Genome-wide assessment of imprinted expression in human cells

Long-read RNA sequencing reveals allele-specific N 6 -methyladenosine modifications

Long-read sequencing technology enables highly accurate detection of allele-specific RNA expression, providing insights into the effects of genetic variation on splicing and RNA abundance. Furthermore, the ability to directly sequence RNA promises the detection of RNA modifications in tandem with ascertaining the allelic origin of each molecule. Here, we leverage these advantages to determine allele-biased patterns of N 6 -methyladenosine (m6A) modifications in native mRNA. We utilized human and mouse cells with known genetic variants to assign allelic origin of each mRNA molecule combined with a supervised machine learning model to detect read-level m6A modification ratios. Our analyses revealed the importance of sequences adjacent to the DRACH- motif in determining m6A deposition, in addition to allelic differences that directly alter the motif. Moreover, we discovered allele-specific m6A modification (ASM) events with no genetic variants in close proximity to the differentially modified nucleotide, demonstrating the unique advantage of using long reads and surpassing the capabilities of antibody-based short-read approaches. This technological advancement promises to advance our understanding of the role of genetics in determining mRNA modifications.

Bayesian Estimation of Allele-Specific Expression in the Presence of Phasing Uncertainty

Motivation

Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors.

Results

We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways.

Availability and Implementation

The software is freely available from https://github.com/x811zou/BEASTIE . BEASTIE is available as Python source code and as a Docker image.

Supplementary information

Additional information is available online.

Monoallelically expressed noncoding RNAs form nucleolar territories on NOR-containing chromosomes and regulate rRNA expression

Out of the several hundred copies of rRNA genes arranged in the nucleolar organizing regions (NOR) of the five human acrocentric chromosomes, ~50% remain transcriptionally inactive. NOR-associated sequences and epigenetic modifications contribute to the differential expression of rRNAs. However, the mechanism(s) controlling the dosage of active versus inactive rRNA genes within each NOR in mammals is yet to be determined. We have discovered a family of ncRNAs, SNULs (Single NUcleolus Localized RNA), which form constrained sub-nucleolar territories on individual NORs and influence rRNA expression. Individual members of the SNULs monoallelically associate with specific NOR-containing chromosomes. SNULs share sequence similarity to pre-rRNA and localize in the sub-nucleolar compartment with pre-rRNA. Finally, SNULs control rRNA expression by influencing pre-rRNA sorting to the DFC compartment and pre-rRNA processing. Our study discovered a novel class of ncRNAs influencing rRNA expression by forming constrained nucleolar territories on individual NORs.

Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects

Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.

Direct haplotype-resolved 5-base HiFi sequencing for genome-wide profiling of hypermethylation outliers in a rare disease cohort

Long-read HiFi genome sequencing allows for accurate detection and direct phasing of single nucleotide variants, indels, and structural variants. Recent algorithmic development enables simultaneous detection of CpG methylation for analysis of regulatory element activity directly in HiFi reads. We present a comprehensive haplotype resolved 5-base HiFi genome sequencing dataset from a rare disease cohort of 276 samples in 152 families to identify rare (~0.5%) hypermethylation events. We find that 80% of these events are allele-specific and predicted to cause loss of regulatory element activity. We demonstrate heritability of extreme hypermethylation including rare cis variants associated with short (~200 bp) and large hypermethylation events (>1 kb), respectively. We identify repeat expansions in proximal promoters predicting allelic gene silencing via hypermethylation and demonstrate allelic transcriptional events downstream. On average 30-40 rare hypermethylation tiles overlap rare disease genes per patient, providing indications for variation prioritization including a previously undiagnosed pathogenic allele in DIP2B causing global developmental delay. We propose that use of HiFi genome sequencing in unsolved rare disease cases will allow detection of unconventional diseases alleles due to loss of regulatory element activity.

Inference of putative cell-type-specific imprinted regulatory elements and genes during human neuronal differentiation

Genomic imprinting results in gene expression bias caused by parental chromosome of origin and occurs in genes with important roles during human brain development. However, the cell-type and temporal specificity of imprinting during human neurogenesis is generally unknown. By detecting within-donor allelic biases in chromatin accessibility and gene expression that are unrelated to cross-donor genotype, we inferred imprinting in both primary human neural progenitor cells and their differentiated neuronal progeny from up to 85 donors. We identified 43/20 putatively imprinted regulatory elements (IREs) in neurons/progenitors, and 133/79 putatively imprinted genes in neurons/progenitors. Although 10 IREs and 42 genes were shared between neurons and progenitors, most putative imprinting was only detected within specific cell types. In addition to well-known imprinted genes and their promoters, we inferred novel putative IREs and imprinted genes. Consistent with both DNA methylation-based and H3K27me3-based regulation of imprinted expression, some putative IREs also overlapped with differentially methylated or histone-marked regions. Finally, we identified a progenitor-specific putatively imprinted gene overlapping with copy number variation that is associated with uniparental disomy-like phenotypes. Our results can therefore be useful in interpreting the function of variants identified in future parent-of-origin association studies.

Increased copy number of imprinted genes in the chromosomal region 20q11-q13.32 is associated with resistance to antitumor agents in cancer cell lines

BACKGROUND

Parent of origin-specific allelic expression of imprinted genes is epigenetically controlled. In cancer, imprinted genes undergo both genomic and epigenomic alterations, including frequent copy number changes. We investigated whether copy number loss or gain of imprinted genes in cancer cell lines is associated with response to chemotherapy treatment.

RESULTS

We analyzed 198 human imprinted genes including protein-coding genes and noncoding RNA genes using data from tumor cell lines from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We examined whether copy number of the imprinted genes in 35 different genome locations was associated with response to cancer drug treatment. We also analyzed associations of pretreatment expression and DNA methylation of imprinted genes with drug response. Higher copy number of BLCAP, GNAS, NNAT, GNAS-AS1, HM13, MIR296, MIR298, and PSIMCT-1 in the chromosomal region 20q11-q13.32 was associated with resistance to multiple antitumor agents. Increased expression of BLCAP and HM13 was also associated with drug resistance, whereas higher methylation of gene regions of BLCAP, NNAT, SGK2, and GNAS was associated with drug sensitivity. While expression and methylation of imprinted genes in several other chromosomal regions was also associated with drug response and many imprinted genes in different chromosomal locations showed a considerable copy number variation, only imprinted genes at 20q11-q13.32 had a consistent association of their copy number with drug response. Copy number values among the imprinted genes in the 20q11-q13.32 region were strongly correlated. They were also correlated with the copy number of cancer-related non-imprinted genes MYBL2, AURKA, and ZNF217 in that chromosomal region. Expression of genes at 20q11-q13.32 was associated with ex vivo drug response in primary tumor samples from the Beat AML 1.0 acute myeloid leukemia patient cohort. Association of the increased copy number of the 20q11-q13.32 region with drug resistance may be complex and could involve multiple genes.

CONCLUSIONS

Copy number of imprinted and non-imprinted genes in the chromosomal region 20q11-q13.32 was associated with cancer drug resistance. The genes in this chromosomal region may have a modulating effect on tumor response to chemotherapy.

Epigenetics in the etiology and management of infertility

BACKGROUND

Epigenetic disruptions have been implicated in some cases of infertility and can serve as therapeutic targets. However, the involvement of epigenetics in infertility has not received adequate attention.

AIM

This study aimed to determine the epigenetic basis of infertility in order to enhance public knowledge.

METHODS

Relevant articles on the subject were collected from PubMed, RCA, Google Scholar, SpringerLink, and Scopus. The articles were pooled together and duplicates were removed using Endnote software.

RESULTS

Available information shows that epigenetic mechanisms, mainly DNA methylation, histone modification, and microRNA interference are necessary for normal gametogenesis and embryogenesis. As a result, epigenetic disruptions in genes that control gametogenesis and embryogenesis, such as DDX3X, ADH4, AZF, PLAG1, D1RAS3, CYGB, MEST, JMJD1A, KCNQ1, IGF2, H19, and MTHFR may result in infertility. Aberrant DNA methylation during genomic imprinting and parental epigenetic mark erasures, in particular, may affect the DNA epigenomes of sperm and oocytes, resulting in reproductive abnormalities. Histone epigenetic dysregulation during oocyte development and histone-protamine replacement in the sperm may also cause reproductive abnormalities. Furthermore, overexpression or repression of certain microRNAs embedded in the ovary, testis, embryo, as well as granulosa cells and oocytes may impair reproduction. Male infertility is characterized by spermatogenesis failure, which includes oligozoospermia, asthenozoospermia, and teratozoospermia, while female infertility is characterized by polycystic ovary syndrome. Some epigenetic modifications can be reversed by deactivating the regulatory enzymes, implying that epigenetic reprogramming could help treat infertility in some cases. For some disorders, epigenetic drugs are available, but none have been formulated for infertility.

CONCLUSION

Some cases of infertility have an epigenetic etiology and can be treated by reversing the same epigenetic mechanism that caused it. As a result, medical practitioners are urged to come up with epigenetic treatments for infertility that have an epigenetic cause.

Genetic, epigenetic, and environmental mechanisms govern allele-specific gene expression

Allele-specific expression (ASE) is a phenomenon in which one allele is preferentially expressed over the other. Genetic and epigenetic factors cause ASE by altering the final composition of a gene's product, leading to expression imbalances that can have functional consequences on phenotypes. Environmental signals also impact allele-specific expression, but how they contribute to this cross talk remains understudied. Here, we explored how genotype, parent-of-origin, tissue, sex, and dietary fat simultaneously influence ASE biases. Male and female mice from a F₁ reciprocal cross of the LG/J and SM/J strains were fed a high or low fat diet. We harnessed strain-specific variants to distinguish between two ASE classes: parent-of-origin-dependent (unequal expression based on parental origin) and sequence-dependent (unequal expression based on nucleotide identity). We present a comprehensive map of ASE patterns in 2853 genes across three tissues and nine environmental contexts. We found that both ASE classes are highly dependent on tissue and environmental context. They vary across metabolically relevant tissues, between males and females, and in response to dietary fat. We also found 45 genes with inconsistent ASE biases that switched direction across tissues and/or environments. Finally, we integrated ASE and QTL data from published intercrosses of the LG/J and SM/J strains. Our ASE genes are often enriched in QTLs for metabolic and musculoskeletal traits, highlighting how this orthogonal approach can prioritize candidate genes. Together, our results provide novel insights into how genetic, epigenetic, and environmental mechanisms govern allele-specific expression, which is an essential step toward deciphering the genotype-to-phenotype map.

Loss of p57 Expression in Conceptions Other Than Complete Hydatidiform Mole: A Case Series With Emphasis on the Etiology, Genetics, and Clinical Significance

Combined p57 immunohistochemistry and DNA genotyping refines classification of products of conception specimens into specific types of hydatidiform moles and various nonmolar entities that can simulate them. p57 expression is highly correlated with genotyping and in practice can reliably be used to identify virtually all complete hydatidiform moles (CHM), but aberrant retained or lost p57 expression in rare CHMs and partial hydatidiform moles (PHM), as well as loss in some nonmolar abortuses, has been reported. Among a series of 2329 products of conceptions, we identified 10 cases for which loss of p57 expression was inconsistent with genotyping results (none purely androgenetic). They displayed a spectrum of generally mild abnormal villous morphology but lacked better developed features of CHMs/early CHMs, although some did suggest subtle forms of the latter. For 5 cases, genotyping (4 cases) and/or ancillary testing (1 case) determined a mechanism for the aberrant p57 results. These included 3 PHMs-2 diandric triploid and 1 triandric tetraploid-and 1 nonmolar specimen with loss of p57 expression attributable to partial or complete loss of the maternal copy of chromosome 11 and 1 nonmolar specimen with Beckwith-Wiedemann syndrome. For 5 cases, including 2 diandric triploid PHMs and 3 biparental nonmolar specimens, genotyping did not identify a mechanism, likely due to other genetic alterations which are below the resolution of or not targeted by genotyping. While overdiagnosis of a PHM as a CHM may cause less harm since appropriate follow-up with serum β-human chorionic gonadotropin levels would take place for both diagnoses, this could cause longer than necessary follow-up due to the expectation of a much greater risk of persistent gestational trophoblastic disease for CHM compared with PHM, which would be unfounded for the correct diagnosis of PHM. Overdiagnosis of a nonmolar abortus with loss of p57 expression as a CHM would lead to unnecessary follow-up and restriction on pregnancy attempts for patients with infertility. Genotyping is valuable for addressing discordance between p57 expression and morphology but cannot elucidate certain mechanisms of lost p57 expression. Future studies are warranted to determine whether chromosomal losses or gains, particularly involving imprinted genes such as p57, might play a role in modifying the risk of persistent gestational trophoblastic disease for PHMs and nonmolar conceptions that are not purely androgenetic but have some abnormal paternal imprinting of the type seen in CHMs.

Megabase-scale methylation phasing using nanopore long reads and NanoMethPhase

The ability of nanopore sequencing to simultaneously detect modified nucleotides while producing long reads makes it ideal for detecting and phasing allele-specific methylation. However, there is currently no complete software for detecting SNPs, phasing haplotypes, and mapping methylation to these from nanopore sequence data. Here, we present NanoMethPhase, a software tool to phase 5-methylcytosine from nanopore sequencing. We also present SNVoter, which can post-process nanopore SNV calls to improve accuracy in low coverage regions. Together, these tools can accurately detect allele-specific methylation genome-wide using nanopore sequence data with low coverage of about ten-fold redundancy.

Inducible uniparental chromosome disomy to probe genomic imprinting at single-cell level in brain and beyond

Genomic imprinting is an epigenetic mechanism that results in parental allele-specific expression of ~1% of all genes in mouse and human. Imprinted genes are key developmental regulators and play pivotal roles in many biological processes such as nutrient transfer from the mother to offspring and neuronal development. Imprinted genes are also involved in human disease, including neurodevelopmental disorders, and often occur in clusters that are regulated by a common imprint control region (ICR). In extra-embryonic tissues ICRs can act over large distances, with the largest surrounding Igf2r spanning over 10 million base-pairs. Besides classical imprinted expression that shows near exclusive maternal or paternal expression, widespread biased imprinted expression has been identified mainly in brain. In this review we discuss recent developments mapping cell type specific imprinted expression in extra-embryonic tissues and neocortex in the mouse. We highlight the advantages of using an inducible uniparental chromosome disomy (UPD) system to generate cells carrying either two maternal or two paternal copies of a specific chromosome to analyze the functional consequences of genomic imprinting. Mosaic Analysis with Double Markers (MADM) allows fluorescent labeling and concomitant induction of UPD sparsely in specific cell types, and thus to over-express or suppress all imprinted genes on that chromosome. To illustrate the utility of this technique, we explain how MADM-induced UPD revealed new insights about the function of the well-studied Cdkn1c imprinted gene, and how MADM-induced UPDs led to identification of highly cell type specific phenotypes related to perturbed imprinted expression in the mouse neocortex. Finally, we give an outlook on how MADM could be used to probe cell type specific imprinted expression in other tissues in mouse, particularly in extra-embryonic tissues.

Parent-of-origin-specific allelic expression in the human placenta is limited to established imprinted loci and it is stably maintained across pregnancy

Background

Genomic imprinting, mediated by parent-of-origin-specific epigenetic silencing, adjusts the gene expression dosage in mammals. We aimed to clarify parental allelic expression in the human placenta for 396 claimed candidate imprinted genes and to assess the evidence for the proposed enrichment of imprinted expression in the placenta. The study utilized RNA-Seq-based transcriptome and genotyping data from 54 parental-placental samples representing the three trimesters of gestation, and term cases of preeclampsia, gestational diabetes, and fetal growth disturbances.

Results

Almost half of the targeted genes (n = 179; 45%) were either not transcribed or showed limited expression in the human placenta. After filtering for the presence of common exonic SNPs, adequacy of sequencing reads and informative families, 91 genes were retained (43 loci form Geneimprint database; 48 recently proposed genes). Only 11/91 genes (12.1%) showed confident signals of imprinting (binomial test, Bonferroni corrected P < 0.05; > 90% transcripts originating from one parental allele). The confirmed imprinted genes exhibit enriched placental expression (PHLDA2, H19, IGF2, MEST, ZFAT, PLAGL1, AIM1) or are transcribed additionally only in the adrenal gland (MEG3, RTL1, PEG10, DLK1). Parental monoallelic expression showed extreme stability across gestation and in term pregnancy complications. A distinct group of additional 14 genes exhibited a statistically significant bias in parental allelic proportions defined as having 65–90% of reads from one parental allele (e.g., KLHDC10, NLRP2, RHOBTB3, DNMT1). Molecular mechanisms behind biased parental expression are still to be clarified. However, 66 of 91 (72.5%) analyzed candidate imprinted genes showed no signals of deviation from biallelic expression.

Conclusions

As placental tissue is not included in The Genotype-Tissue Expression (GTEx) project, the study contributed to fill the gap in the knowledge concerning parental allelic expression. A catalog of parental allelic proportions and gene expression of analyzed loci across human gestation and in term pregnancy complications is provided as additional files. The study outcome suggested that true imprinting in the human placenta is restricted to well-characterized loci. High expression of imprinted genes during mid-pregnancy supports their critical role in developmental programming. Consistent with the data on other GTEx tissues, the number of human imprinted genes appears to be overestimated.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0692-3) contains supplementary material, which is available to authorized users.

RNA-Seq in 296 phased trios provides a high-resolution map of genomic imprinting

Background

Identification of imprinted genes, demonstrating a consistent preference towards the paternal or maternal allelic expression, is important for the understanding of gene expression regulation during embryonic development and of the molecular basis of developmental disorders with a parent-of-origin effect. Combining allelic analysis of RNA-Seq data with phased genotypes in family trios provides a powerful method to detect parent-of-origin biases in gene expression.

Results

We report findings in 296 family trios from two large studies: 165 lymphoblastoid cell lines from the 1000 Genomes Project and 131 blood samples from the Genome of the Netherlands (GoNL) participants. Based on parental haplotypes, we identified > 2.8 million transcribed heterozygous SNVs phased for parental origin and developed a robust statistical framework for measuring allelic expression. We identified a total of 45 imprinted genes and one imprinted unannotated transcript, including multiple imprinted transcripts showing incomplete parental expression bias that was located adjacent to strongly imprinted genes. For example, PXDC1, a gene which lies adjacent to the paternally expressed gene FAM50B, shows a 2:1 paternal expression bias. Other imprinted genes had promoter regions that coincide with sites of parentally biased DNA methylation identified in the blood from uniparental disomy (UPD) samples, thus providing independent validation of our results. Using the stranded nature of the RNA-Seq data in lymphoblastoid cell lines, we identified multiple loci with overlapping sense/antisense transcripts, of which one is expressed paternally and the other maternally. Using a sliding window approach, we searched for imprinted expression across the entire genome, identifying a novel imprinted putative lncRNA in 13q21.2. Overall, we identified 7 transcripts showing parental bias in gene expression which were not reported in 4 other recent RNA-Seq studies of imprinting.

Conclusions

Our methods and data provide a robust and high-resolution map of imprinted gene expression in the human genome.

Electronic supplementary material

The online version of this article (10.1186/s12915-019-0674-0) contains supplementary material, which is available to authorized users.

Novel parent-of-origin-specific differentially methylated loci on chromosome 16

BACKGROUND

Congenital malformations associated with maternal uniparental disomy of chromosome 16, upd(16)mat, resemble those observed in newborns with the lethal developmental lung disease, alveolar capillary dysplasia with misalignment of pulmonary veins (ACDMPV). Interestingly, ACDMPV-causative deletions, involving FOXF1 or its lung-specific upstream enhancer at 16q24.1, arise almost exclusively on the maternally inherited chromosome 16. Given the phenotypic similarities between upd(16)mat and ACDMPV, together with parental allelic bias in ACDMPV, we hypothesized that there may be unknown imprinted loci mapping to chromosome 16 that become functionally unmasked by chromosomal structural variants.

RESULTS

To identify parent-of-origin biased DNA methylation, we performed high-resolution bisulfite sequencing of chromosome 16 on peripheral blood and cultured skin fibroblasts from individuals with maternal or paternal upd(16) as well as lung tissue from patients with ACDMPV-causative 16q24.1 deletions and a normal control. We identified 22 differentially methylated regions (DMRs) with ≥ 5 consecutive CpG methylation sites and varying tissue-specificity, including the known DMRs associated with the established imprinted gene ZNF597 and DMRs supporting maternal methylation of PRR25, thought to be paternally expressed in lymphoblastoid cells. Lastly, we found evidence of paternal methylation on 16q24.1 near LINC01082 mapping to the FOXF1 enhancer.

CONCLUSIONS

Using high-resolution bisulfite sequencing to evaluate DNA methylation across chromosome 16, we found evidence for novel candidate imprinted loci on chromosome 16 that would not be evident in array-based assays and could contribute to the birth defects observed in patients with upd(16)mat or in ACDMPV.

Lateral transfer of organophosphate degradation (opd) genes among soil bacteria: mode of transfer and contributions to organismal fitness

Genes encoding structurally independent phosphotriesterases (PTEs) are identified in soil bacteria. These pte genes, often identified on mobilizable and self-transmissible plasmids are organized as mobile genetic elements. Their dissemination through lateral gene transfer is evident due to the detection of identical organophosphate degradation genes among soil bacteria with little orno taxonomic relationship. Convergent evolution of PTEs provided selective advantages to the bacterial strain as they convert toxic phosphotriesters (PTs) into a source of phosphate. The residues of organophosphate (OP) compounds that accumulate in a soil are proposed to contribute to the evolution of PTEs through substrate-assisted gain-of-function. This review provides comprehensive information on lateral transfer of pte genes and critically examines proposed hypotheses on their evolution in the light of the short half-life of OPs in the environment. The review also proposes alternate factors that have possibly contributed to the evolution and lateral mobility of PTEs by taking into account their biology and analyses of pte genes in genomic and metagenomic databases.

A statistical method for joint estimation of cis-eQTLs and parent-of-origin effects under family trio design

RNA sequencing allows one to study allelic imbalance of gene expression, which may be due to genetic factors or genomic imprinting (i.e., higher expression of maternal or paternal allele). It is desirable to model both genetic and parent-of-origin effects simultaneously to avoid confounding and to improve the power to detect either effect. In studies of genetically tractable model organisms, separation of genetic and parent-of-origin effects can be achieved by studying reciprocal cross of two inbred strains. In contrast, this task is much more challenging in outbred populations such as humans. To address this challenge, we propose a new framework to combine experimental strategies and novel statistical methods. Specifically, we propose to study genetic and imprinting effects in family trios with RNA-seq data from the children and genotype data from both parents and children, and quantify genetic effects by cis-eQTLs. Towards this end, we have extended our method that studies the eQTLs of RNA-seq data (Sun, Biometrics 2012, 68(1): 1-11) to model both cis-eQTL and parent-of-origin effects, and evaluated its performance using extensive simulations. Since sample size may be limited in family trios, we have developed a data analysis pipeline that borrows information from external data of unrelated individuals for cis-eQTL mapping. We have also collected RNA-seq data from the children of 30 family trios, applied our method to analyze this dataset, and identified some previously reported imprinted genes as well as some new candidates of imprinted genes.

Genome-wide survey of parent-of-origin effects on DNA methylation identifies candidate imprinted loci in humans

Genomic imprinting is an epigenetic mechanism leading to parent-of-origin silencing of alleles. So far, the precise number of imprinted regions in humans is uncertain. In this study, we leveraged genome-wide DNA methylation in whole blood measured longitudinally at three time points (birth, childhood and adolescence) and genome-wide association studies (GWAS) data in 740 mother-child duos from the Avon Longitudinal Study of parents and children to identify candidate imprinted loci. We reasoned that cis-meQTLs at genomic regions that were imprinted would show strong evidence of parent-of-origin associations with DNA methylation, enabling the detection of imprinted regions. Using this approach, we identified genome-wide significant cis-meQTLs that exhibited parent-of-origin effects (POEs) at 82 loci, 34 novel and 48 regions previously implicated in imprinting (3.7-10<P < 10-300). Using an independent dataset from the Brisbane Systems Genetic Study, we replicated 76 out of the 82 identified loci. POEs were remarkably consistent across time points and were so strong at some loci that methylation levels enabled good discrimination of parental transmissions at these and surrounding genomic regions. The implication is that parental allelic transmissions could be modelled at many imprinted (and linked) loci in GWAS of unrelated individuals given a combination of genetic and methylation data. Novel regions showing parent of origin effects on methylation will require replication using a different technology and further functional experiments to confirm that such effects arise through a genomic imprinting mechanism.

Assessment of imprinting- and genetic variation-dependent monoallelic expression using reciprocal allele descendants between human family trios

Genomic imprinting is an important epigenetic process that silences one of the parentally-inherited alleles of a gene and thereby exhibits allelic-specific expression (ASE). Detection of human imprinting events is hampered by the infeasibility of the reciprocal mating system in humans and the removal of ASE events arising from non-imprinting factors. Here, we describe a pipeline with the pattern of reciprocal allele descendants (RADs) through genotyping and transcriptome sequencing data across independent parent-offspring trios to discriminate between varied types of ASE (e.g., imprinting, genetic variation-dependent ASE, and random monoallelic expression (RME)). We show that the vast majority of ASE events are due to sequence-dependent genetic variant, which are evolutionarily conserved and may themselves play a cis-regulatory role. Particularly, 74% of non-RAD ASE events, even though they exhibit ASE biases toward the same parentally-inherited allele across different individuals, are derived from genetic variation but not imprinting. We further show that the RME effect may affect the effectiveness of the population-based method for detecting imprinting events and our pipeline can help to distinguish between these two ASE types. Taken together, this study provides a good indicator for categorization of different types of ASE, opening up this widespread and complex mechanism for comprehensive characterization.

Population-genetic models of sex-limited genomic imprinting

Genomic imprinting is a form of epigenetic modification involving parent-of-origin-dependent gene expression, usually the inactivation of one gene copy in some tissues, at least, for some part of the diploid life cycle. Occurring at a number of loci in mammals and flowering plants, this mode of non-Mendelian expression can be viewed more generally as parentally-specific differential gene expression. The effects of natural selection on genetic variation at imprinted loci have previously been examined in a several population-genetic models. Here we expand the existing one-locus, two-allele population-genetic models of viability selection with genomic imprinting to include sex-limited imprinting, i.e., imprinted expression occurring only in one sex, and differential viability between the sexes. We first consider models of complete inactivation of either parental allele and these models are subsequently generalized to incorporate differential expression. Stable polymorphic equilibrium was possible without heterozygote advantage as observed in some prior models of imprinting in both sexes. In contrast to these latter models, in the sex-limited case it was critical whether the paternally inherited or maternally inherited allele was inactivated. The parental origin of inactivated alleles had a different impact on how the population responded to the different selection pressures between the sexes. Under the same fitness parameters, imprinting in the other sex altered the number of possible equilibrium states and their stability. When the parental origin of imprinted alleles and the sex in which they are inactive differ, an allele cannot be inactivated in consecutive generations. The system dynamics became more complex with more equilibrium points emerging. Our results show that selection can interact with epigenetic factors to maintain genetic variation in previously unanticipated ways.

Erasure and reestablishment of random allelic expression imbalance after epigenetic reprogramming

Clonal level random allelic expression imbalance and random monoallelic expression provides cellular heterogeneity within tissues by modulating allelic dosage. Although such expression patterns have been observed in multiple cell types, little is known about when in development these stochastic allelic choices are made. We examine allelic expression patterns in human neural progenitor cells before and after epigenetic reprogramming to induced pluripotency, observing that loci previously characterized by random allelic expression imbalance (0.63% of expressed genes) are generally reset to a biallelic state in induced pluripotent stem cells (iPSCs). We subsequently neuralized the iPSCs and profiled isolated clonal neural stem cells, observing that significant random allelic expression imbalance is reestablished at 0.65% of expressed genes, including novel loci not found to show allelic expression imbalance in the original parental neural progenitor cells. Allelic expression imbalance was associated with altered DNA methylation across promoter regulatory regions, with clones characterized by skewed allelic expression being hypermethylated compared to their biallelic sister clones. Our results suggest that random allelic expression imbalance is established during lineage commitment and is associated with increased DNA methylation at the gene promoter.

Loss of RNA expression and allele-specific expression associated with congenital heart disease

Congenital heart disease (CHD), a prevalent birth defect occurring in 1% of newborns, likely results from aberrant expression of cardiac developmental genes. Mutations in a variety of cardiac transcription factors, developmental signalling molecules and molecules that modify chromatin cause at least 20% of disease, but most CHD remains unexplained. We employ RNAseq analyses to assess allele-specific expression (ASE) and biallelic loss-of-expression (LOE) in 172 tissue samples from 144 surgically repaired CHD subjects. Here we show that only 5% of known imprinted genes with paternal allele silencing are monoallelic versus 56% with paternal allele expression-this cardiac-specific phenomenon seems unrelated to CHD. Further, compared with control subjects, CHD subjects have a significant burden of both LOE genes and ASE events associated with altered gene expression. These studies identify FGFBP2, LBH, RBFOX2, SGSM1 and ZBTB16 as candidate CHD genes because of significantly altered transcriptional expression.

DNA Methylation Profiling of Uniparental Disomy Subjects Provides a Map of Parental Epigenetic Bias in the Human Genome

Genomic imprinting is a mechanism in which gene expression varies depending on parental origin. Imprinting occurs through differential epigenetic marks on the two parental alleles, with most imprinted loci marked by the presence of differentially methylated regions (DMRs). To identify sites of parental epigenetic bias, here we have profiled DNA methylation patterns in a cohort of 57 individuals with uniparental disomy (UPD) for 19 different chromosomes, defining imprinted DMRs as sites where the maternal and paternal methylation levels diverge significantly from the biparental mean. Using this approach we identified 77 DMRs, including nearly all those described in previous studies, in addition to 34 DMRs not previously reported. These include a DMR at TUBGCP5 within the recurrent 15q11.2 microdeletion region, suggesting potential parent-of-origin effects associated with this genomic disorder. We also observed a modest parental bias in DNA methylation levels at every CpG analyzed across ∼1.9 Mb of the 15q11-q13 Prader-Willi/Angelman syndrome region, demonstrating that the influence of imprinting is not limited to individual regulatory elements such as CpG islands, but can extend across entire chromosomal domains. Using RNA-seq data, we detected signatures consistent with imprinted expression associated with nine novel DMRs. Finally, using a population sample of 4,004 blood methylomes, we define patterns of epigenetic variation at DMRs, identifying rare individuals with global gain or loss of methylation across multiple imprinted loci. Our data provide a detailed map of parental epigenetic bias in the human genome, providing insights into potential parent-of-origin effects.

Incorporating parent-of-origin effects in whole-genome prediction of complex traits

Background

Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits. Given that prediction of genetic merit or phenotypic performance is of interest in the study of complex traits, it is relevant to consider parent-of-origin effects in such predictions. We built a whole-genome prediction model that incorporates parent-of-origin effects by considering parental allele substitution effects of single nucleotide polymorphisms and gametic relationships derived from a pedigree (the POE model). We used this model to predict body mass index in a mouse population, a trait that is presumably affected by parent-of-origin effects, and also compared the prediction performance to that of a standard additive model that ignores parent-of-origin effects (the ADD model). We also used simulated data to assess the predictive performance of the POE model under various circumstances, in which parent-of-origin effects were generated by mimicking an imprinting mechanism.

Results

The POE model did not predict better than the ADD model in the real data analysis, probably due to overfitting, since the POE model had far more parameters than the ADD model. However, when applied to simulated data, the POE model outperformed the ADD model when the contribution of parent-of-origin effects to phenotypic variation increased. The superiority of the POE model over the ADD model was up to 8 % on predictive correlation and 5 % on predictive mean squared error.

Conclusions

The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted. Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence. Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

Tools and best practices for data processing in allelic expression analysis

Allelic expression analysis has become important for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. We analyze the properties of allelic expression read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting such errors, show that our quality control measures improve the detection of relevant allelic expression, and introduce tools for the high-throughput production of allelic expression data from RNA-sequencing data.

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

Background

Genomic imprinting is an epigenetic mechanism that can lead to differential gene expression depending on the parent-of-origin of a received allele. While most studies on imprinting address its underlying molecular mechanisms or attempt at discovering genomic regions that might be subject to imprinting, few have focused on the amount of phenotypic variation contributed by such epigenetic process. In this report, we give a brief review of a one-locus imprinting model in a quantitative genetics framework, and provide a decomposition of the genetic variance according to this model. Analytical deductions from the proposed imprinting model indicated a non-negligible contribution of imprinting to genetic variation of complex traits. Also, we performed a whole-genome scan analysis on mouse body mass index (BMI) aiming at revealing potential consequences when existing imprinting effects are ignored in genetic analysis.

Results

10,021 SNP markers were used to perform a whole-genome single marker regression on mouse BMI using an additive and an imprinting model. Markers significant for imprinting indicated that BMI is subject to imprinting. Marked variance changed from 1.218 ×10⁻⁴ to 1.842 ×10⁻⁴ when imprinting was considered in the analysis, implying that one third of marked variance would be lost if existing imprinting effects were not accounted for. When both marker and pedigree information were used, estimated heritability increased from 0.176 to 0.195 when imprinting was considered.

Conclusions

When a complex trait is subject to imprinting, using an additive model that ignores this phenomenon may result in an underestimate of additive variability, potentially leading to wrong inferences about the underlying genetic architecture of that trait. This could be a possible factor explaining part of the missing heritability commonly observed in genome-wide association studies (GWAS).

The landscape of genomic imprinting across diverse adult human tissues

Genomic imprinting is an important regulatory mechanism that silences one of the parental copies of a gene. To systematically characterize this phenomenon, we analyze tissue specificity of imprinting from allelic expression data in 1582 primary tissue samples from 178 individuals from the Genotype-Tissue Expression (GTEx) project. We characterize imprinting in 42 genes, including both novel and previously identified genes. Tissue specificity of imprinting is widespread, and gender-specific effects are revealed in a small number of genes in muscle with stronger imprinting in males. IGF2 shows maternal expression in the brain instead of the canonical paternal expression elsewhere. Imprinting appears to have only a subtle impact on tissue-specific expression levels, with genes lacking a systematic expression difference between tissues with imprinted and biallelic expression. In summary, our systematic characterization of imprinting in adult tissues highlights variation in imprinting between genes, individuals, and tissues.

Genomic imprinting: A missing piece of the Multiple Sclerosis puzzle?

Evidence for parent-of-origin effects in complex diseases such as Multiple Sclerosis (MS) strongly suggests a role for epigenetic mechanisms in their pathogenesis. In this review, we describe the importance of accounting for parent-of-origin when identifying new risk variants for complex diseases and discuss how genomic imprinting, one of the best-characterized epigenetic mechanisms causing parent-of-origin effects, may impact etiology of complex diseases. While the role of imprinted genes in growth and development is well established, the contribution and molecular mechanisms underlying the impact of genomic imprinting in immune functions and inflammatory diseases are still largely unknown. Here we discuss emerging roles of imprinted genes in the regulation of inflammatory responses with a particular focus on the Dlk1 cluster that has been implicated in etiology of experimental MS-like disease and Type 1 Diabetes. Moreover, we speculate on the potential wider impact of imprinting via the action of imprinted microRNAs, which are abundantly present in the Dlk1 locus and predicted to fine-tune important immune functions. Finally, we reflect on how unrelated imprinted genes or imprinted genes together with non-imprinted genes can interact in so-called imprinted gene networks (IGN) and suggest that IGNs could partly explain observed parent-of-origin effects in complex diseases. Unveiling the mechanisms of parent-of-origin effects is therefore likely to teach us not only about the etiology of complex diseases but also about the unknown roles of this fascinating phenomenon underlying uneven genetic contribution from our parents. This article is part of a Directed Issue entitled: Epigenetics dynamics in development and disease.

A functional synonymous coding variant in the IL1RN gene is associated with survival in septic shock

RATIONALE

Death from infection is a highly heritable trait, yet there are few genetic variants with known mechanism influencing survival during septic shock.

OBJECTIVES

We hypothesized that a synonymous coding variant in the IL-1 receptor antagonist gene (IL1RN), rs315952, previously associated with reduced risk for acute respiratory distress syndrome, would be functional and associate with improved survival in septic shock.

METHODS

We used a human endotoxin (LPS) model of evoked inflammatory stress to measure plasma IL-1 receptor antagonist (IL1RA) following low-dose Food and Drug Administration-grade LPS injection (1 ng/kg) in 294 human volunteers. RNA sequencing of adipose tissue pre- and post-LPS was used to test for allelic imbalance at rs315952. In the Vasopressin and Septic Shock Trial cohort, we performed a genetic association study for survival, mortality, and organ failure-free days.

MEASUREMENTS AND MAIN RESULTS

Adipose tissue displayed significant allelic imbalance favoring the rs315952C allele in subjects of European ancestry. Consistent with this, carriers of rs315952C had slightly higher plasma IL1RA at baseline (0.039) and higher evoked IL1RA post-LPS (0.011). In the Vasopressin and Septic Shock Trial cohort, rs315952C associated with improved survival (P = 0.028), decreased adjusted 90-day mortality (P = 0.044), and faster resolution of shock (P = 0.029).

CONCLUSIONS

In European ancestry subjects, the IL1RN variant rs315952C is preferentially transcribed and associated with increased evoked plasma IL1RA and with improved survival from septic shock. It may be that genetically determined IL1RA levels influence survival from septic shock.

Using next-generation RNA sequencing to identify imprinted genes

Genomic imprinting is manifested as differential allelic expression (DAE) depending on the parent-of-origin. The most direct way to identify imprinted genes is to directly score the DAE in a context where one can identify which parent transmitted each allele. Because many genes display DAE, simply scoring DAE in an individual is not sufficient to identify imprinted genes. In this paper, we outline many technical aspects of a scheme for identification of imprinted genes that makes use of RNA sequencing (RNA-seq) from tissues isolated from F1 offspring derived from the pair of reciprocal crosses. Ideally, the parental lines are from two inbred strains that are not closely related to each other. Aspects of tissue purity, RNA extraction, library preparation and bioinformatic inference of imprinting are all covered. These methods have already been applied in a number of organisms, and one of the most striking results is the evolutionary fluidity with which novel imprinted genes are gained and lost within genomes. The general methodology is also applicable to a wide range of other biological problems that require quantification of allele-specific expression using RNA-seq, such as cis-regulation of gene expression, X chromosome inactivation and random monoallelic expression.

Novel approach identifies SNPs in SLC2A10 and KCNK9 with evidence for parent-of-origin effect on body mass index

The phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies (of ∼4,000 trios). Two SNPs replicated: the paternal rs2471083-C allele (located near the imprinted KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P<0.0027) compared to the respective maternal alleles. Real-time PCR experiments of lymphoblastoid cell lines from the CEPH families showed that expression of both genes was dependent on parental origin of the SNPs alleles (P<0.01). Our scheme opens new opportunities to exploit GWAS data of unrelated individuals to identify POEs and demonstrates that they play an important role in adult obesity.

Interrogation of allelic chromatin states in human cells by high-density ChIP-genotyping

Allele-specific (AS) assessment of chromatin has the potential to elucidate specific cis-regulatory mechanisms, which are predicted to underlie the majority of the known genetic associations to complex disease. However, development of chromatin landscapes at allelic resolution has been challenging since sites of variable signal strength require substantial read depths not commonly applied in sequencing based approaches. In this study, we addressed this by performing parallel analyses of input DNA and chromatin immunoprecipitates (ChIP) on high-density Illumina genotyping arrays. Allele-specificity for the histone modifications H3K4me1, H3K4me3, H3K27ac, H3K27me3, and H3K36me3 was assessed using ChIP samples generated from 14 lymphoblast and 6 fibroblast cell lines. AS-ChIP SNPs were combined into domains and validated using high-confidence ChIP-seq sites. We observed characteristic patterns of allelic-imbalance for each histone-modification around allele-specifically expressed transcripts. Notably, we found H3K4me1 to be significantly anti-correlated with allelic expression (AE) at transcription start sites, indicating H3K4me1 allelic imbalance as a marker of AE. We also found that allelic chromatin domains exhibit population and cell-type specificity as well as heritability within trios. Finally, we observed that a subset of allelic chromatin domains is regulated by DNase I-sensitive quantitative trait loci and that these domains are significantly enriched for genome-wide association studies hits, with autoimmune disease associated SNPs specifically enriched in lymphoblasts. This study provides the first genome-wide maps of allelic-imbalance for five histone marks. Our results provide new insights into the role of chromatin in cis-regulation and highlight the need for high-depth sequencing in ChIP-seq studies along with the need to improve allele-specificity of ChIP-enrichment.

Allele-specific expression and eQTL analysis in mouse adipose tissue

Background

The simplest definition of cis-eQTLs versus trans, refers to genetic variants that affect expression in an allele specific manner, with implications on underlying mechanism. Yet, due to technical limitations of expression microarrays, the vast majority of eQTL studies performed in the last decade used a genomic distance based definition as a surrogate for cis, therefore exploring local rather than cis-eQTLs.

Results

In this study we use RNAseq to explore allele specific expression (ASE) in adipose tissue of male and female F1 mice, produced from reciprocal crosses of C57BL/6J and DBA/2J strains. Comparison of the identified cis-eQTLs, to local-eQTLs, that were obtained from adipose tissue expression in two previous population based studies in our laboratory, yields poor overlap between the two mapping approaches, while both local-eQTL studies show highly concordant results. Specifically, local-eQTL studies show ~60% overlap between themselves, while only 15-20% of local-eQTLs are identified as cis by ASE, and less than 50% of ASE genes are recovered in local-eQTL studies. Utilizing recently published ENCODE data, we also find that ASE genes show significant bias for SNPs prevalence in DNase I hypersensitive sites that is ASE direction specific.

Conclusions

We suggest a new approach to analysis of allele specific expression that is more sensitive and accurate than the commonly used fisher or chi-square statistics. Our analysis indicates that technical differences between the cis and local-eQTL approaches, such as differences in genomic background or sex specificity, account for relatively small fraction of the discrepancy. Therefore, we suggest that the differences between two eQTL mapping approaches may facilitate sorting of SNP-eQTL interactions into true cis and trans, and that a considerable portion of local-eQTL may actually represent trans interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-471) contains supplementary material, which is available to authorized users.

Analysis of expressed SNPs identifies variable extents of expression from the human inactive X chromosome

BACKGROUND

X-chromosome inactivation (XCI) results in the silencing of most genes on one X chromosome, yielding mono-allelic expression in individual cells. However, random XCI results in expression of both alleles in most females. Allelic imbalances have been used genome-wide to detect mono-allelically expressed genes. Analysis of X-linked allelic imbalance in females with skewed XCI offers the opportunity to identify genes that escape XCI with bi-allelic expression in contrast to those with mono-allelic expression and which are therefore subject to XCI.

RESULTS

We determine XCI status for 409 genes, all of which have at least five informative females in our dataset. The majority of genes are subject to XCI and genes that escape from XCI show a continuum of expression from the inactive X. Inactive X expression corresponds to differences in the level of histone modification detected by allelic imbalance after chromatin immunoprecipitation. Differences in XCI between populations and between cell lines derived from different tissues are observed.

CONCLUSIONS

We demonstrate that allelic imbalance can be used to determine an inactivation status for X-linked genes, even without completely non-random XCI. There is a range of expression from the inactive X. Genes escaping XCI, including those that do so in only a subset of females, cluster together, demonstrating that XCI and location on the X chromosome are related. In addition to revealing mechanisms involved in cis-gene regulation, determining which genes escape XCI can expand our understanding of the contributions of X-linked genes to sexual dimorphism.

DNMT1 and AIM1 Imprinting in human placenta revealed through a genome-wide screen for allele-specific DNA methylation

BACKGROUND

Genomic imprinting is an epigenetically regulated process wherein genes are expressed in a parent-of-origin specific manner. Many imprinted genes were initially identified in mice; some of these were subsequently shown not to be imprinted in humans. Such discrepancy reflects developmental, morphological and physiological differences between mouse and human tissues. This is particularly relevant for the placenta. Study of genomic imprinting thus needs to be carried out in a species and developmental stage-specific manner. We describe here a new strategy to study allele-specific DNA methylation in the human placenta for the discovery of novel imprinted genes.

RESULTS

Using this methodology, we confirmed 16 differentially methylated regions (DMRs) associated with known imprinted genes. We chose 28 genomic regions for further testing and identified two imprinted genes (DNMT1 and AIM1). Both genes showed maternal allele-specific methylation and paternal allele-specific transcription. Imprinted expression for AIM1 was conserved in the cynomolgus macaque placenta, but not in other macaque tissues or in the mouse.

CONCLUSIONS

Our study indicates that while there are many genomic regions with allele-specific methylation in tissues like the placenta, only a small sub-set of them are associated with allele-specific transcription, suggesting alternative functions for such genomic regions. Nonetheless, novel tissue-specific imprinted genes remain to be discovered in humans. Their identification may help us better understand embryonic and fetal development.

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.

Genomic imprinting and parent-of-origin effects on complex traits

Parent-of-origin effects occur when the phenotypic effect of an allele depends on whether it is inherited from the mother or the father. Several phenomena can cause parent-of-origin effects, but the best characterized is parent-of-origin-dependent gene expression associated with genomic imprinting. The development of new mapping approaches applied to the growing abundance of genomic data has demonstrated that imprinted genes can be important contributors to complex trait variation. Therefore, to understand the genetic architecture and evolution of complex traits, including complex diseases and traits of agricultural importance, it is crucial to account for these parent-of-origin effects. Here, we discuss patterns of phenotypic variation associated with imprinting, evidence supporting its role in complex trait variation and approaches for identifying its molecular signatures.

Stochastic choice of allelic expression in human neural stem cells

Monoallelic gene expression, such as genomic imprinting, is well described. Less well-characterized are genes undergoing stochastic monoallelic expression (MA), where specific clones of cells express just one allele at a given locus. We performed genome-wide allelic expression assessment of human clonal neural stem cells derived from cerebral cortex, striatum, and spinal cord, each with differing genotypes. We assayed three separate clonal lines from each donor, distinguishing stochastic MA from genotypic effects. Roughly 2% of genes showed evidence for autosomal MA, and in about half of these, allelic expression was stochastic between different clones. Many of these loci were known neurodevelopmental genes, such as OTX2 and OLIG2. Monoallelic genes also showed increased levels of DNA methylation compared to hypomethylated biallelic loci. Identified monoallelic gene loci showed altered chromatin signatures in fetal brain, suggesting an in vivo correlate of this phenomenon. We conclude that stochastic allelic expression is prevalent in neural stem cells, providing clonal diversity to developing tissues such as the human brain.

Genotype-based test in mapping cis-regulatory variants from allele-specific expression data

Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome-wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript.

Allele-specific expression analysis methods for high-density SNP microarray data

MOTIVATION

In the past decade, a number of technologies to quantify allele-specific expression (ASE) in a genome-wide manner have become available to researchers. We investigate the application of single-nucleotide polymorphism (SNP) microarrays to this task, exploring data obtained from both cell lines and primary tissue for which both RNA and DNA profiles are available.

RESULTS

We analyze data from two experiments that make use of high-density Illumina Infinium II genotyping arrays to measure ASE. We first preprocess each data set, which involves removal of outlier samples, careful normalization and a two-step filtering procedure to remove SNPs that show no evidence of expression in the samples being analyzed and calls that are clear genotyping errors. We then compare three different tests for detecting ASE, one of which has been previously published and two novel approaches. These tests vary at the level at which they operate (per SNP per individual or per SNP) and in the input data they require. Using SNPs from imprinted genes as true positives for ASE, we observe varying sensitivity for the different testing procedures that improves with increasing sample size. Methods that rely on RNA signal alone were found to perform best across a range of metrics. The top ranked SNPs recovered by all methods appear to be reasonable candidates for ASE.

AVAILABILITY AND IMPLEMENTATION

Analysis was carried out in R (http://www.R-project.org/) using existing functions.

Autosomal monoallelic expression in the mouse

BACKGROUND

Random monoallelic expression defines an unusual class of genes displaying random choice for expression between the maternal and paternal alleles. Once established, the allele-specific expression pattern is stably maintained and mitotically inherited. Examples of random monoallelic genes include those found on the X-chromosome and a subset of autosomal genes, which have been most extensively studied in humans. Here, we report a genome-wide analysis of random monoallelic expression in the mouse. We used high density mouse genome polymorphism mapping arrays to assess allele-specific expression in clonal cell lines derived from heterozygous mouse strains.

RESULTS

Over 1,300 autosomal genes were assessed for allele-specific expression, and greater than 10% of them showed random monoallelic expression. When comparing mouse and human, the number of autosomal orthologs demonstrating random monoallelic expression in both organisms was greater than would be expected by chance. Random monoallelic expression on the mouse autosomes is broadly similar to that in human cells: it is widespread throughout the genome, lacks chromosome-wide coordination, and varies between cell types. However, for some mouse genes, there appears to be skewing, in some ways resembling skewed X-inactivation, wherein one allele is more frequently active.

CONCLUSIONS

These data suggest that autosomal random monoallelic expression was present at least as far back as the last common ancestor of rodents and primates. Random monoallelic expression can lead to phenotypic variation beyond the phenotypic variation dictated by genotypic variation. Thus, it is important to take into account random monoallelic expression when examining genotype-phenotype correlation.

Identification of imprinted loci by transcriptome sequencing

Enabled by high-throughput technologies that are capable of generating millions of sequencing reads, transcriptome sequencing is emerging as an important approach for mapping allelic imbalance (AI), where transcription is biased toward one allele in a diploid system. AI is identified by counting sequencing reads that map to genomic regions containing heterozygous SNPs, where the base identity of the SNP is used to distinguish allelic origin. Genomic imprinting is a special case of AI where bias is toward parental sex and can be identified by transcriptome sequencing of systems that represent reciprocally inherited loci. The focus of this protocol is on experimental design, analysis, and interpretation of genomic imprint discovery using whole transcriptome sequencing.

Folate and DNA methylation: a review of molecular mechanisms and the evidence for folate's role

DNA methylation is an epigenetic modification critical to normal genome regulation and development. The vitamin folate is a key source of the one carbon group used to methylate DNA. Because normal mammalian development is dependent on DNA methylation, there is enormous interest in assessing the potential for changes in folate intake to modulate DNA methylation both as a biomarker for folate status and as a mechanistic link to developmental disorders and chronic diseases including cancer. This review highlights the role of DNA methylation in normal genome function, how it can be altered, and the evidence of the role of folate/folic acid in these processes.

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

BACKGROUND

Common genetic variants that regulate gene expression are widely suspected to contribute to the etiology and phenotypic variability of complex diseases. Although high-throughput, microarray-based assays have been developed to measure differences in mRNA expression among independent samples, these assays often lack the sensitivity to detect rare mRNAs and the reproducibility to quantify small changes in mRNA expression. By contrast, PCR-based allelic expression imbalance (AEI) assays, which use a "marker" single nucleotide polymorphism (mSNP) in the mRNA to distinguish expression from pairs of genetic alleles in individual samples, have high sensitivity and accuracy, allowing differences in mRNA expression greater than 1.2-fold to be quantified with high reproducibility. In this paper, we describe the use of an efficient PCR/next-generation DNA sequencing-based assay to analyze allele-specific differences in mRNA expression for candidate neuropsychiatric disorder genes in human brain.

RESULTS

Using our assay, we successfully analyzed AEI for 70 candidate neuropsychiatric disorder genes in 52 independent human brain samples. Among these genes, 62/70 (89%) showed AEI ratios greater than 1 ± 0.2 in at least one sample and 8/70 (11%) showed no AEI. Arranging log2AEI ratios in increasing order from negative-to-positive values revealed highly reproducible distributions of log2AEI ratios that are distinct for each gene/marker SNP combination. Mathematical modeling suggests that these log2AEI distributions can provide important clues concerning the number, location and contributions of cis-acting regulatory variants to mRNA expression.

CONCLUSIONS

We have developed a highly sensitive and reproducible method for quantifying AEI of mRNA expressed in human brain. Importantly, this assay allowed quantification of differential mRNA expression for many candidate disease genes entirely missed in previously published microarray-based studies of mRNA expression in human brain. Given the ability of next-generation sequencing technology to generate large numbers of independent sequencing reads, our method should be suitable for analyzing from 100- to 200-candidate genes in 100 samples in a single experiment. We believe that this is the appropriate scale for investigating variation in mRNA expression for defined sets candidate disorder genes, allowing, for example, comprehensive coverage of genes that function within biological pathways implicated in specific disorders. The combination of AEI measurements and mathematical modeling described in this study can assist in identifying SNPs that correlate with mRNA expression. Alleles of these SNPs (individually or as sets) that accurately predict high- or low-mRNA expression should be useful as markers in genetic association studies aimed at linking candidate genes to specific neuropsychiatric disorders.

The role of imprinted genes in humans

Detailed comprehensive molecular analysis using families and multiple matched tissues is essential to determine whether imprinted genes have a functional role in humans.