Dataset Information

QuASAR: quantitative allele-specific analysis of reads.

ABSTRACT: 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.http://github.com/piquelab/QuASAR.fluca@wayne.edu or rpique@wayne.eduSupplementary Material is available at Bioinformatics online.

SUBMITTER: Harvey CT

PROVIDER: S-EPMC4393517 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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QuASAR: quantitative allele-specific analysis of reads.

Harvey Chris T CT Moyerbrailean Gregory A GA Davis Gordon O GO Wen Xiaoquan X Luca Francesca F Pique-Regi Roger R

Bioinformatics (Oxford, England) 20141204 8

<h4>Motivation</h4>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 sin ...[more]

PMID: 25480375

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Project description:Motivation:The majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets. Results:We have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data, we found 602 SNPs with significant (false discovery rate 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high-throughput reporter assays. Availability and implementation:http://github.com/piquelab/QuASAR/tree/master/mpra. Contact:fluca@wayne.edu or rpique@wayne.edu. Supplementary information:Supplementary data are available online at Bioinformatics.

Project description:BACKGROUND: The JAK2 V617F mutation is the most frequent somatic change in myeloproliferative neoplasms, making it an important tumour-specific marker for diagnostic purposes and for the detection of minimal residual disease. Sensitive quantitative assays are required for both applications, particularly for the monitoring of minimal residual disease, which requires not only high sensitivity but also very high specificity. METHODS: We developed a highly sensitive probe-free quantitative mutant-allele detection method, Quantitative Threefold Allele-Specific PCR (QuanTAS-PCR), that is performed in a closed-tube system, thus eliminating the manipulation of PCR products. QuantTAS-PCR uses a threefold approach to ensure allele-specific amplification of the mutant sequence: (i) a mutant allele-specific primer, (ii) a 3'dideoxy blocker to suppress false-positive amplification from the wild-type template and (iii) a PCR specificity enhancer, also to suppress false-positive amplification from the wild-type template. Mutant alleles were quantified relative to exon 9 of JAK2. RESULTS: We showed that the addition of the 3'dideoxy blocker suppressed but did not eliminate false-positive amplification from the wild-type template. However, the addition of the PCR specificity enhancer near eliminated false-positive amplification from the wild-type allele. Further discrimination between true and false positives was enabled by using the quantification cycle (Cq) value of a single mutant template as a cut-off point, thus enabling robust distinction between true and false positives. As 10,000 JAK2 templates were used per replicate, the assay had a sensitivity of 1/10(-4) per replicate. Greater sensitivity could be reached by increasing the number of replicates analysed. Variation in replicates when low mutant-allele templates were present necessitated the use of a statistics-based approach to estimate the load of mutant JAK2 copies. QuanTAS-PCR showed comparable quantitative results when validated against a commercial assay. CONCLUSIONS: QuanTAS-PCR is a simple, cost-efficient, closed-tube method for JAK2 V617F mutation quantification that can detect very low levels of the mutant allele, thus enabling analysis of minimal residual disease. The approach can be extended to the detection of other recurrent single nucleotide somatic changes in cancer.

Project description:Customizable endonucleases such as transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) enable rapid generation of mutant strains at genomic loci of interest in animal models and cell lines. With the accelerated pace of generating mutant alleles, genotyping has become a rate-limiting step to understanding the effects of genetic perturbation. Unless mutated alleles result in distinct morphological phenotypes, mutant strains need to be genotyped using standard methods in molecular biology. Classic restriction fragment length polymorphism (RFLP) or sequencing is labor-intensive and expensive. Although simpler than RFLP, current versions of allele-specific PCR may still require post-polymerase chain reaction (PCR) handling such as sequencing, or they are more expensive if allele-specific fluorescent probes are used. Commercial genotyping solutions can take weeks from assay design to result, and are often more expensive than assembling reactions in-house. Key components of commercial assay systems are often proprietary, which limits further customization. Therefore, we developed a one-step open-source genotyping method based on quantitative PCR. The allele-specific qPCR (ASQ) does not require post-PCR processing and can genotype germline mutants through either threshold cycle (Ct) or end-point fluorescence reading. ASQ utilizes allele-specific primers, a locus-specific reverse primer, universal fluorescent probes and quenchers, and hot start DNA polymerase. Individual laboratories can further optimize this open-source system as we completely disclose the sequences, reagents, and thermal cycling protocol. We have tested the ASQ protocol to genotype alleles in five different genes. ASQ showed a 98-100% concordance in genotype scoring with RFLP or Sanger sequencing outcomes. ASQ is time-saving because a single qPCR without post-PCR handling suffices to score genotypes. ASQ is cost-effective because universal fluorescent probes negate the necessity of designing expensive probes for each locus.

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QuASAR: quantitative allele-specific analysis of reads.

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QuASAR: quantitative allele-specific analysis of reads.

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