Novel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes

Korean Clinical Guideline for Autism Spectrum Disorder - Clinical Features, Course, Epidemiology, and Cause

Autism spectrum disorder (ASD) is a heterogeneous developmental disorder characterized by impairments in two core areas: 1) social communication and interaction and 2) restricted and repetitive patterns of behaviors and interests. In general, ASD is known to be a lifelong disorder. Follow-up studies from childhood to adulthood have reported that the severity of the key symptoms ASD decreases over time. However, chronic health problems including mental health occur in many patients with ASD. The prevalence of ASD has increased from around 0.04% in the 1970s to 2.8% at present. The average age of diagnosis in developed countries is 38-120 months of age. Recent evidence suggests that biological factors which include genetic, congenital, immunological, neuroanatomical, biochemical, and environmental ones are important in causing autism. Until now, early signs and various risk factors of ASD have been suggested.

Cross-disorder GWAS meta-analysis of endocannabinoid DNA variations in major depressive disorder, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, and schizophrenia

The endocannabinoid system (ECS) is implicated in multiple mental disorders. In this study, we explored DNA variations in the ECS across major depressive disorder (MDD), bipolar disorder, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia by performing a cross-disorder genome-wide association study (GWAS) meta-analysis. We obtained six datasets from the Psychiatric Genomics Consortium containing GWAS summary statistics from European cohorts (284,023 cases and 508,515 controls). Effective sample size weighted meta-analysis was performed for 2241 single nucleotide polymorphisms (SNPs) pertaining to gene bodies of 33 endocannabinoid genes using METAL, where an overall z-statistic is calculated for each marker based on a weighted sum of individual statistics. Heterogeneity was examined with I2 and X2 tests. MAGMA gene-based analysis was also performed. We identified nine SNPs significantly associated with a change in risk of having a mental disorder. The lead SNP was rs12805732 (Gene: Diacylglycerol Lipase Alpha; DAGLA). Four SNPs had substantial heterogeneity (I2>60 %). DAGLA had the strongest association with disease risk in gene-based analysis. Our findings suggest that the ECS may be a shared pathway in mental disorders. Future studies validating these findings would contribute to the identification of biomarkers of disease risk across multiple mental disorders.

eQTL colocalization analysis highlights novel susceptibility genes in Autism Spectrum Disorders (ASD)

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests. ASD has proven to have a strong genetic component. However, defining causal genes is still one of the main challenges in GWAS, since the vast majority (>90%) of detected signals lie within the non-coding genome. Expression quantitative trait locus (eQTL) colocalization analysis determines whether a specific variant is responsible for both a local eQTL and GWAS association and has helped leverage data and rendering gene discovery for a wide array of diseases. Here we further mine the largest ASD GWAS performed to date (18,381 cases and 27,969 controls) altogether with GWAS summary statistics from the main PGC studies (Schizophrenia, MD (Major Depression) and ADHD (Attention Deficit/Hyperactivity Disorder)), by using eQTpLot, a newly developed tool that illustrates the colocalization of GWAS and eQTL signals in a locus, and the enrichment of and correlation between the candidate gene eQTLs and trait-significant variants. This analysis points up 8 genes with a significant eQTL colocalization signal in ASD (CRHR1, KANSL1, MANBA, MAPT, MMP12, NKX2-2, PTPRE and WNT3) and one gene (SRPK2) with a marginally significant colocalization signal (r = 0.69, p < 1 × 10-6), and specifically highlights the potentially causal role of MAPT (r = 0.76, p < 1 × 10-6), NKX2-2 (r = 0.71, p-value = 2.26-02) and PTPRE (r = 0.97, p-value = 2.63-04) when restricting the analysis to brain tissue.

SOX7: Novel Autistic Gene Identified by Analysis of Multi-Omics Data

Background

Despite thousands of variants identified by genome-wide association studies (GWAS) to be associated with autism spectrum disorder (ASD), it is unclear which mutations are causal because most are noncoding. Consequently, reliable diagnostic biomarkers are lacking. RNA-seq analysis captures biomolecular complexity that GWAS cannot by considering transcriptomic patterns. Therefore, integrating DNA and RNA testing may reveal causal genes and useful biomarkers for ASD.

Methods

We performed gene-based association studies using an adaptive test method with GWAS summary statistics from two large Psychiatric Genomics Consortium (PGC) datasets (ASD2019: 18,382 cases and 27,969 controls; ASD2017: 6,197 cases and 7,377 controls). We also investigated differential expression for genes identified with the adaptive test using an RNA-seq dataset (GSE30573: 3 cases and 3 controls) and DESeq2.

Results

We identified 5 genes significantly associated with ASD in ASD2019 (KIZ-AS1, p = 8.67×10- 10; KIZ, p = 1.16×10- 9; XRN2, p = 7.73×10- 9; SOX7, p = 2.22×10- 7; LOC101929229 (also known as PINX1-DT), p = 2.14×10- 6). Two of the five genes were replicated in ASD2017: SOX7 (p = 0.00087) and LOC101929229 (p = 0.009), and KIZ was close to the replication boundary of replication (p = 0.06). We identified significant expression differences for SOX7 (p = 0.0017, adjusted p = 0.0085), LOC101929229 (p = 5.83×10- 7, adjusted p = 1.18×10- 5), and KIZ (p = 0.00099, adjusted p = 0.0055). SOX7 encodes a transcription factor that regulates developmental pathways, alterations in which may contribute to ASD.

Limitations

The limitation of the gene-based analysis is the reliance on a reference population for estimating linkage disequilibrium between variants. The similarity of this reference population to the population of study is crucial to the accuracy of many gene-based analyses, including those performed in this study. As a result, the extent of our findings is limited to European populations, as this was our reference of choice. Future work includes a tighter integration of DNA and RNA information as well as extensions to non-European populations that have been under-researched.

Conclusions

These findings suggest that SOX7 and its related SOX family genes encode transcription factors that are critical to the downregulation of the canonical Wnt/β-catenin signaling pathway, an important developmental signaling pathway, providing credence to the biologic plausibility of the association between gene SOX7 and autism spectrum disorder.

NK2 homeobox gene cluster: Functions and roles in human diseases

NK2 genes (NKX2 gene cluster in humans) encode for homeodomain-containing transcription factors that are conserved along the phylogeny. According to the most detailed classifications, vertebrate NKX2 genes are classified into two distinct families, NK2.1 and NK2.2. The former is constituted by NKX2-1 and NKX2-4 genes, which are homologous to the Drosophila scro gene; the latter includes NKX2-2 and NKX2-8 genes, which are homologous to the Drosophila vnd gene. Conservation of these genes is not only related to molecular structure and expression, but also to biological functions. In Drosophila and vertebrates, NK2 genes share roles in the development of ventral regions of the central nervous system. In vertebrates, NKX2 genes have a relevant role in the development of several other organs such as the thyroid, lung, and pancreas. Loss-of-function mutations in NKX2-1 and NKX2-2 are the monogenic cause of the brain-lung-thyroid syndrome and neonatal diabetes, respectively. Alterations in NKX2-4 and NKX2-8 genes may play a role in multifactorial diseases, autism spectrum disorder, and neural tube defects, respectively. NKX2-1, NKX2-2, and NKX2-8 are expressed in various cancer types as either oncogenes or tumor suppressor genes. Several data indicate that evaluation of their expression in tumors has diagnostic and/or prognostic value.

Transcriptomic risk scores for attention deficit/hyperactivity disorder

Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

SOX7: Novel Autistic Gene Identified by Analysis of Multi-Omics Data

Background

Genome-wide association studies and next generation sequencing data analyses based on DNA information have identified thousands of mutations associated with autism spectrum disorder (ASD). However, more than 99% of identified mutations are non-coding. Thus, it is unclear which of these mutations might be functional and thus potentially causal variants. Transcriptomic profiling using total RNA-sequencing has been one of the most utilized approaches to link protein levels to genetic information at the molecular level. The transcriptome captures molecular genomic complexity that the DNA sequence solely does not. Some mutations alter a gene's DNA sequence but do not necessarily change expression and/or protein function. To date, few common variants reliably associated with the diagnosis status of ASD despite consistently high estimates of heritability. In addition, reliable biomarkers used to diagnose ASD or molecular mechanisms to define the severity of ASD do not exist.

Objectives

It is necessary to integrate DNA and RNA testing together to identify true causal genes and propose useful biomarkers for ASD.

Methods

We performed gene-based association studies with adaptive test using genome-wide association studies (GWAS) summary statistics with two large GWAS datasets (ASD 2019 data: 18,382 ASD cases and 27,969 controls [discovery data]; ASD 2017 data: 6,197 ASD cases and 7,377 controls [replication data]) which were obtained from the Psychiatric Genomics Consortium (PGC). In addition, we investigated differential expression for genes identified in gene-based GWAS with a RNA-seq dataset (GSE30573: 3 cases and 3 controls) using the DESeq2 package.

Results

We identified 5 genes significantly associated with ASD in ASD 2019 data (KIZ-AS1, p=8.67×10-10; KIZ, p=1.16×10-9; XRN2, p=7.73×10-9; SOX7, p=2.22×10-7; PINX1-DT, p=2.14×10-6). Among these 5 genes, gene SOX7 (p=0.00087), LOC101929229 (p=0.009), and KIZ-AS1 (p=0.059) were replicated in ASD 2017 data. KIZ (p=0.06) was close to the boundary of replication in ASD 2017 data. Genes SOX7 (p=0.0017, adjusted p=0.0085), LOC101929229 (also known as PINX1-DT, p=5.83×10-7, adjusted p=1.18×10-5), and KIZ (p=0.00099, adjusted p=0.0055) indicated significant expression differences between cases and controls in the RNA-seq data. SOX7 encodes a member of the SOX (SRY-related HMG-box) family of transcription factors pivotally contributing to determining of the cell fate and identity in many lineages. The encoded protein may act as a transcriptional regulator after forming a protein complex with other proteins leading to autism.

Conclusion

Gene SOX7 in the transcription factor family could be associated with ASD. This finding may provide new diagnostic and therapeutic strategies for ASD.

Channelopathy of small- and intermediate-conductance Ca2+-activated K+ channels

Small- and intermediate-conductance Ca2+-activated K+ (KCa2.x/KCa3.1 also called SK/IK) channels are gated exclusively by intracellular Ca2+. The Ca2+ binding protein calmodulin confers sub-micromolar Ca2+ sensitivity to the channel-calmodulin complex. The calmodulin C-lobe is constitutively associated with the proximal C-terminus of the channel. Interactions between calmodulin N-lobe and the channel S4-S5 linker are Ca2+-dependent, which subsequently trigger conformational changes in the channel pore and open the gate. KCNN genes encode four subtypes, including KCNN1 for KCa2.1 (SK1), KCNN2 for KCa2.2 (SK2), KCNN3 for KCa2.3 (SK3), and KCNN4 for KCa3.1 (IK). The three KCa2.x channel subtypes are expressed in the central nervous system and the heart. The KCa3.1 subtype is expressed in the erythrocytes and the lymphocytes, among other peripheral tissues. The impact of dysfunctional KCa2.x/KCa3.1 channels on human health has not been well documented. Human loss-of-function KCa2.2 mutations have been linked with neurodevelopmental disorders. Human gain-of-function mutations that increase the apparent Ca2+ sensitivity of KCa2.3 and KCa3.1 channels have been associated with Zimmermann-Laband syndrome and hereditary xerocytosis, respectively. This review article discusses the physiological significance of KCa2.x/KCa3.1 channels, the pathophysiology of the diseases linked with KCa2.x/KCa3.1 mutations, the structure-function relationship of the mutant KCa2.x/KCa3.1 channels, and potential pharmacological therapeutics for the KCa2.x/KCa3.1 channelopathy.

Polygenic risk of paclitaxel-induced peripheral neuropathy: a genome-wide association study

BACKGROUND

Genetic risk factors for chemotherapy-induced peripheral neuropathy (CIPN), a major dose-limiting side-effect of paclitaxel, are not well understood.

METHODS

We performed a genome-wide association study (GWAS) in 183 paclitaxel-treated patients to identify genetic loci associated with CIPN assessed via comprehensive neuropathy phenotyping tools (patient-reported, clinical and neurological grading scales). Bioinformatic analyses including pathway enrichment and polygenic risk score analysis were used to identify mechanistic pathways of interest.

RESULTS

In total, 77% of the cohort were classified with CIPN (n = 139), with moderate/severe neuropathy in 36%. GWAS was undertaken separately for the three measures of CIPN. GWAS of patient-reported CIPN identified 4 chromosomal regions that exceeded genome-wide significance (rs9846958, chromosome 3; rs117158921, chromosome 18; rs4560447, chromosome 4; rs200091415, chromosome 10). rs4560447 is located within a protein-coding gene, LIMCH1, associated with actin and neural development and expressed in the dorsal root ganglia (DRG). There were additional risk loci that exceeded the statistical threshold for suggestive genome-wide association (P < 1 × 10^-5) for all measures. A polygenic risk score calculated from the top 46 ranked SNPs was highly correlated with patient-reported CIPN (r² = 0.53; P = 1.54 × 10^-35). Overlap analysis was performed to identify 3338 genes which were in common between the patient-reported CIPN, neurological grading scale and clinical grading scale GWAS. The common gene set was subsequently analysed for enrichment of gene ontology (GO) and Reactome pathways, identifying a number of pathways, including the axon development pathway (GO:0061564; P = 1.78 × 10^-6) and neuronal system (R-HSA-112316; adjusted P = 3.33 × 10^-7).

CONCLUSIONS

Our findings highlight the potential role of axon development and regeneration pathways in paclitaxel-induced CIPN.

Genetic variants associated with psychiatric disorders are enriched at epigenetically active sites in lymphoid cells

Multiple psychiatric disorders have been associated with abnormalities in both the innate and adaptive immune systems. The role of these abnormalities in pathogenesis, and whether they are driven by psychiatric risk variants, remains unclear. We test for enrichment of GWAS variants associated with multiple psychiatric disorders (cross-disorder or trans-diagnostic risk), or 5 specific disorders (cis-diagnostic risk), in regulatory elements in immune cells. We use three independent epigenetic datasets representing multiple organ systems and immune cell subsets. Trans-diagnostic and cis-diagnostic risk variants (for schizophrenia and depression) are enriched at epigenetically active sites in brain tissues and in lymphoid cells, especially stimulated CD4⁺ T cells. There is no evidence for enrichment of either trans-risk or cis-risk variants for schizophrenia or depression in myeloid cells. This suggests a possible model where environmental stimuli activate T cells to unmask the effects of psychiatric risk variants, contributing to the pathogenesis of mental health disorders.

Shared genetics between autism spectrum disorder and attention-deficit/hyperactivity disorder and their association with extraversion

BACKGROUND

Deciphering the genetic relationships between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) may uncover underlining shared pathophysiology as well as inform treatment.

METHODS

The summary results of genome-wide association studies on ADHD, ASD, and extraversion were utilized for the analyzes. Genetic correlations between ADHD, ASD, and extraversion were tested using linkage disequilibrium score regression. Causal relationships between ADHD, ASD, and extraversion were investigated using Mendelian randomization (MR) analysis. Novel pleiotropic genomic loci shared by ADHD and ASD were identified using a cross-trait meta-analysis.

RESULTS

Extraversion was positively correlated with ADHD (r_g = 0.205) and negatively correlated with ASD (r_g = -0.193). The MR analysis showed that ADHD confers a causal effect on ASD (OR: 1.35, 95% confidence interval (CI):1.20-1.52) and vice versa (1.46, 1.38-1.55). Extraversion exerts a causal effect on ADHD only (1.19, 1.05-1.33). The cross-trait meta-analysis identified three novel pleiotropic genomic loci for ADHD and ASD, involving two pleiotropic genes, LINC00461 and KIZ.

CONCLUSIONS

Our study provides new insights into the shared genetics of ADHD and ASD and their connections with extraversion.

Combining multivariate genomic approaches to elucidate the comorbidity between autism spectrum disorder and attention deficit hyperactivity disorder

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two highly heritable neurodevelopmental disorders. Several lines of evidence point towards the presence of shared genetic factors underlying ASD and ADHD. We conducted genomic analyses of common risk variants (i.e. single nucleotide polymorphisms, SNPs) shared by ASD and ADHD, and those specific to each disorder.

METHODS

With the summary data from two GWAS, one on ASD (N = 46,350) and another on ADHD (N = 55,374) individuals, we used genomic structural equation modelling and colocalization analysis to identify SNPs shared by ASD and ADHD and SNPs specific to each disorder. Functional genomic analyses were then conducted on shared and specific common genetic variants. Finally, we performed a bidirectional Mendelian randomization analysis to test whether the shared genetic risk between ASD and ADHD was interpretable in terms of reciprocal relationships between ASD and ADHD.

RESULTS

We found that 37.5% of the SNPs associated with ASD (at p < 1e-6) colocalized with ADHD SNPs and that 19.6% of the SNPs associated with ADHD colocalized with ASD SNPs. We identified genes mapped to SNPs that are specific to ASD or ADHD and that are shared by ASD and ADHD, including two novel genes INSM1 and PAX1. Our bidirectional Mendelian randomization analyses indicated that the risk of ASD was associated with an increased risk of ADHD and vice versa.

CONCLUSIONS

Using multivariate genomic analyses, the present study uncovers shared and specific genetic variants associated with ASD and ADHD. Further functional investigation of genes mapped to those shared variants may help identify pathophysiological pathways and new targets for treatment.

Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis

Triple-negative breast cancer (TNBC) is generally considered an aggressive breast cancer subtype associated with poor prognostic outcomes. Up to now, the molecular and cellular mechanisms underlying TNBC pathology have not been fully understood. In this manuscript, we propose a novel semiparametric model with kernel for gene-based analysis with a breast cancer GWAS data. The software of SPMGBA (semiparametric method for gene-based analysis) in MATLAB is available at GitHub (https://github.com/zliu3/SPMGBA). Genetic signatures associated with breast cancer are discovered. We further validate the prognostic power of the identified genes with a large cohort of expression data from the European Genome-Phenome Archive, and discover that SEL1L is associated with the overall survival of TNBC with the p-value of .0002. We conclude that gene SEL1L is down-regulated in TNBC and the expression of SEL1L is positively associated with patient survival.

Molecular Dysregulation in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) comprises a heterogeneous group of neurodevelopmental disorders with a strong heritable genetic component. At present, ASD is diagnosed solely by behavioral criteria. Advances in genomic analysis have contributed to numerous candidate genes for the risk of ASD, where rare mutations and s common variants contribute to its susceptibility. Moreover, studies show rare de novo variants, copy number variation and single nucleotide polymorphisms (SNPs) also impact neurodevelopment signaling. Exploration of rare and common variants involved in common dysregulated pathways can provide new diagnostic and therapeutic strategies for ASD. Contributions of current innovative molecular strategies to understand etiology of ASD will be explored which are focused on whole exome sequencing (WES), whole genome sequencing (WGS), microRNA, long non-coding RNAs and CRISPR/Cas9 models. Some promising areas of pharmacogenomic and endophenotype directed therapies as novel personalized treatment and prevention will be discussed.

UTMOST, a single and cross-tissue TWAS (Transcriptome Wide Association Study), reveals new ASD (Autism Spectrum Disorder) associated genes

Autism spectrum disorders (ASD) is a complex neurodevelopmental disorder that may significantly impact on the affected individual's life. Common variation (SNPs) could explain about 50% of ASD heritability. Despite this fact and the large size of the last GWAS meta-analysis, it is believed that hundreds of risk genes in ASD have yet to be discovered. New tools, such as TWAS (Transcriptome Wide Association Studies) which integrate tissue expression and genetic data, are a great approach to identify new ASD susceptibility genes. The main goal of this study is to use UTMOST with the publicly available summary statistics from the largest ASD GWAS meta-analysis as genetic input. In addition, an in silico biological characterization for the novel associated loci was performed. Our results have shown the association of 4 genes at the brain level (CIPC, PINX1, NKX2-2, and PTPRE) and have highlighted the association of NKX2-2, MANBA, ERI1, and MITF at the gastrointestinal level. The gastrointestinal associations are quite relevant given the well-established but unexplored relationship between ASD and gastrointestinal symptoms. Cross-tissue analysis has shown the association of NKX2-2 and BLK. UTMOST-associated genes together with their in silico biological characterization seems to point to different biological mechanisms underlying ASD etiology. Thus, it would not be restricted to brain tissue and it will involve the participation of other body tissues such as the gastrointestinal.

Whole Exome Sequencing Identifies Novel De Novo Variants Interacting with Six Gene Networks in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a highly heritable condition caused by a combination of environmental and genetic factors such as de novo and inherited variants, as well as rare or common variants among hundreds of related genes. Previous genome-wide association studies have identified susceptibility genes; however, most ASD-associated genes remain undiscovered. This study aimed to examine rare de novo variants to identify genetic risk factors of ASD using whole exome sequencing (WES), functional characterization, and genetic network analyses of identified variants using Korean familial dataset. We recruited children with ASD and their biological parents. The clinical best estimate diagnosis of ASD was made according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5^TM), using comprehensive diagnostic instruments. The final analyses included a total of 151 individuals from 51 families. Variants were identified and filtered using the GATK Best Practices for bioinformatics analysis, followed by genome alignments and annotation to the reference genome assembly GRCh37 (liftover to GRCh38), and further annotated using dbSNP 154 build databases. To evaluate allele frequencies of de novo variants, we used the dbSNP, gnomAD exome v2.1.1, and genome v3.0. We used Ingenuity Pathway Analysis (IPA, Qiagen) software to construct networks using all identified de novo variants with known autism-related genes to find probable relationships. We identified 36 de novo variants with potential relations to ASD; 27 missense, two silent, one nonsense, one splice region, one splice site, one 5′ UTR, and one intronic SNV and two frameshift deletions. We identified six networks with functional relationships. Among the interactions between de novo variants, the IPA assay found that the NF-κB signaling pathway and its interacting genes were commonly observed at two networks. The relatively small cohort size may affect the results of novel ASD genes with de novo variants described in our findings. We did not conduct functional experiments in this study. Because of the diversity and heterogeneity of ASD, the primary purpose of this study was to investigate probable causative relationships between novel de novo variants and known autism genes. Additionally, we based functional relationships with known genes on network analysis rather than on statistical analysis. We identified new variants that may underlie genetic factors contributing to ASD in Korean families using WES and genetic network analyses. We observed novel de novo variants that might be functionally linked to ASD, of which the variants interact with six genetic networks.

Variants in the SK2 channel gene (KCNN2) lead to dominant neurodevelopmental movement disorders

KCNN2 encodes the small conductance calcium-activated potassium channel 2 (SK2). Rodent models with spontaneous Kcnn2 mutations show abnormal gait and locomotor activity, tremor and memory deficits, but human disorders related to KCNN2 variants are largely unknown. Using exome sequencing, we identified a de novo KCNN2 frameshift deletion in a patient with learning disabilities, cerebellar ataxia and white matter abnormalities on brain MRI. This discovery prompted us to collect data from nine additional patients with de novo KCNN2 variants (one nonsense, one splice site, six missense variants and one in-frame deletion) and one family with a missense variant inherited from the affected mother. We investigated the functional impact of six selected variants on SK2 channel function using the patch-clamp technique. All variants tested but one, which was reclassified to uncertain significance, led to a loss-of-function of SK2 channels. Patients with KCNN2 variants had motor and language developmental delay, intellectual disability often associated with early-onset movement disorders comprising cerebellar ataxia and/or extrapyramidal symptoms. Altogether, our findings provide evidence that heterozygous variants, likely causing a haploinsufficiency of the KCNN2 gene, lead to novel autosomal dominant neurodevelopmental movement disorders mirroring phenotypes previously described in rodents.

Driessen T, J. Lee P, Tejwani L, Sami A, Nawaz M, Mehmood Baig S, Lim J, Kaukab Raja G. Genetic Risk of Autism Spectrum Disorder in a Pakistani Population

Autism spectrum disorder (ASD) is a group of complex multifactorial neurodevelopmental and neuropsychiatric disorders in children characterized by impairment of communication and social interaction. Several genes with associated single nucleotide polymorphisms (SNPs) have been identified for ASD in different genetic association studies, meta-analyses, and genome-wide association studies (GWAS). However, associations between different SNPs and ASD vary from population to population. Four SNPs in genes CNTNAP2, EIF4E, ATP2B2, CACNA1C, and SNP rs4307059 (which is found between CDH9 and CDH10 genes) have been identified and reported as candidate risk factors for ASD. The aim of the present study was, for the first time, to assess the association of SNPs in these genes with ASD in the Pakistani population. PCR-based genotyping was performed using allele-specific primers in 93 ASD and 93 control Pakistani individuals. All genetic associations, genotype frequencies, and allele frequencies were computed as odds’ ratios (ORs) using logistic regression with a threshold of p ≤ 0.01 to determine statistical significance. We found that the homozygous genotypes of mutant T alleles of CNTNAP2 and ATP2B2 were significantly associated with Pakistani ASD patients in unadjusted ORs (p < 0.01), but their significance score was lost in the adjusted model. Other SNPs such as rs4307059, rs17850950 of EIF4E, and rs1006737 of CACNA1C were not statistically significant. Based on this, we conclude that SNPs are not associated with, or are not the main cause of, autism in the Pakistani population, indicating the involvement of additional players, which need to be investigated in future studies in a large population size. One of the limitations of present study is its small sample size. However, this study, being the first on Pakistani ASD patients, may lay the foundations for future studies in larger samples.

Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene

Some candidate genes have been robustly reported to be associated with complex traits, such as the fat mass and obesity-associated (FTO) gene on body mass index (BMI), and the fibroblast growth factor 5 (FGF5) gene on blood pressure levels. It is of interest to know whether an environmental factor (E) can attenuate or exacerbate the adverse influence of a candidate gene. To this end, we here evaluate the performance of “genetic risk score” (GRS) approaches to detect “gene-environment interactions” (G × E). In the first stage, a GRS is calculated according to the genotypes of variants in a candidate gene. In the second stage, we test whether E can significantly modify this GRS effect. This two-stage procedure can not only provide a p-value for a G × E test but also guide inferences on how E modifies the adverse effect of a gene. With systematic simulations, we compared several ways to construct a GRS. If E exacerbates the adverse influence of a gene, GRS formed by the elastic net (ENET) or the least absolute shrinkage and selection operator (LASSO) is recommended. However, the performance of ENET or LASSO will be compromised if E attenuates the adverse influence of a gene, and using the ridge regression (RIDGE) can be more powerful in this situation. Applying RIDGE to 18,424 subjects in the Taiwan Biobank, we showed that performing regular exercise can attenuate the adverse influence of the FTO gene on four obesity measures: BMI (p = 0.0009), body fat percentage (p = 0.0031), waist circumference (p = 0.0052), and hip circumference (p = 0.0001). As another example, we used RIDGE and found the FGF5 gene has a stronger effect on blood pressure in Han Chinese with a higher waist-to-hip ratio [p = 0.0013 for diastolic blood pressure (DBP) and p = 0.0027 for systolic blood pressure (SBP)]. This study provides an evaluation on the GRS approaches, which is important to infer whether E attenuates or exacerbates the adverse influence of a candidate gene.