On the design and analysis of gene expression studies in human populations

Akey, Joshua M.; Biswas, Shameek; Leek, Jeffrey T.; Storey, John D.

doi:10.1038/ng0707-807

Cited by 124 publications

(122 citation statements)

References 6 publications

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“…After applying ICE eQTL mapping to correct for the confounding effects from regulatory hotspots, the number of cis associations almost doubled and the concordances of cis and trans associations between disjoint subsets significantly improved. Finally in human lymphoblastoid cell lines, where other known batch effects have been suggested (Akey et al 2007), our analysis identified more real cis associations than methods that explicitly correct for the batch effects. Our method is publicly available as an R package at http:// genetics.cs.ucla.edu/ice…”

Section: G Enomewide Analysis Of Gene Expression Data Inmentioning

confidence: 94%

“…Previous studies have shown that many factors contribute to the spurious correlation between microarray samples, including systematic bias from sources such as technical variation in microarray manufacturing (Churchill 2002;Akey et al 2007), variations introduced during sample preparation such as the time postmortem a sample is collected, and variations introduced during expression measurements such as the batch of reagents used or laboratory ozone levels (Fare et al 2003;Branham et al 2007). Such spurious intersample correlations are usually not completely resolved by randomized design of the experiment (Churchill 2002) or through low-level normalization techniques (Yang et al 2002;Irizarry et al 2003).…”

Section: G Enomewide Analysis Of Gene Expression Data Inmentioning

confidence: 99%

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Accurate Discovery of Expression Quantitative Trait Loci Under Confounding From Spurious and Genuine Regulatory Hotspots

Kang¹,

2008

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In genomewide mapping of expression quantitative trait loci (eQTL), it is widely believed that thousands of genes are trans-regulated by a small number of genomic regions called ''regulatory hotspots,'' resulting in ''trans-regulatory bands'' in an eQTL map. As several recent studies have demonstrated, technical confounding factors such as batch effects can complicate eQTL analysis by causing many spurious associations including spurious regulatory hotspots. Yet little is understood about how these technical confounding factors affect eQTL analyses and how to correct for these factors. Our analysis of data sets with biological replicates suggests that it is this intersample correlation structure inherent in expression data that leads to spurious associations between genetic loci and a large number of transcripts inducing spurious regulatory hotspots. We propose a statistical method that corrects for the spurious associations caused by complex intersample correlation of expression measurements in eQTL mapping. Applying our intersample correlation emended (ICE) eQTL mapping method to mouse, yeast, and human identifies many more cis associations while eliminating most of the spurious trans associations. The concordances of cis and trans associations have consistently increased between different replicates, tissues, and populations, demonstrating the higher accuracy of our method to identify real genetic effects.

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Section: G Enomewide Analysis Of Gene Expression Data Inmentioning

confidence: 94%

Section: G Enomewide Analysis Of Gene Expression Data Inmentioning

confidence: 99%

Accurate Discovery of Expression Quantitative Trait Loci Under Confounding From Spurious and Genuine Regulatory Hotspots

Kang¹,

2008

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“…In addition, culture conditions or batch to batch variation could influence the observed differences in gene expression between the two populations. 50 Therefore, to reduce these variables, cell culture protocols were optimized and samples (CEU and YRI) were randomized when cultured and evaluated for miRNA expression in this study.…”

Section: Discussionmentioning

confidence: 99%

“…We have reported in vitro population differences in cellular sensitivity to carboplatin, 25 daunorubicin 25 and araC 26 between the CEU and YRI samples. Cellular sensitivity to these chemotherapeutic agents-quantified by IC 50 (the concentration required to inhibit 50% of cell growth) for carboplatin and daunorubicin, along with AUC (area under the drug concentration cellular percent survival curve) for araC-was evaluated. We performed linear regression analysis between the differentially expressed miRNAs and these drug sensitivity phenotypes in the separate CEU and YRI samples.…”

Section: Samplesmentioning

confidence: 99%

Population differences in microRNA expression and biological implications

et al. 2011

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“…Furthermore, some limitations or confounding factors (both measurable or unmeasurable) could influence the accuracy of these results [26]. A re-analysis by Akey et al of the data from one expression study [14] suggested that a large proportion of the differential genes between CEU and CHB/ JPT samples could be due to systematic and uncorrectable bias [27]. This confounding factor is probably due to the age (in culture) of the CEU samples [28] and time at which analysis was performed (batch effect) relative to the other recently established CHB/JPT and YRI samples.…”

mentioning

confidence: 99%