Abstract
One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11–q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The detailed description of brain samples is provided in Supplementary Table 1. For each tissue, group-based ASE identification results are available in Supplementary Tables 2 and 3. We also include gene lists that we used for gene set enrichment and GO analysis, which include brain-expressed genes that we used for our study (Supplementary Table 6). Raw next-generation sequencing data from human postmortem brain samples are available from published RNA-seq4 and sncRNA-seq42 studies. They have been deposited to the PsychENCODE Knowledge Portal (https://doi.org/10.7303/syn4587609).
Code availability
The R code for the ASE identification using a linear mixed model is provided in the Supplementary Software.
References
Elsabbagh, M. et al. Global prevalence of autism and other pervasive developmental disorders. Autism Res. 5, 160–179 (2012).
Geschwind, D. H. & State, M. W. Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 14, 1109–1120 (2015).
Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011).
Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).
Nardone, S. et al. DNA methylation analysis of the autistic brain reveals multiple dysregulated biological pathways. Transl. Psychiatry 4, e433 (2014).
Karlic, R., Chung, H. R., Lasserre, J., Vlahovicek, K. & Vingron, M. Histone modification levels are predictive for gene expression. Proc. Natl Acad. Sci. USA 107, 2926–2931 (2010).
Wong, C. C. Y. et al. Genome-wide DNA methylation profiling identifies convergent molecular signatures associated with idiopathic and syndromic autism in post-mortem human brain tissue. Hum. Mol. Genet. 28, 2201–2211 (2019).
Ben-David, E., Shohat, S. & Shifman, S. Allelic expression analysis in the brain suggests a role for heterogeneous insults affecting epigenetic processes in autism spectrum disorders. Hum. Mol. Genet. 23, 4111–4124 (2014).
Chess, A. Monoallelic gene expression in mammals. Annu. Rev. Genet. 50, 317–327 (2016).
Gimelbrant, A., Hutchinson, J. N., Thompson, B. R. & Chess, A. Widespread monoallelic expression on human autosomes. Science 318, 1136–1140 (2007).
Gregg, C., Zhang, J., Butler, J. E., Haig, D. & Dulac, C. Sex-specific parent-of-origin allelic expression in the mouse brain. Science 329, 682–685 (2010).
Kukurba, K. R. et al. Allelic expression of deleterious protein-coding variants across human tissues. PLoS Genet. 10, e1004304 (2014).
Degner, J. F. et al. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 25, 3207–3212 (2009).
Pickrell, J. K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).
Kang, E. Y. et al. Discovering single nucleotide polymorphisms regulating human gene expression using allele specific expression from RNA-seq data. Genetics 204, 1057–1064 (2016).
Castel, S. E., Levy-Moonshine, A., Mohammadi, P., Banks, E. & Lappalainen, T. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 16, 195 (2015).
de la Chapelle, A. Genetic predisposition to human disease: allele-specific expression and low-penetrance regulatory loci. Oncogene 28, 3345–3348 (2009).
Gicquel, C. et al. Epimutation of the telomeric imprinting center region on chromosome 11p15 in Silver-Russell syndrome. Nat. Genet. 37, 1003–1007 (2005).
Scoles, H. A., Urraca, N., Chadwick, S. W., Reiter, L. T. & Lasalle, J. M. Increased copy number for methylated maternal 15q duplications leads to changes in gene and protein expression in human cortical samples. Mol. Autism 2, 19 (2011).
Hogart, A. et al. Chromosome 15q11-13 duplication syndrome brain reveals epigenetic alterations in gene expression not predicted from copy number. J. Med. Genet. 46, 86–93 (2009).
Meguro-Horike, M. et al. Neuron-specific impairment of inter-chromosomal pairing and transcription in a novel model of human 15q-duplication syndrome. Hum. Mol. Genet. 20, 3798–3810 (2011).
Savova, V., Patsenker, J., Vigneau, S. & Gimelbrant, A. A. dbMAE: the database of autosomal monoallelic expression. Nucleic Acids Res. 44, D753–D756 (2016).
Adegbola, A. A. et al. Monoallelic expression of the human FOXP2 speech gene. Proc. Natl Acad. Sci. USA 112, 6848–6854 (2015).
Consortium, G. T. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
DeVeale, B., van der Kooy, D. & Babak, T. Critical evaluation of imprinted gene expression by RNA-seq: a new perspective. PLoS Genet. 8, e1002600 (2012).
1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).
Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).
Mukherjee, N. et al. Integrative regulatory mapping indicates that the RNA-binding protein HuR couples pre-mRNA processing and mRNA stability. Mol. Cell 43, 327–339 (2011).
Weyn-Vanhentenryck, S. M. et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 6, 1139–1152 (2014).
Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).
Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).
Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).
Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).
Cook, E. H. Jr. & Scherer, S. W. Copy-number variations associated with neuropsychiatric conditions. Nature 455, 919–923 (2008).
Gardiner, E. et al. Imprinted DLK1-DIO3 region of 14q32 defines a schizophrenia-associated miRNA signature in peripheral blood mononuclear cells. Mol. Psychiatry 17, 827–840 (2012).
Cavaille, J. et al. Identification of brain-specific and imprinted small nucleolar RNA genes exhibiting an unusual genomic organization. Proc. Natl Acad. Sci. USA 97, 14311–14316 (2000).
Cavaille, J., Seitz, H., Paulsen, M., Ferguson-Smith, A. C. & Bachellerie, J. P. Identification of tandemly-repeated C/D snoRNA genes at the imprinted human 14q32 domain reminiscent of those at the Prader–Willi/Angelman syndrome region. Hum. Mol. Genet. 11, 1527–1538 (2002).
Bazeley, P. S. et al. snoTARGET shows that human orphan snoRNA targets locate close to alternative splice junctions. Gene 408, 172–179 (2008).
Wu, Y. E., Parikshak, N. N., Belgard, T. G. & Geschwind, D. H. Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder. Nat. Neurosci. 19, 1463–1476 (2016).
Kishore, S. & Stamm, S. The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C. Science 311, 230–232 (2006).
Kehr, S., Bartschat, S., Stadler, P. F. & Tafer, H. PLEXY: efficient target prediction for box C/D snoRNAs. Bioinformatics 27, 279–280 (2011).
Kryukov, G. V., Pennacchio, L. A. & Sunyaev, S. R. Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am. J. Hum. Genet. 80, 727–739 (2007).
Tycko, B. Allele-specific DNA methylation: beyond imprinting. Hum. Mol. Genet. 19, R210–R220 (2010).
Oberg, A. L. et al. miRNA expression in colon polyps provides evidence for a multihit model of colon cancer. PLoS One 6, e20465 (2011).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: faster genotype imputation. Bioinformatics 31, 782–784 (2015).
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).
Quinn, E. M. et al. Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data. PLoS One 8, e58815 (2013).
Veerappa, A. M., Padakannaya, P. & Ramachandra, N. B. Copy number variation-based polymorphism in a new pseudoautosomal region 3 (PAR3) of a human X-chromosome-transposed region (XTR) in the Y chromosome. Funct. Integr. Genom. 13, 285–293 (2013).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Hulsen, T., de Vlieg, J. & Alkema, W. BioVenn—a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics 9, 488 (2008).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48 (2009).
Eden, E., Lipson, D., Yogev, S. & Yakhini, Z. Discovering motifs in ranked lists of DNA sequences. PLoS Comput. Biol. 3, e39 (2007).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).
Lee, C., Mayfield, R. D. & Harris, R. A. Altered gamma-aminobutyric acid type B receptor subunit 1 splicing in alcoholics. Biol. Psychiatry 75, 765–773 (2014).
Acknowledgements
We thank S. Mangul for helpful advice about his published ASE data-filtering method15. We also thank N. N. Parikshak, Y. E. Wu and T. G. Belgard for providing published RNA-seq4 and sncRNA-seq42 data and helpful advice. We also thank K. Y. Choe for proofreading. This work was supported by Simons Foundation grant no. 401457 to D.H.G., Simons Foundation Bridge to Independence Award to M.J.G., and National Institutes of Health grants no. U01MH116489, no. R01MH110927 and no. R01MH109912 to D.H.G.
Author information
Authors and Affiliations
Contributions
D.H.G. and C.L. designed the study and D.H.G. supervised the project. C.L. analyzed the data. E.Y.K. helped with generating the masked reference file under guidance from E.E., who helped oversee ASE analysis. C.L., D.H.G. and M.J.G. wrote the paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Supplementary Figure 1 Additional data for a schematic of the pipeline for analysis of ASE.
(a) SNP calling pipeline for RNA-seq data. After read mapping, bam files were modified in several steps for SNP calling (Methods). After SNP callings from both GATK and Samtools, we filtered out possible false positive SNPs with a common filter and collected passed SNPs. Sensitivity (b) and specificity (c) to confirm the accuracy of RNA-seq based genotyping. (d) Ancestry identification by a MDS plot with HapMap3 populations. Our samples were plotted at European (EUR), Mexican (MEX), Asian (ASI), African (AFR), and not clear (NC) sections. CEU: Northern and Western European Utah residents; CHB: Han Chinese; YRI: Yoruba, Nigeria; TSI: Tuscan, Italia; JPT: Japanese; CHD: Denver Chinese; MEX: Mexican ancestry in Los Angeles; GIH: Gujarati Indian in Houston; ASW: African-American in Southwest; LWK: Luhya, Kenya; MKK: Maasai, Kenya. (e) The balanced distributions of covariate values across control and ASD. PMI, brain mass, and GC contents (AT and GC dropout values from Picard, Methods) were compared between control (n=48) and ASD (n=40). Their two-tailed unpaired t-test p-values are under the boxplots. The minimum, 1st quantile, median, 3rd quantile, and maximum values of the boxplots show at brain mass (control: 1,090, 1,355, 1,470, 1,555, and 1,815; ASD: 1,070, 1,202, 1,401, 1,571, and 2,100, respectively), PMI (control: 4.75, 21.10, 22.79, 26.16, and 32.92; ASD: 6.75, 15.54, 22.18, 26.62, and 43.25, respectively), AT dropout (control: 7.509, 16.036, 19.353, 22.329, and 28.334; ASD: 14.25, 16.71, 18.99, 22.56, and 30.15, respectively), and GC dropout (control: 0.000607, 0.237233, 0.696491, 1.262749, and 3.015555; ASD: 0.000647, 0.251634, 0.588174, 1.076187, and 2.580510, respectively). (f) and (g) The distributions of log2(fold change) deviations per each haplotype block. The deviations were calculated from the beta values of allele covariate from the group based ASE studies (Methods). The distributions (red line; deviation) of controls (f) and idiopathic ASD (g) were compared with the normal distribution (blue line; normal). Both two-sided KS test p-values were significant (p-value<2.2e-16). Deviation is for the log2(fold change) deviation.
Supplementary Figure 2 Additional data for ASE patterns shared among cases and controls.
(a) The identification of experimentally validated ASE genes from dbMAE. Two-sided Fisher’s Exact Tests results showed at the bottom of table. (b) ASE gene comparisons between two tissues. (c) ASE rates from individual BA9 (n=33), BA41 (n=27), and vermis (n=29) samples in control. As following the method of GTEx study24, we calculated ASE rates and compared them with GTEx data for further external validation (Methods). The minimum, 1st quantile, median, 3rd quantile, and maximum values of the boxplot show at BA9 (0.001333, 0.002873, 0.003324, 0.004083, and 0.007191, respectively), BA41 (0.001008, 0.002581, 0.003254, 0.003825, and 0.009326, respectively), and vermis (0.0006699, 0.0027397, 0.0040822, 0.0056013, and 0.0133876, respectively). (d) The comparison of allele expression between imbalance and other groups. From a BA41 RNA-seq sample, alleles were grouped based on less expressed allele expression fraction (Methods). The minimum, 1st quantile, median, 3rd quantile, and maximum values of the boxplots show at imbalance (-0.51105, -0.37355, 0.07391, 0.75198, and 10.12992, respectively) and other (-0.5111, -0.1325, 0.3369, 1.2114, and 14.8906 respectively). The considered SNP numbers of imbalance and other groups were 10,139 and 23,888, respectively. (e) Cell marker gene fractions of ASE genes in BA9, BA41, and vermis. Since the number of known cell marker genes are different31, the fractions were normalized by the equal number of cell marker genes. (f) Gene set enrichment of ASE genes in BA9 (n=67), BA41 (n=64), and vermis (n=64) with up- and down-regulated genes in idiopathic ASD cortex4.
Supplementary Figure 3 Additional data for ASE patterns distinguished between control and idiopathic ASD brain.
(a) ASE gene comparison for control and idiopathic ASD brain tissues. (b) Hypergeometric test of idiopathic ASD-specific ASE genes in BA9, BA41, and vermis. Their sample sizes were and 30, 32, and 32, respectively. (c) and (d) GO analyses for control-specific (c) and idiopathic ASD-specific (d) ASE genes in cortex. Their p-values and other results of the GO analysis are at Supplementary Table 4. At (c), the white boxes show p-value>10-3, and the yellow boxes represent 10-5
Supplementary Figure 4 Additional data for quantitative allelic imbalance in idiopathic ASD and control cortex.
(a) Difference in SNP number between control and ASD (ASD – control; less expressed allele expression fraction). The red line demarcates where the difference is 0. (b) Minor allele expression fraction distributions of control (blue) and idiopathic ASD (red) per each MAF at autosomes. The density plots at 0% and 100% are magnified 6 times in the right and left rectangular boxes, respectively. The left box at 0≦MAF<0.01 is a 4x magnified image. (c) The comparison of minor allele expression fractions between control (blue) and idiopathic ASD (red) on chromosome X. SNP numbers are 328 and 18 for no PAR (chrX (no PAR)) and PAR (chrX (PAR)), respectively.
Supplementary Figure 5 Genome-wide views of MAE in control, ASD, and dup15q.
Minor allele MAE SNPs (minor) are shown on top, and major allele MAE SNPs (major) are on bottom as labeled. Dup is for dup15q.
Supplementary Figure 6 Additional data for MAE SNPs across control, ASD, and dup15q groups.
(a) and (b) MAE allele visualization at gene bodies. The genes are ZDBF2 (chr2:207,139,365-207,179,150; 39,786bp) (a) and KCNQ1OT1 (chr11:2,629,558-2,721,228; 91,671bp) (b). Control, ASD, and dup15q MAE allele tracks followed by RefSeq Genes model tracks. The values at MAE SNP tracks are 1 and -1 for major and minor MAE SNPs, respectively. (c) The gene set enrichment of MAE genes with cell type specific genes31 and previously identified up- and down-regulated genes in ASD cortex4. Ctl_major, ASD_major, and dup_major are for major allele MAE genes at control, ASD, and dup15q, respectively. Ctl_minor, ASD_minor, and dup_minor are their minor allele MAE genes. (d) Gene set enrichment study of MAE genes with risk variants in psychiatric diseases. For de novo variant datasets32,33, SCZ, ID, and ASD1 were for de novo likely gene disrupting mutations32, and ASD2 was the risk genes integrating de novo copy number variations (FDR≦0.01)33 (Methods). Plot showed ORs and the p-values if significant. The GWAS datasets34,35 of ADHD, BPD, MDD, SCZ, and ASD were used for this study. ASD1 and ASD2 represent the GWAS datasets of Cross-Disorder Group of the Psychiatric Genomics Consortium34 and Grove et al.35, respectively. If significant, FDR corrected p-values were showed (Methods). Dup represents dup15q. For (c) and (d), the sample size of control, ASD, and dup15q were 69, 62, and 15, respectively.
Supplementary Figure 7 Visualization of uniquely mapped reads in RNA-seq and sncRNA-seq data at the significant allele shift rich regions.
From top to bottom, RNA-seq, sncRNA-seq, and RefSeq Genes model were shown at 14q32 (chr14:101,402,937-101,443,821) (a) and at 15q11 (chr15:25,474,024-25,493,548) (b). sncRNA-seq expression is annotated as sncRNAs.
Supplementary Figure 8 SnoRNA target genes and their splicing changes.
(a) The exon numbers between splice junctions and snoRNA targeting sites of 29 splicing changing target genes in ASD (n=62). The comparison between the snoRNA and random target data showed significant difference (one-tailed KS test p-value=0.0029). (b) and (c) The correlation between snoRNA expression and splicing changes of their target genes, ASTN2 (b) and SYNE1 (b), in cortex samples (n=62). PSI(%) is the splicing changing data from qPCR data of the previous study4. (d) The correlation of splicing changes between ASTN2 and SYNE1 in cortex samples (n=62).
Supplementary information
Supplementary Table 1
The detailed information for brain samples used for ASE study. It contains sample ID, brain ID, region, diagnosis, detailed diagnosis, primary cause of death, secondary cause of death, age, sex, RIN, PMI, brain mass, brain bank, sequence batch, SNP annotation, ancestry, AT dropout from Picard and GC dropout from Picard.
Supplementary Table 2
Results of ASE for BA9, BA41, vermis and cortex. For each tissue, the table contains SNP information, the output of linear mixed model and gene information. SNP information contains chromosome, location, reference allele, alternative allele and SNP ID. For the output of linear mixed model, beta value, standard error of the mean (s.e.m.) and P values are provided for allele, age, sex, sequencing batch, RIN, brain bank (bank) and ancestry (ethnicity). Gene information provides SNP location at all known transcripts which contains ENSG, HGNC, biotype, ENST and exon/intron number or promoter location. There are also the gene lists showing ASE (EMSG and HGNC genes). The sample sizes of BA9, BA41, vermis and cortex were 67, 64, 64 and 131, respectively.
Supplementary Table 3
Results of ASE for BA9, BA41, vermis and cortex in control and idiopathic ASD. For each group, the table contains SNP information, the output of linear mixed model and gene information as described at Supplementary Table 2. The ASE gene lists are also provided. The sample sizes of BA9, BA41, vermis and cortex in control were 37, 32, 32 and 69, respectively. In idiopathic ASD, they were 30, 32, 32 and 62, respectively.
Supplementary Table 4
Additional data of ASE GO analyses in cortex. REViGO49 generated the P values and other results from the GO analysis. The table contains its raw GO analysis data for common ASE genes between control and idiopathic ASD groups for above interactive graph of Fig. 3d, control-specific ASE genes (Supplementary Fig. 3c) and idiopathic ASD-specific ASE genes (Supplementary Fig. 3d).
Supplementary Table 6
Gene lists used for gene set enrichment and GO studies. The lists contain ASD risk genes (SFARI36; Methods); known imprinted genes (Methods); PSD32, FMRP28, HuR29 and RBFOX1 target genes30; cell marker genes31 (neuron, astrocyte, oligodendrocyte, microglia and endothelial); up- and downregulated genes in ASD cortex4; and genes containing risk variants in psychiatric disease datasets from de novo variant data (SCZ32, ID32, ASD1 (ref. 32) and ASD2 (ref. 33); Methods). Brain-expressed genes (ENSG and HGNC genes) were used as background genes for the gene set enrichment and GO analyses
Supplementary Software
The R code for the ASE identification using a linear mixed model.
Rights and permissions
About this article
Cite this article
Lee, C., Kang, E.Y., Gandal, M.J. et al. Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage. Nat Neurosci 22, 1521–1532 (2019). https://doi.org/10.1038/s41593-019-0461-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-019-0461-9
This article is cited by
-
CircMiMi: a stand-alone software for constructing circular RNA-microRNA-mRNA interactions across species
BMC Bioinformatics (2022)
-
Trans-genetic effects of circular RNA expression quantitative trait loci and potential causal mechanisms in autism
Molecular Psychiatry (2022)
-
Recovering genotypes and phenotypes using allele-specific genes
Genome Biology (2021)
-
Feasibility of predicting allele specific expression from DNA sequencing using machine learning
Scientific Reports (2021)