title: Robust detection and genotyping of single feature polymorphisms from gene expression data.
abstract: It is well known that Affymetrix microarrays are widely used to predict genome-wide gene expression and genome-wide genetic polymorphisms from RNA and genomic DNA hybridization experiments, respectively. It has recently been proposed to integrate the two predictions by use of RNA microarray data only. Although the ability to detect single feature polymorphisms (SFPs) from RNA microarray data has many practical implications for genome study in both sequenced and unsequenced species, it raises enormous challenges for statistical modelling and analysis of microarray gene expression data for this objective. Several methods are proposed to predict SFPs from the gene expression profile. However, their performance is highly vulnerable to differential expression of genes. The SFPs thus predicted are eventually a reflection of differentially expressed genes rather than genuine sequence polymorphisms. To address the problem, we developed a novel statistical method to separate the binding affinity between a transcript and its targeting probe and the parameter measuring transcript abundance from perfect-match hybridization values of Affymetrix gene expression data. We implemented a Bayesian approach to detect SFPs and to genotype a segregating population at the detected SFPs. Based on analysis of three Affymetrix microarray datasets, we demonstrated that the present method confers a significantly improved robustness and accuracy in detecting the SFPs that carry genuine sequence polymorphisms when compared to its rivals in the literature. The method developed in this paper will provide experimental genomicists with advanced analytical tools for appropriate and efficient analysis of their microarray experiments and biostatisticians with insightful interpretation of Affymetrix microarray data.
authors.affiliation: Laboratory of Population & Quantitative Genetics, The State Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.
authors.forename: Minghui
authors.initials: M
authors.lastname: Wang
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Hu
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Lindsey J
authors.initials: LJ
authors.lastname: Leach
authors.forename: Elena
authors.initials: E
authors.lastname: Potokina
authors.forename: Arnis
authors.initials: A
authors.lastname: Druka
authors.forename: Robbie
authors.initials: R
authors.lastname: Waugh
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Kearsey
authors.forename: Zewei
authors.initials: Z
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011110:Polymorphism, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*