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Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.

ABSTRACT: Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (?1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein-protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS.

SUBMITTER: Kou Y

PROVIDER: S-EPMC3505691 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.

Kou Yan Y Betancur Catalina C Xu Huilei H Buxbaum Joseph D JD Ma'ayan Avi A

American journal of medical genetics. Part C, Seminars in medical genetics 20120412 2

Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (∼1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the geneti ...[more]

PMID: 22499558

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Project description:BackgroundMaternal diabetes has been associated with a risk of neurodevelopmental disorders (NDDs) in offspring, though the common co-occurrence of autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD) and intellectual disability (ID) is rarely considered, nor is the potential for confounding by shared familial factors (e.g. genetics).MethodsThis population-based cohort study used data from Psychiatry Sweden, a linkage of Swedish national registers, to follow 2 369 680 individuals born from 1987 to 2010. We used population-averaged logit models to examine the association between exposure to maternal type 1 diabetes mellitus (T1DM), pre-gestational type 2 diabetes mellitus (T2DM) or gestational diabetes mellitus (GDM), and odds of NDDs in offspring. Subgroup analysis was then performed to investigate the timings of GDM diagnosis during pregnancy and its effect on the odds of NDDs in offspring. We compared these results to models considering paternal lifetime T1DM and T2DM as exposures.ResultsOverall, 45 678 individuals (1.93%) were diagnosed with ASD, 20 823 (0.88%) with ID and 102 018 (4.31%) with ADHD. All types of maternal diabetes were associated with odds of NDDs, with T2DM most strongly associated with any diagnosis of ASD (odds ratioadjusted 1.37, 95% confidence interval 1.03-1.84), ID (2.09, 1.53-2.87) and ADHD (1.43, 1.16-1.77). Considering common co-morbid groups, the associations were strongest between maternal diabetes and diagnostic combinations that included ID. Paternal T1DM and T2DM diagnoses were also associated with offspring NDDs, but these associations were weaker than those with maternal diabetes. Diagnosis of GDM between 27 and 30 weeks of gestation was generally associated with the greatest risk of NDDs in offspring, with the strongest associations for outcomes that included ID.ConclusionThe association of maternal diabetes with NDDs in offspring varies depending on the co-morbid presentation of the NDDs, with the greatest odds associated with outcomes that included ID. Results of paternal-comparison studies suggest that the above associations are likely to be partly confounded by shared familial factors, such as genetic liability.

Project description:ImportanceDepression is a frequently occurring mental disorder and may be common in adults with autism spectrum disorders (ASD), but there is a lack of longitudinal population-based studies examining this association. Whether any increased risk of depression in ASD has a shared familial basis and whether it differs by co-occurring intellectual disability is not well known.ObjectivesTo examine whether individuals with ASD are more likely to be diagnosed as having depression in adulthood than the general population and their nonautistic siblings and to investigate whether these risks differ by the presence or absence of intellectual disability.Design, setting, and participantsPopulation-based cohort study with a nested sibling comparison. The Stockholm Youth Cohort is a total population record linkage study that includes all children and young people (age range, 0-17 years) who were ever resident in Stockholm County, Sweden, between January 2001 and December 2011 (n?=?735?096). Data analysis was conducted between January 5 and November 30, 2017, in Stockholm County, Sweden.Main outcomes and measuresClinical diagnosis of depressive disorders was identified using the Stockholm County Adult Psychiatric Outpatient Register and the Swedish National Patient Register.ResultsParticipants were 223?842 individuals followed up to age 27 years by 2011, of whom 4073 had diagnosed ASD (mean [SD] age, 21.5 [2.7] years; 65.9% male; 2927 without intellectual disability and 1146 with intellectual disability) and 219?769 had no ASD (mean [SD] age, 22.1 [2.8] years; 50.9% male). By age 27 years, 19.8% (n?=?808) of individuals diagnosed having ASD had a diagnosis of depression compared with 6.0% (n?=?13?114) of the general population (adjusted relative risk [RR], 3.64; 95% CI, 3.41-3.88). The risk of a depression diagnosis was higher in ASD without intellectual disability (adjusted RR, 4.28; 95% CI, 4.00-4.58) than in ASD with intellectual disability (adjusted RR, 1.81; 95% CI, 1.51-2.17). Nonautistic full-siblings (adjusted RR, 1.37; 95% CI, 1.23-1.53) and half-siblings (adjusted RR, 1.42; 95% CI, 1.23-1.64) of individuals with ASD also had a higher risk of depression than the general population. Compared with their nonautistic full-siblings, individuals with ASD had more than a 2-fold risk of a depression diagnosis (adjusted odds ratio, 2.50; 95% CI, 1.91-3.27) in young adulthood.Conclusions and relevanceAccording to this study's results, ASD, particularly ASD without intellectual disability, is associated with depression by young adulthood compared with the general population. It appears that this association is unlikely to be explained by shared familial liability. Future research to identify modifiable pathways between ASD and depression may assist in the development of preventive interventions.

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Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.

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Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.

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