Analysis of common genetic variation and rare CNVs in the Australian Autism Biobank

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).

Interactions between the lipidome and genetic and environmental factors in autism

“…Next, we investigated three partially overlapping groups for which there was a priori expectation of outlying lipid levels: (1) statistical outliers ( n = 7); (2) visibly fatty plasma samples ( n = 12); and (3) individuals with copy number variants (CNVs; either clinically significant or large, rare CNVs, both called using genotyping array data 31 ; n = 26) (Fig. 6a,b ).…”

“…There were more males than females in this sample as ASD is diagnosed more frequently in boys and the majority of participants were in the ASD group. Our analyses focused on biological sex rather than gender (confirmed using previously analyzed genetic data 31 ). We also statistically accounted for biological sex by including this as a covariate in all analyses except where specified (for example, sensitivity analyses without covariates).…”

“…With this set of SNPs, we generated PGSs with the beta statistics from the original GWAS in PLINK 57 (version 1.90) using the --score function, which multiplies each SNP effect size by an individual’s allele dosage, then sums across all independent loci. We focused our analysis on European participants, whom we had identified in previous work 31 , so that ancestries were matched to the BHS GWAS dataset, which increased the predictive ability. We standardized the PGSs within the European dataset (including parents and children who provided SNP genotyping samples but did not have lipidomics data measured here) to have a mean of 0 and a standard deviation of 1.…”

Adult Height, 22q11.2 Deletion Extent, and Short Stature in 22q11.2 Deletion Syndrome

“…Next steps should also include investigation of the role of genetic variants within the 22q11.2 microdeletion region and genome-wide, including common variants and polygenic risk score for height, and rare variants of all types [ 29 , 30 , 31 ]. This will be important as there may be effects of reduced gene dosage in the 22q11.2 deletion region that could affect growth from conception onward [ 3 ], and this may add to or interact with genome-wide variants for height.…”

DGH-GO: Dissecting the Genetic Heterogeneity of complex diseases using Gene Ontology

“…A large number of genetic variants, including Single Nucleotide Variants (SNVs) and Copy Number Variants (CNVs) contribute to genetic heterogeneity of several disease by altering different functionally important genes (1,2). For example, CNVs are associated with several complex diseases such as ASD, Schizophrenia, Epilepsy, and Intellectual Disability (ID) (3–8). Previous studies have reported that CNVs contribute to phenotypic heterogeneity of complex diseases (9,10).…”