Review Article | Published:

Autism genetics: opportunities and challenges for clinical translation

Nature Reviews Genetics volume 18, pages 362376 (2017) | Download Citation

Abstract

Genetic studies have revealed the involvement of hundreds of gene variants in autism. Their risk effects are highly variable, and they are frequently related to other conditions besides autism. However, many different variants converge on common biological pathways. These findings indicate that aetiological heterogeneity, variable penetrance and genetic pleiotropy are pervasive characteristics of autism genetics. Although this advancing insight should improve clinical care, at present there is a substantial discrepancy between research knowledge and its clinical application. In this Review, we discuss the current challenges and opportunities for the translation of autism genetics knowledge into clinical practice.

Key points

  • A rapidly growing list of rare genetic causes of autism spectrum disorders (ASDs) is being identified, giving insights into the underlying biology of these disorders.

  • Contrary to what is generally assumed, existing genetic findings are already able to inform our current clinical practice.

  • Genetic findings have great potential to improve the quality of health care provided to individuals with an ASD and to improve their quality of life. However, several initiatives are needed to support the translation of this knowledge into health care.

  • It is important to promote the education of the relevant health care professionals about clinical genetic testing and its possible benefits.

  • We must also adopt a broader view of ASDs that recognizes psychiatric and somatic comorbidity.

  • The field would benefit greatly from unprecedented global cooperation to improve sharing of genotype–phenotype data from cross-sectional and longitudinal studies.

  • Furthermore, researchers and clinicians must work in partnership with the autism community regarding the genetics and health care research agenda.

  • Finally, genetic information should be used to develop future treatments and interventions for psychiatric and somatic comorbidity, and should be evaluated in clinical trials.

  • The question is not so much when ASD genetics will start to influence our clinical practice but rather how we can optimally use the knowledge that we already have and what is required to use its full clinical potential in the future.

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Acknowledgements

The authors are grateful to the investigators from the Autism Genome Project (AGP) who provided insight and expertise. In particular, they would like to thank S. Folstein for bringing them together and starting the discussions that resulted in the writing of this manuscript.

Author information

Affiliations

  1. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, 3485 Utrecht, The Netherlands.

    • Jacob A. S. Vorstman
  2. Institute of Neuroscience, University of Newcastle, Newcastle upon Tyne NE1 4LP, UK.

    • Jeremy R. Parr
  3. Division of Child and Adolescent Psychiatry, Department of Psychiatry and Human Behavior, Brown University, Providence, Rhode Island 02912, USA.

    • Daniel Moreno-De-Luca
  4. Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK.

    • Richard J. L. Anney
  5. Department of Psychiatry, Stark Neurosciences Research Institute, Indiana University School of Medicine.

    • John I. Nurnberger Jr
  6. Department of Medical & Molecular Genetics, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, Indiana 46202, USA.

    • John I. Nurnberger Jr
  7. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, California 94305, USA.

    • Joachim F. Hallmayer

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Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Jacob A. S. Vorstman or Joachim F. Hallmayer.

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    Supplementary information S1 (table)

    Clinical trial data used for the graph in Figure 2.

Glossary

De novo genetic variants

Genetic variants that are identified in individuals but not detected in the genomes of their biological parents. These variants are generally assumed to result from a mutation in the parental germ cell or resulting zygote. However, when mutations arise during the embryonic development of the parent and involve genotypic mosaicism in the parental germ cells (gonadal or gonosomal mosaicism), they can also give rise to mutations in the offspring that are not observed in the parental DNA from typically tested tissues.

Proband

The patient who is the initial member of the family to come under investigation for a medical condition.

Variants of unknown significance

(VUS). Genetic variants for which a phenotypic effect is unknown.

Incidental findings

Genetic discoveries that have an effect on the individuals in which they occur but are not directly relatable to the disease under investigation. An example would be the discovery of a genetic alteration with relevance to familial cancer while interrogating the genome for mutations associated with an autism spectrum disorder.

Private mutations

Rare or unique mutations in the DNA sequence that are restricted to an individual, family or population.

Truncating mutations

Variations in the genetic code that alter the transcripts in such a way that the resultant proteins are shortened and incomplete, or not formed.

Gene set enrichment approaches

Analytical strategies to investigate whether there is enrichment in association signals attributed to a predetermined group of genes.

Weighted gene co-expression network analysis

(WGCNA). An analytical approach that clusters genes into modules according to the strength of the correlations between their expression values.

Machine-learning approaches

Research strategies in which a predictive model is trained using data. Examples of machine-learning approaches include neural nets, support vector machines and decision trees.

Penetrance

The proportion of individuals with a particular genetic variant who display a particular phenotype.

Expressivity

The extent to which an individual exhibits a given trait or phenotype.

Somatic phenotypes

Variations in or symptoms of the body (soma) or bodily functions. Somatic phenotypes can be distinguished from psychiatric phenotypes, which refer to variation in or symptoms of behaviour, cognition, perception and feelings.

Pleiotropy

The association of two or more independent phenotypes with one gene, or variation in that one gene.

Taxonomy

Classification based on a priori defined shared characteristics. The current classification of psychiatric disorders (as used in the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD)) is based mainly on observed symptoms and disease course.

Exposed attributable risk

The difference in the rate of an outcome in an exposed and an unexposed population, expressed as a fraction of the exposed population. In genetics, the exposure is the genotype.

Recurrence rate

(Also known as recurrence risk.) The probability that a condition will be present in subsequent siblings of the proband.

DECIPHER

(Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources). An interactive web-based database that incorporates a suite of tools designed to aid in the interpretation of genomic variation.

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DOI

https://doi.org/10.1038/nrg.2017.4

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