A Novel Stratification Method in Linkage Studies to Address Inter- and Intra-Family Heterogeneity in Autism

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Phenotypic Subtyping and Re-Analysis of Existing Methylation Data from Autistic Probands in Simplex Families Reveal ASD Subtype-Associated Differentially Methylated Genes and Biological Functions

Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders with core deficits in social communication and manifestation of restricted, repetitive, and stereotyped behaviors. Despite the core symptomatology, ASD is extremely heterogeneous with respect to the severity of symptoms and behaviors. This heterogeneity presents an inherent challenge to all large-scale genome-wide omics analyses. In the present study, we address this heterogeneity by stratifying ASD probands from simplex families according to the severity of behavioral scores on the Autism Diagnostic Interview-Revised diagnostic instrument, followed by re-analysis of existing DNA methylation data from individuals in three ASD subphenotypes in comparison to that of their respective unaffected siblings. We demonstrate that subphenotyping of cases enables the identification of over 1.6 times the number of statistically significant differentially methylated regions (DMR) and DMR-associated genes (DAGs) between cases and controls, compared to that identified when all cases are combined. Our analyses also reveal ASD-related neurological functions and comorbidities that are enriched among DAGs in each phenotypic subgroup but not in the combined case group. Moreover, relational gene networks constructed with the DAGs reveal signaling pathways associated with specific functions and comorbidities. In addition, a network comprised of DAGs shared among all ASD subgroups and the combined case group is enriched in genes involved in inflammatory responses, suggesting that neuroinflammation may be a common theme underlying core features of ASD. These findings demonstrate the value of phenotype definition in methylomic analyses of ASD and may aid in the development of subtype-directed diagnostics and therapeutics.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Big data approaches to decomposing heterogeneity across the autism spectrum

Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

ASD Phenotype-Genotype Associations in Concordant and Discordant Monozygotic and Dizygotic Twins Stratified by Severity of Autistic Traits

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder characterized by impaired social communication coupled with stereotyped behaviors and restricted interests. Despite the high concordance rate for diagnosis, there is little information on the magnitude of genetic contributions to specific ASD behaviors. Using behavioral/trait severity scores from the Autism Diagnostic Interview-Revised (ADI-R) diagnostic instrument, we compared the phenotypic profiles of mono- and dizygotic twins where both co-twins were diagnosed with ASD or only one twin had a diagnosis. The trait distribution profiles across the respective twin populations were first used for quantitative trait association analyses using publicly available genome-wide genotyping data. Trait-associated single nucleotide polymorphisms (SNPs) were then used for case-control association analyses, in which cases were defined as individuals in the lowest (Q1) and highest (Q4) quartiles of the severity distribution curves for each trait. While all of the ASD-diagnosed twins exhibited similar trait severity profiles, the non-autistic dizygotic twins exhibited significantly lower ADI-R item scores than the non-autistic monozygotic twins. Case-control association analyses of twins stratified by trait severity revealed statistically significant SNPs with odds ratios that clearly distinguished individuals in Q4 from those in Q1. While the level of shared genomic variation is a strong determinant of the severity of autistic traits in the discordant non-autistic twins, the similarity of trait profiles in the concordantly autistic dizygotic twins also suggests a role for environmental influences. Stratification of cases by trait severity resulted in the identification of statistically significant SNPs located near genes over-represented within autism gene datasets.

Replication of previous GWAS hits suggests the association between rs4307059 near MSNP1AS and autism in a Chinese Han population

Autism is a complex neurodevelopmental disorder with high heritability. Previous genome-wide association studies (GWAS) demonstrated that some single-nucleotide polymorphisms (SNPs) were significantly associated with autism, while other studies focusing on these GWAS hits showed inconsistent results. Besides, the association between these variants and autism in the Chinese Han population remains unclear. Therefore, this family-based association study was performed in 640 Chinese Han autism trios to investigate the association between autism and 7 SNPs with genome-wide significance in previous GWAS (rs4307059 near MSNP1AS, rs4141463 in MACROD2, rs2535629 in ITIH3, rs11191454 in AS3MT, rs1625579 in MIR137HG, rs11191580 in NT5C2, and rs1409313 in CUEDC2). The results showed a nominal association between the T allele of rs4307059 and autism under both additive model (T>C, Z = 2.250, P = .024) and recessive model (T>C, Z = 2.109, P = .035). The findings provided evidence that rs4307059 near MSNP1AS might be a susceptibility variant for autism in the Chinese Han population.

Family-based association study of ZNF804A polymorphisms and autism in a Han Chinese population

BACKGROUND

Autism is a complex neurodevelopmental disorder with high heritability. Zinc finger protein 804A (ZNF804A) was suggested to play important roles in neurodevelopment. Previous studies indicated that single-nucleotide polymorphism (SNP) rs1344706 in ZNF804A was strongly associated with schizophrenia and might influence social interaction. Only one study explored the significance of ZNF804A polymorphisms in autism, which discovered that rs7603001 was nominally associated with autism. Moreover, no previous study investigated the association between ZNF804A and autism in a Han Chinese population. Here, we investigated whether these two SNPs (rs1344706 and rs7603001) in ZNF804A contribute to the risk of autism in a Han Chinese population.

METHODS

We performed a family-based association study in 640 Han Chinese autism trios. Sanger sequencing was used for sample genotyping. Then, single marker association analyses were conducted using the family-based association test (FBAT) program.

RESULTS

No significant association was found between the two SNPs (rs1344706 and rs7603001) in ZNF804A and autism (P > 0.05).

CONCLUSIONS

Our findings suggested that rs1344706 and rs7603001 in ZNF804A might not be associated with autism in a Han Chinese population.

Phenotypic subgrouping and multi-omics analyses reveal reduced diazepam-binding inhibitor (DBI) protein levels in autism spectrum disorder with severe language impairment

BACKGROUND

The mechanisms underlying autism spectrum disorder (ASD) remain unclear, and clinical biomarkers are not yet available for ASD. Differences in dysregulated proteins in ASD have shown little reproducibility, which is partly due to ASD heterogeneity. Recent studies have demonstrated that subgrouping ASD cases based on clinical phenotypes is useful for identifying candidate genes that are dysregulated in ASD subgroups. However, this strategy has not been employed in proteome profiling analyses to identify ASD biomarker proteins for specific subgroups.

METHODS

We therefore conducted a cluster analysis of the Autism Diagnostic Interview-Revised (ADI-R) scores from 85 individuals with ASD to predict subgroups and subsequently identified dysregulated genes by reanalyzing the transcriptome profiles of individuals with ASD and unaffected individuals. Proteome profiling of lymphoblastoid cell lines from these individuals was performed via 2D-gel electrophoresis, and then mass spectrometry. Disrupted proteins were identified and compared to the dysregulated transcripts and reported dysregulated proteins from previous proteome studies. Biological functions were predicted using the Ingenuity Pathway Analysis (IPA) program. Selected proteins were also analyzed by Western blotting.

RESULTS

The cluster analysis of ADI-R data revealed four ASD subgroups, including ASD with severe language impairment, and transcriptome profiling identified dysregulated genes in each subgroup. Screening via proteome analysis revealed 82 altered proteins in the ASD subgroup with severe language impairment. Eighteen of these proteins were further identified by nano-LC-MS/MS. Among these proteins, fourteen were predicted by IPA to be associated with neurological functions and inflammation. Among these proteins, diazepam-binding inhibitor (DBI) protein was confirmed by Western blot analysis to be expressed at significantly decreased levels in the ASD subgroup with severe language impairment, and the DBI expression levels were correlated with the scores of several ADI-R items.

CONCLUSIONS

By subgrouping individuals with ASD based on clinical phenotypes, and then performing an integrated transcriptome-proteome analysis, we identified DBI as a novel candidate protein for ASD with severe language impairment. The mechanisms of this protein and its potential use as an ASD biomarker warrant further study.

Investigation of epigenetic regulatory networks associated with autism spectrum disorder (ASD) by integrated global LINE-1 methylation and gene expression profiling analyses

BACKGROUND

The exact cause and mechanisms underlying the pathobiology of autism spectrum disorder (ASD) remain unclear. Dysregulation of long interspersed element-1 (LINE-1) has been reported in the brains of ASD-like mutant mice and ASD brain tissues. However, the role and methylation of LINE-1 in individuals with ASD remain unclear. In this study, we aimed to investigate whether LINE-1 insertion is associated with differentially expressed genes (DEGs) and to assess LINE-1 methylation in ASD.

METHODS

To identify DEGs associated with LINE-1 in ASD, we reanalyzed previously published transcriptome profiles and overlapped them with the list of LINE-1-containing genes from the TranspoGene database. An Ingenuity Pathway Analysis (IPA) of DEGs associated with LINE-1 insertion was conducted. DNA methylation of LINE-1 was assessed via combined bisulfite restriction analysis (COBRA) of lymphoblastoid cell lines from ASD individuals and unaffected individuals, and the methylation levels were correlated with the expression levels of LINE-1 and two LINE-1-inserted DEGs, C1orf27 and ARMC8.

RESULTS

We found that LINE-1 insertion was significantly associated with DEGs in ASD. The IPA showed that LINE-1-inserted DEGs were associated with ASD-related mechanisms, including sex hormone receptor signaling and axon guidance signaling. Moreover, we observed that the LINE-1 methylation level was significantly reduced in lymphoblastoid cell lines from ASD individuals with severe language impairment and was inversely correlated with the transcript level. The methylation level of LINE-1 was also correlated with the expression of the LINE-1-inserted DEG C1orf27 but not ARMC8.

CONCLUSIONS

In ASD individuals with severe language impairment, LINE-1 methylation was reduced and correlated with the expression levels of LINE-1 and the LINE-1-inserted DEG C1orf27. Our findings highlight the association of LINE-1 with DEGs in ASD blood samples and warrant further investigation. The molecular mechanisms of LINE-1 and the effects of its methylation in ASD pathobiology deserve further study.

Phenotyping, Etiological Factors, and Biomarkers: Toward Precision Medicine in Autism Spectrum Disorders

Despite the progress made in understanding the biology of autism spectrum disorder (ASD), effective biological interventions for the core symptoms remain elusive. Because of the etiological heterogeneity of ASD, identification of a "one-size-fits-all" treatment approach will likely continue to be challenging. A meeting was convened at the University of Missouri and the Thompson Center to discuss strategies for stratifying patients with ASD for the purpose of moving toward precision medicine. The "white paper" presented here articulates the challenges involved and provides suggestions for future solutions.

Genetic variation in melatonin pathway enzymes in children with autism spectrum disorder and comorbid sleep onset delay

Sleep disruption is common in individuals with autism spectrum disorder (ASD). Genes whose products regulate endogenous melatonin modify sleep patterns and have been implicated in ASD. Genetic factors likely contribute to comorbid expression of sleep disorders in ASD. We studied a clinically unique ASD subgroup, consisting solely of children with comorbid expression of sleep onset delay. We evaluated variation in two melatonin pathway genes, acetylserotonin O-methyltransferase (ASMT) and cytochrome P450 1A2 (CYP1A2). We observed higher frequencies than currently reported (p < 0.04) for variants evidenced to decrease ASMT expression and related to decreased CYP1A2 enzyme activity (p ≤ 0.0007). We detected a relationship between genotypes in ASMT and CYP1A2 (r(2) = 0.63). Our results indicate that expression of sleep onset delay relates to melatonin pathway genes.

A Pooled Genome-Wide Association Study of Asperger Syndrome

Asperger Syndrome (AS) is a neurodevelopmental condition characterized by impairments in social interaction and communication, alongside the presence of unusually repetitive, restricted interests and stereotyped behaviour. Individuals with AS have no delay in cognitive and language development. It is a subset of Autism Spectrum Conditions (ASC), which are highly heritable and has a population prevalence of approximately 1%. Few studies have investigated the genetic basis of AS. To address this gap in the literature, we performed a genome-wide pooled DNA association study to identify candidate loci in 612 individuals (294 cases and 318 controls) of Caucasian ancestry, using the Affymetrix GeneChip Human Mapping version 6.0 array. We identified 11 SNPs that had a p-value below 1x10-5. These SNPs were independently genotyped in the same sample. Three of the SNPs (rs1268055, rs7785891 and rs2782448) were nominally significant, though none remained significant after Bonferroni correction. Two of our top three SNPs (rs7785891 and rs2782448) lie in loci previously implicated in ASC. However, investigation of the three SNPs in the ASC genome-wide association dataset from the Psychiatric Genomics Consortium indicated that these three SNPs were not significantly associated with ASC. The effect sizes of the variants were modest, indicating that our study was not sufficiently powered to identify causal variants with precision.