Replicate sequencing libraries are important for quantification of allelic imbalance

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

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QuASAR: Quantitative Allele Specific Analysis of Reads

10.1101/007492 ◽

2014 ◽

Author(s):

Chris Harvey ◽

Gregory A Moyebrailean ◽

Omar Davis ◽

Xiaoquan Wen ◽

Francesca Luca ◽

...

Keyword(s):

Gene Expression ◽

Single Individual ◽

Rna Seq ◽

Specific Expression ◽

Model Base ◽

Specific Analysis ◽

Genotype Information ◽

Contrast Analysis ◽

Allele Specific ◽

Over Dispersion

Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression, enabling a better understanding of the functional role of non-coding sequences. However, eQTL studies are costly, requiring large sample sizes and genome-wide genotyping of each sample. In contrast, analysis of allele specific expression (ASE) is becoming a popular approach to detect the effect of genetic variation on gene expression, even within a single individual. This is typically achieved by counting the number of RNA-seq reads matching each allele at heterozygous sites and testing the null hypothesis of a 1:1 allelic ratio. In principle, when genotype information is not readily available it could be inferred from the RNA-seq reads directly. However, there are currently no existing methods that jointly infer genotypes and conduct ASE inference, while considering uncertainty in the genotype calls. We present QuASAR, Quantitative Allele Specific Analysis of Reads, a novel statistical learning method for jointly detecting heterozygous genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high quality genotypes are available. Results for an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available.

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Inferring the genetic architecture of expression variation from replicated high throughput allele-specific expression experiments

10.1101/699074 ◽

2019 ◽

Author(s):

Xinwen Zhang ◽

J.J. Emerson

Keyword(s):

False Positive ◽

Statistical Significance ◽

False Positive Rate ◽

Regulatory Sequences ◽

Binomial Model ◽

Specific Expression ◽

Allele Specific Expression ◽

Expression Variation ◽

Positive Rate ◽

Allele Specific

AbstractGene expression variation between alleles in a diploid cell is mediated by variation in cis regulatory sequences, which usually refers to the differences in DNA sequence between two alleles near the gene of interest. Expression differences caused by cis variation has been estimated by the ratio of the expression level of the two alleles under a binomial model. However, the binomial model underestimates the variance among replicated experiments resulting in the exaggerated statistical significance of estimated cis effects and thus many false discoveries of cis-affected genes. Here we describe a beta-binomial model that estimates the cis-effect for each gene while permitting overdispersion of variance among replicates. We demonstrated with simulated null data (data without true cis-effect) that the new model fits the true distribution better, resulting in approximately 5% false positive rate under 5% significance level in all null datasets, considerably better than the 6%-40% false positive rate of the binomial model. Additional replicates increase the performance of the beta-binomial model but not of the binomial model. We also collected new allele-specific expression data from an experiment comprised of 20 replicates of a yeast hybrid (YPS128/RM11-1a). We eliminated the mapping bias problem with de novo assemblies of the two parental genomes. By applying the beta-binomial model to this dataset, we found that cis effects are ubiquitous, affecting around 70% of genes. However, most of these changes are small in magnitude. The high number of replicates enabled us a better approximation of cis landscape within species and also provides a resource for future exploration for better models.

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Assessment of kinship detection using RNA-seq data

Nucleic Acids Research ◽

10.1093/nar/gkz776 ◽

2019 ◽

Vol 47 (21) ◽

pp. e136-e136

Author(s):

Natalia Blay ◽

Eduard Casas ◽

Iván Galván-Femenía ◽

Jan Graffelman ◽

Rafael de Cid ◽

...

Keyword(s):

Allelic Imbalance ◽

Population Based ◽

Rna Seq ◽

Pairwise Identity ◽

Specific Expression ◽

Whole Exome ◽

High Quality Snps ◽

Allele Specific ◽

Related Individuals ◽

Family Based

Abstract Analysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Prediction of kinship from RNA-seq data would be useful for confirming the expected relationships in family based studies and for highlighting samples from related individuals in case-control or population based studies. Currently, reconstruction of pedigrees is largely based on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression. In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity by descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth.

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BaseQTL: a Bayesian method to detect eQTLs from RNA-seq data with or without genotypes

10.1101/2020.07.16.203851 ◽

2020 ◽

Author(s):

Elena Vigorito ◽

Wei-Yu Lin ◽

Colin Starr ◽

Paul DW Kirk ◽

Simon R White ◽

...

Keyword(s):

Gene Regulation ◽

Quantitative Trait ◽

Bayesian Method ◽

Specific Effect ◽

Error Rates ◽

Rna Seq ◽

Sequencing Data ◽

Specific Expression ◽

Expression Studies ◽

Allele Specific

AbstractAvailable methods to detect molecular quantitative trait loci (QTL) require study individuals to be genotyped. Here, we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads even when no genotypes are available. When used with genotypes, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100kB-window that could be tested, contained 26% of QTL variants detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific effect for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.

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Unexpected variability of allelic imbalance estimates from RNA sequencing

10.1101/2020.02.18.948323 ◽

2020 ◽

Cited By ~ 1

Author(s):

Asia Mendelevich ◽

Svetlana Vinogradova ◽

Saumya Gupta ◽

Andrey A. Mironov ◽

Shamil Sunyaev ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Allelic Imbalance ◽

Experimental Methods ◽

Specific Gene ◽

Differential Analysis ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific ◽

Noise Models

RNA sequencing and other experimental methods that produce large amounts of data are increasingly dominant in molecular biology. However, the noise properties of these techniques have not been fully understood. We assessed the reproducibility of allele-specific expression measurements by conducting replicate sequencing experiments from the same RNA sample. Surprisingly, variation in the estimates of allelic imbalance (AI) between technical replicates was up to 7-fold higher than expected from commonly applied noise models. We show that AI overdispersion varies substantially between replicates and between experimental series, appears to arise during the construction of sequencing libraries, and can be measured by comparing technical replicates. We demonstrate that compensation for AI overdispersion greatly reduces technical variation and enables reliable differential analysis of allele-specific expression across samples and across experiments. Conversely, not taking AI overdispersion into account can lead to a substantial number of false positives in analysis of allele-specific gene expression

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Assessment of kinship detection using RNA-seq data

10.1101/546937 ◽

2019 ◽

Author(s):

Natalia Blay ◽

Eduard Casas ◽

Iván Galván-Femenía ◽

Jan Graffelman ◽

Rafael de Cid ◽

...

Keyword(s):

Allelic Imbalance ◽

Rna Seq ◽

Data Quality Control ◽

Identity By Descent ◽

Pairwise Identity ◽

Specific Expression ◽

Whole Exome ◽

High Quality Snps ◽

Allele Specific ◽

Related Individuals

AbstractAnalysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Sample labelling mistakes are estimated to affect more than 4% of published samples. Therefore, as a method of data quality control, a way to reconstruct pedigrees from RNA-seq data would be useful for confirming the expected relationships. Currently, reconstruction of pedigrees is based mainly on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression.In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity-by-descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth.

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Elimination of reference mapping bias reveals robust immune related allele-specific expression in crossbred sheep

10.1101/619122 ◽

2019 ◽

Cited By ~ 1

Author(s):

Mazdak Salavati ◽

Stephen J. Bush ◽

Sergio Palma-Vera ◽

Mary E. B. McCulloch ◽

David A. Hume ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Allelic Imbalance ◽

Cell Types ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Gene Expression Atlas ◽

Expression Atlas ◽

Allele Specific

AbstractPervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript we describe an unbiased standardised computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open licence. The analysis pipeline we present is designed to minimise reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel x Scottish Blackface sheep, using the sheep gene expression atlas dataset. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited and instead they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programmes for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq datasets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterisation of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling, to provide both a novel analysis of the multi-dimensional sheep gene expression atlas dataset, and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.

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Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

Scientific Reports ◽

10.1038/s41598-021-83459-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Joseph Tomlinson ◽

Shawn W. Polson ◽

Jing Qiu ◽

Juniper A. Lake ◽

William Lee ◽

...

Keyword(s):

Broiler Chickens ◽

Nucleotide Polymorphisms ◽

Rna Seq ◽

Specific Expression ◽

Single Nucleotide ◽

Allele Specific Expression ◽

Detection Tool ◽

Commercial Broiler ◽

Significant Phenomenon ◽

Allele Specific

AbstractDifferential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

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Analysis of Allele-Specific Expression in Mouse Liver by RNA-Seq: A Comparison With Cis-eQTL Identified Using Genetic Linkage

Genetics ◽

10.1534/genetics.113.153882 ◽

2013 ◽

Vol 195 (3) ◽

pp. 1157-1166 ◽

Cited By ~ 34

Author(s):

Sandrine Lagarrigue ◽

Lisa Martin ◽

Farhad Hormozdiari ◽

Pierre-François Roux ◽

Calvin Pan ◽

...

Keyword(s):

Mouse Liver ◽

Genetic Linkage ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

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Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

10.1101/011767 ◽

2014 ◽

Author(s):

Andreas Tuerk ◽

Gregor Wiktorin ◽

Serhat Güler

Keyword(s):

Probability Distributions ◽

False Positive Rate ◽

Synthetic Data ◽

True Positive Rate ◽

Rna Seq ◽

Microarray Quality Control ◽

Data Set ◽

Rna Transcripts ◽

Positive Rate ◽

Fragment Distribution

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. "mixquare") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

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