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QuASAR: Quantitative Allele Specific Analysis of Reads  

2014-08-18 08:40:45|  分类: 生物信息分析 |  标签: |举报 |字号 订阅

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ABSTRACT

Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression and have enabled a better understanding of the functional role of non-coding sequences. However, eQTL studies are generally quite expensive, requiring large sample sizes and genome-wide genotyping of each sample. On the other hand, allele specific expression (ASE) is becoming a very 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 rejecting the null hypothesis of 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 methods that jointly infer genotype and test for ASE or that include the uncertainty in the genotype calls within the ASE inference step. Here, 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.

QuASAR: Quantitative Allele Specific Analysis of Reads - 喜欢吃桃子 - wangyufeng的博客

QQplot comparing the p-value distribution of 3 alternative methods for determining ASE. The x-axis shows the log10 quantiles of the p-values expected from the null distribution. The y-axis shows the log10 quantiles of the p-values computed from the real data using 3 different methods: i) Binomial (black) assumes M = 1no overdispersion; ii) Betabinomial (blue) considers overdispersion but does not consider uncertainty in the genotype; iii) QuASAR uses the Beta-binomial distribution and uncertainty in the genotype calls. In all three cases the same set of SNPs are considered

Chris HarveyGregory A MoyebraileanOmar DavisXiaoquan WenFrancesca Luca,Roger Pique-Regi
doi: http://dx.doi.org/10.1101/007492

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