Chloe X Yap1,2,3, Gail A Alvares3,4, Anjali K Henders2,3, Tian Lin2, Leanne Wallace2, Alaina Farrelly2, Tiana McLaren2, Jolene Berry2, Anna A E Vinkhuyzen2, Maciej Trzaskowski2,5, Jian Zeng2, Yuanhao Yang1,2, Dominique Cleary3,4, Rachel Grove3,6, Claire Hafekost3,4, Alexis Harun3,4, Helen Holdsworth1,3,7, Rachel Jellett3,8, Feroza Khan3,6, Lauren Lawson3,8, Jodie Leslie3,4, Mira Levis Frenk1,3,7, Anne Masi3,6, Nisha E Mathew3,6, Melanie Muniandy3,8, Michaela Nothard1,3,7, Peter M Visscher2,9, Paul A Dawson1,3, Cheryl Dissanayake3,8, Valsamma Eapen3,6,10, Helen S Heussler3,7,11, Andrew J O Whitehouse3,4, Naomi R Wray2,3,9, Jacob Gratten12,13,14. 1. Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia. 2. Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. 3. Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia. 4. Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia. 5. Max Kelsen, Fortitude Valley, QLD, Australia. 6. School of Psychiatry, University of New South Wales, Sydney, NSW, Australia. 7. Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia. 8. Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia. 9. Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia. 10. Academic Unit of Child Psychiatry South West Sydney, Ingham Institute, Liverpool Hospital, Sydney, NSW, Australia. 11. Child Development Program, Children's Health Queensland, Brisbane, QLD, Australia. 12. Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia. jacob.gratten@mater.uq.edu.au. 13. Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. jacob.gratten@mater.uq.edu.au. 14. Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia. jacob.gratten@mater.uq.edu.au.
Abstract
BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose biological basis is yet to be elucidated. The Australian Autism Biobank (AAB) is an initiative of the Cooperative Research Centre for Living with Autism (Autism CRC) to establish an Australian resource of biospecimens, phenotypes and genomic data for research on autism. METHODS: Genome-wide single-nucleotide polymorphism genotypes were available for 2,477 individuals (after quality control) from 546 families (436 complete), including 886 participants aged 2 to 17 years with diagnosed (n = 871) or suspected (n = 15) ASD, 218 siblings without ASD, 1,256 parents, and 117 unrelated children without an ASD diagnosis. The genetic data were used to confirm familial relationships and assign ancestry, which was majority European (n = 1,964 European individuals). We generated polygenic scores (PGS) for ASD, IQ, chronotype and height in the subset of Europeans, and in 3,490 unrelated ancestry-matched participants from the UK Biobank. We tested for group differences for each PGS, and performed prediction analyses for related phenotypes in the AAB. We called copy-number variants (CNVs) in all participants, and intersected these with high-confidence ASD- and intellectual disability (ID)-associated CNVs and genes from the public domain. RESULTS: The ASD (p = 6.1e-13), sibling (p = 4.9e-3) and unrelated (p = 3.0e-3) groups had significantly higher ASD PGS than UK Biobank controls, whereas this was not the case for height-a control trait. The IQ PGS was a significant predictor of measured IQ in undiagnosed children (r = 0.24, p = 2.1e-3) and parents (r = 0.17, p = 8.0e-7; 4.0% of variance), but not the ASD group. Chronotype PGS predicted sleep disturbances within the ASD group (r = 0.13, p = 1.9e-3; 1.3% of variance). In the CNV analysis, we identified 13 individuals with CNVs overlapping ASD/ID-associated CNVs, and 12 with CNVs overlapping ASD/ID/developmental delay-associated genes identified on the basis of de novo variants. LIMITATIONS: This dataset is modest in size, and the publicly-available genome-wide-association-study (GWAS) summary statistics used to calculate PGS for ASD and other traits are relatively underpowered. CONCLUSIONS: We report on common genetic variation and rare CNVs within the AAB. Prediction analyses using currently available GWAS summary statistics are largely consistent with expected relationships based on published studies. As the size of publicly-available GWAS summary statistics grows, the phenotypic depth of the AAB dataset will provide many opportunities for analyses of autism profiles and co-occurring conditions, including when integrated with other omics datasets generated from AAB biospecimens (blood, urine, stool, hair).
BACKGROUND:Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose biological basis is yet to be elucidated. The Australian Autism Biobank (AAB) is an initiative of the Cooperative Research Centre for Living with Autism (Autism CRC) to establish an Australian resource of biospecimens, phenotypes and genomic data for research on autism. METHODS: Genome-wide single-nucleotide polymorphism genotypes were available for 2,477 individuals (after quality control) from 546 families (436 complete), including 886 participants aged 2 to 17 years with diagnosed (n = 871) or suspected (n = 15) ASD, 218 siblings without ASD, 1,256 parents, and 117 unrelated children without an ASD diagnosis. The genetic data were used to confirm familial relationships and assign ancestry, which was majority European (n = 1,964 European individuals). We generated polygenic scores (PGS) for ASD, IQ, chronotype and height in the subset of Europeans, and in 3,490 unrelated ancestry-matched participants from the UK Biobank. We tested for group differences for each PGS, and performed prediction analyses for related phenotypes in the AAB. We called copy-number variants (CNVs) in all participants, and intersected these with high-confidence ASD- and intellectual disability (ID)-associated CNVs and genes from the public domain. RESULTS: The ASD (p = 6.1e-13), sibling (p = 4.9e-3) and unrelated (p = 3.0e-3) groups had significantly higher ASDPGS than UK Biobank controls, whereas this was not the case for height-a control trait. The IQ PGS was a significant predictor of measured IQ in undiagnosed children (r = 0.24, p = 2.1e-3) and parents (r = 0.17, p = 8.0e-7; 4.0% of variance), but not the ASD group. Chronotype PGS predicted sleep disturbances within the ASD group (r = 0.13, p = 1.9e-3; 1.3% of variance). In the CNV analysis, we identified 13 individuals with CNVs overlapping ASD/ID-associated CNVs, and 12 with CNVs overlapping ASD/ID/developmental delay-associated genes identified on the basis of de novo variants. LIMITATIONS: This dataset is modest in size, and the publicly-available genome-wide-association-study (GWAS) summary statistics used to calculate PGS for ASD and other traits are relatively underpowered. CONCLUSIONS: We report on common genetic variation and rare CNVs within the AAB. Prediction analyses using currently available GWAS summary statistics are largely consistent with expected relationships based on published studies. As the size of publicly-available GWAS summary statistics grows, the phenotypic depth of the AAB dataset will provide many opportunities for analyses of autism profiles and co-occurring conditions, including when integrated with other omics datasets generated from AAB biospecimens (blood, urine, stool, hair).
Entities:
Keywords:
Australian autism biobank; Autism spectrum disorder; Copy number variation; Genetics; Polygenic score
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