Single nucleotide polymorphisms affect both cis- and trans-eQTLs

Department of Biostatistics, Section on Statistical Genetics, School of Public Health, University of Alabama at Birmingham, AL 35209, USA.
Genomics (Impact Factor: 2.79). 03/2009; 93(6):501-8. DOI: 10.1016/j.ygeno.2009.01.011
Source: PubMed

ABSTRACT Single nucleotide polymorphisms (SNPs) between microarray probes and RNA targets can affect the performance of expression array by weakening the hybridization. In this paper, we examined the effect of the SNPs on Affymetrix GeneChip probe set summaries and the expression quantitative trait loci (eQTL) mapping results in two eQTL datasets, one from mouse and one from human. We showed that removing SNP-containing probes significantly changed the probe set summaries and the more SNP-containing probes we removed the greater the change. Comparison of the eQTL mapping results between with and without SNP-containing probes showed that less than 70% of the significant eQTL peaks were concordant regardless of the significance threshold. These results indicate that SNPs do affect both probe set summaries and eQTLs (both cis and trans), thus SNP-containing probes should be filtered out to improve the performance of eQTL mapping.

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    • "We downloaded the whole-adult Affymetrix Drosophila 2.0 array expression data (accession number E-MEXP-1594) reported in Ayroles et al. (2009) and the original 37 sequences of the DGRP first released in 2009 (Mackay et al. 2012). Probes with underlying SNPs were removed or masked (Benovoy et al. 2008; Chen et al. 2009). The sex effect for each gene was removed and the residuals rescaled to standardized deviates using the total sample variance. "
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    Molecular Biology and Evolution 04/2014; 31(8). DOI:10.1093/molbev/msu146 · 14.31 Impact Factor
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    • "Transcript abundance may act as intermediate phenotype between loci and macroscopic phenotypes, and can be considered as expression quantitative trait (e-trait) in order to identify chromosomal regions where genotypes significantly affect gene expression [120]. By using cis- and trans- mapping approaches, other interesting questions regarding gene expression regulation could be addressed by combining QTL and eQTL: for instance the relative contributions of cis-regulatory elements versus trans-regulatory elements [121], or the exploration of the effect of gene duplications on the genetic regulatory network [122]. Because of the virtually unlimited types of data that can be integrated in QTL mapping for an "overall genomic information system" (e.g., eQTL, proteomics, metabolomics, association studies), the increase of gene mapping efforts in conifer species shall represent an important stage for conifer comparative genomics, simultaneously opening stimulating perspectives for evolutionary studies and molecular breeding applications. "
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    BMC Genomics 03/2011; 12:145. DOI:10.1186/1471-2164-12-145 · 4.04 Impact Factor
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    • "It would be important to know how many of these cisacting candidate QTL are genuine and how many are caused by qualitative differences between isoforms. As has been established for several years, SNP position and the number of SNPs overlapping the microarray probes have a significant impact on the discovery rate and the size of apparent expression differences for cis-(Schadt et al. 2003; Alberts et al., 2005, 2007; Doss et al. 2005; Chen et al. 2009) and trans-modulated transcripts (Chen et al. 2009). Our analysis found that the longer Illumina 50-mers have approximately the same sensitivity to sequence differences as the shorter Affymetrix 25-mer probes (Figure S1 and Figure S2). "
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    ABSTRACT: Common sequence variants within a gene often generate important differences in expression of corresponding mRNAs. This high level of local (allelic) control-or cis modulation-rivals that produced by gene targeting, but expression is titrated finely over a range of levels. We are interested in exploiting this allelic variation to study gene function and downstream consequences of differences in expression dosage. We have used several bioinformatics and molecular approaches to estimate error rates in the discovery of cis modulation and to analyze some of the biological and technical confounds that contribute to the variation in gene expression profiling. Our analysis of SNPs and alternative transcripts, combined with eQTL maps and selective gene resequencing, revealed that between 17 and 25% of apparent cis modulation is caused by SNPs that overlap probes rather than by genuine quantitative differences in mRNA levels. This estimate climbs to 40-50% when qualitative differences between isoform variants are included. We have developed an analytical approach to filter differences in expression and improve the yield of genuine cis-modulated transcripts to approximately 80%. This improvement is important because the resulting variation can be successfully used to study downstream consequences of altered expression on higher-order phenotypes. Using a systems genetics approach we show that two validated cis-modulated genes, Stk25 and Rasd2, are likely to control expression of downstream targets and affect disease susceptibility.
    Genetics 11/2009; 184(1):119-28. DOI:10.1534/genetics.109.107474 · 4.87 Impact Factor
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