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PLOS ONE  2011 

The Choice of the Filtering Method in Microarrays Affects the Inference Regarding Dosage Compensation of the Active X-Chromosome

DOI: 10.1371/journal.pone.0023956

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Abstract:

Background The hypothesis of dosage compensation of genes of the X chromosome, supported by previous microarray studies, was recently challenged by RNA-sequencing data. It was suggested that microarray studies were biased toward an over-estimation of X-linked expression levels as a consequence of the filtering of genes below the detection threshold of microarrays. Methodology/Principal Findings To investigate this hypothesis, we used microarray expression data from circulating monocytes in 1,467 individuals. In total, 25,349 and 1,156 probes were unambiguously assigned to autosomes and the X chromosome, respectively. Globally, there was a clear shift of X-linked expressions toward lower levels than autosomes. We compared the ratio of expression levels of X-linked to autosomal transcripts (X:AA) using two different filtering methods: 1. gene expressions were filtered out using a detection threshold irrespective of gene chromosomal location (the standard method in microarrays); 2. equal proportions of genes were filtered out separately on the X and on autosomes. For a wide range of filtering proportions, the X:AA ratio estimated with the first method was not significantly different from 1, the value expected if dosage compensation was achieved, whereas it was significantly lower than 1 with the second method, leading to the rejection of the hypothesis of dosage compensation. We further showed in simulated data that the choice of the most appropriate method was dependent on biological assumptions regarding the proportion of actively expressed genes on the X chromosome comparative to the autosomes and the extent of dosage compensation. Conclusion/Significance This study shows that the method used for filtering out lowly expressed genes in microarrays may have a major impact according to the hypothesis investigated. The hypothesis of dosage compensation of X-linked genes cannot be firmly accepted or rejected using microarray-based data.

References

[1]  Payer B, Lee JT (2008) X chromosome dosage compensation: how mammals keep the balance. Annu Rev Genet 42: 733–772. doi:10.1146/annurev.genet.42.110807.091711.
[2]  Ohno S (1967) Sex Chromosomes and Sex-linked Genes. Berlin: Springer-Verlag.
[3]  Gupta V, Parisi M, Sturgill D, Nuttall R, Doctolero M, et al. (2006) Global analysis of X-chromosome dosage compensation. J Biol 5: 1–22. doi:10.1186/jbiol30.
[4]  Nguyen DK, Disteche CM (2006) Dosage compensation of the active X chromosome in mammals. Nat Genet 38: 47–53. doi:10.1038/ng1705.
[5]  Johnston CM, Lovell FL, Leongamornlert DA, Stranger BE, Dermitzakis ET, et al. (2008) Large-scale population study of human cell lines indicates that dosage compensation is virtually complete. PLoS Genet 4: e9. doi:10.1371/journal.pgen.0040009.
[6]  Vicoso B, Bachtrog D (2009) Progress and prospects toward our understanding of the evolution of dosage compensation. Chromosome Res 17: 585–602. doi:10.1007/s10577-009-9053-y.
[7]  Vicoso B, Bachtrog D (2011) Lack of global dosage compensation in Schistosoma mansoni, a female-heterogametic parasite. Genome Biol Evol 3: 230–235. doi:10.1093/gbe/evr010.
[8]  Walters JR, Hardcastle TJ (2011) Getting a full dose? Reconsidering sex chromosome dosage compensation in the silkworm, Bombyx mori. Genome Biol Evol 3: 491–504. doi:10.1093/gbe/evr036.
[9]  Xiong Y, Chen X, Chen Z, Wang X, Shi S, et al. (2010) RNA sequencing shows no dosage compensation of the active X-chromosome. Nat Genet 42: 1043–1047. doi:10.1038/ng.711.
[10]  Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, et al. (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321: 956–960. doi:10.1126/science.1160342.
[11]  Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18: 1509–1517. doi:10.1101/gr.079558.108.
[12]  Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, et al. (2010) Genetics and beyond–the transcriptome of human monocytes and disease susceptibility. PLoS ONE 5: e10693. doi:10.1371/journal.pone.0010693.
[13]  Hebenstreit D, Fang M, Gu M, Charoensawan V, van Oudenaarden A, et al. (2011) RNA sequencing reveals two major classes of gene expression levels in metazoan cells. Mol Syst Biol 7: 497. doi:10.1038/msb.2011.28.
[14]  Ideker T, Dutkowski J, Hood L (2011) Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power. Cell 144: 860–863. doi:10.1016/j.cell.2011.03.007.
[15]  Barbosa-Morais NL, Dunning MJ, Samarajiwa SA, Darot JFJ, Ritchie ME, et al. (2010) A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res 38: e17. doi:10.1093/nar/gkp942.
[16]  Lin SM, Du P, Huber W, Kibbe WA (2008) Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res 36: e11. doi:10.1093/nar/gkm1075.
[17]  Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24: 1547–1548. doi:10.1093/bioinformatics/btn224.

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