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Scientific studies to date suggest that autism tends to run in families, and that it develops due to genetic and environmental factors. Recently a team of researchers from Oak Ridge National Laboratory discovered a genetic mutation associated with nonverbal types of autism spectrum disorder (ASD) using a novel method and artificial intelligence (AI) machine learning. Their study will be published in the January 2023 edition of the scientific journal HGG Advances.
“The heritability of autism spectrum disorder, based on family studies, has been estimated to be between 50% and 90%,” wrote the researchers. “A recent study of more than 2 million individuals and 680,000 families from multiple countries provides a best estimate of 80%, yet like many complex diseases very little of this heritability has been explained by significant genome-wide association study (GWAS) loci despite ever-increasing sample sizes.”
Approximately 40 percent of people with ASD are nonverbal according to Autism Speaks. An estimated one in 100 children globally has autism spectrum disorder (ASD) or autism, according to the World Health Organization. This prevalence is even higher in the United States. According to a 2021 U.S. Centers for Disease Control (CDC) report, one in 44 children in America was diagnosed with ASD in 2018.
Autism is a spectrum disorder with a wide range of conditions. The American Psychiatric Association unified Asperger syndrome, childhood disintegrative disorder, pervasive developmental disorder-not otherwise specified (PDD-NOS), and autistic disorder under one unified diagnosis of autism spectrum disorder (ASD) in the fifth edition of its Diagnostic and Statistical Manual of Mental Disorders (DSM-5) released in 2013.
People with autism may have deficits in social-emotional skills, verbal and nonverbal communication, and developing and maintaining relationships. Those with autism may have repetitive movements or speech, rigid thinking, difficulty with transitions, abnormally intense focus in certain interests, and hyper- or hyporeactivity to sensory input.
In short, those on the autism spectrum have social communication challenges and restricted, repetitive behaviors. These symptoms may begin in early childhood. According to Autism Speaks, early intervention may improve brain development, communication skills, learning and social skills.
In this study, the researchers aimed to find genetic markers for non-verbal types of autism by focusing on genomic mutations, also called structural variants (SVs). According to the researchers, although structural variants are a known risk factor for developmental disorders like autism, as with single nucleotide polymorphisms (SNPs), there has not been a discovery of genomic mutations that are consistently replicated in large autistic populations.’
“Despite being difficult to detect accurately, it is becoming increasingly clear that SVs are a major component of heritability in complex traits including human disease,” the scientists wrote. “This is not surprising given that SVs affect approximately five times the amount of genomic space, are three times more likely to be associated with a GWAS signal, and are 50 times more likely to affect the regulation of a gene compared with SNPs.”
Single nucleotide polymorphisms are the most common type of genetic variation in humans according to the National Institute of Health (NIH). These genetic variations are often found in the DNA between genes and may act as biological markers to help researchers find the genes that are associated with diseases. SNPs may be used to assess disease risk, discover the inheritability of genetic variants associated with diseases in families, predict reaction to medication, and vulnerability to environmental factors.
“Here we use a method to identify potential SVs based on non-Mendelian inheritance patterns in pedigrees using parent-child genotypes from ASD families and demonstrate that they are enriched in ASD-risk genes,” the researchers wrote.
The datasets used for the study included an array-based genotypes of autistic individuals and their parents from the database of Genotypes and Phenotypes (dbGAP). For structural variant discovery, a dataset of over 1,170 autistic individuals from over 380 families genotyped at over 1,048,800 nuclear SNP loci from a University of Miami ASD study was used. For validation the researchers used a dataset of over 4,160 individuals from over 1,380 families genotyped at over 1,072,600 nuclear loci from a study by the Autism Genomic Project Consortium (AGPC).
The researchers’ approach was to find genetic variations called single nucleotide polymorphisms (SNPs) that show patterns of non-Mendelian inheritance (NMI) in an autism database, validate these with a second database, and then filter out the potential false positives.
In biology, non-Mendelian inheritance refers to the inheritance of traits that do not follow Mendel’s laws developed in the mid-19th century by Austrian-born monk named Gregor Mendel (1822–1884). Through cross-fertilization experiments with pea plants, Mendel developed the law of dominance , the law of segregation, and the law of independent assortment and is considered the father of modern genetics.
Examples of non-Mendelian inheritance include incomplete dominance, codominance where both alleles are equally expressed, multiple alleles where over two alleles code for any one characteristic, and sex-linked traits found the sex chromosomes of the species. Individuals inherit variant forms of a gene called alleles or allelomorphs, specifically two versions of each gene, from each parent.
To find potential non-Mendelian inheritance genetic markers, the scientists used an open-source whole genome analysis toolset called PLINK that was developed by researchers affiliated with the Center for Human Genetic Research at Massachusetts General Hospital and the Broad Institute of Harvard & MIT.
The Oak Ridge National Laboratory researchers discovered a significant site in the ACMSD (Aminocarboxymuconate Semialdehyde Decarboxylase) gene that is part of the kynurenine pathway and plays a role in the breakdown of tryptophan. According to the researchers, the ACMSD gene “lies at the nexus of numerous ASD-associated traits including neuroinflammation, sleep disorder, mitochondrial dysfunction, gastrointestinal abnormalities, and altered circadian rhythms.”
The researchers used an explainable AI algorithm, an iterative random forest classifier, to identify the most important genes that contain structural variants for autism to define subgroups.
“This could provide a crucial tool for precision medicine in the context of neuropsychiatric disorders,” the scientists concluded.
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