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Statistical inference of allelic imbalance from transcriptome data
Author(s) -
Nothnagel Michael,
Wolf Andreas,
Herrmann Alexander,
Szafranski Karol,
Vater Inga,
Brosch Mario,
Huse Klaus,
Siebert Reiner,
Platzer Matthias,
Hampe Jochen,
Krawczak Michael
Publication year - 2011
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.21396
Subject(s) - biology , genetics , international hapmap project , allele , context (archaeology) , computational biology , genotyping , inference , genotype , transcriptome , dna sequencing , rna seq , gene , computer science , gene expression , artificial intelligence , paleontology
Next‐generation sequencing and the availability of high‐density genotyping arrays have facilitated an analysis of somatic and meiotic mutations at unprecedented level, but drawing sensible conclusions about the functional relevance of the detected variants still remains a formidable challenge. In this context, the study of allelic imbalance in intermediate RNA phenotypes may prove a useful means to elucidate the likely effects of DNA variants of unknown significance. We developed a statistical framework for the assessment of allelic imbalance in next‐generation transcriptome sequencing (RNA‐seq) data that requires neither an expression reference nor the underlying nuclear genotype(s), and that allows for allele miscalls. Using extensive simulation as well as publicly available whole‐transcriptome data from European‐descent individuals in HapMap, we explored the power of our approach in terms of both genotype inference and allelic imbalance assessment under a wide range of practically relevant scenarios. In so doing, we verified a superior performance of our methodology, particularly at low sequencing coverage, compared to the more simplistic approach of completely ignoring allele miscalls. Because the proposed framework can be used to assess somatic mutations and allelic imbalance in one and the same set of RNA‐seq data, it will be particularly useful for the analysis of somatic genetic variation in cancer studies. Hum Mutat 32:98–106, 2011. © 2010 Wiley‐Liss, Inc.