research paper related to genetic disorders

Genetics of neurodegenerative diseases: an overview

Affiliations.

1 Institute of Clinical Medicine, University of Oslo, Oslo, Norway; UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.
2 UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Center for Neurology and Hertie Institute for Clinical Brain Research, Eberhard-Karls-University, Tübingen, Germany.
3 UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom. Electronic address: [email protected].
PMID: 28987179
DOI: 10.1016/B978-0-12-802395-2.00022-5

Genetic factors are central to the etiology of neurodegeneration, both as monogenic causes of heritable disease and as modifiers of susceptibility to complex, sporadic disorders. Over the last two decades, the identification of disease genes and risk loci has led to some of the greatest advances in medicine and invaluable insights into pathogenic mechanisms and disease pathways. Large-scale research efforts, novel study designs, and advances in methodology are rapidly expanding our understanding of the genome and the genetic architecture of neurodegenerative disease. Here, we review major developments in the field to date, highlighting overarching historic trends and general insights. Monogenic neurodegenerative diseases are discussed from the perspectives of both rare Mendelian forms of common disorders, such as Alzheimer disease and Parkinson disease, and heterogeneous heritable conditions, including ataxias and spastic paraplegias. Next, we summarize the experiences from investigations of complex neurodegenerative disorders, including genomewide association studies. In the final section, we reflect upon the limitations of current findings and outline important future directions. Genetics plays an essential role in translational research, ultimately aiming to develop novel disease-modifying therapies for neurodegenerative disorders. We anticipate that individual genetic profiling will also be increasingly relevant in a clinical context, with implications for patient care in line with the proposed ideal of personalized medicine.

Keywords: Alzheimer's diseases; Genetics; Parkinson's disease; genome-wide association study (GWAS); neurodegeneration.

Publication types

Genetic Predisposition to Disease*
Genome-Wide Association Study
Neurodegenerative Diseases / genetics*
Neurodegenerative Diseases / physiopathology

Grants and funding

G0802760/MRC_/Medical Research Council/United Kingdom
G1001253/MRC_/Medical Research Council/United Kingdom
G108/638/MRC_/Medical Research Council/United Kingdom
MR/J004758/1/MRC_/Medical Research Council/United Kingdom

EDITORIAL article

Editorial: the genetics and epigenetics of mental health.

1 Instituto de Pesquisa Pelé Pequeno Príncipe, Curitiba, Brazil
2 Faculdades Pequeno Príncipe, Curitiba, Brazil
3 Department of Genetics, Federal University of Parana, Post-graduation Program in Genetics, Curitiba, Brazil
4 Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova-Santa Maria, Biomedical Research Institute of Lleida (IRBLleida), Lleida, Spain
5 CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain

Editorial on the Research Topic The genetics and epigenetics of mental health

Mental health conditions cover a broad spectrum of disturbances, including neurological and substance use disorders, suicide risk, and associated psychosocial, cognitive, and intellectual disabilities (WHO, 2022). Despite a substantial amount of evidence, the interaction of genetic variants, epigenetic mechanisms, and environmental risk factors involved in mental health is poorly understood. Through distinct perspectives and different experimental approaches, the genetics and epigenetics of mental health were addressed in seven relevant articles included in this Research Topic, briefly summarized below.

Stress has severe consequences on the epigenome, but the timing of its occurrence, as well as the intensity and number of events, are critical for the severity of mental health symptoms. In particular, Serpeloni et al. demonstrated that stress generated in the form of intimate partner violence (IPV) during and/or after pregnancy impacts the offspring’s epigenome, shaping its resilience. They observed that individuals exposed to maternal IPV after birth presented psychiatric issues similar to their mothers, with different outcomes if the exposure to maternal IPV occurred both prenatally and postnatally. Prenatal IPV was associated with differential methylation in CpG sites in the genes encoding the glucocorticoid receptor ( NR3C1 ) and its repressor FKBP51 ( FKBP5 ), associated with the ability to terminate hormonal stress responses. Also considering early-life experiences and data from 2008 to 2016 of the Health and Retirement Study, Shin et al. concluded that early life experiences and relationships have a significant influence, attenuating or exacerbating the risk of suffering from mental health problems among individuals with a higher polygenic risk score predisposing to autism.

Environmental and developmental factors are also strongly linked to obsessive-compulsive disorder (OCD). They may explain the apparent discrepancy between the relatively high heritability scores and the inconsistent results found in genetic association studies, owing to their impact on gene expression and regulation. Based on this, Deng et al. stratified OCD patients by the age of disease onset. The findings revealed associations between the early onset and variants of genes whose products play a role in neural development, corroborating the age-associated genetic heterogeneity of OCD.

Further exploring environmental and genetic etiological clues, Li et al. used genome-wide association study (GWAS) data to calculate polygenic risk scores for salivary and tongue dorsum microbiomes associated with anxiety and depression. Additionally, causal relationships between the oral microbiome, anxiety, and depression were detected through Mendelian randomization, unraveling potential pathogenic mechanisms and interventional targets. Constructing a similar line of evidence, Becerra et al. found associations between the epigenetic regulation of inflammatory processes, the composition of gut microbiome, and modified Rosenberg self-esteem scores in samples from the Native Hawaiian and other Pacific Islander (NHPI) populations, which present a high prevalence and mortality from chronic and immunometabolic diseases, as well as mental health problems. This warrants further investigation into the relationship of microbiota to brain activity and mental health.

There is a lot of debate regarding suicidal behavior and its relationship with psychiatric disorders, but the extent to which they share the same genetic architecture is unknown. This Research Topic was investigated by Kootbodien et al. through the use of genomic structural equation modeling and Mendelian randomization with a large genomic dataset. The authors observed a strong genetic correlation between suicidal ideation, attempts, and self-harm, as well as a moderate to strong genetic correlation between suicidal behavioral traits and a range of psychiatric disorders, most notably major depressive disorder, involving pathways related to developmental biology, signal transduction, and RNA degradation. In conclusion, the study provided evidence of a shared etiology between suicidal behavior and psychiatric disorders, with overlapping pathophysiological pathways.

Malekpour et al. , in their investigation of psychogenic non-epileptic seizures (PNES), also uncovered shared pathways with psychiatric conditions. PNES, the most prevalent non-epileptic disorder among patients referring to epilepsy centers, carries a mortality rate akin to drug-resistant epilepsy. Employing a systems biology approach, the authors pinpointed several key components influencing the disease pathogenesis network. These include brain-derived neurotrophic factor (BDNF), cortisol, norepinephrine, proopiomelanocortin (POMC), neuropeptide Y (NPY), the growth hormone receptor signaling pathway, phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling, and the neurotrophin signaling pathway.

In general, these studies have some limitations: small sample sizes, leading to low statistical power in some cases, environmental confounding factors (such as diet and physical activity), which were not considered in the microbiome studies, incomplete phenotype descriptions, and partial coverages of human genetic diversity. Childhood adversities and adult comorbidities are among the variables that were not controlled for as possible causes of the investigated psychiatric and neurological disorders, and some results still claim for functional studies to be validated. Thus, the findings brought more elaborated questions, each of which shed some light on knowledge gaps that remain very difficult to fill. How do early-life epigenetic processes regulate our mental health resilience and disease resistance? What is the role of the microbiome in this process and how do genetic variants influence its composition? How does the impact of all these elements shape the resistance of human populations to psychiatric and neurological diseases and, most importantly, translate into public health measures in the future? We hope to engage more researchers in the pursuit of these answers.

Author contributions

GCK: Conceptualization, Data curation, Writing–original draft, Writing–review and editing. ABWB: Writing–original draft, Writing–review and editing. ADST: Conceptualization, Data curation, Writing–original draft, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Empresa Brasileira de Serviços Hospitalares (Ebserh) grant numbers 423317/2021-0 and 313741/2021-2 (8520137521584230), Research for the United Health SUS System (PPSUS-MS), CNPq, Fundação Araucária and SESA-PR, Protocol N°: SUS2020131000106. ABWB receives CNPq research productivity scholarships (protocols 313741/2021). ADST receives financial support from Instituto de Salud Carlos III (Miguel Servet, 2023: CP23/00095), co-funded by Fondo Social Europeo Plus (FSE+).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: methylation, GWAS-genome-wide association study, microbiome & dysbiosis, poligenic risk score, neurological conditions, epigenome, genome

Citation: Kretzschmar GC, Boldt ABW and Targa ADS (2024) Editorial: The genetics and epigenetics of mental health. Front. Genet. 15:1402495. doi: 10.3389/fgene.2024.1402495

Received: 17 March 2024; Accepted: 26 March 2024; Published: 09 April 2024.

Edited and reviewed by:

Copyright © 2024 Kretzschmar, Boldt and Targa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gabriela Canalli Kretzschmar, [email protected] ; Angelica Beate Winter Boldt, [email protected] ; Adriano D. S. Targa, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Rare genetic disorders in India: Current status, challenges, and CRISPR-based therapy

Published: 19 February 2024
Volume 49 , article number 28 , ( 2024 )

Cite this article

research paper related to genetic disorders

Pallabi Bhattacharyya 1 na1 ,
Kanikah Mehndiratta 1 , 2 na1 ,
Souvik Maiti 1 , 3 &
Debojyoti Chakraborty ORCID: orcid.org/0000-0003-1460-7594 1 , 3

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Rare genetic diseases are a group of life-threatening disorders affecting significant populations worldwide and posing substantial challenges to healthcare systems globally. India, with its vast population, is also no exception. The country harbors millions of individuals affected by these fatal disorders, which often result from mutations in a single gene. The emergence of CRISPR-Cas9 technology, however, has ushered in a new era of hope in genetic therapies. CRISPR-based treatments hold the potential to precisely edit and correct disease-causing mutations, offering tailored solutions for rare genetic diseases in India. This review explores the landscape of rare genetic diseases in India along with national policies and major challenges, and examines the implications of CRISPR-based therapies for potential cure. It delves into the potential of this technology in providing personalized and effective treatments. However, alongside these promising prospects, some ethical considerations, regulatory challenges, and concerns about the accessibility of CRISPR therapies are also discussed since addressing these issues is crucial for harnessing the full power of CRISPR in tackling rare genetic diseases in India. By taking a multidisciplinary approach that combines scientific advancements, ethical principles, and regulatory frameworks, these complexities can be reconciled, paving the way for innovative and impactful healthcare solutions for rare diseases in India.

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Acknowledgements

This work was supported by an EMBO Investigator Award to DC and was carried out at CSIR-Institute of Genomics & Integrative Biology, India.

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Pallabi Bhattacharyya and Kanikah Mehndiratta contributed equally.

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Council of Scientific and Industrial Research, Institute of Genomics and Integrative Biology, New Delhi, India

Pallabi Bhattacharyya, Kanikah Mehndiratta, Souvik Maiti & Debojyoti Chakraborty

Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, 60596, Frankfurt am Main, Germany

Kanikah Mehndiratta

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

Souvik Maiti & Debojyoti Chakraborty

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PB and KM equally contributed to writing the manuscript. SM and DC critically supervised and finalized the manuscript.

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Correspondence to Debojyoti Chakraborty .

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This article is part of the Topical Collection: The Rare Genetic Disease Research Landscape in India.

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Bhattacharyya, P., Mehndiratta, K., Maiti, S. et al. Rare genetic disorders in India: Current status, challenges, and CRISPR-based therapy. J Biosci 49 , 28 (2024). https://doi.org/10.1007/s12038-023-00413-8

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Using AI to improve diagnosis of rare genetic disorders

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Researchers from the Baylor Genetics clinical diagnostic laboratory noted that AIM's module can contribute to predictions independent of clinical knowledge of the gene of interest, helping to advance the discovery of novel disease mechanisms. “The diagnostic rate for rare genetic disorders is only about 30%, and on average, it is six years from the time of symptom onset to diagnosis. There is an urgent need for new approaches to enhance the speed and accuracy of diagnosis,” said co-corresponding author Dr. Pengfei Liu , associate professor of molecular and human genetics and associate clinical director at Baylor Genetics.

AIM is trained using a public database of known variants and genetic analysis called Model organism Aggregated Resources for Rare Variant ExpLoration ( MARRVEL ) previously developed by the Baylor team . The MARRVEL database includes more than 3.5 million variants from thousands of diagnosed cases. Researchers provide AIM with patients’ exome sequence data and symptoms, and AIM provides a ranking of the most likely gene candidates causing the rare disease.

Researchers compared AIM’s results to other algorithms used in recent benchmark papers. They tested the models using three data cohorts with established diagnoses from Baylor Genetics, the National Institutes of Health-funded Undiagnosed Diseases Network (UDN) and the Deciphering Developmental Disorders (DDD) project. AIM consistently ranked diagnosed genes as the No. 1 candidate in twice as many cases than all other benchmark methods using these real-world data sets.

“We trained AIM to mimic the way humans make decisions, and the machine can do it much faster, more efficiently and at a lower cost. This method has effectively doubled the rate of accurate diagnosis,” said co-corresponding author Dr. Zhandong Liu , associate professor of pediatrics – neurology at Baylor and investigator at the Jan and Dan Duncan Neurological Research Institute (NRI) at Texas Children’s Hospital.

AIM also offers new hope for rare disease cases that have remained unsolved for years. Hundreds of novel disease-causing variants that may be key to solving these cold cases are reported every year; however, determining which cases warrant reanalysis is challenging because of the high volume of cases. The researchers tested AIM’s clinical exome reanalysis on a dataset of UDN and DDD cases and found that it was able to correctly identify 57% of diagnosable cases.

“We can make the reanalysis process much more efficient by using AIM to identify a high-confidence set of potentially solvable cases and pushing those cases for manual review,” Zhandong Liu said. “We anticipate that this tool can recover an unprecedented number of cases that were not previously thought to be diagnosable.”

Researchers also tested AIM’s potential for discovery of novel gene candidates that have not been linked to a disease. AIM correctly predicted two newly reported disease genes as top candidates in two UDN cases.

“AIM is a major step forward in using AI to diagnose rare diseases. It narrows the differential genetic diagnoses down to a few genes and has the potential to guide the discovery of previously unknown disorders,” said co-corresponding author Dr. Hugo Bellen , Distinguished Service Professor in molecular and human genetics at Baylor and chair in neurogenetics at the Duncan NRI.

“When combined with the deep expertise of our certified clinical lab directors, highly curated datasets and scalable automated technology, we are seeing the impact of augmented intelligence to provide comprehensive genetic insights at scale, even for the most vulnerable patient populations and complex conditions,” said senior author Dr. Fan Xia , associate professor of molecular and human genetics at Baylor and vice president of clinical genomics at Baylor Genetics. “By applying real-world training data from a Baylor Genetics cohort without any inclusion criteria, AIM has shown superior accuracy. Baylor Genetics is aiming to develop the next generation of diagnostic intelligence and bring this to clinical practice.”

Other authors of this work include Dongxue Mao, Chaozhong Liu, Linhua Wang, Rami AI-Ouran, Cole Deisseroth, Sasidhar Pasupuleti, Seon Young Kim, Lucian Li, Jill A.Rosenfeld, Linyan Meng, Lindsay C. Burrage, Michael Wangler, Shinya Yamamoto, Michael Santana, Victor Perez, Priyank Shukla, Christine Eng, Brendan Lee and Bo Yuan. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Al Hussein Technical University, Baylor Genetics and the Human Genome Sequencing Center at Baylor.

This work was supported by the Chan Zuckerberg Initiative and the National Institute of Neurological Disorders and Stroke (3U2CNS132415). Read the full publication here .

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Researchers at Baylor College of Medicine have tested the feasibility of using human cell transdifferentiation with RNA sequencing to facilitate diagnoses of Mendelian disorders. The approach generated an overall diagnostic yield of 25.4% in a cohort of Undiagnosed Diseases Network cases. The findings are published in the American Journal of Human Genetics .

RNA sequencing, which reads the transcriptome or gene expression in a cell, is often needed to support a genetic diagnosis obtained through exome sequencing or whole genome sequencing. However, the effectiveness of RNA sequencing is limited by expression of disease-associated genes in clinically accessible tissues like blood or skin fibroblasts, cells that contribute to the formation of connective tissue.

“This is especially problematic in neurological diseases because the gene causing the disorder may not be expressed in blood and skin cells,” said corresponding author Dr. Pengfei Liu , associate professor of molecular and human genetics and director of the ACGME-accredited Laboratory Genetics and Genomics Fellowship Program at Baylor.

Research method helps overcome obstacles to genetic diagnoses

To overcome this challenge, researchers led by first author Dr. Shenglan Li , staff scientist in Liu’s lab at Baylor, converted fibroblasts obtained in skin biopsies to neurons in a process called transdifferentiation. “This method activates neuron-specific gene expression and increases the probability that we can accurately characterize disease-causing mutations in these cells,” Li said.

For clinical validation, researchers generated these induced neurons for a cohort of 71 individuals with neurological characteristics in the Undiagnosed Diseases Network. RNA sequencing of the induced neurons led to a diagnosis in 18 individuals (25.4%); five of those cases could not have been diagnosed with RNA sequencing of fibroblasts alone.

This study shows that fibroblast-to-neuron transdifferentiation followed by RNA sequencing is a simple, low-cost and reproducible approach with a reasonable turnaround time, making it feasible for clinical implementation,” Liu said. “This new testing method represents a paradigm shift in laboratory genetics, moving from the traditional DNA-centric approach to one that focuses on the patient’s cells.”

“It’s exciting to apply this well-established technique, which previously has been used to study mechanisms of neurodegenerative diseases, for a new use in genetic diagnostics,” Li said.

Other authors of this work include Sen Zhao, Jefferson C. Sinson, Aleksandar Bajic, Jill A. Rosenfeld, Matthew B. Neeley, Mezthly Pena, Kim C. Worley, Lindsay C. Burrage, Monika Weisz-Hubshman, Shamika Ketkar, William J. Craigen, Gary D. Clark, Seema Lalani, Carlos A. Bacino, Keren Machol, Hsiao-Tuan Chao, Lorraine Potocki, Lisa Emrick, Jennifer Sheppard, My T.T. Nguyen, Anahita Khoramnia, Paula Patricia Hernandez, Sandesh C. S. Nagamani, Zhandong Liu, Christine M. Eng and Brendan Lee. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Texas Children’s Hospital, McNair Medical Institute at the Robert and Janice McNair Foundation and Baylor Genetics.

The research was supported by the National Institutes of Health Common Fund (U01HG007709 and U01HG007942), the National Human Genome Research Institute (R35HG011311) and the BCM Intellectual and Developmental Disabilities Research Center funded by the Eunice Kennedy Shriver National Institute of Child Health & Human Development (P50HD103555). For a full list of funding sources, see the publication .

By Molly Chiu

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Brain organoids and assembloids are new models for elucidating, treating neurodevelopmental disorders

Stanford Medicine research on Timothy syndrome — which predisposes newborns to autism and epilepsy — may extend well beyond the rare genetic disorder to schizophrenia and other conditions.

April 24, 2024 - By Bruce Goldman, Erin Digitale

In this 2019 photo, Timothy syndrome patient Holden Hulet, left, rides in a side-by-side ATV driven by his dad, Kelby Hulet, at sand dunes near their home in southern Utah. Courtesy of the Hulet family

For a long time, no one understood that Holden Hulet was having seizures.

“He would just say ‘I feel tingly, and my vision kind of goes blurry,’” said Holden’s mom, JJ Hulet. “But he couldn’t communicate exactly what was going on.”

JJ and Kelby Hulet could see their son was having short spells of incoherent speech, rapid back-and-forth eye movements and odd physical changes. “He’d kind of go — I don’t want to say ‘limp’ because he would stand just fine — but his body would just be in zombie mode,” JJ said. The episodes lasted less than a minute.

The parents were puzzled and worried, as they had been many times since Holden was born in 2008 and they learned that their newborn had an extremely rare genetic disease. “I was thinking it was his heart,” Kelby Hulet, Holden’s dad, said.

Holden’s condition, Timothy syndrome, causes long, irregular gaps in heart rhythm. He spent his first six months hospitalized in a neonatal intensive care unit in his family’s home state of Utah while he grew big enough to receive an implantable cardioverter defibrillator. The device sends an electrical signal to restart his heart when it pauses for too long.

As a small child, Holden would sometimes pass out before the defibrillator shocked his heart back into action. When Holden started telling his parents about the blurry-vision episodes at age 6, Kelby initially believed it was a new version of the same problem, and he kept a time stamp on his phone for each episode. But the records from Holden’s defibrillator showed that these times did not line up with any heart-rhythm problems.

The family’s pediatrician was confused, too. Perhaps Holden was having periods of low blood sugar, another possible Timothy syndrome complication, he suggested. Initial testing at the local medical center did not turn up clear answers.

But Kelby, who was training to become an operating room nurse, realized Holden’s episodes reminded him of what he was learning about warning signs for stroke. JJ called Holden’s cardiologist in Utah and asked for a detailed neurologic evaluation, which enabled the mysterious episodes to be diagnosed as seizures. Holden began taking anti-seizure medication, which helped, to his parents’ great relief.

Researching a rare disease

A few months after Holden was born, Sergiu Pasca , MD, arrived at Stanford Medicine to pursue a postdoctoral fellowship in the lab of Ricardo Dolmetsch, PhD, then an assistant professor of neurobiology, who was redirecting his research to autism spectrum disorder. At the time, Pasca did not know the Hulet family. But his work soon became focused on the disorder that has shaped Holden’s life.

Caused by a defective gene on the 12th chromosome, Timothy syndrome is vanishingly rare, with no more than 70 diagnosed cases. Children with this disorder rarely survive to late adolescence. It is caused by a mutation in the gene coding for a type of calcium channel — a protein containing a pore that selectively opens or closes, respectively permitting or blocking the flow of calcium across cells’ membranes. While a prominent feature — severe heart malfunction — can be tackled with a pacemaker, most children with Timothy syndrome will end up with lifelong brain disorders including autism, epilepsy and schizophrenia.

By mid-2009, Pasca had succeeded in generating nerve cells from induced pluripotent stem cells (which can be induced to form virtually any of the body’s numerous cell types). These included cells derived from the skin of two patients with Timothy syndrome. Later that year he observed defects in how the patient-derived neurons were handling calcium. This advance — the creation of one of the initial in-a-dish models of brain disease, built from neurons with defects that precisely mirrored those of a patient’s brain — was published in Nature Medicine in 2011.

Pasca and colleagues continued to monitor these Timothy-syndrome neurons in standard two-dimensional culture — growing as single layers in petri dishes — over the next few years. While this two-dimensional culture method was limited in its ability to sustain viable neurons, it was soon superseded by a genuine scientific breakthrough.

Pioneering the first assembloids

The constraints of two-dimensional culture, including the inability to keep these neurons for long periods of time so that they could reach key stages of neural development, prompted Pasca in 2011 to start developing an unprecedented three-dimensional method. The novel technology produced what came to be known as brain organoids. These constructs recapitulated some of the architecture and physiology of the human cerebral cortex. The organoids can survive for several years in culture, enabling neuroscientists to view, non-invasively, the developing human brain up close and in real time. The scientists wrote a seminal Nature Methods paper , published in 2015, that described their discovery.

Pasca’s group subsequently showed that culturing brain organoids in different ways could generate organoids representing different brain regions (in this case, the cerebral cortex and a fetal structure called the subpallium). In a breakthrough set of experiments, Pasca’s team found ways to bring these organoids into contact so that they fuse and forge complex neuronal connections mimicking those that arise during natural fetal and neonatal development. Pasca named such constructs assembloids.

In their paper on the research, which was published in Nature in 2017, Pasca’s team showed that after fusion, a class of inhibitory neurons originating in the subpallium migrates to the cortex, proceeding in discrete, stuttering jumps. (See animation .) These migrating neurons, called interneurons, upon reaching their destinations — excitatory neurons of the cortex — form complex circuits with those cortical neurons.

But in assembloids derived from Timothy syndrome patients, the motion of interneurons as they migrate from the subpallium is impaired — they jump forward more often, but each jump is considerably shorter, so they fail to integrate into the appropriate circuitry in the cortex. This wreaks havoc with signaling in cortical circuits. Pasca’s team tied this aberrant neuronal behavior on the part of Timothy syndrome neurons to the key molecular consequence of the genetic defect responsible for the condition: namely, malfunction of the critical channels through which calcium must pass to cross neurons’ outer membranes.

A family’s struggles

While Pasca was developing assembloids, the Hulet family was progressing through their own journey of discovery with Holden. They faced painful uncertainty at every stage, starting when Holden was discharged from the NICU in the summer of 2009, after several months of hospitalization and multiple heart surgeries.

“Even when we brought him home, [his doctors] said ‘Don’t get your hopes up. We don’t usually see them make it past age 2,” JJ recalled. Many children with Timothy syndrome die from cardiac failure in early life.

“It’s really hard to be positive in that kind of situation, and for a long time I did let it get to me,” JJ said. “I finally got to a point where I said, ‘I have to live my life and we just keep fighting.’”

JJ runs a child care center and has years of experience working with special-needs kids, which motivated her to push for an autism evaluation when she saw signs of autism in Holden. He’s much more verbal than many children with autism, which paradoxically made it more difficult to get an official diagnosis.

“That was frustrating,” JJ said. Although the family’s pediatric cardiologist in Salt Lake City was familiar with the vagaries of Timothy syndrome, their local caregivers in the small town where they live in southern Utah were not. “They kept saying ‘Oh, no, it’s just developmental delays because he was so premature,’” she said. She wonders whether it would have been easier to have Holden’s autism diagnosed had more been known about Timothy syndrome at the time.

“I think research is important so that parents and children have the support they need,” she said, noting how lonely and painful it can be to advocate for a child when his condition is poorly understood — and when, as a parent, you may be doubted by medical professionals. “It’s a really hard thing to deal with.”

Her voice breaks briefly. She continues, “I think research brings validity to that.”

Sergiu Pasca

Implanting organoids

In 2022, Pasca published a study in Nature describing the transplantation of human cortical organoids into neonatal rats’ brains, which resulted in the integration of human neurons along with supporting brain cells into the brain tissue of rats to form hybridized working circuits. The implanted human organoids survived, thrived and grew. Individual neurons from the human organoids integrated into young rats’ brains were at least six times as big as those — generated the same way, at the same time — that remained in a dish. The transplanted neurons also exhibited much more sophisticated branching patterns. Pasca and his colleagues observed marked differences in the electrical activity of, on one hand, human neurons generated from a Timothy syndrome patient, cultured as organoids and transplanted into one side of a rat’s brain, and, on the other hand, those generated from a healthy individual and transplanted, as an organoid, into the corresponding spot on the other side of the same rat’s brain. The Timothy syndrome neurons were also much smaller and were deficient in sprouting branching, brush-like extensions called dendrites, which act as antennae for input from nearby neurons.

“We’ve learned a lot about Timothy syndrome by studying organoids and assembloids kept in a dish,” Pasca said. “But only with transplantation were we able to convincingly see these neuronal-activity-related differences.”

That same year, the FDA Modernization Act 2.0 was signed into law, exempting certain categories of new drug-development protocols from previously mandated animal testing. The act was predicated on the understanding that recent advancements in science offer increasingly viable alternatives to animal testing, so the findings based on the organoid- and assembloid-culture technologies may be adequate to justify clinical trials in some neurodevelopmental conditions.

Most recently, in a Nature paper published April 24, Pasca and his colleagues demonstrated, in principle, the ability of antisense oligonucleotides (ASOs) to correct the fundamental defects that lead to Timothy syndrome by nudging calcium-channel production toward another form of the gene that does not carry the disease-causing mutation. Using ASOs to guide production of the functional rather than defective form of this channel reversed the defect’s detrimental downstream effects: Interneuronal migration proceeded similarly to that procedure in healthy brains, and the altered electrical properties of the calcium channel reverted to normalcy. This therapeutic correction was demonstrated in a lab dish — and, critically, in rat-transplantation experiments, suggesting that this therapeutic approach can work in a living organism.

Pasca is now actively searching the globe for carriers of the genetic defect, in preparation for the pursuit of a clinical trial at Stanford Medicine to test the safety and therapeutic potential of ASOs in mitigating the pathological features of Timothy syndrome.

“We are also actively engaged in conversations with other scientists, clinicians in the field and ethicists about the best way to move forward and safely bring this therapeutic approach into the clinic,” he said.

Pasca added that the calcium channel that is mutated in Timothy syndrome is, in fact, “the hub” of several neuropsychiatric diseases including schizophrenia and bipolar disorder. So it may be that the lessons learned — and the therapies derived — from his 15-year focus on a rare disease may have broad application in a number of widespread and troubling psychiatric conditions.

‘Amazing’ teenager

Today, in defiance of his doctors’ warning that he might not live past age 2, Holden Hulet is 15 years old and doing well.

“I think a lot of times, autism is perceived as ‘They’re not neurotypical and they’re not capable of certain things.’ But he is brilliant,” JJ said. “He’s amazing with techie stuff or Legos. He’s funny and super honest and very self-aware.”

Kelby often takes Holden to visit the farm where he grew up. Holden loves to ride the farm equipment and enjoys hanging out with the animals, especially the farm dogs and calves. Like a lot of kids, he keeps an eye out for good rocks, Kelby said with a chuckle.

“He’s always either throwing them or collecting them,” JJ said. “That’s something I really like about him: He’s always got a pocket full of something.”

Although navigating a rare disease is one of the most challenging things they have faced, the Hulets see light in their situation, and would offer encouragement to any family facing a new Timothy syndrome diagnosis.

“There is hope,” JJ said. “There are people out there who care, people out there who fight for you who don’t even know you. I think that’s what is so important about research — that you’re fighting a battle for people you don’t even know.”

The study published April 24 was supported by the National Institute of Mental Health (grants R01 MH115012 and K99 MH119319P), the Wu Tsai Neuroscience Institute, the Autism Speaks Postdoctoral Fellowship, the Kwan Funds, the Senkut Funds, the Coates Foundation, the Ludwig Family Foundation, the Alfred E. Mann Foundation, and the Stanford Maternal and Child Health Research Institute Postdoctoral Fellowship.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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Genetic Disease Research Branch

The Genetic Disease Research Branch studies the mechanisms by which genetic changes affect the structure and function of gene products leading to human disease.

A major focus of the branch's research lies in understanding how disruptions in signaling pathways and transcription factors contribute to disease. Our investigators use genetics and genomic approaches in both human and mouse systems to identify and better understand pathways involved in human genetic diseases and normal development. Model systems, including genetically altered mice and in vitro cell and organ culture systems, are major components of these investigations. Ongoing efforts include research aimed at understanding genetic contributions to a number of human diseases, particularly those affecting the nervous, immune and musculoskeletal systems. As a major emphasis of their work, our investigators use genetic and genomic tools to understand normal development and differentiation.

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Using AI to improve diagnosis of rare genetic disorders

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Diagnosing rare Mendelian disorders is a labor-intensive task, even for experienced geneticists. Investigators at Baylor College of Medicine are trying to make the process more efficient using artificial intelligence. The team developed a machine learning system called AI-MARRVEL (AIM) to help prioritize potentially causative variants for Mendelian disorders. The study is published today in NEJM AI . Researchers from the Baylor Genetics clinical diagnostic laboratory noted that AIM's module can contribute to predictions independent of clinical knowledge of the gene of interest, helping to advance the discovery of novel disease mechanisms. “The diagnostic rate for rare genetic disorders is only about 30%, and on average, it is six years from the time of symptom onset to diagnosis. There is an urgent need for new approaches to enhance the speed and accuracy of diagnosis,” said co-corresponding author Dr. Pengfei Liu , associate professor of molecular and human genetics and associate clinical director at Baylor Genetics.

This work was supported by the Chan Zuckerberg Initiative and the National Institute of Neurological Disorders and Stroke (3U2CNS132415). Read the full publication here .

In the brain, bursts of beta rhythms implement cognitive control

Bursts of brain rhythms with “beta” frequencies control where and when neurons in the cortex process sensory information and plan responses. Studying these bursts would improve understanding of cognition and clinical disorders, researchers argue in a new review.

The brain processes information on many scales. Individual cells electrochemically transmit signals in circuits but at the large scale required to produce cognition, millions of cells act in concert, driven by rhythmic signals at varying frequencies. Studying one frequency range in particular, beta rhythms between about 14-30 Hz, holds the key to understanding how the brain controls cognitive processes—or loses control in some disorders—a team of neuroscientists argues in a new review article.

Drawing on experimental data, mathematical modeling and theory, the scientists make the case that bursts of beta rhythms control cognition in the brain by regulating where and when higher gamma frequency waves can coordinate neurons to incorporate new information from the senses or formulate plans of action. Beta bursts, they argue, quickly establish flexible but controlled patterns of neural activity for implementing intentional thought.

“Cognition depends on organizing goal-directed thought, so if you want to understand cognition, you have to understand that organization,” said co-author Earl K. Miller , Picower Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “Beta is the range of frequencies that can control neurons at the right spatial scale to produce organized thought.”

Miller and colleagues Mikael Lundqvist, Jonatan Nordmark and Johan Liljefors at the Karolinska Institutet and Pawel Herman at the KTH Royal Institute of Technology in Sweden, write that studying bursts of beta rhythms to understand how they emerge and what they represent would not only help explain cognition, but also aid in diagnosing and treating cognitive disorders.

“Given the relevance of beta oscillations in cognition, we foresee a major change in the practice for biomarker identification, especially given the prominence of beta bursting in inhibitory control processes … and their importance in ADHD, schizophrenia and Alzheimer’s disease,” they write in the journal Trends in Cognitive Sciences .

Experimental studies covering several species including humans, a variety of brain regions, and numerous cognitive tasks have revealed key characteristics of beta waves in the cortex, the authors write: Beta rhythms occur in quick but powerful bursts; they inhibit the power of higher frequency gamma rhythms; and though they originate in deeper brain regions, they travel within specific locations of cortex. Considering these properties together, the authors write that they are all consistent with precise and flexible regulation, in space and time, of the gamma rhythm activity that experiments show carry signals of sensory information and motor plans.

A chart from a study plots bursts of brain waves of varying frequency at specific times. The bursts are represented as warm colors against a the blue background. When there are low frequency bursts there aren't high frequency bursts and vice versa.

“Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions,” the authors write.

For one example, Miller and colleagues have shown in animals that in the prefrontal cortex in working memory tasks, beta bursts direct when gamma activity can store new sensory information, read out the information when it needs to be used, and then discard it when it’s no longer relevant. For another example, other researchers have shown that beta rises when human volunteers are asked to suppress a previously learned association between word pairs, or to forget a cue because it will no longer be used in a task.

In a paper last year, Lundqvist, Herman, Miller and others cited several lines of experimental evidence to hypothesize that beta bursts implement cognitive control spatially in the brain , essentially constraining patches of the cortex to represent the general rules of a task even as individual neurons within those patches represent the specific contents of information. For example, if the working memory task is to remember a pad lock combination, beta rhythms will implement patches of cortex for the general steps “turn left,” “turn right,” “turn left again,” allowing gamma to enable neurons within each patch to store and later recall the specific numbers of the combination. The two-fold value of such an organizing principle, they noted, is that the brain can rapidly apply task rules to many neurons at a time and do so without having to re-establish the overall structure of the task if the individual numbers change (i.e. you set a new combination).

Another important phenomenon of beta bursts, the authors write, is that they propagate across long distances in the brain, spanning multiple regions. Studying the direction of their spatial travels, as well as their timing, could shed further light on how cognitive control is implemented.

New ideas beget new questions

Beta rhythm bursts can differ not only in their frequency, but also their duration, amplitude, origin and other characteristics. This variety speaks to their versatility, the authors write, but also obliges neuroscientists to study and understand these many different forms of the phenomenon and what they represent to harness more information from these neural signals.

“It quickly becomes very complicated, but I think the most important aspect of beta bursts is the very simple and basic premise that they shed light on the transient nature of oscillations and neural processes associated with cognition,” Lundqvist said.“This changes our models of cognition and will impact everything we do. For a long time we implicitly or explicitly assumed oscillations are ongoing which has colored experiments and analyses. Now we see a first wave of studies based on this new thinking, with new hypothesis and ways to analyze data, and it should only pick up in years to come.”

The authors acknowledge another major issue that must be resolved by further research—How do beta bursts emerge in the first place to perform their apparent role in cognitive control?

“It is unknown how beta bursts arise as a mediator of an executive command that cascades to other regions of the brain,” the authors write.

The authors don’t claim to have all the answers. Instead, they write, because beta rhythms appear to have an integral role in controlling cognition, the as yet unanswered questions are worth asking.

“We propose that beta bursts provide both experimental and computational studies with a window through which to explore the real-time organization and execution of cognitive functions,” they conclude. “To fully leverage this potential there is a need to address the outstanding questions with new experimental paradigms, analytical methods and modeling approaches.”

Paper: to understand cognition—and its dysfunction—neuroscientists must learn its rhythms.

Study reveals a universal pattern of brain wave frequencies

Anesthesia blocks sensation by cutting off communication within the cortex

Anesthesia technology precisely controls unconsciousness in animal tests

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Brief Communication
Published: 04 May 2023

Global trends and themes in genetic counseling research

Wan Nur Amalina Zakaria ORCID: orcid.org/0000-0001-5709-1861 1 ,
Sook-Yee Yoon 2 ,
Adi Wijaya ORCID: orcid.org/0000-0001-5339-0231 3 ,
Asma Hayati Ahmad ORCID: orcid.org/0000-0001-5447-0356 4 ,
Rahimah Zakaria 4 &
Zahiruddin Othman 5

European Journal of Human Genetics volume 31 , pages 1181–1184 ( 2023 ) Cite this article

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This article seeks to highlight the most recent trends and themes in genetic counseling that are of broad interest. A total of 3505 documents were published between 1952 and 2021, with a trend toward increase in paper/year. The most common documents are original articles (2515, 71.8%), followed by review articles (341, 9.7%). Journal of Genetic Counseling publishes the highest number of genetic counseling articles (587, 16.7%), followed by Clinical Genetics (103, 2.9%) and the South American Journal of Medical Genetics (95, 2.7%). Co-occurrence analysis revealed five research themes: genetic testing, cancer, genetic counselor, prenatal diagnosis, and psychiatry. The genetic counselor theme contained most of the recent keywords, including “covid-19,” “underrepresented population,” “service delivery models,” “workforce,” “disparities,” “service delivery,” “professional development,” “cultural competence,” “access,” “diversity,” “telemedicine,” and “health literacy.” Genetic counseling researchers may use these keywords to find topics pertinent to their future research and practice.

Genetic counseling and testing for Asian Americans: a systematic review

Where is genetic medicine headed? Exploring the perspectives of Canadian genetic professionals on future trends using the Delphi method

The development of the Clinician-reported Genetic testing Utility InDEx (C-GUIDE): a novel strategy for measuring the clinical utility of genetic testing

Introduction.

The term “genetic counseling” was first coined by an American scientist, Sheldon C. Reed, in 1947 [ 1 ]. According to the Ad Hoc Committee of the American Society of Human Genetics, genetic counseling addresses human issues related to the incidence or risk of a genetic disorder in a family [ 2 ]. Seymour Kessler later defined genetic counseling as psychological contact or psychotherapy [ 3 ]. The 2006 National Society of Genetic Counselors task force described genetic counseling as helping people understand and adapt to the physical, psychological, and familial implications of genetic contributions to disease [ 4 ].

Future trends in clinical genetic and genetic services are fast evolving and have been the subject of recent publications [ 5 , 6 , 7 , 8 ]. However, the global situation of genetic counseling research, as well as trends and subjects of broad interest in this area throughout the last 70 years, have not been adequately covered since the focus was mainly on specific journals [ 5 ] or countries [ 6 , 7 , 8 ]. We, therefore, mapped genetic counseling research globally using the Scopus database and the VOSviewer tool to provide academics and practitioners in this field, information on the scientific landscape as well as important and emerging themes.

Materials and methods

Search strategy.

The literature was searched and retrieved from the Scopus database on January 19, 2022. The Scopus database is regarded as one of the most significant and exhaustive collections of scientific information in the world [ 9 ]. The following search string was used in the article title: “genetic counsel*ing”. All document types written in the English language and released between 1952 and 2021 were included, except for erratum. The retrieved documents were subjected to bibliometric analysis using VOSviewer.

Publication outputs

A total of 3505 documents were retrieved. The most common document type was original articles (2515, 71.8%), followed by review articles (341, 9.7%). Letters, book chapters and conference papers, each contributed about 4.7–4.8% of the documents. Notes, editorials, short surveys and books contributed about 4.4% in total. From 1952 to 1967, the document number was one digit. From 1968 to 2010, the number of papers increased to two digits; from 2011 to 2021, it was three digits (Fig. 1 ).

Trends in the number of publications from 1952 to 2021 in the field of genetic counseling.

The top genetic counseling journals published 1163 documents, accounting for 33.2 per cent of all publications (Table 1 ). The Journal of Genetic Counseling had 587 publications with a Cite-Score of 3.8 and SJR 2020 of 0.867. Only three journals, the American Journal of Human Genetics, Genetics in Medicine, and Journal of Medical Genetics, have an SJR greater than two. The Scopus database has discontinued its coverage of the two of the top journals, while the American Journal of Medical Genetics has been renamed the American Journal of Medical Genetics, Parts A, B, and C.

Author Keywords

Network visualization of keyword co-occurrence map was constructed with a threshold of nine keyword co-occurrences in VOSviewer. The top keywords ranked by frequency were ‘genetic counseling’ ( n = 1156), ‘genetic testing’ ( n = 205), ‘prenatal diagnosis’ ( n = 104), ‘breast cancer’ ( n = 97), ‘genetic counselor’ ( n = 68), ‘genetics’ ( n = 59), ‘brca1’ ( n = 49), ‘education’ ( n = 48), ‘brca2’ ( n = 45), and ‘hereditary cancer’ ( n = 44).

To identify themes in the literature, we studied the terms in each cluster. Five clusters appear from the map in Fig. 2A with the following themes: genetic testing (red cluster), cancer (green cluster), genetic counselor (blue cluster), prenatal diagnosis (yellow cluster) and psychiatry (purple cluster). The overlay visualization of keyword co-occurrence map indicates the publishing dates when specific topics were most popular. Many newer keywords (yellow nodes) were found mainly in the blue cluster (genetic counselor theme) compared to other clusters (Fig. 2B ).

A Network visualization, B Overlay visualization.

The increasing number of publications per year may indicate the development trend in the field of genetic counseling. The observed trend may be attributable to the rapid evolution of clinical genetic services [ 5 , 6 , 7 , 8 ]. Genetic counselors collaborate with geneticists and other medical specialists to provide genetic counseling services. Genetic counseling research, which strives to improve the quality and availability of genetic counseling services, benefits from these clinical data. Original articles contributed to a large proportion of the publication outputs and increased significantly over the last decade. This finding indicates that the field is expanding in line with the development of genomic medicine [ 10 ]. Funding for implementation and outcome research is urgently needed to expand the scientific evidence for genetic counseling practice [ 10 ].

The present analysis revealed that 1163 documents or 33.2% of studies were published in the top 10 journals. Of these, more than half of the relevant documents were published in the Journal of Genetic Counseling . This indicates that this is the most active journal publishing a large number of documents and has a substantial impact on the field of genetic counseling. Among the top journals, the results revealed only three journals have SCImago Journal Rank (SJR) greater than two; the American Journal of Human Genetics, Genetics in Medicine , and Journal of Medical Genetics . As a result, it can be deduced that approximately 170 documents, or 4.9% of all genetic counseling publications, were published in high-impact journals.

The keyword co-occurrence map identified five major clusters in the genetic counseling field (Fig. 2A ). These are genetic testing (red cluster), cancer (green cluster), genetic counselor (blue cluster), prenatal diagnosis (yellow cluster) and psychiatry (purple cluster) themes. Some of these topics (cancer and prenatal) have earlier been identified by content analysis study [ 5 ]. Genetic counseling should be regarded as an integral part of the genetic testing process [ 11 ]. Thus, genetic testing appeared as the main theme with the largest cluster. There are several categories of genetic testing that require genetic counseling. In diagnostic genetic testing, the test is performed on symptomatic individuals. While pre-test genetic counseling may not always be necessary, post-test genetic counseling should be offered especially when the result is positive. Conversely, pre-and post-test genetic counseling should be offered in other types of genetic testing such as predictive, susceptibility (risk), carrier, prenatal, preimplantation genetic diagnosis (PGD), and genetic screening. For pharmacogenetic testing, the need for genetic counseling will depend on whether the results have other implications rather than decisions about drug treatment [ 11 ].

The second theme in the genetic counseling field is cancer. Pathogenic variants in genes can be broadly grouped into sporadic, familial, and hereditary cancers. Sporadic cancers, making up 75–80% of all cancers, are caused by acquired mutations in tumour cells. About 10–20% of cancers are familial but not caused by a gene mutation. On the other hand, familial cancers are attributed to shared family factors. Finally, hereditary cancer is evaluated when a person has a maternal or paternal family history with suggestive symptoms, depending on the type of cancer and specific hereditary syndrome. More studies are needed to build a cancer genetic counseling service to enable fair access for all patients [ 12 ]. In order to establish a cancer genetic counseling service, more studies are needed to look into the requirements and obstacles of patients, family members, and specialists particularly, in different settings/countries to ensure equitable access to all patients [ 12 ].

Genetic counselor, the third theme, is a health practitioner who has been trained to evaluate genetic testing results, both scientifically and medically, while also considering psychological, ethical, and family concerns [ 13 ]. The genetic counselor theme has the highest number of recent keywords based on the number of yellow nodes (Fig. 2B ). These keywords with the average publication year 2018 onwards include ‘covid-19’, ‘underrepresented population’, ‘service delivery models’, ‘workforce’, ‘disparities’, ‘service delivery’, ‘professional development’, ‘cultural competence’, ‘access’, ‘diversity’, ‘telemedicine’ and ‘health literacy’. These keywords are related to the genetic counselors’ challenges and needs related to genetic service delivery models.

The fourth theme in the genetic counseling field is prenatal diagnosis. The need for prenatal genetic counselling is increasing worldwide due to advances in prenatal screening and diagnostic tests [ 14 ]. Given the complexity of genetic testing information, the role of genetic counseling at pre-and post-test is to increase knowledge, decrease anxiety, avoid decisional conflicts, and aid in the interpretation of test results, thereby helping an individual make an informed decision [ 14 , 15 ].

The last theme in the genetic counseling field is psychiatry. Psychiatric genetic counseling has benefits for people with psychiatric disorders and their families despite the limited genetic testing available [ 16 , 17 , 18 ]. The role of genetic counseling is to help people understand that mental illness is not their fault, but there are things that they can do to protect their mental health [ 19 ]. It is an emerging speciality within clinical genetics [ 20 ].

The main limitation of our study is that, despite Scopus’ vast coverage [ 9 ], we might have overlooked some important studies in other databases. Second, new research is being released almost daily, and the data in this analysis was up to January 19, 2022. Third, we might have missed several relevant documents if the authors had not included the term “genetic counseling” in the article titles. Lastly, the research themes in this study were derived from keyword co-occurrence thematic analysis without a deeper analysis.

Conclusions

The field of genetic counseling is growing as genomic medicine develops. Genetic counseling research has identified the following themes: genetic testing, cancer, genetic counselor, prenatal diagnosis, and psychiatry. Future directions in genetic counseling research may be related to the requirements and challenges of genetic counselors, according to the emerging topics.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Zakaria, W.N.A., Yoon, SY., Wijaya, A. et al. Global trends and themes in genetic counseling research. Eur J Hum Genet 31 , 1181–1184 (2023). https://doi.org/10.1038/s41431-023-01371-3

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Received : 13 October 2022

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Genetic contributions to autism spectrum disorder

1 Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway

2 Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway

3 Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway

M. Niarchou

4 Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA

A. Starnawska

5 The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark

6 Department of Biomedicine, Aarhus University, Denmark

7 Center for Genomics for Personalized Medicine, CGPM, and Center for Integrative Sequencing, iSEQ, Aarhus, Denmark

8 College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE

C. van der Merwe

9 Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, MA, USA

10 Department of Psychiatry, Autism Research Centre, University of Cambridge, UK

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous ( de novo ). More than 100 risk genes have been implicated by rare, often de novo , potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse , each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Definition of autism

Kanner defined autism in 1943 with detailed case descriptions of children showing social aloofness, communication impairments, and stereotyped behaviors and interests, often accompanied by intellectual disability (ID) (Kanner, 1943 ). A year later, Asperger independently published an article on children presenting marked difficulties in social communication and unusually circumscribed and intense interests, despite advanced intellectual and language skills (Asperger, 1944 ). Three decades later, Wing and Gould united Asperger and Kanner's descriptions and conceptualized a spectrum of autistic conditions (Wing and Gould, 1978 , 1979 ).

The onset of autism is during the first years of life, although symptoms may not be fully apparent or recognized until later (American Psychiatric Association, 2013 ). Autism is a heterogeneous and complex group of conditions with considerable variation in core symptoms, language level, intellectual functioning, and co-occurring psychiatric and medical difficulties. Subtype diagnoses such as childhood autism and Asperger's syndrome were previously used to specify more homogeneous presentations, but were unstable over time within individuals and used unreliably by clinicians (Lord et al., 2020 ). Current editions of the major diagnostic manuals have replaced the subtypes with an overarching autism spectrum disorder diagnosis and instead require specification of key sources of heterogeneity; language level, intellectual functioning, and co-occurring conditions (APA, 2013 ; World Health Organization, 2018 ).

Epidemiology

Prevalence estimates of autism have steadily increased from less than 0.4% in the 1970s to current estimates of 1–2% (Fombonne, 2018 ; Lyall et al., 2017 ). The increase is largely explained by broadening diagnostic criteria to individuals without ID and with milder impairments, and increased awareness and recognition of autistic traits (Lord et al., 2020 ; Taylor et al., 2020 ). There are marked sex and gender differences in autism (Halladay et al., 2015 ; Warrier et al., 2020 ). The male-to-female ratio is approximately 4:1 in clinical and health registry cohorts but closer to 3:1 in general population studies with active case-finding (Loomes, Hull, & Mandy, 2017 ) and 1–2:1 in individuals with moderate-to-severe ID (Fombonne, 1999 ; Yeargin-Allsopp et al., 2003 ). The mechanisms underlying the sex difference are mostly unknown, and hypotheses include a female protective effect (aspects of the female sex conferring resilience to risk factors for autism), prenatal steroid hormone exposure, and social factors such as underdiagnosis and misdiagnosis in women (Ferri, Abel, & Brodkin, 2018 ; Halladay et al., 2015 ).

Co-occurring conditions are the rule rather than the exception, estimated to affect at least 70% of people with autism from childhood (Lai et al., 2019 ; Simonoff et al., 2008 ). Common co-occurring conditions include attention-deficit hyperactivity disorder (ADHD), anxiety, depression, epilepsy, sleep problems, gastrointestinal and immune conditions (Davignon, Qian, Massolo, & Croen, 2018 ; Warrier et al., 2020 ). There is an elevated risk of premature mortality from various causes, including medical comorbidities, accidental injury, and suicide (Hirvikoski et al., 2016 ).

Autism is also associated with positive traits such as attention to detail and pattern recognition (Baron-Cohen & Lombardo, 2017 ; Bury, Hedley, Uljarević, & Gal, 2020 ). Further, there is wide variability in course and adulthood outcomes with regard to independence, social relationships, employment, quality of life, and happiness (Howlin & Magiati, 2017 ; Mason et al., 2020 ; Pickles, McCauley, Pepa, Huerta, & Lord, 2020 ). Rigorous longitudinal studies and causally informative designs are needed to determine the factors affecting developmental trajectories and outcomes.

Environmental factors

Twin studies suggest that 9–36% of the variance in autism predisposition might be explained by environmental factors (Tick, Bolton, Happé, Rutter, & Rijsdijk, 2016 ). There is observational evidence for association with pre- and perinatal factors such as parental age, asphyxia-related birth complications, preterm birth, maternal obesity, gestational diabetes, short inter-pregnancy interval, and valproate use (Lyall et al., 2017 ; Modabbernia, Velthorst, & Reichenberg, 2017 ). Mixed results are reported for pregnancy-related nutritional factors and exposure to heavy metals, air pollution, and pesticides, while there is strong evidence that autism risk is unrelated to vaccination, maternal smoking, or thimerosal exposure (Modabbernia et al., 2017 ). It is challenging to infer causality from observed associations, given that confounding by lifestyle, socioeconomic, or genetic factors contributes to non-causal associations between exposures and autism. Many putative exposures are associated with parental genotype (e.g. obesity, age at birth) (Gratten et al., 2016 ; Taylor et al., 2019a , Yengo et al., 2018 ), and some are associated both with maternal and fetal genotypes (e.g. preterm birth) (Zhang et al., 2017 ). Studies triangulating genetically informative designs are needed to disentangle these relationships (Davies et al., 2019 ; Leppert et al., 2019 ; Thapar & Rutter, 2019 ).

Twin and pedigree studies

In 1944, Kanner noted that parents shared common traits with their autistic children, introducing the ‘broader autism phenotype’ (i.e. sub-threshold autistic traits) and recognizing the importance of genetics (Harris, 2018 ; Kanner, 1944 ). Thirty years later, twin studies revolutionized the field of autism research (Ronald & Hoekstra, 2011 ).

Twin studies were the first to demonstrate the heritability of autism. In 1977, the first twin-heritability estimate was published, based on a study of 10 dizygotic (DZ) and 11 monozygotic (MZ) pairs (Folstein & Rutter, 1977 ). Four out of the 11 MZ pairs (36%) but none of the DZ pairs were concordant for autism. Subsequently, over 30 twin studies have been published, further supporting the high heritability of autism (Ronald & Hoekstra, 2011 ). A meta-analysis of seven primary twin studies reported that the heritability estimates ranged from 64% to 93% (Tick et al., 2016 ). The correlations for MZ twins were at 0.98 [95% confidence interval (CI) 0.96–0.99], while the correlations for DZ twins were at 0.53 (95% CI 0.44–0.60) when the autism prevalence rate was assumed to be 5% (based on the broader autism phenotype) and increased to 0.67 (95% CI 0.61–0.72) when the prevalence was 1% (based on the stricter definition) (Tick et al., 2016 ). Additionally, family studies have found that the relative risk of a child having autism relates to the amount of shared genome with affected relatives ( Fig. 1 ) (Bai et al., 2019 ; Constantino et al., 2013 ; Georgiades et al., 2013 ; Grønborg, Schendel, & Parner, 2013 ; Risch et al., 2014 ; Sandin et al., 2014 ).

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Relative risk of autism by degree of relatedness with a person with autism. Relative risk for full and half siblings, and full cousins was provided in Hansen et al. ( 2019 ). Relative risk for half first cousins was estimated based on Xie et al. ( 2019 ). GS, genome shared.

Early twin and pedigree studies demonstrated that the biological relatives of individuals with autism who did not meet the criteria for an autism diagnosis themselves commonly showed elevated autistic traits such as communication and social interaction difficulties (Le Couteur et al., 1996 ), indicating that the heritability is not restricted to the traditional diagnostic boundaries of autism. Twin studies also indicate that although social communication and repetitive behavior trait dimensions each show strong heritability, there is a limited genetic correlation between them (e.g. for a review, see Ronald & Hoekstra, 2011 ). Further, twin studies have found substantial genetic overlap between autistic traits and symptoms of other psychiatric conditions, including language delay (e.g. Dworzynski et al., 2008 ), ID (e.g. Nishiyama et al., 2009 ), ADHD (e.g. Ronald, Edelson, Asherson, & Saudino, 2010 ), and anxiety (e.g. Lundström et al., 2011 ) (for a review, see Ronald & Hoekstra, 2014 ). Moreover, twin and family studies indicate that the sibling recurrence rate of autism is lower in female than male siblings (Palmer et al., 2017 ; Werling & Geschwind, 2015 ), suggesting the female protective effect hypothesis as a potential explanation for the male preponderance in the diagnosis of autism. The hypothesis was supported by results showing that the siblings of autistic females had a higher likelihood of high autistic trait scores and autism than the siblings of autistic males (Ferri et al., 2018 ; Palmer et al., 2017 ; Robinson, Lichtenstein, Anckarsäter, Happé, & Ronald, 2013 ), consistent with females having a higher liability threshold.

Genetic variants differ in the frequency at which they occur in the population (e.g. rare v. common), the type (i.e. SNPs/CNVs/translocations and inversions/indels), and whether they are inherited or de novo . Here, we summarize the findings on genetic risk for autism from linkage and candidate gene studies, common and rare genetic variation studies, epigenomics, and transcriptomics. A glossary of important terms is in Box 1 .

Candidate gene association study: A study that examines the association between a phenotype and a genetic variant chosen a priori based on knowledge of the gene's biology or functional impact.

Complex trait: A trait that does not follow Mendelian inheritance patterns, but is likely the result of multiple factors including a complex mixture of variation within multiple genes.

Copy number variant (CNV): Deletion or duplication of large genomic regions.

de novo mutation: A mutation that is present in the offspring but is either absent in parents or is present only in parental germ cells.

DNA methylation (DNAm): Epigenetic modification of DNA characterized by the addition of a methyl group (-CH 3 ) to the 5 th position of the pyrimidine ring of cytosine base resulting in 5-methylcytosine (5mC).

Epigenetics: The science of heritable changes in gene regulation and expression that do not involve changes to the underlying DNA sequence.

Epigenome-Wide Association Study (EWAS): A study that investigates associations between DNA methylation levels quantified at tens/hundreds of thousands of sites across the human genome, and the trait of interest.

Genome-Wide Association Study (GWAS): A study scanning genome-wide genetic variants for associations with a given trait.

Genetic correlation: An estimate of the proportion of variance shared between two traits due to shared genetics.

Heritability: An estimate of the proportion of variation in a given trait that is due to differences in genetic variation between individuals in a given population.

Heritability on the liability scale : A heritability estimate adjusted for the population prevalence of a given binary trait, typically disorders.

Genetic linkage studies: A statistical method of mapping genes of heritable traits to their chromosomal locations by using chromosomal co-segregation with the phenotype.

Mendelian inheritance: When the inheritance of traits is passed down from parents to children and is controlled by a single gene for which one allele is dominant and the other recessive.

Methylation Quantitative Trait Locus (mQTL): A SNP at which genotype is correlated with the variation of DNA methylation levels at a nearby ( cis- mQTL) or distal ( trans- mQTL) site.

Phenotype: The observable characteristics of an individual.

Polygenic risk score (PRS): An estimate of an individual's genetic liability for a condition calculated based on the cumulative effect of many common genetic variants.

Single nucleotide polymorphism (SNP): A single base pair change that is common (>1%) in the population.

Single nucleotide variant (SNV): A variation in a single nucleotide without any limitation of frequency.

SNP heritability: The proportion of variance in a given phenotype in a population that is attributable to the additive effects of all SNPs tested. Typically, SNPs included have a minor allele frequency >1%.

Linkage and candidate gene studies

Initial linkage studies were conducted to identify chromosomal regions commonly inherited in affected individuals. Susceptibility loci implicated a range of regions, but only two have been replicated (Ramaswami & Geschwind, 2018 ): at chromosome 20p13 (Weiss, Arking, Daly, & Chakravarti, 2009 ) and chromosome 7q35 (Alarcón, Cantor, Liu, Gilliam, & Geschwind, 2002 ). Lack of replication and inconsistent findings were largely due to low statistical power (Kim & Leventhal, 2015 ). Candidate gene association studies identified over 100 positional and/or functional candidate genes for associations with autism (Bacchelli & Maestrini, 2006 ). However, there was no consistent replication for any of these findings (Warrier, Chee, Smith, Chakrabarti, & Baron-Cohen, 2015 ), likely due to limitations in study design (e.g. low statistical power, population diversity, incomplete coverage of variation within the candidate genes, and false positives arising from publication bias) (Ioannidis, 2005 ; Ioannidis, Ntzani, Trikalinos, & Contopoulos-Ioannidis, 2001 ). The advancement of genome-wide association studies (GWAS) and next-generation sequencing techniques has significantly enhanced gene and variant discovery.

Common genetic variation

The SNP-heritability (proportion of variance attributed to the additive effects of common genetic variants) of autism ranges from 65% in multiplex families (Klei et al., 2012 ) to 12% in the latest Psychiatric Genomics Consortium GWAS ( Fig. 2 a ) (Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, 2017 ; Grove et al., 2019 ). Variation is largely attributable to sample heterogeneity and differences in methods used to estimate SNP-heritability.

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Variance explained by different classes of genetic variants in autism. ( a ) Donut chart of the variance explained by different classes of variants. The narrow-sense heritability (82.7%, Nordic average, shades of green) has been estimated using familial recurrence data from Bai et al. ( 2019 ). The total common inherited heritability (12%) has been estimated using LDSC-based SNP-heritability (additive) from Grove et al. ( 2019 ) and the total rare inherited heritability (3%) has been obtained from Gaugler et al. ( 2014 ). The currently unexplained additive heritability is thus 67.7% (total narrow-sense heritability minus common and rare inherited heritabilities combined). This leaves a total of 17.3% of the variance to shared and unique environmental estimates (Bai et al., 2019 ). The term environmental refers to non-additive and non-inherited factors that contribute to variation in autism liability. Of this, de novo missense and protein-truncating variants (Satterstrom et al., 2020 ) and variation in non-genic regions (An et al., 2018 ) together explain 2.5% of the variance. Whilst de novo variation can be inherited in some cases (germline mutation in the parent) and thus shared between siblings, it is unlikely that this will be shared by other related individuals, and thus unlikely to be included in the narrow-sense heritability in Bai et al. ( 2019 ). This is likely to be a lower-bound of the estimate as we have not included the variance explained by de novo structural variants and tandem repeats. Additionally, non-additive variation accounts for ~4% of the total variance (Autism Sequencing Consortium et al., 2019 ). Thus, ~11% of the total variance is currently unaccounted for, though this is likely to be an upper bound. ( b ) The variance explained is likely to change in phenotypic subgroups. For instance, the risk ratio for de novo protein-truncating variants in highly constrained genes (pLI > 0.9) is higher in autistic individuals with ID compared to those without ID (point estimates and 95% confidence intervals provided; Kosmicki et al., 2017 ). ( c ) Similarly, the proportion of the additive variance explained by common genetic variants is higher in autistic individuals without ID compared to autistic individuals with ID (Grove et al., 2019 ). Point estimates and 95% confidence intervals provided.

Early GWASs of autism were underpowered, partly due to overestimating potential effect sizes. Grove et al. ( 2019 ) conducted a large GWAS of autism combining data from over 18 000 autistic individuals and 27 000 non-autistic controls and an additional replication sample. They identified five independent GWAS loci ( Fig. 3 ). Another recent study (Matoba et al., 2020 ) identified a further novel locus by meta-analyzing the results from Grove et al. ( 2019 ) with over 6000 case-pseudocontrol pairs from the SPARK cohort by employing a massively parallel reporter assay to identify a potential causal variant (rs7001340) at this locus which regulates DDH2 in the fetal brain. The sample sizes are still relatively small compared to other psychiatric conditions (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2020 ; Howard et al., 2019 ), though ongoing work aims to double the sample size and identify additional loci.

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Karyogram showing the 102 genes implicated by rare variant findings at a false discovery rate of 0.1 or less (Satterstrom et al., 2020 ) and the five index SNPs identified in GWAS (Grove et al., 2019 ) of autism.

Using genetic correlations and polygenic score analyses, studies have identified modest shared genetics between autism and different definitions of autistic traits in the general population (Askeland et al., 2020 ; Bralten et al., 2018 ; Robinson et al., 2016 ; Taylor et al., 2019 b ). There is some evidence for developmental effects, with greater shared genetics in childhood compared to adolescence (St Pourcain et al., 2018 ). These methods have also identified modest polygenic associations between autism and other neurodevelopmental and mental conditions such as schizophrenia, ADHD, and major depressive disorder, related traits such as age of walking, language delays, neuroticism, tiredness, and self-harm, as well as risk of exposure to childhood maltreatment and other stressful life events (Brainstorm Consortium et al., 2018 ; Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ; Hannigan et al., 2020 ; Lee et al., 2019 , b ; Leppert et al., 2019 ; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013 ; Warrier & Baron-Cohen, 2019 ). Notably, autism is positively genetically correlated with measures of intelligence and educational attainment (EA) (Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ), an observation supported by polygenic score association (Clarke et al., 2016 ). Polygenic Transmission Disequilibrium Tests have identified an over-transmission of polygenic scores for EA, schizophrenia, and self-harm from parents to autistic children, but an absence of such over-transmission to non-autistic siblings (Warrier & Baron-Cohen, 2019 ; Weiner et al., 2017 ), suggesting that these genetic correlations are not explained by ascertainment biases or population stratification. However, a genetic correlation does not necessarily imply a causal relationship between the two phenotypes and may simply index biological pleiotropy. Causal inference methods such as Mendelian randomization can be used to disentangle such relationships (Davies et al., 2019 ; Pingault et al., 2018 ).

The relatively low SNP-heritability in autism compared to other psychiatric conditions may partly be due to phenotypic heterogeneity. In an attempt to reduce phenotypic heterogeneity, Chaste et al. ( 2015 ) identified 10 phenotypic combinations to subgroup autistic individuals. Family-based association analyses did not identify significant loci, and SNP-heritability for the subgroups was negligent. It is unclear if reducing phenotypic heterogeneity increases genetic homogeneity, and investigating this in larger samples is warranted. Another study identified no robust evidence of genetic correlation between social and non-social (restricted and repetitive behavior patterns) autistic traits (Warrier et al., 2019 ). A few studies have investigated the common variant genetic architecture of social and non-social autistic traits in individuals with autism (Alarcón et al., 2002 ; Cannon et al., 2010 ; Cantor et al., 2018 ; Lowe, Werling, Constantino, Cantor, & Geschwind, 2015 ; Tao et al., 2016 ; Yousaf et al., 2020 ) and in the general population (St Pourcain et al., 2014 ; Warrier et al., 2018 , 2019 ), but replication of the identified loci is needed.

Diagnostic classification is another source of heterogeneity: SNP-heritability of Asperger's syndrome (ICD-10 diagnosis) was twice (0.097 ± 0.001) that of childhood autism and unspecified pervasive developmental disorders (Grove et al., 2019 ) [due to overlap in subtype diagnoses, a hierarchy was used: childhood autism>atypical autism>Asperger's syndrome>unspecified subtypes (Grove et al., 2019 )]. Supporting this, polygenic scores for intelligence and EA had larger loadings in the Asperger's syndrome and childhood autism subgroups compared to other subgroups (Grove et al., 2019 ). Additionally, the SNP-heritability of autism (all subtypes) without co-occurring ID diagnosis (0.09 ± 0.005) was three times that of autism with ID (Grove et al., 2019 ) ( Fig. 2 c ).

Rare genetic variation

Rare genetic variants confer significant risk in the complex etiology of autism. They are typically non-Mendelian, with substantial effect sizes and low population attributable risk. It is estimated that ~10% of autistic individuals have been diagnosed with an identifiable rare genetic syndrome characterized by dysmorphia, metabolic, and/or neurologic features (Carter & Scherer, 2013 ; Tammimies et al., 2015 ). Associated syndromes include the 15q11-q13 duplication of the Prader-Willi/Angelman syndrome, fragile X syndrome, 16p11.2 deletion syndrome, and 22q11 deletion syndrome (Sztainberg & Zoghbi, 2016 ). Prevalence estimates for autism vary widely between genetic syndromes; for example, 11% in 22q11.2 deletion syndrome and 54% in Cohen's syndrome (Richards, Jones, Groves, Moss, & Oliver, 2015 ). Of note, estimating the prevalence of autism in the context of genetic syndromes is complex (Havdahl et al., 2016 ; Richards et al., 2015 ).

The rate of gene discovery in autism is a linear function of increasing sample size (De Rubeis et al., 2014 ). Early studies implicated nine genes in the first 1000 autism cases (Neale et al., 2012 ; Sanders et al., 2012 ), increasing to 27 and 33 associated genes from separate analyses of Simons Simplex Collection and Autism Sequencing Consortium (ASC) samples (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Integrating these samples using the TADA framework implicated a total of 65 autism genes (Sanders et al., 2015 ).

The MSSNG initiative analyzed whole genomes from 5205 individuals ( N cases = 2636), and identified 61 autism-risk genes, of which 18 were new candidates (Yuen et al., 2017 ). More recently, the largest whole-exome sequencing analysis to date conducted by the ASC ( N = 35 584, N cases = 11 986) identified 102 autism-associated genes ( Fig. 3 ), many of which are expressed during brain development with roles in the regulation of gene expression and neuronal communication (Satterstrom et al., 2020 ). Rare CNVs and SNVs associated with autism have pleiotropic effects, thus increasing the risk for other complex disorders such as schizophrenia, ADHD, ID, and epilepsy (Gudmundsson et al., 2019 ; Satterstrom et al., 2019 , 2020 ).

CNVs can impact one or multiple genes and can occur at common or rare frequencies in a population. All CNVs associated with autism have been rare. Recurrent CNVs are among the most convincing rare inherited risk variations for autism, and have a prevalence of about 3% in affected patients (Bourgeron, 2016 ). In comparison, approximately 4–10% of autistic individuals have de novo deletions or duplications (Bourgeron, 2016 ; Pinto et al., 2010 ; Sebat et al., 2007 ) frequently mapped to established risk loci 1q21.1, 3q29, 7q11.23, 15q11.2-13, and 22q11.2 (Sanders et al., 2015 ). A higher global frequency of de novo CNVs is observed in idiopathic autism cases from simplex families (10%) compared to multiplex families (2%) and controls (1%) (Halladay et al., 2015 ; Itsara et al., 2010 ; Sebat et al., 2007 ). Inherited CNVs can be present in unaffected siblings and parents, suggesting a model of incomplete penetrance dependent on the dosage sensitivity and function of the gene(s) they affect (Vicari et al., 2019 ).

Damaging SNVs include nonsense, frameshift, and splice site mutations (collectively referred to as protein-truncating variants, or PTVs), and missense variants. Rare inherited variants have a smaller average effect size and reduced penetrance compared to de novo pathogenic mutations. Early studies on whole exomes from trios established a key role for de novo germline mutations in autism. Whilst analysis in smaller sample sizes indicated only modest increase in de novo mutation rates in autism cases (Neale et al., 2012 ), the rate rose significantly in excess of expectation as the sample size increased (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Most recently, the ASC observed a 3.5-fold case enrichment of damaging de novo PTVs and a 2.1-fold enrichment for damaging de novo missense variants (Satterstrom et al., 2020 ), concluding that all exome de novo SNVs explain 1.92% of the variance in autism liability (Satterstrom et al., 2020 ) ( Fig. 2 a ).

Comparatively, the ASC discovered a 1.2-fold enrichment of rare inherited damaging PTVs in cases compared to unaffected siblings (Satterstrom et al., 2020 ). Similarly, recent whole-genome analysis found no excess of rare inherited SNVs, and no difference in the overall rate of these variants in affected subjects compared to unaffected siblings (Ruzzo et al., 2019 ).

New advancements

It is estimated that de novo mutations in protein-coding genes contribute to risk in ~30% of simplex autism cases (Yuen et al., 2017 ; Zhou et al., 2019 ). However, recent work has also shown that de novo mutations in non-coding regions of the genome (particularly gene promoters) contribute to autism (An et al., 2018 ; Zhou et al., 2019 ). Adapting machine learning techniques may be key to providing novel neurobiological insights to the genetic influences on autism in the future (An et al., 2018 ; Ruzzo et al., 2019 ; Zhou et al., 2019 ). Additionally, rare tandem repeat expansions in genic regions are more prevalent among autism cases than their unaffected siblings, with a combined contribution of ~2.6% to the risk of autism (Trost et al., 2020 ).

Common and rare variant interplay

The largest component of genetic risk is derived from common variants of additive effect with a smaller contribution from de novo and rare inherited variation ( Fig. 2 a ) (de la Torre-Ubieta, Won, Stein, & Geschwind, 2016 ; Gaugler et al., 2014 ). Notably, KMT2E was implicated in both the latest GWAS (Grove et al., 2019 ) and exome sequencing (Satterstrom et al., 2020 ) analyses. It is hypothesized that common genetic variation in or near the genes associated with autism influences autism risk, although current sample sizes lack the power to detect the convergence of the two (Satterstrom et al., 2020 ).

Whilst higher SNP-heritability is observed in autistic individuals without ID ( Fig. 2 b ), de novo PTVs in constrained genes are enriched in autistic individuals with ID ( Fig. 2 a ). However, the genetic architecture of autism is complex and diverse. For example, common genetic variants also contribute to risk in autistic individuals with ID and in autistic individuals carrying known large-effect de novo variants in constrained genes (Weiner et al., 2017 ). Furthermore, an excess of disruptive de novo variants is also observed in autistic individuals without co-occurring ID compared to non-autistic individuals (Satterstrom et al., 2020 ).

Epigenetics

DNA methylation (DNAm), an epigenetic modification, allows for both genetic and environmental factors to modulate a phenotype (Martin & Fry, 2018 ; Smith et al., 2014 ). DNAm affects gene expression, regulatory elements, chromatin structure, and alters neuronal development, functioning, as well as survival (Kundaje et al., 2015 ; Lou et al., 2014 ; Peters et al., 2015 ; Sharma, Klein, Barboza, Lohdi, & Toth, 2016 ; Yu et al., 2012 ; Zlatanova, Stancheva, & Caiafa, 2004 ). Additionally, putative prenatal environmental risk factors impact the offspring's methylomic landscape (Anderson, Gillespie, Thiele, Ralph, & Ohm, 2018 ; Cardenas et al., 2018 ; Joubert et al., 2016 ), thus providing a plausible molecular mechanism to modulate the neurodevelopmental origins of autism.

Autism Epigenome-Wide Association Study (EWAS) meta-analysis performed in blood from children and adolescents from SEED and SSC cohorts ( N cases = 796, N controls = 858) identified seven differentially methylated positions (DMPs) associated ( p < 10 × 10 −05 ) with autism, five of them also reported to have brain-based autism associations. The associated DMPs annotated to CENPM , FENDRR , SNRNP200 , PGLYRP4 , EZH1 , DIO3 , and CCDC181 genes, with the last site having the largest effect size and the same direction of association with autism across the prefrontal cortex, temporal cortex, and cerebellum (Andrews et al., 2018 ). The study reported moderate enrichment of methylation Quantitative Trait Loci (mQTLs) among the associated findings, suggesting top autism DMPs to be under genetic control (Andrews et al., 2018 ). These findings were further extended by the MINERvA cohort that added 1263 neonatal blood samples to the meta-analysis. The SEED-SSC-MINERvA meta-EWAS identified 45 DMPs, with the top finding showing the consistent direction of association across all three studies annotated to ITLN1 (Hannon et al., 2018 ). The MINERvA sample was also used for EWAS of autism polygenic score, hypothesizing that the polygenic score-associated DNAm variation is less affected by environmental risk factors, which can confound case–control EWAS. Elevated autism polygenic score was associated with two DMPs ( p < 10 × 10 −06 ), annotated to FAM167A / C8orf12 and RP1L1 . Further Bayesian co-localization of mQTL results with autism GWAS findings provided evidence that several SNPs on chromosome 20 are associated both with autism risk and DNAm changes in sites annotated to KIZ , XRN2 , and NKX2-4 (Hannon et al., 2018 ). The mQTL effect of autism risk SNPs was corroborated by an independent study not only in blood, but also in fetal and adult brain tissues, providing additional evidence that autism risk variants can act through DNAm to mediate the risk of the condition (Hammerschlag, Byrne, Bartels, Wray, & Middeldorp, 2020 ).

Since autism risk variants impact an individual's methylomic landscape, studies that investigate DNAm in the carriers of autism risk variants are of interest to provide insight into their epigenetic profiles. A small blood EWAS performed in 52 cases of autism of heterogeneous etiology, nine carriers of 16p11.2del, seven carriers of pathogenic variants in CHD8 , and matched controls found that DNAm patterns did not clearly distinguish autism of the heterogeneous etiology from controls. However, the homogeneous genetically-defined 16p11.2del and CHD8 +/− subgroups were characterized by unique DNAm signatures enriched in biological pathways related to the regulation of central nervous system development, inhibition of postsynaptic membrane potential, and immune system (Siu et al., 2019 ). This finding highlights the need to combine genomic and epigenomic information for a better understanding of the molecular pathophysiology of autism.

It must be noted that a very careful interpretation of findings from peripheral tissues is warranted. DNAm is tissue-specific and therefore EWAS findings obtained from peripheral tissues may not reflect biological processes in the brain. Using the mQTL analytical approach may reduce this challenge, as mQTLs are consistently detected across tissues, developmental stages, and populations (Smith et al., 2014 ). However, not all mQTLs will be detected across tissues and will not necessarily have the same direction of effect (Smith et al., 2014 ). Therefore, it is recommended that all epigenetic findings from peripheral tissues are subjected to replication analyses in human brain samples, additional experimental approaches, and/or Mendelian randomization to strengthen causal inference and explore molecular mediation by DNAm (Walton, Relton, & Caramaschi, 2019 ).

EWASs performed in post-mortem brains have typically been conducted using very small sample sizes, due to limited access to brain tissue (Ladd-Acosta et al., 2014 ; Nardone et al., 2014 ). One of the largest autism EWAS performed in post-mortem brains (43 cases and 38 controls) identified multiple DMPs ( p < 5 × 10 −05 ) associated with autism (31 DMPs in the prefrontal cortex, 52 in the temporal cortex, and two in the cerebellum) (Wong et al., 2019 ), and autism-related co-methylation modules to be significantly enriched for synaptic, neuronal, and immune dysfunction genes (Wong et al., 2019 ). Another post-mortem brain EWAS reported DNAm levels at autism-associated sites to resemble the DNAm states of early fetal brain development (Corley et al., 2019 ). This finding suggests an epigenetic delay in the neurodevelopmental trajectory may be a part of the molecular pathophysiology of autism.

Overall, methylomic studies of autism provide increasing evidence that common genetic risk variants of autism may alter DNAm across tissues, and that the epigenetic dysregulation of neuronal processes can contribute to the development of autism. Stratification of study participants based on their genetic risk variants may provide deeper insight into the role of aberrant epigenetic regulation in subgroups within autism.

Transcriptomics

Transcriptomics of peripheral tissues.

Gene expression plays a key role in determining the functional consequences of genes and identifying genetic networks underlying a disorder. One of the earliest studies on genome-wide transcriptome (Nishimura et al., 2007 ) investigated blood-derived lymphoblastoid cells gene expression from a small set of males with autism ( N = 15) and controls. Hierarchical clustering on microarray expression data followed by differentially expressed gene (DEG) analysis revealed a set of dysregulated genes in autism compared to controls. This approach was adopted (Luo et al., 2012 ) to investigate DEGs in a cohort of 244 families with autism probands (index autism case in a family) known to carry de novo pathogenic or variants of unknown significance and discordant sibling carriers of non-pathogenic CNVs. From genome-wide microarray transcriptome data, this study identified significant enrichment of outlier genes that are differentially expressed and reside within the proband rare/ de novo CNVs. Pathway enrichment of these outlier genes identified neural-related pathways, including neuropeptide signaling, synaptogenesis, and cell adhesion. Distinct expression changes of these outlier genes were identified in recurrent pathogenic CNVs, i.e. 16p11.2 microdeletions, 16p11.2 microduplications, and 7q11.23 duplications. Recently, multiple independent genome-wide blood-derived transcriptome analysis (Filosi et al., 2020 ; Lombardo et al., 2018 ; Tylee et al., 2017 ) showed the efficiency of detecting dysregulated genes in autism, including aberrant expression patterns of long non-coding RNAs (Sayad, Omrani, Fallah, Taheri, & Ghafouri-Fard, 2019 ).

Transcriptomics of post-mortem brain tissue

Although blood-derived transcriptome can be feasible to study due to easy access to the biological specimen, blood transcriptome results are not necessarily representative of the transcriptional machinery in the brain (GTEx Consortium, 2017 ). Hence, it is extremely hard to establish a causal relationship between blood transcriptional dysregulations and phenotypes in autism. A landmark initiative by Allen Brain Institute to profile human developing brain expression patterns (RNA-seq) from post-mortem tissue enabled neurodevelopmental research to investigate gene expression in the brain (Sunkin et al., 2013 ). Analyzing post-mortem brain tissue, multiple studies identified dysregulation of genes at the level of gene exons impacted by rare/ de novo mutations in autism (Uddin et al., 2014 ; Xiong et al., 2015 ), including high-resolution detection of exon splicing or novel transcript using brain tissue RNA sequencing (RNA-seq). High-resolution RNA-seq enabled autism brain transcriptome analysis on non-coding elements, and independent studies identified an association with long non-coding RNA and enhancer RNA dysregulation (Wang et al., 2015 ; Yao et al., 2015 ; Ziats & Rennert, 2013 ).

Although it is difficult to access post-mortem brain tissue from autistic individuals, studies of whole-genome transcriptome from autism and control brains have revealed significantly disrupted pathways ( Fig. 4 ) related to synaptic connectivity, neurotransmitter, neuron projection and vesicles, and chromatin remodeling pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ). Recently, an integrated genomic study also identified from autism brain tissue a component of upregulated immune processes associated with hypomethylation (Ramaswami et al., 2020 ). These reported pathways are in strong accordance with numerous independent autism studies that integrated genetic data with brain transcriptomes (Courchesne, Gazestani, & Lewis, 2020 ; Uddin et al., 2014 ; Yuen et al., 2017 ). A large-scale analysis of brain transcriptome from individuals with autism identified allele-specific expressions of genes that are often found to be impacted by pathogenic de novo mutations (Lee et al., 2019 a ). The majority of the studies are in consensus that genes that are highly active during prenatal brain development are enriched for clinically relevant mutations in autism (Turner et al., 2017 ; Uddin et al., 2014 ; Yuen et al., 2017 ). Recently, a large number (4635) of expression quantitative trait loci were identified that were enriched in prenatal brain-specific regulatory regions comprised of genes with distinct transcriptome modules that are associated with autism (Walker et al., 2019 ).

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Most commonly reported three pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ) associated with autism. ( a ) The synaptic connectivity and neurotransmitter pathway involves genes (yellow rectangular box) within presynaptic and postsynaptic neurons. Neurotransmitter transport through numerous receptors is an essential function of this pathway; ( b ) the chromatin remodeling pathway involves binding of remodeling complexes that initiate the repositioning (move, eject, or restructure) of nucleosomes that potentially can disrupt gene regulation; and ( c ) the neural projection pathway [adapted from Greig, Woodworth, Galazo, Padmanabhan, & Macklis ( 2013 )] involves the projection of neural dendrite into distant regions and the migration of neuronal cells through ventricular (VZ) and subventricular zones (SVZ) into the different cortical layers (I-VI).

Single-cell transcriptomics

Recent advancement of single-cell transcriptomics enables the detection of cell types that are relevant to disorder etiology. A recent case–control study conducted single-cell transcriptomics analysis on 15 autism and 16 control cortical post-mortem brain tissues generating over 100 000 single-cell transcriptomics data (Velmeshev et al., 2019 ). Cell-type analysis revealed dysregulations of a specific group of genes in cortico-cortical projection neurons that correlate with autism severity (Velmeshev et al., 2019 ). Deciphering cell-type identification has future implications, in particular for the implementation of precision medicine. However, single-cell technology is at very early stages of development and computationally it is still very complex to classify cell-type identity.

The emergence of CRISPR/Cas9 genome editing technology can potentially become an effective tool in future therapeutics of genetic conditions associated with autism. Although introducing and reversing DNA mutation is becoming a mature technology within in vitro systems, much work needs to be done for in vivo use of genome editing. Single-cell OMICs is another emerging field that has the potential to decipher developmental (spatio-temporally) brain cell types that are associated with autism. Identifying cell clusters and defining cell identity is a major computational challenge. Artificial intelligence can significantly improve these computational challenges to identify the molecular associations of autism at the single-cell level.

Clinical and therapeutic implications

In some, but not all, best practice clinical guidelines, genetic tests such as fragile X testing, chromosomal microarray, and karyotype testing are part of the standard medical assessment in a diagnostic evaluation of autism to identify potentially etiologically relevant rare genetic variants (Barton et al., 2018 ). The guidelines vary with respect to whether genetic testing is recommended for all people with autism, or based on particular risk factors, such as ID, seizures, or dysmorphic features. The DSM-5 diagnosis of autism includes a specifier for associated genetic conditions (APA, 2013 ). Although genetic test results may not usually have consequences for treatment changes, the results could inform recurrence risk and provide families with access to information about symptoms and prognosis. In the future, gene therapy, CRISPR/Cas9, and genome editing technologies may lead to the gene-specific design of precision medicine for rare syndromic forms of autism (Benger, Kinali, & Mazarakis, 2018 ; Gori et al., 2015 ).

Given that a substantial proportion of the genetic liability to autism is estimated to be explained by the cumulative effect of a large number of common SNPs, polygenic scores have gained traction as potential biomarkers. However, the predictive ability of polygenic scores from the largest autism GWAS to date is too low to be clinically useful. The odds ratio when comparing the top and bottom polygenic score decile groups is only 2.80 (95% CI 2.53–3.10) (Grove et al., 2019 ). Additionally, polygenic scores based on the samples of European ancestry do not translate well in populations with diverse ancestry (Palk, Dalvie, de Vries, Martin, & Stein, 2019 ).

Genetic testing can in the future become useful for informing screening or triaging for diagnostic assessments or identifying who may be more likely to respond to which type of intervention (Wray et al., 2021 ). Genetics may also help identify individuals with autism who are at a high risk of developing co-occurring physical and mental health conditions or likely to benefit from treatments of such conditions. A top research priority for autistic people and their families is addressing co-occurring mental health problems (Autistica, 2016 ), which may sometimes be the primary treatment need as opposed to autism per se . Genomics may also be helpful to repurpose existing treatments and better identify promising treatments. There are active clinical trials to repurpose drugs in autism (Hong & Erickson, 2019 ). Moreover, genetics can be used to identify social and environmental mediating and moderating factors (Pingault et al., 2018 ), which could inform interventions to improve the lives of autistic people.

Notably, there are important ethical challenges related to clinical translation of advances in genetics, including concerns about discriminatory use, eugenics concerning prenatal genetic testing, and challenges in interpretation and feedback (Palk et al., 2019 ). People with autism and their families are key stakeholders in genetic studies of autism and essential to include in discussions of how genetic testing should be used.

Conclusions and future directions

Recent large-scale and internationally collaborative investigations have led to a better understanding of the genetic contributions to autism. This includes identifying the first robustly associated common genetic variants with small individual effects (Grove et al., 2019 ) and over 100 genes implicated by rare, mostly de novo , variants of large effects (Sanders et al., 2015 ; Satterstrom et al., 2020 ). These and other findings show that the genetic architecture of autism is complex, diverse, and context-dependent, highlighting a need to study the interplay between different types of genetic variants, identify genetic and non-genetic factors influencing their penetrance, and better map the genetic variants to phenotypic heterogeneity within autism.

Immense collaborative efforts are needed to identify converging and distinct biological mechanisms for autism and subgroups within autism, which can in turn inform treatment (Thapar & Rutter, 2020 ). It is crucial to invest in multidimensional and longitudinal measurements of both core defining traits and associated traits such as language, intellectual, emotional, and behavioral functioning, and to collaboratively establish large omics databases including genomics, epigenomics, transcriptomics, proteomics, and brain connectomics (Searles Quick, Wang, & State, 2020 ). Indeed, large-scale multi-omic investigations are becoming possible in the context of large population-based family cohorts with rich prospective and longitudinal information on environmental exposures and developmental trajectories of different neurodevelopmental traits. Finally, novel methods (Neumeyer, Hemani, & Zeggini, 2020 ) can help investigate causal molecular pathways between genetic variants and autism and autistic traits.

Acknowledgements

We thank the Psychiatric Genomics Consortium, Anders Børglum, and Elise Robinson for their support and advice.

Financial support

Alexandra Havdahl was supported by the South-Eastern Norway Regional Health Authority (#2018059, career grant #2020022) and the Norwegian Research Council (#274611 PI Ted Reichborn-Kjennerud and #288083 PI Espen Røysamb). Maria Niarchou was supported by Autism Speaks (#11680). Anna Starnawska was supported by The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark (R155-2014-1724). Varun Warrier is supported by the Bowring Research Fellowship (St. Catharine's College, Cambridge), the Templeton World Charity Foundation, Inc., the Autism Research Trust, and the Wellcome Trust. Celia van der Merwe is supported by the Simons Foundation NeuroDev study (#599648) and the NIH R01MH111813 grant.

Conflict of interest

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Using AI to improve diagnosis of rare genetic disorders. Molly Chiu. 713-798-4710. Houston, TX - Apr 25, 2024. Share this article. Diagnosing rare Mendelian disorders is a labor-intensive task, even for experienced geneticists. Investigators at Baylor College of Medicine are trying to make the process more efficient using artificial intelligence.
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