000 0.113 0.992 0.749 1.000
NA rs7030851 A (minor allele) 1.6 (1.3–2.0) 1.4 × 10−4 0.006 0.285 0.864 1.000 0.113 0.992 0.749 1.000
Anney et al., 2012 [34] RASSF5 rs11118968 A 0.44 (0.32–0.61) 2.452 × 10−7 0.000 0.006 0.930 1.000 0.117 0.993 0.504 0.999
DNER rs6752370 G 1.62 (1.33–1.96) 8.526 × 10−7 0.001 0.214 0.407 0.999 0.003 0.764 0.089 0.990
YEATS2 rs263035 G 1.39 (1.22–1.57) 2.258 × 10−7 0.009 0.890 0.013 0.928 0.000 0.115 0.009 0.898
None rs29456 A 1.65 (1.37–1.99) 1.226 × 10−7 0.000 0.159 0.272 0.997 0.001 0.504 0.028 0.967
None rs1936295 A 1.69 (1.37–2.09) 6.636 × 10−7 0.001 0.136 0.620 0.999 0.009 0.905 0.179 0.995
None rs4761371 A 0.46 (0.34–0.63) 3.914 × 10−7 0.000 0.010 0.924 1.000 0.111 0.992 0.521 0.999
None rs288604 G 1.58 (1.32–1.88) 2.975 × 10−7 0.001 0.279 0.207 0.996 0.001 0.473 0.032 0.971
MACROD2 rs6110458 A 1.46 (1.27–1.69) 1.806 × 10−7 0.004 0.641 0.084 0.989 0.001 0.383 0.033 0.971
MACROD2 NCRNA00186 rs14135 G 1.49 (1.28–1.74) 1.778 × 10−7 0.003 0.534 0.130 0.993 0.001 0.467 0.042 0.977
NCRNA00186 MACROD2 rs1475531 C 1.53 (1.30–1.79) 2.011 × 10−7 0.001 0.402 0.083 0.989 0.000 0.213 0.013 0.929
PARD3B rs4675502 NA 1.28 (1.16–1.41) 4.34 × 10−7 0.095 0.999 0.006 0.856 0.001 0.362 0.030 0.969
NA rs7711337 NA 0.82 (0.76–0.89) 8.25 × 10−7 0.350 1.000 0.006 0.854 0.002 0.672 0.091 0.990
NA rs7834018 NA 0.64 (0.53–0.77) 7.54 × 10−7 0.003 0.333 0.465 0.999 0.007 0.871 0.186 0.996
TAF1C rs4150167 NA 0.51 (0.39–0.66) 2.91 × 10−7 0.000 0.021 0.764 1.000 0.015 0.937 0.142 0.994
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] CYFIP1c rs7170637 G > A 0.85 (0.75, 0.96) 0.007 0.625 1.000 0.934 1.000 0.898 1.000 0.993 1.000
CAMK4c rs25925 C > G 1.31 (1.04, 1.64) 0.021 0.222 0.881 0.988 1.000 0.954 1.000 0.996 1.000
Anney et al., 2017 [31] NA rs1436358 T vs. C 0.86 (0.79–0.93) 0.00001473 0.785 1.000 0.168 0.995 0.137 0.994 0.844 1.000
MACROD2 MACROD2-AS1 rs6079556 A vs. C 0.94 (0.91–0.97) 0.00001731 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
LINC00535 chr8_94389815_I I vs. D 0.92 (0.89–0.96) 0.00002102 1.000 1.000 0.109 0.992 0.109 0.992 0.867 1.000
LINCR-0001 PRSS55 rs4840484 T vs. C 1.07 (1.04–1.11) 0.00002307 1.000 1.000 0.232 0.997 0.232 0.997 0.945 1.000
Anney et al., 2017 (continued) ADTRP rs10947543 C vs. G 0.94 (0.91–0.97) 0.000031 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
LRRC4 MIR593 SND1 SND1-IT1 chr7_127644308_D D vs. I 0.93 (0.90–0.97) 0.00003235 1.000 1.000 0.422 0.999 0.422 0.999 0.972 1.000
CCDC93 DDX18 INSIG2 chr2_118616767_D I vs. D 0.85 (0.78–0.93) 0.00003531 0.667 1.000 0.374 0.998 0.285 0.997 0.921 1.000
NA chr14_99235398_I I vs. D 0.87 (0.81–0.94) 0.00003765 0.862 1.000 0.327 0.998 0.296 0.998 0.930 1.000
TTBK1 rs2756174 A vs. C 0.94 (0.91–0.97) 0.00005245 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
HCG4B HLA-A HLA-H rs115254791 T vs. G 0.94 (0.90–0.97) 0.00005321 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
MIR2113 rs9482120 A vs. C 0.94 (0.91–0.97) 0.00009513 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
CRTAP SUSD5 chr3_33191013_D I vs. D 0.93 (0.89–0.97) 0.0000957 1.000 1.000 0.422 0.999 0.422 0.999 0.972 1.000
NA rs9285005 A vs. G 0.91 (0.86–0.96) 0.0001147 0.999 1.000 0.354 0.998 0.354 0.998 0.956 1.000
LOC100505609 rs73065342 T vs. C 0.89 (0.83–0.95) 0.0001169 0.976 1.000 0.322 0.998 0.317 0.998 0.941 1.000
DCAF4 DPF3 PAPLN PSEN1 RBM25 ZFYVE1 rs1203311 A vs. C 0.86 (0.79–0.94) 0.0001394 0.756 1.000 0.540 0.999 0.470 0.999 0.960 1.000
MACROD2 rs192259652 A vs. T 0.91 (0.85–0.96) 0.0001438 0.999 1.000 0.354 0.998 0.354 0.998 0.956 1.000
FOXP1 rs76188283 T vs. C 1.09 (1.05–1.14) 0.0002093 1.000 1.000 0.142 0.994 0.142 0.994 0.892 1.000
CCDC38 NTN4 SNRPF chr12_96221819_D I vs. D 0.94 (0.91–0.97) 0.0002128 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
NA chr3_182308608_I D vs. I 0.94 (0.90–0.97) 0.0002755 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
ASTN2 PAPPA PAPPA-AS1 rs7026354 A vs. G 1.05 (1.03–1.08) 0.0003018 1.000 1.000 0.407 0.999 0.407 0.999 0.979 1.000
NA rs2368140 A vs. G 0.94 (0.91–0.98) 0.0003049 1.000 1.000 0.783 1.000 0.783 1.000 0.993 1.000
NA rs13016472 T vs. C 0.94 (0.91–0.98) 0.0003629 1.000 1.000 0.783 1.000 0.783 1.000 0.993 1.000
DSCAM rs62235658 T vs. C 0.92 (0.87–0.97) 0.0004132 1.000 1.000 0.668 1.000 0.668 1.000 0.986 1.000
NA rs3113169 C vs. G 0.93 (0.90–0.97) 0.0004234 1.000 1.000 0.422 0.999 0.422 0.999 0.972 1.000
CASKIN2 GGA3 GRB2 LOC100287042 MIF4GD MIR3678 MIR6785 MRPS7 NUP85 SLC25A19 TMEM94 TSEN54 rs12950709 A vs. G 0.92 (0.87–0.97) 0.0004387 1.000 1.000 0.668 1.000 0.668 1.000 0.986 1.000
CAMP CDC25A CSPG5 DHX30 MAP4 MIR1226 MIR4443 SMARCC1 ZNF589 rs7429990 A vs. C 0.94 (0.91–0.97) 0.0004525 1.000 1.000 0.102 0.991 0.102 0.991 0.887 1.000
NA chr8_84959513_D D vs. I 0.89 (0.83–0.96) 0.0004634 0.956 1.000 0.728 1.000 0.718 1.000 0.985 1.000
ACTN2 rs4659712 A vs. G 0.95 (0.92–0.98) 0.0004976 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
ASB4 rs113706540 T vs. C 0.93 (0.88–0.97) 0.0005006 1.000 1.000 0.422 0.999 0.422 0.999 0.972 1.000
GJD4 rs7897060 C vs. G 0.95 (0.91–0.98) 0.0005789 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
AK5 DNAJB4 FAM73A FUBP1 GIPC2
MGC27382 NEXN NEXN-AS1 USP33 ZZZ3		 rs12126604 T vs. C 0.92 (0.87–0.97) 0.0006161 1.000 1.000 0.668 1.000 0.668 1.000 0.986 1.000
SEMA6D rs17387110 T vs. G 0.95 (0.92–0.98) 0.0006996 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
NA chr16_62649826_D D vs. I 0.87 (0.80–0.95) 0.0007369 0.831 1.000 0.697 1.000 0.657 0.999 0.979 1.000
NA rs4239875 A vs. G 1.06 (1.03–1.10) 0.0008018 1.000 1.000 0.672 1.000 0.672 1.000 0.990 1.000
CTNNA3 DNAJC12 HERC4 MYPN POU5F1P5 SIRT1 chr10_69763783_D I vs. D 0.91 (0.86–0.97) 0.0008401 0.997 1.000 0.792 1.000 0.791 1.000 0.991 1.000
CLIC5 ENPP4 ENPP5 rs7762549 A vs. G 0.95 (0.92–0.98) 0.00085 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
NA chr18_76035713_D D vs. I 0.93 (0.88–0.97) 0.000884 1.000 1.000 0.422 0.999 0.422 0.999 0.972 1.000
BRICD5 CASKIN1 DNASE1L2 E4F1 MIR3180-5 MIR4516 MLST8 PGP PKD1 RAB26 SNHG19 SNORD60 TRAF7 rs2078282 A vs. G 0.94 (0.91–0.98) 0.0009187 1.000 1.000 0.783 1.000 0.783 1.000 0.993 1.000
OPCML rs7952100 C vs. G 1.06 (1.03–1.10) 0.0009399 1.000 1.000 0.672 1.000 0.672 1.000 0.990 1.000
LOC101927907 LRRTM4 rs58500924 A vs. G 0.90 (0.84–0.96) 0.0009721 0.990 1.000 0.581 0.999 0.579 0.999 0.977 1.000
RNGTT rs35675874 A vs. G 0.94 (0.91–0.98) 0.001031 1.000 1.000 0.783 1.000 0.783 1.000 0.993 1.000
LOC101928505 LOC101928539 chr5_57079215_I D vs. I 1.07 (1.03–1.11) 0.001076 1.000 1.000 0.232 0.997 0.232 0.997 0.945 1.000
DPP4 SLC4A10 rs2909451 T vs. C 0.94 (0.90–0.98) 0.001078 1.000 1.000 0.783 1.000 0.783 1.000 0.993 1.000
ERAP2 LNPEP rs55767008 T vs. C 0.89 (0.82–0.96) 0.001182 0.956 1.000 0.728 1.000 0.718 1.000 0.985 1.000
C2orf15 KIAA1211L LIPT1 LOC101927070 TSGA10 rs10202643 A vs. T 0.95 (0.92–0.98) 0.001269 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
AUTS2 rs2293507 T vs. G 0.88 (0.81–0.96) 0.001337 0.890 1.000 0.817 1.000 0.799 1.000 0.989 1.000
NA rs138457704 A vs. G 1.07 (1.03–1.11) 0.001357 1.000 1.000 0.232 0.997 0.232 0.997 0.945 1.000
GLDC rs13288399 C vs. G 0.95 (0.91–0.98) 0.001357 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
MTFR1 PDE7A rs1513723 C vs. G 0.95 (0.92–0.98) 0.001447 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
ASTN2 ASTN2-AS1 PAPPA TRIM32 rs146737360 T vs. G 0.95 (0.92–0.98) 0.001534 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
NA chr6_45726254_D D vs. I 0.90 (0.83–0.96) 0.001606 0.990 1.000 0.581 0.999 0.579 0.999 0.977 1.000
NA rs6742513 C vs. G 1.07 (1.03–1.11) 0.001611 1.000 1.000 0.232 0.997 0.232 0.997 0.945 1.000
NA rs73204738 A vs. C 0.92 (0.88–0.97) 0.001617 1.000 1.000 0.668 1.000 0.668 1.000 0.986 1.000
LINC01553 rs11817353 A vs. C 0.95 (0.92–0.98) 0.001678 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
Anney et al., 2017 (continued) RAD51B rs2842330 A vs. C 1.10 (1.04–1.16) 0.001845 0.999 1.000 0.303 0.998 0.303 0.998 0.946 1.000
RBFOX1 rs12930616 C vs. G 1.05 (1.02–1.09) 0.001985 1.000 1.000 0.913 1.000 0.913 1.000 0.998 1.000
GRID2 rs6811974 T vs. C 0.95 (0.93–0.98) 0.001995 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
NA rs7135621 T vs. C 0.96 (0.93–0.98) 0.002059 1.000 1.000 0.094 0.991 0.094 0.991 0.915 1.000
GFER NOXO1 NPW RNF151 RPS2 SNHG9 SNORA78 SYNGR3 TBL3 ZNF598 rs55742253 T vs. C 0.93 (0.88–0.98) 0.002075 1.000 1.000 0.868 1.000 0.868 1.000 0.995 1.000
PTPRB rs10784860 T vs. C 0.95 (0.91–0.98) 0.002211 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
LOC101927768 rs9387201 C vs. G 1.09 (1.03–1.14) 0.002427 1.000 1.000 0.142 0.994 0.142 0.994 0.892 1.000
BTBD11 LOC101929162 PRDM4 PWP1 rs4964602 T vs. G 0.95 (0.91–0.98) 0.00256 1.000 1.000 0.550 0.999 0.550 0.999 0.986 1.000
NA rs1376888 T vs. C 1.05 (1.02–1.08) 0.002668 1.000 1.000 0.407 0.999 0.407 0.999 0.979 1.000
KLHL29 rs10182178 A vs. G 1.05 (1.02–1.08) 0.003508 1.000 1.000 0.407 0.999 0.407 0.999 0.979 1.000
UBE2H rs78661858 A vs. G 0.91 (0.85–0.97) 0.003665 0.997 1.000 0.792 1.000 0.791 1.000 0.991 1.000
VAPA rs29063 A vs. G 1.04 (1.01–1.07) 0.004075 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
NA rs190401890 A vs. T 1.12 (1.04–1.20) 0.004114 0.975 1.000 0.568 0.999 0.562 0.999 0.975 1.000
LOC102723427 rs192668887 T vs. C 0.91 (0.84–0.97) 0.004205 0.997 1.000 0.792 1.000 0.791 1.000 0.991 1.000
SLC12A7 rs73031119 A vs. C 0.91 (0.84–0.97) 0.004399 0.997 1.000 0.792 1.000 0.791 1.000 0.991 1.000
ADGRL2 rs75695875 A vs. G 0.93 (0.87–0.98) 0.004715 1.000 1.000 0.868 1.000 0.868 1.000 0.995 1.000
NA rs1943999 C vs. G 0.96 (0.92–0.99) 0.004915 1.000 1.000 0.903 1.000 0.903 1.000 0.998 1.000
DNAH6 rs2222734 A vs. G 0.92 (0.87–0.98) 0.005058 0.999 1.000 0.906 1.000 0.906 1.000 0.996 1.000
OR8A1 OR8B12 rs2226753 T vs. C 0.96 (0.93–0.99) 0.005074 1.000 1.000 0.903 1.000 0.903 1.000 0.998 1.000
TUSC5 rs35713482 A vs. G 1.05 (1.01–1.08) 0.005154 1.000 1.000 0.407 0.999 0.407 0.999 0.979 1.000
C5orf15 VDAC1 rs67120295 T vs. C 1.06 (1.02–1.10) 0.005745 1.000 1.000 0.672 1.000 0.672 1.000 0.990 1.000
NA rs76010911 A vs. G 1.11 (1.04–1.19) 0.006255 0.986 1.000 0.769 1.000 0.767 1.000 0.989 1.000
MTMR9 SLC35G5 TDH rs6601581 T vs. C 1.06 (1.02–1.11) 0.006463 1.000 1.000 0.930 1.000 0.930 1.000 0.998 1.000
HSDL2 MIR3134 PTBP3 SUSD1 rs7024761 A vs. G 1.05 (1.02–1.09) 0.00648 1.000 1.000 0.913 1.000 0.913 1.000 0.998 1.000
CRTC3 GABARAPL3 IQGAP1 ZNF774 rs2601187 A vs. G 1.05 (1.01–1.08) 0.006859 1.000 1.000 0.407 0.999 0.407 0.999 0.979 1.000
LOC101927189 LRRC1 rs4715431 A vs. G 1.04 (1.01–1.08) 0.007007 1.000 1.000 0.977 1.000 0.977 1.000 0.999 1.000
NA rs646680 A vs. G 0.95 (0.92–0.99) 0.00723 1.000 1.000 0.937 1.000 0.937 1.000 0.998 1.000
CCNE1 rs12609867 A vs. G 0.95 (0.91–0.99) 0.00743 1.000 1.000 0.937 1.000 0.937 1.000 0.998 1.000
NOS1AP OLFML2B rs75192393 T vs. C 1.07 (1.02–1.12) 0.007697 1.000 1.000 0.787 1.000 0.787 1.000 0.993 1.000
KDM4A KDM4A-AS1 LOC101929592
MIR6079 PTPRF ST3GAL3		 rs79857083 T vs. C 1.04 (1.01–1.08) 0.007758 1.000 1.000 0.977 1.000 0.977 1.000 0.999 1.000
NA rs142968358 T vs. G 1.04 (1.01–1.07) 0.007789 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
C3orf30 IGSF11 IGSF11-AS1 UPK1B rs1102586 A vs. G 1.06 (1.02–1.10) 0.007844 1.000 1.000 0.672 1.000 0.672 1.000 0.990 1.000
NA chr11_98107192_D D vs. I 1.04 (1.01–1.08) 0.00785 1.000 1.000 0.977 1.000 0.977 1.000 0.999 1.000
C9orf135 rs76014157 A vs. G 0.90 (0.82–0.98) 0.007946 0.962 1.000 0.941 1.000 0.939 1.000 0.997 1.000
NA rs6437449 A vs. G 1.07 (1.02–1.11) 0.008708 1.000 1.000 0.232 0.997 0.232 0.997 0.945 1.000
MYO5A chr15_52811815_D I vs. D 0.90 (0.81–0.98) 0.008799 0.962 1.000 0.941 1.000 0.939 1.000 0.997 1.000
NA rs9466619 A vs. G 0.95 (0.92–0.99) 0.009071 1.000 1.000 0.937 1.000 0.937 1.000 0.998 1.000
NA rs6117854 A vs. G 0.96 (0.93–0.99) 0.01012 1.000 1.000 0.903 1.000 0.903 1.000 0.998 1.000
C7orf33 rs6955951 A vs. T 1.04 (1.01–1.07) 0.01015 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
LHX6 rs72767788 A vs. C 0.95 (0.91–0.99) 0.01093 1.000 1.000 0.937 1.000 0.937 1.000 0.998 1.000
NA rs2028664 A vs. C 1.04 (1.01–1.07) 0.01095 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
ELAVL2 rs180861134 A vs. T 1.05 (1.01–1.09) 0.01104 1.000 1.000 0.913 1.000 0.913 1.000 0.998 1.000
RASGEF1C rs12659560 T vs. C 1.04 (1.01–1.07) 0.0112 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
MIR548AZ SYNE2 rs2150291 T vs. C 1.05 (1.01–1.09) 0.0113 1.000 1.000 0.913 1.000 0.913 1.000 0.998 1.000
WDFY4 rs118059975 A vs. C 0.95 (0.91–0.99) 0.01146 1.000 1.000 0.937 1.000 0.937 1.000 0.998 1.000
LINC01525 MAN1A2 rs3820500 A vs. G 1.04 (1.01–1.07) 0.0116 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
GALNT10 rs17629195 T vs. C 1.04 (1.01–1.07) 0.012 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
MIR597 TNKS rs78853604 T vs. C 1.05 (1.01–1.08) 0.01256 1.000 1.000 0.407 0.999 0.407 0.999 0.979 1.000
EXT1 rs7835763 A vs. T 1.04 (1.01–1.08) 0.01283 1.000 1.000 0.977 1.000 0.977 1.000 0.999 1.000
NA rs4652928 A vs. G 0.96 (0.92–0.99) 0.01384 1.000 1.000 0.903 1.000 0.903 1.000 0.998 1.000
PDE1C rs11976985 T vs. C 0.95 (0.92–0.99) 0.0141 1.000 1.000 0.937 1.000 0.937 1.000 0.998 1.000
BAX FTL GYS1 rs2230267 T vs. C 1.04 (1.01–1.07) 0.01429 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
Anney et al., 2017 (continued) GRID2 rs6854329 C vs. G 0.92 (0.86–0.99) 0.01486 0.996 1.000 0.963 1.000 0.963 1.000 0.998 1.000
NA rs1926229 C vs. G 1.05 (1.01–1.08) 0.01496 1.000 1.000 0.407 0.999 0.407 0.999 0.979 1.000
NA rs261351 T vs. C 0.96 (0.93–0.99) 0.01498 1.000 1.000 0.903 1.000 0.903 1.000 0.998 1.000
RAPGEF2 rs4440173 A vs. G 1.04 (1.01–1.07) 0.01564 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
MIR4650-1 MIR4650-2 POM121 SBDSP1 SPDYE7P TYW1B rs4392770 T vs. C 1.05 (1.01–1.09) 0.01564 1.000 1.000 0.913 1.000 0.913 1.000 0.998 1.000
NA rs138493916 C vs. G 1.08 (1.02–1.14) 0.01783 1.000 1.000 0.840 1.000 0.840 1.000 0.994 1.000
NA rs615512 A vs. G 1.08 (1.02–1.14) 0.01811 1.000 1.000 0.840 1.000 0.840 1.000 0.994 1.000
EP400 EP400NL PUS1 SNORA49 rs11608890 T vs. G 0.94 (0.88–0.99) 0.0187 1.000 1.000 0.951 1.000 0.951 1.000 0.998 1.000
DIAPH3 chr13_60161890_I I vs. D 1.05 (1.01–1.09) 0.01984 1.000 1.000 0.913 1.000 0.913 1.000 0.998 1.000
ADAM12 rs1674923 T vs. C 0.96 (0.93–0.99) 0.0203 1.000 1.000 0.903 1.000 0.903 1.000 0.998 1.000
ATP2B2 GHRL GHRLOS IRAK2 LINC00852
MIR378B MIR885 SEC13 TATDN2		 rs7619385 A vs. G 1.04 (1.01–1.07) 0.02102 1.000 1.000 0.873 1.000 0.873 1.000 0.997 1.000
UNC13C rs75099274 A vs. G 1.08 (1.01–1.14) 0.02123 1.000 1.000 0.840 1.000 0.840 1.000 0.994 1.000
ZSWIM6 rs10053166 A vs. G 0.95 (0.90–0.99) 0.02226 1.000 1.000 0.937 1.000 0.937 1.000 0.998 1.000
HIVEP3 rs2786484 T vs. C 0.93 (0.86–0.99) 0.0237 1.000 1.000 0.958 1.000 0.958 1.000 0.998 1.000
FJX1 TRIM44 rs76847144 T vs. C 0.93 (0.86–0.99) 0.02643 1.000 1.000 0.958 1.000 0.958 1.000 0.998 1.000
WBSCR17 rs148521358 C vs. G 0.94 (0.88–0.99) 0.02731 1.000 1.000 0.951 1.000 0.951 1.000 0.998 1.000
MIR3134 SUSD1 rs2564899 T vs. C 0.97 (0.94–1.00) 0.02735 1.000 1.000 0.980 1.000 0.980 1.000 0.999 1.000
NA chr8_138837351_I I vs. D 1.05 (1.01–1.09) 0.0284 1.000 1.000 0.913 1.000 0.913 1.000 0.998 1.000
LINC01393 MDFIC rs7799732 A vs. G 1.03 (1.00–1.06) 0.03114 1.000 1.000 0.978 1.000 0.978 1.000 0.999 1.000
TBX18 TBX18-AS1 rs76397051 A vs. G 1.05 (1.01–1.10) 0.034 1.000 1.000 0.975 1.000 0.975 1.000 0.999 1.000
NA rs171794 T vs. C 1.06 (1.01–1.12) 0.03587 1.000 1.000 0.974 1.000 0.974 1.000 0.999 1.000
GDA rs4327921 A vs. G 0.97 (0.94–1.00) 0.03938 1.000 1.000 0.980 1.000 0.980 1.000 0.999 1.000
NA rs2167341 T vs. G 1.05 (1.00–1.10) 0.04203 1.000 1.000 0.975 1.000 0.975 1.000 0.999 1.000
EVA1C rs62216215 A vs. C 1.04 (1.00–1.08) 0.04598 1.000 1.000 0.977 1.000 0.977 1.000 0.999 1.000
LINC01036 rs17589281 T vs. C 0.95 (0.89–1.00) 0.04716 1.000 1.000 0.980 1.000 0.980 1.000 0.999 1.000
LOC283585 rs61979775 T vs. C 0.97 (0.93–1.00) 0.04813 1.000 1.000 0.980 1.000 0.980 1.000 0.999 1.000
CHMP4A GMPR2 MDP1 NEDD8
NEDD8-MDP1 TM9SF1 TSSK4		 rs72694312 T vs. G 1.06 (1.00–1.11) 0.04814 1.000 1.000 0.930 1.000 0.930 1.000 0.998 1.000
Ma et al., 2009 [32] NA rs10065041 T(minor)/C(major) 1.21 (1.08–1.36) 3.24 × 10−4 0.445 1.000 0.757 1.000 0.581 0.999 0.970 1.000
NA rs10038113 C(minor)/T(major) 0.75 (0.70–0.90) 3.40 × 10−6 0.129 0.897 0.939 1.000 0.688 1.000 0.979 1.000
NA rs6894838 T(minor)/C(major) 1.26 (1.12–1.42) 8.00 × 10−5 0.212 0.998 0.416 0.999 0.131 0.993 0.827 1.000
Anney et al., 2010 [30] HAT1 rs6731562 G (minor allele) 1.25 (1.11–1.41) 2.0 × 10−4 0.253 0.998 0.527 0.999 0.220 0.996 0.891 1.000
POU6F2 rs10258862 G (minor allele) 1.09 (1.00–1.18) 4.6 × 10−2 0.991 1.000 0.971 1.000 0.971 1.000 0.998 1.000
NA rs6557675 A (minor allele) 0.84 (0.76–0.93) 1.0 × 10−3 0.561 1.000 0.583 0.999 0.440 0.999 0.953 1.000
MYH11 rs17284809 A (minor allele) 0.63 (0.50–0.79) 5.7 × 10−5 0.008 0.312 0.891 1.000 0.168 0.995 0.821 1.000
GSG1L rs205409 G (minor allele) 0.91 (0.84–0.99) 2.8 × 10−2 0.980 1.000 0.966 1.000 0.966 1.000 0.998 1.000
TAF1C rs4150167 A (minor allele) 0.54 (0.40–0.73) 2.1 × 10−5 0.002 0.085 0.963 1.000 0.420 0.999 0.905 1.000
Kuo et al., 2015 [33] GLIS1 rs12082358 C (minor allele) 1.3 (1.1–1.5) 2.2 × 10−4 0.136 0.975 0.705 1.000 0.251 0.997 0.906 1.000
GLIS1 rs12080993 A (minor allele) 1.3 (1.1–1.5) 1.5 × 10−4 0.136 0.975 0.705 1.000 0.251 0.997 0.906 1.000
GPD2 rs3916984 A (minor allele) 1.3 (1.1–1.5) 3.1 × 10−4 0.136 0.975 0.705 1.000 0.251 0.997 0.906 1.000
LRP2/BBS5 rs13014164 C (minor allele) 1.7 (1.3–2.3) 8.6 × 10−5 0.012 0.209 0.980 1.000 0.735 1.000 0.974 1.000
PDGFRA rs7697680 G (minor allele) 1.5 (1.2–1.9) 9.2 × 10−4 0.032 0.500 0.960 1.000 0.607 0.999 0.967 1.000
FSTL4 rs11741756 A (minor allele) 1.3 (1.1–1.5) 1.2 × 10−2 0.136 0.975 0.705 1.000 0.251 0.997 0.906 1.000
NA rs13211684 G (minor allele) 1.3 (1.1–1.5) 2.5 × 10−3 0.136 0.975 0.705 1.000 0.251 0.997 0.906 1.000
NA rs10966205 T (minor allele) 1.3 (1.2–1.5) 2.9 × 10−5 0.136 0.975 0.705 1.000 0.251 0.997 0.906 1.000
C10orf68 rs10763893 A (minor allele) 1.6 (1.2–2.2) 6.1 × 10−4 0.038 0.346 0.990 1.000 0.917 1.000 0.992 1.000
NA rs12366025 A (minor allele) 1.3 (1.1–1.6) 3.8 × 10−3 0.225 0.912 0.983 1.000 0.936 1.000 0.995 1.000
NA rs11030597 G (minor allele) 1.3 (1.1–1.6) 4.1 × 10−3 0.225 0.912 0.983 1.000 0.936 1.000 0.995 1.000
NA rs7933990 A (minor allele) 1.3 (1.1–1.6) 2.5 × 10−3 0.225 0.912 0.983 1.000 0.936 1.000 0.995 1.000
NA rs11030606 A (minor allele) 1.3 (1.1–1.6) 5.6 × 10−3 0.225 0.912 0.983 1.000 0.936 1.000 0.995 1.000
MACROD2 rs17263514 A (minor allele) 1.2 (1.0–1.4) 1.4 × 10−2 0.500 0.998 0.976 1.000 0.953 1.000 0.996 1.000
BCAS1/CYP24A1 rs12479663 C (minor allele) 1.5 (1.3–1.9) 4.0 × 10−5 0.032 0.500 0.960 1.000 0.607 0.999 0.967 1.000
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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.

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).

Table 3.

Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8 ≤ p-value < 0.05) in the genome-wide association studies (GWAS) catalog.

Author, Year Gene Variant Comparison OR (95% CI) p-Value Power
OR 1.2		 Power
OR 1.5		 FPRP Values at Prior Probability BFDP
0.001		 BFDP
0.000001
OR 1.2 OR 1.5
0.001 0.000001 0.001 0.000001
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] MACROD2 rs4141463 NA 1.37 (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] AL163541.1 rs4773054 NA 2.66 (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] PPP2R5C rs7142002 NA 1.56 (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] TAF1C rs4150167 NA 1.96 (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] PARD3B rs4675502 NA 1.28 (1.16–1.41) 4.00 × 10−7 0.095 0.999 0.006 0.856 0.001 0.362 0.030 0.969
Anney et al., 2012 [34] AC113414.1 rs7711337 NA 1.22 (1.12–1.32) 8.00 × 10−7 0.340 1.000 0.002 0.689 0.001 0.429 0.038 0.975
Anney et al., 2012 [34] AC009446.1, EYA1 rs7834018 NA 1.56 (1.3–1.89) 8.00 × 10−7 0.004 0.344 0.602 0.999 0.016 0.942 0.338 0.998
Anney et al., 2017 [31] AL133270.1, AL139093.1 rs142968358 T (risk allele) 1.1 (1.06–1.14) 1.00 × 10−6 1.000 1.000 0.000 0.145 0.000 0.145 0.014 0.936
Anney et al., 2017 [31] EXT1 rs7835763 A (risk allele) 1.1 (1.06–1.14) 2.00 × 10−6 1.000 1.000 0.000 0.145 0.000 0.145 0.014 0.936
Chaste et al., 2014 [35] INHCAP rs1867503 NA 1.55 (1.30–1.84) 4.00 × 10−7 0.002 0.354 0.241 0.997 0.002 0.608 0.058 0.984
Chaste et al., 2014 [35] CUEDC2 rs1409313 NA 1.75 (1.40–2.18) 4.00 × 10−7 0.000 0.085 0.610 0.999 0.007 0.876 0.121 0.993
Chaste et al., 2014 [35] CTU2 rs11641365 NA 2.06 (1.54–2.76) 3.00 × 10−7 0.000 0.017 0.897 1.000 0.071 0.987 0.433 0.999
Chaste et al., 2014 [35] AC067752.1, AC024598.1, ZNF365 rs93895 NA 1.91 (1.48–2.47) 2.00 × 10−7 0.000 0.033 0.804 1.000 0.024 0.961 0.241 0.997
Kuo et al., 2015 [33] LINC01151, AC108136.1 rs12543592 G (risk allele) 1.43 (1.25–1.67) 3.00 × 10−6 0.013 0.727 0.318 0.998 0.008 0.895 0.275 0.997
Kuo et al., 2015 [33] NAALADL2 rs3914502 A (risk allele) 1.4 (1.20–1.60) 4.00 × 10−6 0.012 0.844 0.062 0.985 0.001 0.482 0.051 0.982
Kuo et al., 2015 [33] OR2M4 rs10888329 NA 1.82 (1.39–2.33) 8.00 × 10−6 0.000 0.062 0.809 1.000 0.031 0.970 0.338 0.998
Kuo et al., 2015 [33] SGSM2 rs2447097 A (risk allele) 1.53 (1.27–1.85) 9.00 × 10−6 0.006 0.419 0.652 0.999 0.026 0.965 0.467 0.999
Ma et al., 2009 [32] Intergenic (RNU6-374P - MSNP1) rs10038113 T (risk allele) 1.33 (1.11–1.43] 3.00 × 10−6 0.003 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] AL163541.1 rs4773054 NA 2.9 (1.91–4.39) 7.00 × 10−8 0.000 0.001 0.970 1.000 0.345 0.998 0.741 1.000
Anney et al., 2017 [31] HLA-A, AL671277.1 rs115254791 G (risk allele) 1.0869565 (1.05–1.14) 4.00 × 10−6 1.000 1.000 0.376 0.998 0.376 0.998 0.963 1.000
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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.

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.

Table 4.

Re-analysis results of gene variants with genome wide statistical significance (p-value < 5 × 10−8) and borderline statistical significance (5 × 10−8 ≤ p-value < 0.05) in the GWAS datasets included in GWAS meta-analyses (results of FPRP < 0.2 and BFDP < 0.8).

Author, Year Trait Gene(s) Variant Comparison OR (95% CI) p-Value Power OR 1.2 Power OR 1.5 FPRP Values at Prior Probability BFDP
0.001		 BFDP
0.000001
OR 1.2 OR 1.5
0.001 0.000001 0.001 0.000001
Anney et al., 2012 [34] ASD (European) ERBB4 rs1879532 A 2.02 (1.57–2.59) 1.55 × 10−8 0.000 0.009 0.595 0.999 0.003 0.757 0.026 0.964
Anney et al., 2012 [34] Autism (European) None rs289932 A 0.49 (0.38–0.64) 5.04 × 10−8 0.000 0.012 0.772 1.000 0.014 0.932 0.114 0.992
Anney et al., 2012 [34] ASD TMEM132B rs16919315 A 0.53 (0.42–0.67) 5.12 × 10−8 0.000 0.028 0.589 0.999 0.004 0.800 0.049 0.981
Anney et al., 2012 [34] Autism (European) ERBB4 rs1879532 A 1.72 (1.39–2.11) 1.66 × 10−7 0.000 0.095 0.416 0.999 0.002 0.676 0.044 0.979
Anney et al., 2010 [30] Autism NA rs6557675 A (minor allele) 0.61 (0.51–0.71) 2.20 × 10−7 0.000 0.126 0.006 0.861 0.000 0.001 0.000 0.048
Anney et al., 2012 [34] Autism (European) None rs289858 A 0.52 (0.40–0.67) 2.81 × 10−7 0.000 0.027 0.762 1.000 0.015 0.940 0.161 0.995
Anney et al., 2012 [34] ASD SYNE2 rs2150291 A 1.72 (1.40–2.13) 2.83 × 10−7 0.000 0.105 0.579 0.999 0.006 0.864 0.119 0.993
Anney et al., 2012 [34] ASD (European) RPH3AL rs7207517 A 1.97 (1.51–2.57) 3.05 × 10−7 0.000 0.022 0.817 1.000 0.025 0.963 0.226 0.997
Anney et al., 2012 [34] Autism (European) None rs4761371 A 0.46 (0.34–0.63) 3.91 × 10−7 0.000 0.010 0.924 1.000 0.111 0.992 0.521 0.999
Anney et al., 2012 [34] ASD (European) PRAMEF12 rs1812242 A 1.44 (1.25–1.66) 4.29 × 10−7 0.006 0.713 0.077 0.988 0.001 0.411 0.038 0.975
Anney et al., 2012 [34] ASD None rs10904487 G 0.63 (0.52–0.75) 4.29 × 10−7 0.001 0.262 0.198 0.996 0.001 0.440 0.028 0.966
Anney et al., 2012 [34] Autism (European) None rs289932 A 0.67 (0.57–0.79) 5.42 × 10−7 0.005 0.524 0.286 0.998 0.004 0.784 0.135 0.994
Anney et al., 2010 [30] Autism MACROD2 rs4141463 A (minor allele) 0.62 (0.52–0.73) 5.50 × 10−7 0.000 0.192 0.047 0.980 0.000 0.048 0.002 0.655
Anney et al., 2012 [34] Autism None rs9608521 A 1.46 (1.25–1.69) 7.62 × 10−7 0.004 0.641 0.084 0.989 0.001 0.383 0.033 0.971
Anney et al., 2012 [34] ASD None rs1408744 A 0.65 (0.54–0.77) 8.06 × 10−7 0.002 0.385 0.235 0.997 0.002 0.618 0.062 0.985
Anney et al., 2017 [31] ASD LINC00535 chr8_94389815_I I vs. D 1.14 (1.09–1.19) 9.47 × 10−7 0.990 1.000 0.000 0.002 0.000 0.002 0.686 1.000
Anney et al., 2012 [34] ASD (European) PC rs7122539 A 0.60 (0.49–0.74) 9.64 × 10−7 0.001 0.162 0.628 0.999 0.011 0.917 0.213 0.996
Anney et al., 2010 [30] Autism MACROD2 rs4814324 A (minor allele) 1.58 (1.34–1.86) 9.80 × 10−7 0.000 0.266 0.076 0.988 0.000 0.128 0.006 0.859
Anney et al., 2010 [30] Autism MACROD2 rs6079544 A (minor allele) 1.57 (1.33–1.84) 1.20 × 10−6 0.000 0.287 0.053 0.982 0.000 0.081 0.004 0.797
Anney et al., 2017 [31] ASD EXOC4 rs6467494 T vs. C 1.12 (1.07–1.16) 1.43 × 10−6 1.000 1.000 0.000 0.000 0.000 0.000 0.197 0.996
Anney et al., 2010 [30] Autism MACROD2 rs6079536 A (minor allele) 0.64 (0.54–0.75) 1.60 × 10−6 0.001 0.307 0.059 0.984 0.000 0.102 0.005 0.837
Anney et al., 2010 [30] ASD MYH11 rs17284809 A (minor allele) 0.52 (0.39–0.69) 1.70 × 10−6 0.001 0.043 0.915 1.000 0.121 0.993 0.636 0.999
Anney et al., 2010 [30] Autism MACROD2 rs6079553 A (minor allele) 1.55 (1.31–1.82) 2.10 × 10−6 0.001 0.344 0.090 0.990 0.000 0.204 0.011 0.920
Anney et al., 2010 [30] Autism MACROD2 rs6074798 A (minor allele) 1.56 (1.32–1.84) 2.10 × 10−6 0.001 0.321 0.123 0.993 0.000 0.287 0.017 0.945
Anney et al., 2017 [31] ASD OPCML rs7952100 C vs.G 1.14 (1.09–1.19) 2.49 × 10−6 0.990 1.000 0.000 0.002 0.000 0.002 0.686 1.000
Anney et al., 2010 [30] Autism MACROD2 rs10446030 G (minor allele) 1.54 (1.30–1.81) 3.20 × 10−6 0.001 0.375 0.116 0.992 0.000 0.301 0.019 0.951
Kuo et al., 2015 [33] ASD STYK1 rs16922945 C (minor allele) 1.86 (1.43–2.43) 3.43 × 10−6 0.001 0.057 0.891 1.000 0.085 0.989 0.572 0.999
Anney et al., 2010 [30] ASD POU5F2 rs10258862 G (minor allele) 1.41 (1.23–1.61) 3.70 × 10−6 0.009 0.820 0.043 0.978 0.000 0.319 0.027 0.966
Anney et al., 2010 [30] Autism MACROD2 rs6079540 A (minor allele) 0.65 (0.55–0.77) 3.70 × 10−6 0.002 0.385 0.235 0.997 0.002 0.618 0.062 0.985
Anney et al., 2010 [30] Autism MACROD2 rs6074787 A (minor allele) 1.53 (1.30–1.80) 4.10 × 10−6 0.002 0.406 0.147 0.994 0.001 0.418 0.031 0.970
Anney et al., 2010 [30] ASD MACROD2 rs6074798 A (minor allele) 1.38 (1.22–1.56) 4.80 × 10−6 0.013 0.909 0.020 0.954 0.000 0.224 0.018 0.948
Anney et al., 2010 [30] Autism MACROD2 rs980319 G (minor allele) 1.52 (1.29–1.79) 5.10 × 10−6 0.002 0.437 0.184 0.996 0.001 0.543 0.050 0.981
Anney et al., 2010 [30] Autism MACROD2 rs6079537 G (minor allele) 1.52 (1.29–1.79) 6.00 × 10−6 0.002 0.437 0.184 0.996 0.001 0.543 0.050 0.981
Kuo et al., 2015 [33] ASD NA rs10966205 A (minor allele) 1.52 (1.27–1.83) 6.25 × 10−6 0.006 0.444 0.609 0.999 0.022 0.957 0.426 0.999
Kuo et al., 2015 [33] ASD OR2M4 rs10888329 T (minor allele) 0.55 (0.43–0.72) 8.05 × 10−6 0.001 0.081 0.916 1.000 0.144 0.994 0.718 1.000
Anney et al., 2010 [30] ASD MACROD2 rs6079536 A (minor allele) 0.73 (0.65–0.83) 8.50 × 10−6 0.022 0.917 0.067 0.986 0.002 0.628 0.084 0.989
Anney et al., 2010 [30] ASD NA rs6557675 A (minor allele) 0.72 (0.63–0.82) 8.70 × 10−6 0.014 0.877 0.051 0.982 0.001 0.457 0.047 0.980
Kuo et al., 2015 [33] ASD NA rs7933990 A (minor allele) 1.72 (1.35–2.19) 9.40 × 10−6 0.002 0.133 0.861 1.000 0.075 0.988 0.606 0.999
Kuo et al., 2015 [33] ASD MNT rs2447097 A (minor allele) 1.53 (1.27–1.85) 9.45 × 10−6 0.006 0.419 0.652 0.999 0.026 0.965 0.467 0.999
Anney et al., 2010 [30] ASD GSG1L rs205409 G (minor allele) 0.72 (0.64–0.82) 9.60 × 10−6 0.014 0.877 0.051 0.982 0.001 0.457 0.047 0.980
Kuo et al., 2015 [33] ASD OR2M4 rs6672981 C (minor allele) 0.55 (0.42–0.72) 9.64 × 10−6 0.001 0.081 0.916 1.000 0.144 0.994 0.718 1.000
Kuo et al., 2015 [33] ASD OR2M4 rs4397683 C (minor allele) 0.55 (0.42–0.72) 9.86 × 10−6 0.001 0.081 0.916 1.000 0.144 0.994 0.718 1.000
Anney et al., 2010 [30] ASD MACROD2 rs980319 G (minor allele) 1.36 (1.20–1.54) 1.00 × 10−5 0.024 0.939 0.049 0.981 0.001 0.570 0.068 0.987
Kuo et al., 2015 [33] ASD BCAS1/CYP24A1 rs12479663 G (minor allele) 1.81 (1.38–2.36) 1.08 × 10−5 0.001 0.083 0.907 1.000 0.124 0.993 0.687 1.000
Anney et al., 2010 [30] ASD MACROD2 rs4814324 A (minor allele) 1.36 (1.20–1.54) 1.10 × 10−5 0.024 0.939 0.049 0.981 0.001 0.570 0.068 0.987
Kuo et al., 2015 [33] ASD KRR1 rs3741496 C (minor allele) 1.49 (1.24–1.78) 1.15 × 10−5 0.009 0.529 0.565 0.999 0.020 0.954 0.430 0.999
Kuo et al., 2015 [33] ASD OR2M4 rs4642918 C (minor allele) 0.56 (0.43–0.73) 1.24 × 10−5 0.002 0.099 0.917 1.000 0.155 0.995 0.745 1.000
Anney et al., 2010 [30] ASD MACROD2 rs6079544 A (minor allele) 1.35 (1.20–1.53) 1.30 × 10−5 0.033 0.951 0.074 0.988 0.003 0.733 0.124 0.993
Kuo et al., 2015 [33] ASD NA rs13211684 G (minor allele) 1.56 (1.28–1.91) 1.36 × 10−5 0.006 0.352 0.750 1.000 0.045 0.979 0.572 0.999
Kuo et al., 2015 [33] ASD MNT rs2447095 A (minor allele) 1.52 (1.26–1.84) 1.45 × 10−5 0.008 0.446 0.695 1.000 0.038 0.975 0.552 0.999
Kuo et al., 2015 [33] ASD NA rs12543592 G (minor allele) 0.67 (0.56–0.81) 1.63 × 10−5 0.012 0.521 0.744 1.000 0.063 0.985 0.678 1.000
Anney et al., 2010 [30] ASD MACROD2 rs6079553 A (minor allele) 1.35 (1.19–1.52) 1.70 × 10−5 0.026 0.959 0.027 0.965 0.001 0.424 0.041 0.977
Kuo et al., 2015 [33] ASD KRR1 rs1051446 C (minor allele) 1.47 (1.23–1.76) 1.77 × 10−5 0.014 0.587 0.669 1.000 0.045 0.979 0.614 0.999
Anney et al., 2010 [30] ASD NA rs4078417 C (minor allele) 1.38 (1.21–1.57) 1.90 × 10−5 0.017 0.897 0.055 0.983 0.001 0.524 0.059 0.984
Anney et al., 2010 [30] ASD MACROD2 rs10446030 G (minor allele) 1.34 (1.19–1.52) 2.20 × 10−5 0.043 0.960 0.110 0.992 0.006 0.847 0.210 0.996
Kuo et al., 2015 [33] ASD GPD2 rs3916984 T (minor allele) 0.62 (0.49–0.77) 2.25 × 10−5 0.004 0.256 0.804 1.000 0.056 0.984 0.595 0.999
Kuo et al., 2015 [33] ASD NA rs12366025 T (minor allele) 1.67 (1.31–2.11) 2.49 × 10−5 0.003 0.184 0.860 1.000 0.086 0.989 0.662 0.999
Ma et al., 2009 [32] Autism NA rs10038113 C(minor)/T(major) 0.67 (0.56–0.81) 2.75 × 10−5 0.012 0.521 0.744 1.000 0.063 0.985 0.678 1.000
Anney et al., 2010 [30] ASD MACROD2 rs6079540 A (minor allele) 0.75 (0.66–0.84) 2.90 × 10−5 0.034 0.979 0.019 0.950 0.001 0.399 0.037 0.975
Anney et al., 2010 [30] Autism HAT1 rs6731562 G (minor allele) 1.51 (1.27–1.81) 3.30 × 10−5 0.006 0.471 0.562 0.999 0.017 0.946 0.383 0.998
Anney et al., 2010 [30] ASD MACROD2 rs6074787 A (minor allele) 1.33 (1.18–1.50) 3.40 × 10−5 0.047 0.975 0.067 0.986 0.003 0.776 0.147 0.994
Kuo et al., 2015 [33] ASD GLIS1 rs12080933 A (minor allele) 1.48 (1.23–1.78) 3.57 × 10−5 0.013 0.557 0.707 1.000 0.053 0.983 0.648 0.999
Kuo et al., 2015 [33] ASD FSTL4 rs11741756 T (minor allele) 1.67 (1.31–2.13) 3.64 × 10−5 0.004 0.194 0.903 1.000 0.157 0.995 0.785 1.000
Kuo et al., 2015 [33] ASD STYK1 rs7953930 G (minor allele) 1.65 (1.30–2.09) 3.83 × 10−5 0.004 0.215 0.888 1.000 0.133 0.994 0.761 1.000
Anney et al., 2010 [30] Autism NA rs4078417 C (minor allele) 1.50 (1.26–1.79) 4.10 × 10−5 0.007 0.500 0.509 0.999 0.014 0.933 0.339 0.998
Anney et al., 2010 [30] ASD MACROD2 rs4141463 A (minor allele) 0.75 (0.66–0.85) 4.30 × 10−5 0.049 0.967 0.118 0.993 0.007 0.873 0.243 0.997
Kuo et al., 2015 [33] ASD OR2M3 rs11204613 G (minor allele) 0.58 (0.45–0.75) 4.60 × 10−5 0.003 0.144 0.920 1.000 0.185 0.996 0.799 1.000
Anney et al., 2010 [30] ASD MACROD2 rs6079537 G (minor allele) 1.32 (1.17–1.49) 5.40 × 10−5 0.062 0.981 0.103 0.991 0.007 0.878 0.249 0.997
Anney et al., 2010 [30] Autism GSG1L rs205409 G (minor allele) 0.69 (0.58–0.81) 1.10 × 10−4 0.011 0.663 0.353 0.998 0.009 0.896 0.271 0.997
Anney et al., 2010 [30] Autism POU5F2 rs10258862 G (minor allele) 1.43 (1.21–1.71) 1.80 × 10−4 0.027 0.700 0.764 1.000 0.112 0.992 0.799 1.000
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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.

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).

Figure 2.

Figure 2

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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.

Table 5.

Lists of genes involved in the PPI network.

Gene Function of the Encoding Proteins
OXTR Receptor for oxytocin associated with social recognition and emotion processing
MTHFR Influences susceptibility to neural tube defect by changing folate metabolism
RELN Control cell positioning and neural migration during brain development
DRD3 D3 subtype of the five dopamine receptors; localized to the limbic areas of the brain
MNT Protein member of the Myc/Max/Mad network; transcriptional repressor and an antagonist of Myc-dependent transcriptional activation and cell growth
OPCML Member of the IgLON subfamily in the immunoglobulin protein superfamily of proteins; localized in the plasma membrane; accessory role in opioid receptor function
PC Pyruvate carboxylase; gluconeogenesis, lipogenesis, insulin secretion and synthesis of neurotransmitter glutamate
ERBB4 Tyr protein kinase family and the epidermal growth factor receptor subfamily; binds to and is activated by neuregulins, and induces mitogenesis and differentiation
OR2M4 Members of a large family of GPCR; olfactory receptors initiating a neuronal response that triggers the perception of a smell
BCAS1 Oncogene; highly expressed in three amplified breast cancer cell lines and in one breast tumor without amplification at 20q13.2.
CYP24A1 Cytochrome P450 superfamily of enzymes; drug metabolism and synthesis of cholesterol, steroids and other lipids
TMEM132B The function remains poorly understood despite their mutations associated with non-syndromic hearing loss, panic disorder, and cancer
KRR1 Nucleolar protein; 18S rRNA synthesis and 40S ribosomal assembly
HAT1 Type B histone acetyltransferase; rapid acetylation of newly synthesized cytoplasmic histones; replication-dependent chromatin assembly
SGSM2 GTPase activator; regulators of membrane trafficking
EXT1 Endoplasmic reticulum-resident type II transmembrane glycosyltransferase; involved in the chain elongation step of heparan sulfate biosynthesis
OR2T33 Members of a large family of GPCR; share a 7-transmembrane domain structure with many neurotransmitter and hormone receptors
TAF1C Binds to the core promoter of ribosomal RNA genes to position the polymerase properly; acts as a channel for regulatory signals
HDAC4 Class II of the histone deacetylase/acuc/apha family; represses transcription when tethered to a promoter
MEGF10 Member 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
NFKB2 Subunit of the transcription factor complex nuclear factor-kappa-B; central activator of genes involved in inflammation and immune function
BNC2 Conserved zinc finger protein; skin color saturation
NMB Member of the bombesin-like family of neuropeptides; negatively regulate eating behavior; regulate colonic smooth muscle contraction
HPS6 Organelle biogenesis associated with melanosomes, platelet dense granules, and lysosomes
ELOVL3 GNS1/SUR4 family; elongation of long chain fatty acids to provide precursors for synthesis of sphingolipids and ceramides
PITX3 Member of the RIEG/PITX homeobox family; transcription factors; lens formation during eye development
NAALADL2 Not well-known, but diseases associated with NAALADL2 include Chromosome 6Pter-P24 Deletion Syndrome and Cornelia De Lange Syndrome.
MACROD2 Deacetylase removing ADP-ribose from mono-ADP-ribosylated proteins; translocate from the nucleus to the cytoplasm upon DNA damage
CUEDC2 CUE domain-containing protein; down-regulate ESR1 protein levels through progesterone-induced and degradation of receptors
FBXL15 Substrate recognition component of SCF E3 ubiquitin-protein ligase complex; mediates the ubiquitination and subsequent proteasomal degradation of SMURF1
EXOC4 Component of the exocyst complex; targeting exocytic vesicles to specific docking sites on the plasma membrane
NOLC1 Nucleolar protein; act as a regulator of RNA polymerase I; neural crest specification; nucleologenesis
PPRC1 Similar to PPAR-gamma coactivator 1; activate mitochondrial biogenesis through NRF1 in response to proliferative signals
SEC11A Member of the peptidase S26B family; subunit of the signal peptidase complex; cell migration and invasion, gastric cancer and lymph node metastasis
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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.

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.

Click here for additional data file. (163KB, zip)

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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