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A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex

Nature Genetics volume 54, pages 1284–1292 (2022)Cite this article

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A Publisher Correction to this article was published on 29 June 2022

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Abstract

The genetic etiology of autism spectrum disorder (ASD) is multifactorial, but how combinations of genetic factors determine risk is unclear. In a large family sample, we show that genetic loads of rare and polygenic risk are inversely correlated in cases and greater in females than in males, consistent with a liability threshold that differs by sex. De novo mutations (DNMs), rare inherited variants and polygenic scores were associated with various dimensions of symptom severity in children and parents. Parental age effects on risk for ASD in offspring were attributable to a combination of genetic mechanisms, including DNMs that accumulate in the paternal germline and inherited risk that influences behavior in parents. Genes implicated by rare variants were enriched in excitatory and inhibitory neurons compared with genes implicated by common variants. Our results suggest that a phenotypic spectrum of ASD is attributable to a spectrum of genetic factors that impact different neurodevelopmental processes.

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Fig. 1: Risk for ASD is attributable to multiple genetic factors including DNMs, rare inherited variants and polygenic risk.
Fig. 2: Multivariable regression of six genetic factors to create a composite GRS.
Fig. 3: Increased genetic load in females with ASD compared with males.
Fig. 4: Negative correlation of rare variants and polygenic risk is consistent with a liability threshold model.
Fig. 5: Differential effects of rare and common variation on behavioral traits in cases, sibling controls and parents.
Fig. 6: The genetic basis of parental age effects on ASD risk in offspring is multifactorial.
Fig. 7: ASD susceptibility genes implicated by rare variants are enriched in neuronal cell types of the developing brain.

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Data availability

WGS data from the SSC and exome and SNP genotyping data from SPARK are available from SFARI (https://www.sfari.org/resource/autism-cohorts). Summary genetic data from WGS and exomes, including individual counts for dnLoF, dnMIS, inhLoF and PSs for all subjects in the present study and input data files for all analysis code, are also available from SFARI. WGS data from the REACH project are available from the National Institute of Mental Health Data Archive (NDA), including the structural variant callset and raw sequence (FASTQ), alignment (BAM) and VCF files from the REACH cohort (https://nda.nih.gov/edit_collection.html?id=2019). GWAS summary statistics are available from the Psychiatric Genomics Consortium (ASD and SZ) (https://www.med.unc.edu/pgc/download-results) and the Social Science Genetic Association Consortium (EA) (http://www.thessgac.org/data). Bulk tissue expression data on ASD susceptibility genes was obtained from the BrainSpan developmental transcriptome dataset (v.0; https://www.brainspan.org/api/v2/well_known_file_download/267666525). Cell-type expression levels of ASD susceptibility genes in fetal cortex were obtained through the web interface of the CoDEx viewer (http://solo.bmap.ucla.edu/shiny/webapp).

Code availability

Analysis code for all major statistical genetic analyses in the paper and for generating Figs. 1–7 is available as a Google Colab notebook on Github (https://github.com/sebatlab/Antaki2021).

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Acknowledgements

We thank the families who participated in the genetic studies of the REACH, SSC and SPARK cohorts. The large samples used in the present study were made possible through the strong commitment to sharing of individual-level data by SFARI, the National Institutes of Health (NIH) and UCSD, and the sharing of summary-level data by the Psychiatric Genomics Consortium. We thank W. Pfeiffer, the San Diego Supercomputer Center and Amazon Web Services for hosting the computing infrastructure necessary for completing this project. We thank W. Chung and SFARI for providing data and materials for validation of DNM calls. We thank N. Baya for assistance with data processing. We thank H. Wong for providing the list of high-confidence GWAS genes. This work was supported by grants to J.S. from the SFARI (grant no. SFARI 606768), NIH (grant nos. MH113715, MH119746, 1MH109501) and the Escher fund for Autism (grant no. 20171603), a grant to C.M.N. from the NIH (grant no. MH106595), and a grant to A.R.M. from the NIH (MH127077). L.M.I. was supported by grants from the NIH (MH109885, MH108528, MH105524, MH104766) and SFARI (#345469). D.A. was supported by a T32 training grant from the NIH (grant no. GM008666). M.K. was supported by grants from the Dutch Research Council (grant nos. 09150162010073 and 45219212).

Author information

Author notes

  1. These authors contributed equally: Danny Antaki, James Guevara.

Authors and Affiliations

  1. Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA

    Danny Antaki

  2. Beyster Center for Psychiatric Genomics, University of California San Diego, La Jolla, CA, USA

    Danny Antaki, James Guevara, Marieke Klein, Madhusudan Gujral, Oanh Hong & Jonathan Sebat

  3. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Danny Antaki, James Guevara, Adam X. Maihofer, Marieke Klein, Madhusudan Gujral, Oanh Hong, Lilia M. Iakoucheva, Caroline M. Nievergelt & Jonathan Sebat

  4. Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA

    Danny Antaki, Alysson R. Muotri & Jonathan Sebat

  5. Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA

    Danny Antaki, Alysson R. Muotri, Lilia M. Iakoucheva, Joseph G. Gleeson & Jonathan Sebat

  6. Department of Neurosciences, University of California San Diego, La Jolla, CA, USA

    Danny Antaki, Eric Courchesne, Karen Pierce & Joseph G. Gleeson

  7. Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Jakob Grove

  8. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark

    Jakob Grove

  9. Center for Genomics and Personalized Medicine and Center for Integrative Sequencing, iSEQ, Aarhus, Denmark

    Jakob Grove

  10. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

    Jakob Grove

  11. Harvard T.H. Chan School of Public Health, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    Caitlin E. Carey & Elise B. Robinson

  12. Research Laboratory Unit, Fundacio Docencia i Recerca Mutua, Terrassa, Spain

    Maria J. Arranz

  13. Child and Adolescent Mental Health Unit, Hospital Universitari Mútua de Terrassa, Barcelona, Spain

    Amaia Hervas

  14. TEACCH Autism Program, University of North Carolina, Chapel Hill, NC, USA

    Christina Corsello

  15. Human Longevity Inc., San Diego, CA, USA

    Keith K. Vaux

  16. Department of Pediatrics and Department of Cellular & Molecular Medicine, University of California San Diego, School of Medicine, Center for Academic Research and Training in Anthropogeny, Archealization Center, Kavli Institute for Brain and Mind, La Jolla, CA, USA

    Alysson R. Muotri

  17. Autism Center of Excellence, University of California San Diego, La Jolla, CA, USA

    Eric Courchesne & Karen Pierce

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Contributions

J.S. conceived and coordinated the study. D.A., M.G. and J. Guevara performed data processing, variant calling and annotation of WGS and exome datasets. J.S., C.M.N. and A.X.M. supervised statistical genetic analyses. D.A., J. Guevara, A.X.M., M.K. and J.S. performed statistical genetic analyses. J. Guevara, D.A., L.M.I. and J.S. performed analysis of RNA-sequencing datasets. E.R., C.E.C. and J. Grove performed analysis of SNP genotypes and meta-analysis of summary statistics. J.S., C.C., K.K.V., A.H., M.J.A., K.P., E.C., J.G.G., A.R.M. and O.H. coordinated recruitment and DNA sample processing for the UCSD dataset.

Corresponding author

Correspondence to Jonathan Sebat.

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Competing interests

A.R.M. is a co-founder and has an equity interest in TISMOO, a company focusing on applications of genetics and human brain organoids to personalized medicine. The terms of this arrangement have been reviewed and approved by the UCSD, in accordance with its conflict-of-interest policies. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Rates of de novo mutations stratified by cohort and evaluation of potential confounders.

a, Rates of de novo synonymous (dnSyn) variants were not associated with ASD in the combined sample, but were enriched 1.1-fold in the SPARK cohort (P = 0.021). b, We evaluated whether quality metrics or other confounders could explain the slight excess of dnSyn variants in SPARK cases. Quality metrics did not differ in cases and controls including coverage, transition:transversion ratio (Ti/Tv) or ratio of heterozygous calls (Het/Hom). c, Paternal age did not differ significantly between cases and controls.

Extended Data Fig. 2 The combined effects of dnLoF, inhLoF and sex on the transmission of rare variants in families.

a, A significant liability threshold for rare variants was evident based on a negative correlation of dnLoF and inhLoF (linear regression P = 0.03), and this effect did not differ significantly by sex. b, Case-control odds ratios were compared for the transmission rates in families by sex (father-daughter, mother-daughter, father-son, mother-son). Both maternal and paternal rare variants contribute to ASD with a significant over-transmission from mother to daughter and from father to son. We did not observe a significant sex bias in the transmission of rare variants in families. In particular, we did not observe an enriched transmission from mother to male cases as we have previously hypothesized8.

Extended Data Fig. 3 Sex differences in the correlation of rare variant and common variant risk was not robust across multiple polygenic scoring methods.

a, An early analysis of this dataset using polygenic score estimates from PRSice observed that the negative correlation of RVRS and CVRS was stronger in males than in females, consistent with males having less tolerance of genetic risk. The heatmap displays the correlations between polygenic scores and rare variants in males and females separately. Correlations were tested by linear regression controlling for cohort, case status and ancestry PCs, and a gene-by-sex interaction was tested in the combined sample (ǂgene-by-sex P < 0.05). b, With polygenic scores calculated using SBayesR, there was a similar trend with the correlation of CVRS and RVRS being stronger in males; however, the gene-by-sex interaction was not statistically significant.

Extended Data Fig. 4 Correlation of de novo mutation rate with parental age.

a,b, Correlation of total autosomal de novo SNVs with age of fathers (a) and mothers (b). See also Fig. 6a. n = 4,518 trios for which age-at-birth was available for the mother and father.

Extended Data Fig. 5 Comparison of the predictive values of polygenic scoring methods PRSice and SBayesR.

Polygenic scores calculated using SBayesR had greater predictive value for polygenic scores for ASD (PSASD), schizophrenia (PSSZ) and educational attainment (PSEA).

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Antaki, D., Guevara, J., Maihofer, A.X. et al. A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex. Nat Genet 54, 1284–1292 (2022). https://doi.org/10.1038/s41588-022-01064-5

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