Proper Use of Allele-Specific Expression Improves Statistical Power for <i>cis</i>-eQTL Mapping with RNA-Seq Data

<div><p>Studies of expression quantitative trait loci (eQTLs) offer insight into the molecular mechanisms of loci that were found to be associated with complex diseases and the mechanisms can be classified into <i>cis-</i> and <i>trans-</i>acting regulation. At present, high-throughput RNA sequencing (RNA-seq) is rapidly replacing expression microarrays to assess gene expression abundance. Unlike microarrays that only measure the total expression of each gene, RNA-seq also provides information on allele-specific expression (ASE), which can be used to distinguish <i>cis-</i>eQTLs from <i>trans-</i>eQTLs and, more importantly, enhance <i>cis-</i>eQTL mapping. However, assessing the <i>cis</i>-effect of a candidate eQTL on a gene requires knowledge of the haplotypes connecting the candidate eQTL and the gene, which can not be inferred with certainty. The existing two-stage approach that first phases the candidate eQTL against the gene and then treats the inferred phase as observed in the association analysis tends to attenuate the estimated <i>cis</i>-effect and reduce the power for detecting a <i>cis</i>-eQTL. In this article, we provide a maximum-likelihood framework for <i>cis</i>-eQTL mapping with RNA-seq data. Our approach integrates the inference of haplotypes and the association analysis into a single stage, and is thus unbiased and statistically powerful. We also develop a pipeline for performing a comprehensive scan of all local eQTLs for all genes in the genome by controlling for false discovery rate, and implement the methods in a computationally efficient software program. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to empirical breast cancer data from The Cancer Genome Atlas project. Supplementary materials for this article are available online.</p></div

Micro-Scale Genomic DNA Copy Number Aberrations as Another Means of Mutagenesis in Breast Cancer

<div><h3>Introduction</h3><p>In breast cancer, the basal-like subtype has high levels of genomic instability relative to other breast cancer subtypes with many basal-like-specific regions of aberration. There is evidence that this genomic instability extends to smaller scale genomic aberrations, as shown by a previously described micro-deletion event in the <em>PTEN</em> gene in the Basal-like SUM149 breast cancer cell line.</p> <h3>Methods</h3><p>We sought to identify if small regions of genomic DNA copy number changes exist by using a high density, gene-centric Comparative Genomic Hybridizations (CGH) array on cell lines and primary tumors. A custom tiling array for CGH (244,000 probes, 200 bp tiling resolution) was created to identify small regions of genomic change, which was focused on previously identified basal-like-specific, and general cancer genes. Tumor genomic DNA from 94 patients and 2 breast cancer cell lines was labeled and hybridized to these arrays. Aberrations were called using SWITCHdna and the smallest 25% of SWITCHdna-defined genomic segments were called micro-aberrations (<64 contiguous probes, ∼ 15 kb).</p> <h3>Results</h3><p>Our data showed that primary tumor breast cancer genomes frequently contained many small-scale copy number gains and losses, termed micro-aberrations, most of which are undetectable using typical-density genome-wide aCGH arrays. The basal-like subtype exhibited the highest incidence of these events. These micro-aberrations sometimes altered expression of the involved gene. We confirmed the presence of the <em>PTEN</em> micro-amplification in SUM149 and by mRNA-seq showed that this resulted in loss of expression of all exons downstream of this event. Micro-aberrations disproportionately affected the 5′ regions of the affected genes, including the promoter region, and high frequency of micro-aberrations was associated with poor survival.</p> <h3>Conclusion</h3><p>Using a high-probe-density, gene-centric aCGH microarray, we present evidence of small-scale genomic aberrations that can contribute to gene inactivation. These events may contribute to tumor formation through mechanisms not detected using conventional DNA copy number analyses.</p> </div

Copy Number Micro-aberrations by Subtype.

<p>The mean and median numbers of micro-aberrations for samples within each subtype are shown, as is the percentage of samples within each subtype that exhibited any micro-aberrations.</p

Higher levels of micro-aberrations are associated with worse survival outcomes.

<p>A) Kaplan-Meier plots for overall survival and B) relapse-free survival are shown for the patients in the tiling array datasets. The patients were split into two groups, the top 67% in terms of total micro-aberration versus the bottom 33%. (N = 94).</p

Micro-aberration Frequency by Gene.

<p>A) The top 17 genes that displayed the most micro-aberrations are shown, along with the number of micro-aberrations (All)/micro-amplifications (Amp)/micro-deletions (Del) seen within each gene. The expected number of micro-aberrations for each gene, corrected for gene size, is calculated based on the distribution of log₂ ratio values for our dataset and the probability of observing a segment meeting our micro-aberration cutoffs. p-values calculated by Chi-square test.</p>*<p>Based on the distribution of log-ratio data within our dataset and the probability given this distribution of contiguous probes meeting the cutoffs for micro-aberrations, with correction for gene size.</p>**<p>p-value based on Chi-square test.</p

Analysis of Gene Expression Relative to Median Expression by Aberration Type.

<p>For micro-amplifications, micro-deletions, and all micro-aberrations combined, the number of samples with or without micro-aberration and with greater than median or less than median expression are displayed. P-value calculated by Fisher’s Exact Test.</p

Selected examples of intra-genic micro-aberrations.

<p>A) The previously identified <i>PTEN</i> exon 2 micro-amplification in SUM149 cell line DNA. B) Intra-genic deletion in <i>PTEN</i> in basal-like tumor BR-970137B. C) Focal deletion of <i>RB1</i> in basal-like tumor UNC020510B, and D) amplification of <i>RB1</i> gene in basal-like tumor UNC030459B. The locations of the tiling array probes are indicated with black crosses. Exons are highlighted with grey bars. SWITCHdna called segments are indicated in blue.</p

The presence of micro-aberrations can result in differential expression by copy number status.

<p>Samples with micro-amplifications in A) <i>NUF2</i> and B) <i>UBE2T</i> are associated with significantly higher expression of the gene than samples without these aberrations. Samples with micro-deletion in C) <i>ZNF217</i> are associated with significantly lower expression of the gene than samples without these aberrations. Samples with micro-deletion in D) <i>SLC7A6</i> are associated with significantly higher expression of the gene than samples without these aberrations.</p

IRS isoforms mediate distinct gene expression profiles, functional pathways, and breast cancer subtype association.

<p>(A) Venn diagrams depicting four distinct IRS isoform gene signatures were derived from overlapping and differential global gene expression patterns in response to IGF-I. (B) Target gene validation confirms both distinct and overlapping patterns of IRS-regulated gene expression. Gene expression was normalized to RPLP0 and is presented as fold-change of treatment (black bars) vs. serum-free (white bars) conditions. Error bars represent standard deviation and all results are representative of at least three independent replicates. (C) IRS gene signature enrichment in breast tumor subtypes in the UNC337 cohort. Median expression values are represented here in graphical format with p-values included for each of the IRS gene signatures.</p

Multivariate Cox regression analysis of RFS & OS in Luminal B breast cancer tumors.

<p>Multivariate Cox regression analysis of RFS & OS in Luminal B breast cancer tumors.</p