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Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage

Nature Neuroscience volume 22, pages 1521–1532 (2019)Cite this article

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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.

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Fig. 1: A schematic of the pipeline for analysis of ASE. RNA-seq data were generated from 263 brain samples across 3 brain regions from 56 people with ASDs and 40 controls.
Fig. 2: ASE patterns shared among cases and controls.
Fig. 3: ASE patterns distinguish control and idiopathic ASD brains.
Fig. 4: Quantitative allelic imbalance in idiopathic ASD and control cortex.
Fig. 5: MAE SNPs across control, ASD and dup15q groups.
Fig. 6: Allele-shift-rich regions at ASD and dup15q.
Fig. 7: Characterization of allele-shift-rich regions in ASD and dup15q.

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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.

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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.

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Author notes

  1. Changhoon Lee

    Present address: Department of Neuroscience, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

Authors and Affiliations

  1. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Changhoon Lee, Michael J. Gandal & Daniel H. Geschwind

  2. Department of Computer Science, Henry Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    Eun Yong Kang & Eleazar Eskin

  3. Center for Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Michael J. Gandal & Daniel H. Geschwind

  4. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Eleazar Eskin & Daniel H. Geschwind

  5. Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Eleazar Eskin

  6. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

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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.

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Correspondence to Daniel H. Geschwind.

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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 ≦10-3. At (d), the bubble color and bubble size indicate the p-value and the frequency of each GO term, respectively. Similar GO terms are linked by edges, and the line width shows the degree of similarity. (e) Gene set enrichment study of ASE genes in control and idiopathic ASD with risk variants in psychiatric diseases. For de novo variant datasets32,33, SCZ, ID, and ASD1 gene lists contained 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 were considered for ADHD, BPD, MDD, SCZ, and ASD. ASD1 and ASD2 represent data from Cross-Disorder Group of the Psychiatric Genomics Consortium34 and Grove et al.35 respectively. If significant, plot showed FDR corrected p-values for GWAS (Methods). The sample size of control and idiopathic ASD were 69 and 62, respectively.

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 Figs. 1–8 and Supplementary Table 5.

Reporting Summary

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.

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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

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