Allelic imbalance reveals widespread germline-somatic regulatory differences and prioritizes risk loci in Renal Cell Carcinoma

Gusev, Alexander; Spisák, Sándor; Fay, André P.; Carol, Hallie; Vavra, Kevin C.; Signoretti, Sabina; Tisza, Viktória; Pomerantz, Mark; Abbasi, Forough; Seo, Ji-Heui; Choueiri, Toni K.; Lawrenson, Kate; Freedman, Matthew L.

doi:10.1101/631150

Cited by 14 publications

(22 citation statements)

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“…13 The behavior and performance of CAVIAR is representative of similar QTL-based methods such as CAVIARBF, FINEMAP, and PAINTOR without functional annotation data. [14][15][16] The versions of PLASMA are furthermore compared against the only other publicly released fine-mapping method (to our knowledge) that integrates AS data described in the pre-print of Zou et al 24 This unnamed method, denoted as ''AS-Meta,'' utilizes the association between SNP heterozygosity and a binary indicator of allelic imbalance. By binarizing allelic imbalance, AS-Meta is expected to lose power relative to treating imbalance as a quantitative phenotype but may be more robust to spurious AS signal.…”

Section: Comparison Of Existing Models With Plasmamentioning

confidence: 99%

Allele-Specific QTL Fine Mapping with PLASMA

Wang

Shetty

O’Connor

et al. 2020

The American Journal of Human Genetics

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Although quantitative trait locus (QTL) associations have been identified for many molecular traits such as gene expression, it remains challenging to distinguish the causal nucleotide from nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are a powerful measure of cis-regulation that are concordant with traditional QTLs but typically less susceptible to technical/environmental noise. However, existing methods for estimating causal variant probabilities (i.e., fine mapping) cannot produce valid estimates from asQTL signals due to complexities in linkage disequilibrium (LD). We introduce PLASMA (Population Allele-Specific Mapping), a fine-mapping method that integrates QTL and asQTL information to improve accuracy. In simulations, PLASMA accurately prioritizes causal variants over a wide range of genetic architectures. Applied to RNA-seq data from 524 kidney tumor samples, PLASMA achieves a greater power at 50 samples than conventional QTL-based fine mapping at 500 samples, with more than 17% of loci fine mapped to within five causal variants, compared to 2% by QTL-based fine mapping, and a 6.9-fold overall reduction in median credible set size compared to QTL-based fine mapping when applied to H3K27AC ChIP-seq from just 28 prostate tumor/normal samples. Variants in the PLASMA credible sets for RNA-seq and ChIP-seq were enriched for open chromatin and chromatin looping, respectively, at a comparable or greater degree than credible variants from existing methods while containing far fewer markers. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data.

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Section: Comparison Of Existing Models With Plasmamentioning

confidence: 99%

Allele-Specific QTL Fine Mapping with PLASMA

Wang

Shetty

O’Connor

et al. 2020

The American Journal of Human Genetics

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“…Lastly, we look at how PLASMA prioritizes experimentally-verified causal variants at GWAS risk loci. Figure 6 shows the strength AS and QTL associations for DPF3 and SCARB1, genes in two kidney GWAS loci that have verified causal variants [23, 33]. At each sample size threshold, the AS statistic more confidently identifies the true causal variant than the QTL statistic.…”

Section: Resultsmentioning

confidence: 99%

“…The StratAS algorithm was used to quantify allele-specific signal and identify initially significant features for fine-mapping [23]. For each peak/gene (the feature) and individual all reads at heterozygous SNPs in the feature were aggregated to compute the haplotype-specific read counts, and summed across the two haplotypes of each individual to compute the QTL read counts.…”

Section: Methodsmentioning

confidence: 99%

“…The AS effect-size has also been shown to be highly correlated to conventional QTL effect-sizes, implying that both features typically the same underlying cis-regulatory patterns [22]. Several methods have recently been developed to robustly identify asQTLs [19, 20, 23], but the calculated association statistics follow a different distribution than QTL summary statistics and cannot be directly integrated into existing fine-mapping software to produce valid posterior measures and credible sets.…”

Section: Introductionmentioning

confidence: 99%

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Allele-Specific QTL Fine-Mapping with PLASMA

Wang

Shetty

O’Connor

et al. 2019

Preprint

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1Although quantitative trait locus (QTL) associations have been identified for many molecular 2 traits such as gene expression, it remains challenging to distinguish the causal nucleotide from 3 nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are 4 a powerful measure of cis-regulation that are largely concordant with traditional QTLs, and 5 can be less susceptible to technical/environmental noise. However, existing asQTL analysis 6 methods do not produce probabilities of causality for each marker, and do not take into account 7 correlations among markers at a locus in linkage disequilibrium (LD). We introduce PLASMA 8 (PopuLation Allele-Specific MApping), a novel, LD-aware method that integrates QTL and 9 asQTL information to fine-map causal regulatory variants while drawing power from both the 10 number of individuals and the number of allelic reads per individual. We demonstrate through 11 simulations that PLASMA successfully detects causal variants over a wide range of genetic 12 architectures. We apply PLASMA to RNA-Seq data from 524 kidney tumor samples and show 13 that over 17 percent of loci can be fine-mapped to within 5 causal variants, compared less than 2 14 percent of loci using existing QTL-based fine-mapping. PLASMA furthermore achieves a greater 15 power at 50 samples than conventional QTL fine-mapping does at over 500 samples. Overall, 16 PLASMA achieves a 6.9-fold reduction in median 95% credible set size compared to existing 17 QTL-based fine-mapping. We additionally apply PLASMA to H3K27AC ChIP-Seq from 28 18 prostate tumor/normal samples and demonstrate that PLASMA is able to prioritize markers 19 even at small samples, with PLASMA achieving a 1.3-fold reduction in median 95% credible set 20 sizes over existing QTL-based fine-mapping. Variants in the PLASMA credible sets for RNA-21 Seq and ChIP-Seq were enriched for open chromatin and chromatin looping (respectively) at a 22 comparable or greater degree than credible variants from existing methods, while containing far 23 fewer markers. Our results demonstrate how integrating AS activity can substantially improve 24 the detection of causal variants from existing molecular data and at low sample size. 25 1 Introduction 26 A major open problem in genetics is understanding the biological mechanisms underlying complex 27traits, which are largely driven by non-coding variants. A widely adopted approach for elucidating 28 1 these regulatory patterns is the identification of disease variants that also modify molecular pheno-29 types (such as gene expression) [1][2][3][4]. These variants, known as quantitative trait loci (QTLs), are 30 typically single nucleotide polymorphisms (SNPs) that exhibit a statistical association with overall 31 gene expression abundance [5][6][7][8]. Although QTL association analysis is now mature, it remains 32 challenging to identify the precise variants that causally influence the molecular trait (as opposed 33 to variants in linkage disequilibrium (LD) with causal variants...

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“…According to the authors, the risk allele then translates into higher PARP1 levels, which may promote melanoma formation by rescuing cells from BRAF V600E oncogene-induced senescence. Another example involves intronic cis-acting variants in individuals predisposed to breast and ovarian cancer that lead to haploinsufficiency of the DNA repair proteins PALB2 and BRCA1 [ 21 , 22 ], and regions in the genome with differential promoter/enhancer activity between matched tumor and normal samples, that overlap GWAS-associated variants in renal cell carcinoma [ 23 ]. In an instance that exemplifies the complexities of cancer risk and gene regulation, Hua et al identified that prostate cancer risk SNP rs11672691 falls within an intron of lncRNA PCAT19 , in a region with both promoter and enhancer function.…”

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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Allelic imbalance reveals widespread germline-somatic regulatory differences and prioritizes risk loci in Renal Cell Carcinoma

Cited by 14 publications

References 65 publications

Allele-Specific QTL Fine Mapping with PLASMA

Allele-Specific QTL Fine Mapping with PLASMA

Allele-Specific QTL Fine-Mapping with PLASMA

Allele-specific expression: applications in cancer and technical considerations

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