Next-generation DNA sequencing-based assay for measuring allelic expression imbalance (AEI) of candidate neuropsychiatric disorder genes in human brain

Transcript specific regulation of expression influences susceptibility to multiple sclerosis

Genome-wide association studies (GWAS) have identified over 100 loci containing single nucleotide variants (SNVs) that influence the risk of developing multiple sclerosis (MS). Most of these loci lie in non-coding regulatory regions of the genome that are active in immune cells and are therefore thought to modify risk by altering the expression of key immune genes. To explore this hypothesis we screened genes flanking MS-associated variants for evidence of allele specific expression (ASE) by quantifying the transcription of coding variants in linkage disequilibrium with MS-associated SNVs. In total, we were able to identify and successfully analyse 200 such coding variants (from 112 genes) in both CD4+ and CD8+ T cells from 106 MS patients and 105 controls. Fifty-six of these coding variants (from 43 genes) showed statistically significant evidence of ASE in one or both cell types. In the Lck interacting transmembrane adaptor 1 gene (LIME1), for example, we were able to show that in both cell types, the MS-associated variant rs2256814 increased the expression of some transcripts while simultaneously reducing the expression of other transcripts. In CD4+ cells from an additional independent set of 96 cases and 93 controls we were able to replicate the effect of this SNV on the balance of alternate LIME1 transcripts using qPCR (p = 5 × 10^-24). Our data thus indicate that some of the MS-associated SNVs identified by GWAS likely exert their effects on risk by distorting the balance of alternate transcripts rather than by changing the overall level of gene expression.

Analysis of porcine OSBPL5 gene allelic expression in skeletal muscle and association of a single-nucleotide polymorphism in the coding region with production traits

Genes that exhibit allelic expression imbalance and imprinted genes play important roles in the survival of the embryo and postnatal growth regulation. In this study, the porcine oxysterol-binding protein-related 5 (OSBPL5) gene was examined, and the 2140G>A mutation (rs318687202) was found in its coding region by a comparison of Laiwu and Landrace pigs. By allele-specific expression analysis based on a specific single-nucleotide polymorphism (SNP), the imprinting status of OSBPL5 gene in skeletal muscle from both neonate and adult pigs was determined. The results showed that the OSBPL5 was paternally imprinted in skeletal muscle from adults but biallelically expressed with predominantly maternal imprinting in neonates. The distribution of the 2140G>A SNP in four pig populations was analyzed, which showed that GG genotype was dominant in Duroc and Dapulian populations, whereas the AG genotype was dominant in Junmu-1 and Laiwu populations. Pigs with the GG genotype had significantly larger litters and greater cannon bone circumferences but a lower average daily gain than pigs with the AA genotype. In conclusion, we determined the difference in the allelic expression of OSBPL5 between adult and neonate pigs and identified an SNP in its coding region that is associated with production traits.

Quantification of allelic differential expression using a simple Fluorescence primer PCR-RFLP-based method

Allelic differential expression (ADE) is common in diploid organisms, and is often the key reason for specific phenotype variations. Thus, ADE detection is important for identification of major genes and causal mutations. To date, sensitive and simple methods to detect ADE are still lacking. In this study, we have developed an accurate, simple, and sensitive method, named fluorescence primer PCR-RFLP quantitative method (fPCR-RFLP), for ADE analysis. This method involves two rounds of PCR amplification using a pair of primers, one of which is double-labeled with an overhang 6-FAM. The two alleles are then separated by RFLP and quantified by fluorescence density. fPCR-RFLP could precisely distinguish ADE cross a range of 1- to 32-fold differences. Using this method, we verified PLAG1 and KIT, two candidate genes related to growth rate and immune response traits of pigs, to be ADE both at different developmental stages and in different tissues. Our data demonstrates that fPCR-RFLP is an accurate and sensitive method for detecting ADE on both DNA and RNA level. Therefore, this powerful tool provides a way to analyze mutations that cause ADE.

HapIso: An Accurate Method for the Haplotype- Specific Isoforms Reconstruction From Long Single-Molecule Reads

Sequencing of RNA provides the possibility to study an individual's transcriptome landscape and determine allelic expression ratios. Single-molecule protocols generate multi-kilobase reads longer than most transcripts, allowing sequencing of complete haplotype isoforms. This allows partitioning the reads into two parental haplotypes. While the read length of the single-molecule protocols is long, the relatively high error rate limits the ability to accurately detect the genetic variants and assemble them into the haplotype-specific isoforms. In this paper, we present Haplotype-specific Isoform reconstruction (HapIso), a method able to tolerate the relatively high error rate of the single-molecule platform and partition the isoform reads into the parental alleles. Phasing the reads according to the allele of origin allows our method to efficiently distinguish between the read errors and the true biological mutations. HapIso uses a k -means clustering algorithm aiming to group the reads into two meaningful clusters maximizing the similarity of the reads within the cluster and minimizing the similarity of the reads from different clusters. Each cluster corresponds to a parental haplotype. We used the family pedigree information to evaluate our approach. Experimental validation suggests that HapIso is able to tolerate the relatively high error rate and accurately partition the reads into the parental alleles of the isoform transcripts. We also applied HapIso to novel clinical single-molecule RNA-Seq data to estimate allele-specific expression of genes of interest. Our method was able to correct reads and determine Glu1883Lys point mutation of clinical significance validated by GeneDx HCM panel. Furthermore, our method is the first method able to reconstruct the haplotype-specific isoforms from long single-molecule reads.

The genetic architecture of autism spectrum disorders (ASDs) and the potential importance of common regulatory genetic variants

Currently, there is great interest in identifying genetic variants that contribute to the risk of developing autism spectrum disorders (ASDs), due in part to recent increases in the frequency of diagnosis of these disorders worldwide. While there is nearly universal agreement that ASDs are complex diseases, with multiple genetic and environmental contributing factors, there is less agreement concerning the relative importance of common vs rare genetic variants in ASD liability. Recent observations that rare mutations and copy number variants (CNVs) are frequently associated with ASDs, combined with reduced fecundity of individuals with these disorders, has led to the hypothesis that ASDs are caused primarily by de novo or rare genetic mutations. Based on this model, large-scale whole-genome DNA sequencing has been proposed as the most appropriate method for discovering ASD liability genes. While this approach will undoubtedly identify many novel candidate genes and produce important new insights concerning the genetic causes of these disorders, a full accounting of the genetics of ASDs will be incomplete absent an understanding of the contributions of common regulatory variants, which are likely to influence ASD liability by modifying the effects of rare variants or, by assuming unfavorable combinations, directly produce these disorders. Because it is not yet possible to identify regulatory genetic variants by examination of DNA sequences alone, their identification will require experimentation. In this essay, I discuss these issues and describe the advantages of measurements of allelic expression imbalance (AEI) of mRNA expression for identifying cis-acting regulatory variants that contribute to ASDs.

Evidence for genetic regulation of mRNA expression of the dosage-sensitive gene retinoic acid induced-1 (RAI1) in human brain

RAI1 (retinoic acid induced-1) is a dosage-sensitive gene that causes Smith-Magenis syndrome (SMS) when mutated or deleted and Potocki-Lupski Syndrome (PTLS) when duplicated, with psychiatric features commonly observed in both syndromes. How common genetic variants regulate this gene, however, is unknown. In this study, we found that RAI1 mRNA expression in Chinese prefrontal and temporal cortex correlate with genotypes of common single nucleotide polymorphisms (SNPs) located in the RAI1 5′-upstream region. Using genotype imputation, “R²-Δ²” analysis, and data from the RegulomeDB database, we identified SNPs rs4925102 and rs9907986 as possible regulatory variants, accounting for approximately 30–40% of the variance in RAI1 mRNA expression in both brain regions. Specifically, rs4925102 and rs9907986 are predicted to disrupt the binding of retinoic acid RXR-RAR receptors and the transcription factor DEAF1 (Deformed epidermal autoregulatory factor-1), respectively. Consistent with these predictions, we observed binding of RXRα and RARα to the predicted RAI1 target in chromatin immunoprecipitation assays. Retinoic acid is crucial for early development of the central neural system, and DEAF1 is associated with intellectual disability. The observation that a significant portion of RAI1 mRNA expression is genetically controlled raises the possibility that common RAI1 5′-region regulatory variants contribute more generally to psychiatric disorders.

Allelome.PRO, a pipeline to define allele-specific genomic features from high-throughput sequencing data

Detecting allelic biases from high-throughput sequencing data requires an approach that maximises sensitivity while minimizing false positives. Here, we present Allelome.PRO, an automated user-friendly bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses of two genetically distinct mouse strains to detect allele-specific expression and chromatin modifications. Allelome.PRO extends approaches used in previous studies that exclusively analyzed imprinted expression to give a complete picture of the ‘allelome’ by automatically categorising the allelic expression of all genes in a given cell type into imprinted, strain-biased, biallelic or non-informative. Allelome.PRO offers increased sensitivity to analyze lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data. We used RNA-seq data from mouse embryonic fibroblasts from F1 reciprocal crosses to determine a biologically relevant allelic ratio cutoff, and define for the first time an entire allelome. Furthermore, we show that Allelome.PRO detects differential enrichment of H3K4me3 over promoters from ChIP-seq data validating the RNA-seq results. This approach can be easily extended to analyze histone marks of active enhancers, or transcription factor binding sites and therefore provides a powerful tool to identify candidate cis regulatory elements genome wide.

An intronic PICALM polymorphism, rs588076, is associated with allelic expression of a PICALM isoform

BACKGROUND

Although genome wide studies have associated single nucleotide polymorphisms (SNP)s near PICALM with Alzheimer's disease (AD), the mechanism underlying this association is unclear. PICALM is involved in clathrin-mediated endocytosis and modulates Aß clearance in vitro. Comparing allelic expression provides the means to detect cis-acting regulatory polymorphisms. Thus, we evaluated whether PICALM showed allele expression imbalance (AEI) and whether this imbalance was associated with the AD-associated polymorphism, rs3851179.

RESULTS

We measured PICALM allelic expression in 42 human brain samples by using next-generation sequencing. Overall, PICALM demonstrated equal allelic expression with no detectable influence by rs3851179. A single sample demonstrated robust global PICALM allelic expression imbalance (AEI), i.e., each of the measured isoforms showed AEI. Moreover, the PICALM isoform lacking exons 18 and 19 (D18-19 PICALM) showed significant AEI in a subset of individuals. Sequencing these individuals and subsequent genotyping revealed that rs588076, located in PICALM intron 17, was robustly associated with this imbalance in D18-19 PICALM allelic expression (p = 9.54 x 10-5). This polymorphism has been associated previously with systolic blood pressure response to calcium channel blocking agents. To evaluate whether this polymorphism was associated with AD, we genotyped 3269 individuals and found that rs588076 was modestly associated with AD. However, when both the primary AD SNP rs3851179 was added to the logistic regression model, only rs3851179 was significantly associated with AD.

CONCLUSIONS

PICALM expression shows no evidence of AEI associated with rs3851179. Robust global AEI was detected in one sample, suggesting the existence of a rare SNP that strongly modulates PICALM expression. AEI was detected for the D18-19 PICALM isoform, and rs588076 was associated with this AEI pattern. Conditional on rs3851179, rs588076 was not associated with AD risk, suggesting that D18-19 PICALM is not critical in AD. In summary, this analysis of PICALM allelic expression provides novel insights into the genetics of PICALM expression and AD risk.

Common GSAP promoter variant contributes to Alzheimer's disease liability

Toxic amyloid-β40-42 (Aβ40-42) peptide cleaved from Aβ protein precursor by β- and γ secretases plays a crucial role in the etiology of Alzheimer's disease (AD). Recently, Paul Greengard laboratory described a novel γ-secretase activating protein (gSAP) that specifically increases Aβ40-42 production without affecting the cleavage of another γ-secretase substrate, Notch. In this study, we show that expression of messenger RNA for GSAP, the gene that encodes the gSAP precursor protein, in human temporal cortex correlates with genotypes of 6 linked single-nucleotide polymorphisms (SNPs) located within the 5' region of GSAP in both Han Chinese and Caucasian populations. One of these SNPs, rs4727380, associates with AD diagnosis in a Han Chinese-based case-control study comprising 397 AD cases and 474 controls and in a Caucasian-based sample comprising 1906 cases and 1475 controls. As predicted, the high-expression allele of rs4727380 was identified as the AD risk allele in both samples. We also determined that rs4727380 correlates with AD diagnosis primarily among APOE4 noncarriers. To our knowledge, this is the first report providing genetic evidence linking GSAP to AD liability.

The schizophrenia/bipolar disorder candidate gene GNB1L is regulated in human temporal cortex by a cis-acting element located within the 3'-region

22q11.2 deletion syndrome (DS) is a complex developmental disorder with a high incidence of psychiatric illnesses, including schizophrenia and mood disorders. Recent studies have identified Guanine Nucleotide Binding Protein (G protein) Beta Polypeptide 1-Like (GNB1L), located within the 1.5 Mbp 22q11.2 DS critical region, as a candidate liability gene for schizophrenia and bipolar disorder. In this study, we used mRNA expression measurements in Han Chinese postmortem temporal cortex and linkage disequilibrium (LD) analysis to show that GNB1L is regulated by a cis-acting genetic variant within the 3'-region of the gene. Significantly, this variant is located within an LD block that contains all of the common SNPs previously shown to associate with schizophrenia and bipolar disorder in Han Chinese and Caucasian populations. Contrary to our expectations, re-analysis of previously published case-control study data in light of our mRNA expression results implies that the GNB1L high-expression allele is the risk allele for schizophrenia and bipolar disorder in the Han Chinese population.

Whole transcriptome RNA-Seq allelic expression in human brain

Background

Measuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.

Results

We find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.

Conclusions

These results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

Research progress in allele-specific expression and its regulatory mechanisms

Although the majority of genes are expressed equally from both alleles, some genes are differentially expressed. Organisms possess characteristics to preferentially express a particular allele under regulatory factors, which is termed allele-specific expression (ASE). It is one of the important genetic factors that lead to phenotypic variation and can be used to identify the variance of gene regulation factors. ASE indicates mechanisms such as DNA methylation, histone modifications, and non-coding RNAs function. Here, we review a broad survey of progress in ASE studies, and what this simple yet very effective approach can offer in functional genomics, and possible implications toward our better understanding of the underlying mechanisms of complex traits.

Allelic gene expression imbalance of bovine IGF2, LEP and CCL2 genes in liver, kidney and pituitary

Allelic expression imbalance (AEI) is an important genetic factor being the cause of differences in phenotypic traits that can be heritable. Studying AEI can be useful in searching for factors that modulate gene expression and help to understand molecular mechanisms underlying phenotypic changes. Although it was commonly recognized in many species and we know many genes show allelic expression imbalance, this phenomena was not studied on a larger scale in cattle. Using the pyrosequencing method we analyzed a set of 29 bovine genes in order to find those that have preferential allelic expression. The study was conducted in three tissues: liver, pituitary and kindey. Out of the studied group of genes 3 of them—LEP (leptin), IGF2 (insulin-like growth factor 2), CCL2 (chemokine C–C motif ligand 2) showed allelic expression imbalance.