Allele-Specific QTL Fine Mapping with PLASMA

Wang, Austin T.; Shetty, Anamay; O’Connor, Edward; Bell, Connor; Pomerantz, Mark; Freedman, Matthew L.; Gusev, Alexander

doi:10.1016/j.ajhg.2019.12.011

Cited by 17 publications

(21 citation statements)

References 49 publications

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“…Overall, the combined method was more powerful for identifying causal variants, which is consistent with recent reports 10,11 . Step 1 simulates a gene body configuration by first simulating the number of polymorphic sites of the gene followed by positioning these polymorphic sites uniformly across the gene body.…”

Section: Resultssupporting

confidence: 89%

“…However, running these methods on sample sizes beyond a few hundred is generally computationally intractable, and as a result they have not been applied to large-scale studies like GTEx, which includes over 15,000 samples across 49 tissues. For fine-mapping, two approaches that combine both allele-specific expression and eQTL mapping via meta-analysis have been recently proposed 10 , 11 . However, to our knowledge, no existing method provides a scalable unified framework combining total and allele-specific counts with explicit multi-SNP modeling for QTL mapping, fine-mapping, and prediction.…”

Section: Introductionmentioning

confidence: 99%

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A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

et al. 2021

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Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs (FDR < 0.05, per tissue) that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

et al. 2021

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show abstract

“…Overall, the combined method was more powerful for identifying causal variants, which is consistent with recent reports [Zou et al, 2019; Wang et al, 2020].…”

Section: Resultssupporting

confidence: 90%

“…However, these methods are computationally too costly to be applied to sample sizes beyond a few hundred and as a result have not been applied to large-scale studies like GTEx, which includes over 17,000 samples across 49 tissues. Recently, two fine-mapping approaches have been proposed utilizing effect size estimates obtained from both ASE and eQTL mapping via meta-analysis [Zou et al, 2019; Wang et al, 2020]. However, no existing methods, to our knowledge, provides a unified framework of total and allele-specific counts with explicit multi-SNP modeling for QTL mapping, fine-mapping, and prediction.…”

Section: Introductionmentioning

confidence: 99%

Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

Liang

Aguet

Barbeira

et al. 2020

Preprint

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9Genome-wide association studies (GWAS) have been highly successful in identifying genomic 10 loci associated with complex traits. However, identification of the causal genes that mediate 11 these associations remains challenging, and many approaches integrating transcriptomic data 12 with GWAS have been proposed. However, there currently exist no computationally scalable 13 methods that integrate total and allele-specific gene expression to maximize power to detect 14 genetic effects on gene expression. Here, we describe a unified framework that is scalable to 15 studies with thousands of samples. Using simulations and data from GTEx, we demonstrate 16 an average power gain equivalent to a 29% increase in sample size for genes with sufficient 17 allele-specific read coverage. We provide a suite of freely available tools, mixQTL, mixFine, and 18 mixPred, that apply this framework for mapping of quantitative trait loci, fine-mapping, and 19 prediction. 20 1 Introduction 21 Genome-wide association studies (GWAS) have identified tens of thousands of genomic loci asso-22 ciated with complex traits. A large majority of these loci lie in non-coding regions of the genome, 23 1 which hinders identification of the underlying molecular mechanisms and causal genes. Multi-24 ple methods have been developed to integrate GWAS results with expression quantitatite trait 25 loci (eQTLs), to test whether complex trait associations are mediated through regulation of gene 26 expression. Two strategies are commonly employed: 1) association-based approaches including 27 PrediXcan [Gamazon et al., 2015], fusion [Gusev et al., 2016], and smr [Zhu et al., 2016]; and 28 2) colocalization-based approaches including coloc [Giambartolomei et al., 2014], eCAVIAR [Hor-29 mozdiari et al., 2016], and enloc [Wen et al., 2017]. These approaches rely on high-quality eQTL 30 mapping, fine-mapping, and gene expression predictions. 31 In cis-eQTL analysis, allele-specific expression (ASE), i.e., the relative expression difference 32 between the two haplotypes, captures the genetic effect of nearby variants. ASE provides additional 33 signal to total read count, and several methods have been proposed to combine total and allele-34 specific read count for QTL mapping, such as TReCASE [Sun, 2012], WASP [Van De Geijn et al., 35 2015], and RASQUAL [Kumasaka et al., 2016]). However, these methods are computationally too 36 costly to be applied to sample sizes beyond a few hundred and as a result have not been applied to 37 large-scale studies like GTEx, which includes over 17,000 samples across 49 tissues. Recently, two 38 fine-mapping approaches have been proposed utilizing effect size estimates obtained from both ASE 39 and eQTL mapping via meta-analysis [Zou et al., 2019; Wang et al., 2020]. However, no existing 40 methods, to our knowledge, provides a unified framework of total and allele-specific counts with 41 explicit multi-SNP modeling for QTL mapping, fine-mapping, and prediction. 42 By assuming a log-linear model for transcript expression level...

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“…This long form then promotes prostate cancer development by cooperating to activate a number of cell-cycle genes. To facilitate performing these analyses systematically, methods such as PLASMA [ 25 ] and the statistical framework developed by Zou et al [ 26 ] have been introduced.…”

Section: Allele-specific Expression In Cancermentioning

confidence: 99%

Allele-specific expression: applications in cancer and technical considerations

Robles-Espinoza

Mohammadi

Bonilla

et al. 2021

Current Opinion in Genetics & Development

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Allele-specific gene expression can influence disease traits. Non-coding germline genetic variants that alter regulatory elements can cause allele-specific gene expression and contribute to cancer susceptibility. In tumors, both somatic copy number alterations and somatic single nucleotide variants have been shown to lead to allele-specific expression of genes, many of which are considered drivers of tumor growth. Here, we review recent studies revealing the pervasive presence of this phenomenon in cancer susceptibility and progression. Furthermore, we underscore the importance of careful experimental design and computational analysis for accurate allelic expression quantification and avoidance of false positives. Finally, we discuss additional methodological challenges encountered in cancer studies and in the burgeoning field of single-cell transcriptomics.

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Allele-Specific QTL Fine Mapping with PLASMA

Cited by 17 publications

References 49 publications

A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

Allele-specific expression: applications in cancer and technical considerations

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