Rare variant phasing and haplotypic expression from RNA sequencing with phASER

Castel, Stephane E.; Mohammadi, Pejman; Chung, Wendy K.; Shen, Yufeng; Lappalainen, Tuuli

doi:10.1038/ncomms12817

Cited by 127 publications

(124 citation statements)

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“…Haplotype-level data was generated using phASER v1.0.1 (Castel, 2016). phASER was run using whole genome sequencing genotype calls that were population-phased with Shapeit v2.837 in read-backed phasing mode with whole genome sequencing reads (Delaneau, 2013).…”

Section: Data Generation and Availabilitymentioning

confidence: 99%

“…The original phASER package produced gene-level haplotypic expression per individual (Castel, 2016). We developed new additions to phASER (phASER-POP) that make it easier to analyze data across many samples, as is often done with gene expression quantifications.…”

Section: Software and Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…1b). We have previously developed a tool, phASER, which does this systematically, in a way that uses the information contained within reads to improve phasing, while preventing double counting of reads across SNPs to improve the quality of data generated (Castel, 2016).In this work, we present an ASE resource generated using the Genotype Tissue Expression (GTEx) version 8 data release comprising RNA-seq data from 54 tissues and 838 individuals, for a total of 15,253 samples. We generated both SNP-level and haplotype-level ASE data.…”

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A vast resource of allelic expression data spanning human tissues

Castel

Aguet

Mohammadi

et al. 2019

Preprint

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Allele specific expression (ASE) analysis robustly measures cis regulatory effects. Here, we present a vast ASE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of ASE at the SNP-level and 153 million measurements at the haplotype-level. In addition, we developed an extension of our tool phASER that allows effect sizes of cis regulatory variants to be estimated using haplotypelevel ASE data. This ASE resource is the largest to date and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues. BackgroundAllele specific expression (ASE), or allelic expression analysis is a powerful technique that can be used to measure the expression of gene alleles relative to one another within single individuals. This makes it well suited to measure cis-acting regulatory variation using imbalance between alleles in heterozygous individuals ( Fig. 1a) (Mohammadi, 2017). ASE analysis can capture both common cis-regulatory variation, for example, expression quantitative trait loci (eQTL), and rare regulatory variation (GTEx Consortium, 2017). It can also be used to measure allele-specific epigenetic effects such as parent of origin imprinting (Baran, 2015).In practice, ASE analysis uses RNA-seq reads that overlap heterozygous single nucleotide polymorphisms (SNPs), where the SNP can be used to assign the read to an allele.These heterozygous SNPs capture the cumulative effects of cis-regulatory variation acting on each allele. In some cases, these effects can be caused by the SNPs being used to measure ASE themselves, for example, stop gain variants that cause nonsense-mediated decay (NMD; Rivas, 2015), but often they simply capture effects of other cis-acting variation. Traditionally, a single SNP has been used to measure ASE, by taking the SNP with the highest coverage per gene. However, as a result of improvements in genome phasing, data can be aggregated across SNPs to produce estimates of allelic expression at the haplotype-level (Fig. 1b). We have previously developed a tool, phASER, which does this systematically, in a way that uses the information contained within reads to improve phasing, while preventing double counting of reads across SNPs to improve the quality of data generated (Castel, 2016).In this work, we present an ASE resource generated using the Genotype Tissue Expression (GTEx) version 8 data release comprising RNA-seq data from 54 tissues and 838 individuals, for a total of 15,253 samples. We generated both SNP-level and haplotype-level ASE data. While the SNP-level data is available to approved users through dbGaP, the haplotype-level data does not contain identifiable information, and we were thus able to make it publicly available on the GTEx portal. Results and DiscussionBoth SNP-level and haplotype-level ASE data were generated for each GTEx sample using current best practices, both with and w...

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Section: Data Generation and Availabilitymentioning

confidence: 99%

Section: Software and Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A vast resource of allelic expression data spanning human tissues

Castel

Aguet

Mohammadi

et al. 2019

Preprint

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“…Next, we consider reference allele alignment bias that is when the allelic expression ratio is systematically skewed towards one of the alleles due to sequence alignment other artifacts. Several approached have been developed to alleviate this problem, and novel alignment approaches may resolve it completely (31,32). However, since it is still present in most current data sets, it needs to be addressed.…”

Section: Aneva-dotmentioning

confidence: 99%

Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

Mohammadi

Castel

Cummings

et al. 2019

Preprint

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Transcriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraint in a gene. We next used ANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AE data form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variants in previously resolved cases, and lead to one confirmed and several potential new diagnoses in cases previously unresolved. Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data.Large reference databases of human exomes and genomes have enabled characterization of genomic variation in human populations (1-3). These data have been used to summarize genic intolerance to damaging variants (1,4,5), which has been essential for prioritizing rare and de novo coding variants to achieve genetic diagnosis of Mendelian disease currently for 25-50% patients (6, 7). Yet, a large number of patients remain undiagnosed after exome and/or whole genome sequencing. Despite remarkable advances in DNA sequencing, the search for rare disease-causing variants outside the coding sequence has been hindered by the difficulty of interpreting rare regulatory variants and identifying their target genes. P.M.

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Integrative omics approaches to advance rare disease diagnostics

Smirnov

Konstantinovskiy

Prokisch

2023

J of Inher Metab Disea

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Over the past decade high‐throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity, and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease‐causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA‐sequencing, proteomics, metabolomics and DNA‐methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.This article is protected by copyright. All rights reserved.

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Rare variant phasing and haplotypic expression from RNA sequencing with phASER

Cited by 127 publications

References 20 publications

A vast resource of allelic expression data spanning human tissues

A vast resource of allelic expression data spanning human tissues

Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

Integrative omics approaches to advance rare disease diagnostics

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