<figure><img src="image_1.png" /></figure>

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<figure><img src="image_2.png" /></figure>

# The 5p12 breast cancer susceptibility locus affects MRPS30 expression in estrogen-receptor positive tumors

<figure><img src="image_3.png" /></figure>

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David A. Quigley<sup>a,b,c</sup>, Elisa Fiorito<sup>d</sup>, Silje Nord<sup>a,b</sup>, Peter Van Loo<sup>e,f</sup>, Grethe Grenaker Alnæs<sup>a,b</sup>, Thomas Fleischer<sup>a,b</sup>, Jørg Tost<sup>g,h</sup>, Hans Kristian Moen Vollan<sup>a,b</sup>, Trine Tramm<sup>i</sup>, Jens Overgaard<sup>j</sup>, Ida R. Bukholm<sup>j,k</sup>, Antoni Hurtado<sup>a,b,d</sup>, Allan Balmain<sup>c</sup>, Anne-Lise Børresen-Dale<sup>a,b</sup>, Vessela Kristensen<sup>a,b,l,*</sup>

## ARTICLE INFO

### Article history:

Received 10 September 2013

Received in revised form

19 November 2013

## ABSTRACT

Genome-wide association studies have identified numerous loci linked to breast cancer susceptibility, but the mechanism by which variations at these loci influence susceptibility is usually unknown. Some variants are only associated with particular clinical subtypes of breast cancer. Understanding how and why these variants influence subtype-specific cancer risk contributes to our understanding of cancer etiology. We conducted a genome-wide

Abbreviations: eQTL, expression Quantitative Trait Locus; FAIRE, Formaldehyde-Assisted Isolation of Regulatory Elements; ChIP-PCR, Chromatin Immunoprecipitation-Polymerase Chain Reaction; ER, Estrogen Receptor; GWAS, Genome-wide Association Study; SNP, Single Nucleotide Polymorphism; ATP, Adenosine Triphosphate.

* Corresponding author. Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian, Radium Hospital, Montebello, 0310 Oslo, Norway. Tel.: +47 22 78 13 75.

E-mail addresses: dquigley@cc.ucsf.edu (D.A. Quigley), elisa.fiorito@ncmm.uio.no (E. Fiorito), silje.nord@rr-research.no (S. Nord), pvl@sanger.ac.uk (P. Van Loo), Grethe.I.Grenaker.Alnas@rr-research.no (G.G. Alnæs), thofle@rr-research.no (T. Fleischer), tost@cng.fr (J. Tost), Hans.Kristian.Moen.Vollan@rr-research.no (H.K. Moen Vollan), TRAMM@ONCOLOGY.DK (T. Tramm), jens@oncology.dk (J. Overgaard), i.r.k.bukholm@medisin.uio.no (I.R. Bukholm), a.h.rodriguez@ncmm.uio.no (A. Hurtado), abalmain@cc.ucsf.edu (A. Balmain), a.l.borresen-dale@medisin.uio.no (A.-L. Børresen-Dale), Vessela.N.Kristensen@rr-research.no, vessela@ulrik.uio.no (V. Kristensen). 1574-7891/$ – see front matter © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.molonc.2013.11.008

<footnote><sup>a</sup>Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway</footnote>

<footnote><sup>b</sup>K.G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway</footnote>

<footnote><sup>c</sup>Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, USA</footnote>

<footnote><sup>d</sup>Breast Cancer Research Group, Nordic EMBL Partnership, Centre for Molecular Medicine Norway, (NCMM), University of Oslo, Norway</footnote>

<footnote><sup>e</sup>Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton UK</footnote>

<footnote><sup>f</sup>Department of Human Genetics, VIB and KU Leuven, Leuven, Belgium</footnote>

<footnote><sup>g</sup>Laboratory for Functional Genomics, Fondation Jean Dausset, Centre Etude Polymorphism Humain, (CEPH), Paris, France</footnote>

<footnote><sup>h</sup>Laboratory of Epigenetics, Centre National de Génotypage, Commissariat à l'énergie Atomique et, aux énergies Alternatives (CEA)—Institut de Génomique, Evry, France</footnote>

<footnote><sup>i</sup>Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark</footnote>

<footnote><sup>j</sup>Department of Breast-Endocrine Surgery, Akershus University Hospital, Oslo, Norway</footnote>

<footnote><sup>k</sup>Department of Oncology, Division of Cancer Medicine, Surgery and Transplantation, Oslo University Hospital, Oslo, Norway</footnote>

<footnote><sup>l</sup>Department of Clinical Molecular Biology (EpiGen), Medical Division, Akershus University Hospital, Lørenskog, Norway</footnote>
Accepted 21 November 2013
Available online 3 December 2013

Keywords:

Breast cancer
Genetics
Expression Quantitative Trait Locus
Estrogen
chromatin

expression Quantitative Trait Locus (eQTL) study in a discovery set of 287 breast tumors and 97 normal mammary tissue samples and a replication set of 235 breast tumors. We found that the risk-associated allele of rs7716600 in the 5p12 estrogen receptor-positive (ER-positive) susceptibility locus was associated with elevated expression of the nearby gene MRPS30 exclusively in ER-positive tumors. We replicated this finding in 235 independent tumors. Further, we showed the rs7716600 risk genotype was associated with decreased MRPS30 promoter methylation exclusively in ER-positive breast tumors. *In vitro* studies in MCF-7 cells carrying the protective genotype showed that estrogen stimulation decreased MRPS30 promoter chromatin availability and mRNA levels. In contrast, in 600MPE cells carrying the risk genotype, estrogen increased MRPS30 expression and did not affect promoter availability. Our data suggest the 5p12 risk allele affects MRPS30 expression in estrogen-responsive tumor cells after tumor initiation by a mechanism affecting chromatin availability. These studies emphasize that the genetic architecture of breast cancer is context-specific, and integrated analysis of gene expression and chromatin remodeling in normal and tumor tissues will be required to explain the mechanisms of risk alleles.

© 2013 Federation of European Biochemical Societies.

Published by Elsevier B.V. All rights reserved.

# 1. Introduction

Germline variants at hundreds of loci in the human genome are associated with modest increases in heritable cancer susceptibility. Breast cancer susceptibility has been the subject of intense scrutiny by GWAS (Hunter et al., 2007; Michailidou et al., 2013). Breast tumors can be divided into several subtypes at the molecular level which may have fundamentally distinct etiologies (Curtis et al., 2012; Polyak, 2007; Sorlie et al., 2001). The most fundamental subtype distinction is whether the tumor responds to mitogenic signals from the Estrogen Receptor (ER). Some of the variants associated with breast cancer susceptibility are associated only with ER-positive or ER-negative disease (Garcia-Closas et al., 2013; Hunter et al., 2007; Michailidou et al., 2013), indicating that their heritable influence on cancer susceptibility is context-dependent. These subtype-specific loci may predispose normal breast tissue to develop ER-positive cancer, or they may preferentially affect the progression of ER-positive but not ER-negative cancer. Susceptibility alleles may act either by direct cell-autonomous effects on growth of the epithelial target cells, or through indirect effects on the tumor microenvironment, suggesting that analysis of both normal tissue and tumor tissue may inform mechanistic understanding of the genetic basis of heritable susceptibility.

Although some susceptibility loci are in linkage with one or more genes, association studies cannot identify the causal variations or explain their mechanism. To generate mechanistic hypotheses, germline variants can be associated with constitutive gene expression levels to identify expression Quantitative Trait Loci (eQTL). These eQTL act in a tissue-specific and context-specific manner, reflecting changes to the genetic architecture as cells differentiate or transform into malignant tumors (Brem et al., 2002; Dimas et al., 2009; Emilsson et al., 2008; Li et al., 2013; Morley et al., 2004; Nica et al., 2011; Quigley et al., 2009; Quigley and Balmain, 2009; Quigley et al., 2011). When a variant is linked to both tumor susceptibility and expression of a nearby gene, this suggests that the mechanism of the susceptibility variant may involve transcriptional control of that gene. We searched for loci

linked to gene expression in normal breast tissue and in breast adenocarcinomas. Here we show that rs7716600, a Single Nucleotide Polymorphism (SNP) located at chromosome 5p12 and specifically linked to ER-positive breast tumor susceptibility in several GWAS (Kim et al., 2012; Li et al., 2011; Milne et al., 2011; Ruiz-Narvaez et al., 2010; Stacey et al., 2008), was significantly associated with MRPS30 gene expression in ER-positive tumors in two independent datasets. We also provide *in vivo* and *in vitro* data suggesting a mechanism for how this locus influences expression of MRPS30.

# 2. Materials and methods

## 2.1. Genetic and genomic data

Three previously published breast adenocarcinoma gene expression datasets (MicMa: GSE19783, DBCG82bc: GSE24117, MDG: GSE18672) were combined for the discovery tumor dataset. MicMa data (Agilent Whole Genome 44k) and DBCG82bc (ABI Genome Survey) were separately background-corrected and quantile normalized. MDG (an Agilent two-color array) was normalized within arrays. Probes were collapsed to 8147 genes by *entrez identifier* using the mean of probes annotated as measuring the same gene with Pearson correlation > 0.8; where no pair of probes met this cut-off, the mean value of all probes assigned to a gene was used. All microarray normalization was performed using the *limma* package (Gentleman, 2005) in the R statistical environment version 2.14 (R Development Core Team, 2012). Discovery expression data were mean-centered and standardized within datasets. Two cohorts were combined for the normal dataset (MDG: GSE18672, Ahus: GSE48067). The normal datasets share probe identities and were standardized and batch-corrected using *ComBat* (Johnson et al., 2007). The normal breast dataset consisted of 16,651 genes. The validation dataset (Illumina Human Genome microarray) was published in (Curtis et al., 2012). DNA methylation status was assessed with the Illumina HumanMethylation450 microarray, testing probe cg04713108_MRPS30. Discovery genotypes were measured
using the Illumina Human 660k platform and CRLMM (Ritchie et al., 2009), rejecting any array with >10% missing calls. Individual SNPs with >10% missing calls or a Minor Allele Frequency <5% were discarded. Genotype imputation was performed with IMPUTE (Howie et al., 2009) and HapMap3 R2 B36 reference genotypes. Linkage scores were calculated with PLINK (Purcell et al., 2007), and linkage heat maps were plotted with the LDheatmap library (Ji-Hyung Shin et al., 2006). The study was approved by REC SouthEast (Regional Ethical Committee for Medical and Health Research Ethics) and by the Ethical Committee for Aarhus county and by “Data-tilsynet” (The Data Inspectorate, an independent administrative body under the Ministry of Government Administration and Reform). All patients have given consent to the use of material for research purposes in adherence with the Declaration of Helsinki Principles.

## 2.2. Statistical analysis

Association between genotypes and gene expression was assessed by linear regression. For each gene we identified the SNP most significantly associated with that gene's expression in a 1 Mb window around the transcription start site and reported this as the raw P value. eQTL significance was assessed by permuting the genotype assignments for SNPs in the cis-window 10,000 times and identifying the rank of the observed statistic in the permuted statistics. This process indicated a 5% False Discovery Rate would be obtained by a permutation P value cut-off of $P \le 0.0002$ in the normal dataset and $P \le 0.001$ in the tumor dataset. Spearman rank correlation significance was assessed with a permutation method which established a 5% Genome-wide error rate as in (Churchill and Doerge, 1994). Gene Ontology enrichment analysis was calculated with BiNGO (Maere et al., 2005).

## 2.3. Cell culture

MCF-7 cells were plated and grown for 24 h in DMEM containing phenol red and supplemented with 10% serum, 2 mM L-glutamine, 50 U/ml penicillin and 50 µg/ml streptomycin (all from Life Technologies GmbH). For hormone deprivation experiments, cells were grown for three days in DMEM without phenol red (Life Technologies GmbH) and supplemented with 5% charcoal stripped heat-inactivated FBS (HyClone), 2 mM L-glutamine, 50 U/ml penicillin and 50 µg/ml streptomycin. At day three, cells were stimulated with vehicle (ethanol) or 10 nM estradiol (Sigma-Aldrich) for 12 h.

## 2.4. Formaldehyde-assisted isolation of regulatory elements (FAIRE)

For FAIRE experiments, we followed the protocol described in (Giresi and Lieb, 2009) with minor modifications. Nucleosome-free DNA was detected by Real-time PCR.

## 2.5. Chromatin immunoprecipitation (ChIP)

After estrogen stimulation cells were crosslinked with 1% formaldehyde (diluted in clear DMEM) for 10 min at room temperature. Glycine was added (125 mM final concentration) and

left at room temperature for 5 min to quench formaldehyde. Cells were then scraped in cold PBS containing protease inhibitors (Roche). For lysate, each plate of cells (around 80% confluent) was resuspended in 250 µl of lysis buffer (1% SDS, 10 mM EDTA, 50 mM Tris-HCl [pH 8.1]) supplemented with protease inhibitors (Roche). Chromatin was sonicated with a Bioruptor sonicator (Diagenode) to obtain fragments of 200–600 bp in size (setting: 15 min, 30 s ON/30 s OFF). After centrifugation (10 min, 14,000 g, 4 °C), the sheared chromatin was diluted in dilution buffer (1% Triton, 2 mM EDTA, 150 mM NaCl, 20 mM Tris-HCl [pH 8.1]) supplemented with protease inhibitors (Roche). For each ChIP, 5 µg of antibody pre-conjugated with 50 µl of Protein A- or G-magnetic beads (Life Technologies GmbH) were then added to the diluted chromatin. Antibodies used were: ERα (sc-543, Santa Cruz Biotechnology) and CTCF (07-729 from Millipore). Chromatin and the combination of antibody/magnetic beads were mixed at 4 °C with rotation O/N, in a final volume of 1.5 ml. DNA-protein interactions were washed 6 times with RIPA buffer (50 mM Hepes [pH7.6], 1 mM EDTA, 0.7% Na-deoxycholate, 1% NP-40, 0.5 M LiCl). Chromatin was eluted with 100 µl of elution buffer (1% SDS, 50 mM NaHCO3) and reverse cross-linked at 65 °C for 6–16 h. For real-time PCR, DNA was purified with NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) following the manufacturer's instructions.

Primers: MRPS30 promoter.

FW 5'-CTCCCAGCGTCAGGTCTAAG-3' REV 5'-TGTTGCGTCATTCATGGTTT-3'.

Primers: CTCF site.

FW 5'-TGTGAAGCATGAGGTGAACAA-3' REV 5'-TCTGGCAGAGATGCAGCTATTA-3'.

Primers: DNase I hypersensitive region:

FW 5'-TCCTGTCTTCCATCCCAGAG-3' REV 5'-CAAGCAGCCCTCATGAAACT-3'.

# 3. Results

## 3.1. MRPS30 expression is associated with rs7716600 in ER-positive tumors

We first performed a genome-wide analysis of cis-acting eQTLs in 97 normal breast tissue samples and 284 breast adenocarcinomas. The tumor cohort comprised 216 ER-positive tumors and 68 ER-negative tumors. We limited the study to cis-acting loci because of insufficient power to identify trans-eQTLs, defining cis-acting loci as residing in a one megabase window around each gene's annotated transcription start site. Statistical significance was assessed by permutation (see Methods and experimental summary in Supplemental Figure 1). This analysis identified 57 genes in the normal dataset and 170 genes in the tumor dataset with cis-eQTLs significant at a 5% false discovery rate (Figure 1a, Supplemental Tables 1 and 2). Breast tumor eQTLs were distributed across the genome and no region of the genome was significantly over-represented in the number of eQTLs detected. When we analyzed ER-positive and ER-negative cohorts separately, 24 genes were significant only in ER-positive tumors while two were significant only in ER-negative tumors. Seventeen
<figure><img src="image_4.png" /><figcaption>Figure 1 – Variation at rs7716600 is associated with *MRPS30* expression in ER-positive breast cancer. (a) Manhattan plot of the most significant *cis*-eQTL for each gene in the tumor dataset. Horizontal line indicates genome-wide significance. (b) Manhattan plot of the association between genotypes in the region near rs7716600 and expression of *MRPS30* with a significant peak at rs7716600, drawn in blue. The location of *MRPS30* is noted as a blue box. Below this, a heat map of linkage of loci near *MRPS30* (*r*²); rs7716600 indicated by arrow. (c) Figure (b) drawn for validation data, indicating the same locus was most significantly associated with *MRPS30* expression. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

genes had significant eQTL in both normal tissue and tumor datasets. The larger number of significant eQTLs detected in ER-positive tumors may reflect both the larger number of samples in this cohort and differences in the genetic architecture between cohorts.

To select for loci likely to be relevant specifically in breast cancer, we compared our list of genes with significant eQTLs to a database of genes with previously reported eQTLs in tissues such as lymphoblastoid cell lines, liver, and brain tissue (Myers et al., 2007; Schadt et al., 2008; Stranger et al., 2007; eqtl.uchicago.edu). Most of the significant variants we detected affect genes whose expression have been reported to be under genetic control in several human tissues, such as endoplasmic reticulum aminopeptidase 2 (ERAP2) (Dimas et al., 2009; Pickrell et al., 2010). Of the 170 genes with significant eQTL in tumors, 135 had previously been identified in studies of other normal

human tissues. This suggested that we were powered to detect eQTLs with relatively large effects, and that many of the loci that we found affect more than one tissue type.

In Table 1 we report 29 genes with significant breast tumor cis-eQTL for which no previous study has, to our knowledge, reported an eQTL. One of these was rs7716600 on chromosome 5 at 44,875,004 bases, which was significantly associated with expression of MRPS30 (Figure 1b). Locus 5p12 has been associated with susceptibility to ER-positive breast cancer in several GWAS (Kim et al., 2012; Li et al., 2011; Milne et al., 2011; Ruiz-Narvaez et al., 2010; Stacey et al., 2008). The rs7716600 locus is approximately 60 kilobases (kB) downstream from MRPS30. Li et al. reported the strongest association on 5p12 was rs7716600 ($P = 7.06 \times 10^{-7}$, with each A allele increasing the odds ratio of breast cancer by 1.24). Our data support previous reports (Stacey et al., 2008) indicating
<table><caption>Table 1 — eQTLs significant in tumors and not previously identified in the UChicago eQTL database.</caption><thead><tr><th>Symbol</th><th>SNP</th><th>Locus</th><th>Chr.</th><th>Mb</th><th>P disc.</th><th>P perm. disc.</th><th>P validation</th></tr></thead><tbody><tr><td>CYP2J2</td><td>rs11572255</td><td>1p31.3-p31.2</td><td>1</td><td>60.15</td><td>6.8 × 10<sup>-6</sup></td><td>0.0007</td><td>0.012</td></tr><tr><td>PRMT6</td><td>rs1623927</td><td>1p13.3</td><td>1</td><td>107.40</td><td>6.1 × 10<sup>-12</sup></td><td>&lt;0.0001</td><td>7.9 × 10<sup>-12</sup></td></tr><tr><td>C1D</td><td>rs10203061</td><td>2p13-p12</td><td>2</td><td>68.14</td><td>2.8 × 10<sup>-24</sup></td><td>&lt;0.0001</td><td>0.304</td></tr><tr><td>VILL</td><td>rs6806209</td><td>3p21.3</td><td>3</td><td>38.43</td><td>2.1 × 10<sup>-5</sup></td><td>0.0008</td><td>0.630</td></tr><tr><td>ZNF654</td><td>rs6805957</td><td>3p11.1</td><td>3</td><td>88.34</td><td>5.7 × 10<sup>-7</sup></td><td>&lt;0.0001</td><td>0.006</td></tr><tr><td>NDUFB4</td><td>rs373188</td><td>3q13.33</td><td>3</td><td>121.99</td><td>2.0 × 10<sup>-5</sup></td><td>0.0007</td><td>0.770</td></tr><tr><td>RAB33B</td><td>rs7681614</td><td>4q28</td><td>4</td><td>140.52</td><td>2.7 × 10<sup>-13</sup></td><td>&lt;0.0001</td><td>NA</td></tr><tr><td>FAM198B</td><td>rs 1928267</td><td>4q32.1</td><td>4</td><td>159.36</td><td>5.1 × 10<sup>-6</sup></td><td>0.0005</td><td>NA</td></tr><tr><td>MRPS30</td><td>rs7716600</td><td>5p12</td><td>5</td><td>44.91</td><td>7.0 × 10<sup>-7</sup></td><td>&lt;0.0001</td><td>4.0 × 10<sup>-4</sup></td></tr><tr><td>ZCCHC10</td><td>rs10793814</td><td>5q31.1</td><td>5</td><td>132.77</td><td>8.3 × 10<sup>-6</sup></td><td>0.0008</td><td>0.285</td></tr><tr><td>TBCC</td><td>rs10948067</td><td>6p21.1</td><td>6</td><td>43.31</td><td>4.7 × 10<sup>-6</sup></td><td>0.0004</td><td>0.154</td></tr><tr><td>PSPH</td><td>rs1113765</td><td>7p11.2</td><td>7</td><td>55.86</td><td>8.2 × 10<sup>-20</sup></td><td>&lt;0.0001</td><td>0.746</td></tr><tr><td>GTPBP10</td><td>rs1029365</td><td>7q21.13</td><td>7</td><td>89.75</td><td>2.7 × 10<sup>-6</sup></td><td>0.0003</td><td>NA</td></tr><tr><td>RASEF</td><td>rs4391505</td><td>9q21.32</td><td>9</td><td>84.79</td><td>9.3 × 10<sup>-29</sup></td><td>&lt;0.0001</td><td>4.0 × 10<sup>-20</sup></td></tr><tr><td>PYROXD1</td><td>rs1476905</td><td>12p12.1</td><td>12</td><td>21.49</td><td>9.3 × 10<sup>-9</sup></td><td>&lt;0.0001</td><td>3.8 × 10<sup>-5</sup></td></tr><tr><td>FAM60A</td><td>rs256722</td><td>12p11</td><td>12</td><td>30.91</td><td>1.0 × 10<sup>-5</sup></td><td>0.0008</td><td>0.787</td></tr><tr><td>ZBTB1</td><td>rs1542313</td><td>14q23.3</td><td>14</td><td>64.07</td><td>9.4 × 10<sup>-10</sup></td><td>&lt;0.0001</td><td>0.053</td></tr><tr><td>PTGR2</td><td>rs8500</td><td>14q24.3</td><td>14</td><td>73.50</td><td>1.2 × 10<sup>-6</sup></td><td>0.0001</td><td>NA</td></tr><tr><td>EIF3C</td><td>rs6565259</td><td>16p11.2</td><td>16</td><td>28.81</td><td>8.0 × 10<sup>-8</sup></td><td>&lt;0.0001</td><td>2.0 × 10<sup>-7</sup></td></tr><tr><td>TMEM220</td><td>rs440655</td><td>17p13.1</td><td>17</td><td>10.56</td><td>1.3 × 10<sup>-7</sup></td><td>&lt;0.0001</td><td>0.091</td></tr><tr><td>PSMG2</td><td>rs11080611</td><td>18p11.21</td><td>18</td><td>12.90</td><td>3.1 × 10<sup>-6</sup></td><td>0.0004</td><td>0.531</td></tr><tr><td>UBL5</td><td>rs3815746</td><td>19p13.3</td><td>19</td><td>9.94</td><td>3.0 × 10<sup>-6</sup></td><td>0.0001</td><td>0.102</td></tr><tr><td>KLF2</td><td>rs3765070</td><td>19p13.11</td><td>19</td><td>15.90</td><td>8.1 × 10<sup>-6</sup></td><td>0.0007</td><td>0.425</td></tr><tr><td>IGFLR1</td><td>rs2871921</td><td>19q13.12</td><td>19</td><td>40.92</td><td>3.3 × 10<sup>-18</sup></td><td>&lt;0.0001</td><td>0.003</td></tr><tr><td>IRF3</td><td>rs7259683</td><td>19q13.3-q13.4</td><td>19</td><td>54.85</td><td>5.5 × 10<sup>-6</sup></td><td>0.0003</td><td>0.007</td></tr><tr><td>C20orf54</td><td>rs6140090</td><td>20p13</td><td>20</td><td>0.68</td><td>2.1 × 10<sup>-6</sup></td><td>0.0005</td><td>0.001</td></tr><tr><td>YBEY</td><td>rs2839186</td><td>21q22.3</td><td>21</td><td>46.51</td><td>3.8 × 10<sup>-7</sup></td><td>&lt;0.0001</td><td>0.034</td></tr><tr><td>ARFGAP3</td><td>rs1807591</td><td>22q13.2</td><td>22</td><td>41.52</td><td>5.1 × 10<sup>-6</sup></td><td>0.0008</td><td>8.3 × 10<sup>-4</sup></td></tr></tbody></table>

Genes listed as NA were called not expressed in the validation dataset. The *P disc.* and *P validation* columns report the raw *P* value of the eQTL in discovery and validation datasets, while the *P perm. disc.* column reports the permutation-adjusted *P* value in the discovery dataset.

that the region near MRPS30 has a low recombination rate (Figure 1b).

We replicated the eQTL analysis for the 29 associations listed in Table 1 in an independent cohort of 235 breast tumors where both genotype and expression data were available (Curtis et al., 2012). In light of a recent report suggesting many published cis-eQTLs are technical artifacts caused by SNPs located in the binding region of microarray probes (Ramasamy et al., 2013), we note that both gene expression and genotypes in the discovery and validation microarray datasets were measured on distinct platforms. The genotypes of SNPs present in the discovery, but not validation dataset were imputed (see Methods). In this analysis rs7716600 was the 5p12 SNP most significantly associated with MRPS30 expression ($P = 4 \times 10^{-4}$, permutation $P = 0.019$, Figure 1c), with association only in ER-positive tumors. This finding, significant after Bonferroni correction for 29 tests, confirmed the significance of a genetic link between this locus and expression of MRPS30 mRNA. We focused our subsequent analysis on MRPS30 because it was the only candidate gene from a GWAS among the 29 breast-specific genes that were identified.

## 3.2. MRPS30 response to estrogen stimulation is associated with rs7716600

MRPS30 is a constituent of the mitochondrial ribosome, which generates proteins which produce the bulk of the ATP in

eukaryotic cells. Mitochondrial ribosomal proteins are translated in the cytoplasm from nuclear genes and imported into mitochondria, where they are essential for oxidative phosphorylation. We found that the less frequent risk-associated A allele of rs7716600 was significantly associated with higher expression of MRPS30 in ER-positive but not ER-negative tumors in the discovery and validation cohorts (Figure 2a, b). This is compatible with GWAS results showing that the locus affects susceptibility to ER-positive but not ER-negative breast cancer (Kim et al., 2012; Li et al., 2011; Milne et al., 2011; Ruiz-Narvaez et al., 2010; Stacey et al., 2008). In both the discovery and validation datasets MRPS30 expression levels in ER-negative tumors and in ER-positive tumors homozygous for the C allele of rs7716600 were comparable (Figure 2a, b). Elevated MRPS30 expression was exclusively associated with both estrogen sensitivity and presence of an A allele.

Since the 5p12 locus is relevant only in ER-positive tumors, we compared MRPS30 co-expression networks in ER-positive and ER-negative tumors separately to suggest estrogen-mediated differences in MRPS30 function. The genes most significantly correlated in each condition were distinct and functionally enriched for different functions. In ER-positive tumors MRPS30 expression was correlated significantly with 282 genes (Supplemental Table 3). Among the strongest direct correlations were genes in the estrogen signaling pathway, including the estrogen receptor itself (ESR1) and other genes
<figure><img src="image_5.png" /><figcaption>Figure 2 – Expression of *MRPS30* is influenced by estrogen levels in MCF-7 cells. (a) Mean-centered standardized expression of *MRPS30* plotted against rs7716600 genotype in discovery ER-negative and ER-positive tumors, demonstrating that this locus is only associated with *MRPS30* expression in ER-positive tumors. Horizontal bars indicate mean expression level per genotype. (b) Log<sub>2</sub> expression of *MRPS30* plotted against rs7716600 genotype in validation ER-negative and ER-positive tumors. (c) Expression of *MRPS30* in ER-positive MCF-7 and 600MPE and ER-negative SUM159 cell lines after 12 h of estrogen exposure (blue bar) compared to vehicle (gray bar), indicating *MRPS30* expression responds directly to estrogen exposure in ER-positive cells and that the direction of response at 12 h is associated with rs7716600 genotype. Error bars indicate 1 s.d.</figcaption></figure>

crucial for the estrogen response such as GATA3 and FOXA1 (Table 2). The 282 ER-positive genes were significantly enriched for roles in response to estrogen stimulus, anti-apoptosis, positive regulation of epithelial cell proliferation, and cell differentiation (all corrected *P* < 8 × 10<sup>-4</sup>, hypergeometric test, see Methods).

The ER-negative correlation list (Table 3) comprised 248 genes enriched for roles in the M phase of the mitotic cell cycle (*P* = 6 × 10<sup>-13</sup>, see Methods and Supplemental Table 4) and RNA metabolism (*P* = 4 × 10<sup>-5</sup>), which was not the case in ER-positive tumors. The strongest correlations were with PES1 (pescadillo ribosomal biogenesis factor 1) and BRIX1 (biogenesis of ribosomes), genes essential for ribosomal assembly (Kaser et al., 2001; Lerch-Gaggl et al., 2002). In the normal breast tissue cohort no gene was significantly correlated with MRPS30, and no tested SNP had a significant eQTL for MRPS30 expression (*P* > 0.05). Searching at lower

stringency, we did not find evidence for a correlation between MRPS30 and genes in the estrogen response signature in normal tissue (data not shown). These results were concordant with role of MRPS30 in mitochondrial protein synthesis in ER-negative tissues, but suggested that MRPS30 transcription levels are affected by the estrogen signaling pathway in ER-positive tumors.

We next tested whether MRPS30 transcription responds directly to estrogen stimulus, and whether this response is distinct depending on rs7716600 genotype. Published SNP data (Heiser et al., 2012) predicted the luminal breast cancer lines cell lines 600MPE and MCF-7 would have the genotypes AA and CC respectively at rs7716600. We confirmed by Sanger sequencing that MCF-7 has the more common CC genotype, while 600MPE has the rarer risk-associated AA genotype. After 12 h of estrogen treatment, 600MPE cells express on average 1.5 times more MRPS30 (*P* = 0.002, t test, Figure 2c, see
<table><caption>Table 2 – *MRPS30* expression is correlated with estrogen signaling genes in ER-positive tumors.</caption><thead><tr><th rowspan="2">symbol</th><th colspan="2">ER-positive</th><th colspan="2">ER-negative</th></tr><tr><th>rho</th><th>P</th><th>rho</th><th>P</th></tr></thead><tbody><tr><td>SLC39A6</td><td>0.50</td><td>2 × 10<sup>-17</sup></td><td>0.23</td><td>0.02</td></tr><tr><td>TPRG1</td><td>0.48</td><td>2 × 10<sup>-15</sup></td><td>-0.12</td><td>0.30</td></tr><tr><td>ESR1</td><td>0.45</td><td>2 × 10<sup>-14</sup></td><td>0.04</td><td>0.70</td></tr><tr><td>P4HTM</td><td>0.44</td><td>4 × 10<sup>-14</sup></td><td>0.10</td><td>0.30</td></tr><tr><td>CA12</td><td>0.44</td><td>8 × 10<sup>-14</sup></td><td>-0.06</td><td>0.60</td></tr><tr><td>MAGED2</td><td>0.44</td><td>1 × 10<sup>-13</sup></td><td>0.09</td><td>0.40</td></tr><tr><td>SIAH2</td><td>0.43</td><td>5 × 10<sup>-13</sup></td><td>0.30</td><td>4 × 10<sup>-3</sup></td></tr><tr><td>ELP2</td><td>0.42</td><td>6 × 10<sup>-13</sup></td><td>0.27</td><td>8 × 10<sup>-3</sup></td></tr><tr><td>HEXIM2</td><td>0.41</td><td>4 × 10<sup>-12</sup></td><td>-0.02</td><td>0.80</td></tr><tr><td>C17orf75</td><td>0.41</td><td>4 × 10<sup>-12</sup></td><td>0.29</td><td>5 × 10<sup>-3</sup></td></tr><tr><td>HPN</td><td>0.41</td><td>5 × 10<sup>-12</sup></td><td>0.09</td><td>0.40</td></tr><tr><td>GATA3</td><td>0.40</td><td>4 × 10<sup>-11</sup></td><td>0.04</td><td>0.70</td></tr><tr><td>WWP1</td><td>0.40</td><td>1 × 10<sup>-11</sup></td><td>0.02</td><td>0.90</td></tr><tr><td>SEMA3B</td><td>0.40</td><td>2 × 10<sup>-11</sup></td><td>NA</td><td>NA</td></tr><tr><td>POLB</td><td>0.40</td><td>2 × 10<sup>-11</sup></td><td>0.37</td><td>2 × 10<sup>-4</sup></td></tr><tr><td>COMMD4</td><td>0.39</td><td>4 × 10<sup>-11</sup></td><td>0.22</td><td>0.03</td></tr><tr><td>UGCG</td><td>0.39</td><td>7 × 10<sup>-11</sup></td><td>-0.13</td><td>0.20</td></tr><tr><td>C6orf211</td><td>0.39</td><td>9 × 10<sup>-11</sup></td><td>0.24</td><td>0.02</td></tr><tr><td>FOXA1</td><td>0.39</td><td>8 × 10<sup>-11</sup></td><td>-0.07</td><td>0.50</td></tr><tr><td>C6orf97</td><td>0.39</td><td>1 × 10<sup>-10</sup></td><td>NA</td><td>NA</td></tr></tbody></table>

This ranked list of the 20 genes most significantly correlated with MRPS30 in ER-positive tumors shows MRPS30 expression is significantly correlated with key estrogen pathway genes including ESR1, GATA3, and FOXA1. The rho columns indicate Spearman's rho values, while the P columns indicate the P values.

<table><caption>Table 3 – *MRPS30* expression is correlated with Mitochondrial & Mitotic genes in ER-negative tumors.</caption><thead><tr><th rowspan="2">symbol</th><th colspan="2">ER-positive</th><th colspan="2">ER-negative</th></tr><tr><th>rho</th><th>P</th><th>rho</th><th>P</th></tr></thead><tbody><tr><td>PES1</td><td>-0.05</td><td>0.40</td><td>0.51</td><td>1 × 10<sup>-7</sup></td></tr><tr><td>BRIX1</td><td>0.06</td><td>0.30</td><td>0.47</td><td>2 × 10<sup>-6</sup></td></tr><tr><td>KIAA1524</td><td>0.04</td><td>0.50</td><td>0.46</td><td>3 × 10<sup>-6</sup></td></tr><tr><td>SKP2</td><td>NA</td><td>NA</td><td>0.45</td><td>9 × 10<sup>-6</sup></td></tr><tr><td>CECR5</td><td>-0.01</td><td>0.90</td><td>0.44</td><td>1 × 10<sup>-5</sup></td></tr><tr><td>UBQLN4</td><td>0.05</td><td>0.40</td><td>0.44</td><td>1 × 10<sup>-5</sup></td></tr><tr><td>PAIP1</td><td>0.25</td><td>5 × 10<sup>-5</sup></td><td>0.43</td><td>2 × 10<sup>-5</sup></td></tr><tr><td>MRPL3</td><td>0.10</td><td>0.10</td><td>0.43</td><td>4 × 10<sup>-5</sup></td></tr><tr><td>RAD1</td><td>0.18</td><td>3 × 10<sup>-3</sup></td><td>0.42</td><td>3 × 10<sup>-5</sup></td></tr><tr><td>CKM</td><td>NA</td><td>NA</td><td>0.42</td><td>6 × 10<sup>-5</sup></td></tr><tr><td>NHEJ1</td><td>-0.03</td><td>0.70</td><td>0.40</td><td>6 × 10<sup>-5</sup></td></tr><tr><td>TADA1</td><td>0.13</td><td>0.03</td><td>0.40</td><td>7 × 10<sup>-5</sup></td></tr><tr><td>HDDC2</td><td>-0.08</td><td>0.20</td><td>0.40</td><td>6 × 10<sup>-5</sup></td></tr><tr><td>MAD2L2</td><td>-0.01</td><td>0.90</td><td>0.40</td><td>8 × 10<sup>-5</sup></td></tr><tr><td>NIPSNAP1</td><td>0.06</td><td>0.40</td><td>0.40</td><td>7 × 10<sup>-5</sup></td></tr><tr><td>MINPP1</td><td>0.19</td><td>2 × 10<sup>-3</sup></td><td>0.40</td><td>7 × 10<sup>-5</sup></td></tr><tr><td>NIF3L1</td><td>0.18</td><td>4 × 10<sup>-3</sup></td><td>0.40</td><td>9 × 10<sup>-5</sup></td></tr><tr><td>RASA1</td><td>0.21</td><td>7 × 10<sup>-4</sup></td><td>0.39</td><td>8 × 10<sup>-5</sup></td></tr><tr><td>OIP5</td><td>0.12</td><td>0.06</td><td>0.39</td><td>1 × 10<sup>-4</sup></td></tr><tr><td>NADKD1</td><td>0.22</td><td>3 × 10<sup>-4</sup></td><td>0.39</td><td>9 × 10<sup>-5</sup></td></tr></tbody></table>

This ranked list of the 20 genes most significantly correlated with MRPS30 shows that in ER-negative tumors, by contrast, the most significant correlations are with ribosomal biogenesis genes such as PES1 and BRIX1 and cell cycle checkpoint genes such as SKP2, RAD1, and MAD2L2. The rho columns indicate Spearman's rho values, while the P columns indicate the P values.

Methods). In contrast, after 12 h of estrogen exposure, MCF-7 cells express modestly but significantly less MRPS30 at the same time point (0.75-fold decrease, P = 0.004, t test, Figure 2c). As a negative control we also tested ER-negative SUM159 cells, which showed as expected no significant change in MRPS30 expression levels after 12 h of exposure (P = 0.35, t test, Figure 2c). These data suggested that MRPS30 expression was responsive to estrogen stimulus in estrogen-responsive tumor cells and that the direction of the response was associated with rs7716600 genotype.

### 3.3. MRPS30 promoter availability is associated with variation at rs7716600

Reversible increases in histone methylation at gene promoters wind chromatin loops more tightly, reducing DNA availability and thereby affecting transcription levels. We tested whether allelic variation in rs7716600 was associated with methylation of DNA at the MRPS30 promoter using the Illumina HumanMethylation450 microarray in 123 tumor

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

Figure 3 – Allelic variation at rs7716600 is associated with methylation of *MRPS30*. (a) Degree of methylation of the region immediately upstream of the *MRPS30* transcription start site plotted against genotype in ER-negative and ER-positive tumors. Horizontal bars indicate mean methylation level per genotype. (b) Methylation percentage of the same locus plotted against expression of *MRPS30* measured in the same tumors in ER-negative (c) and ER-positive (d) tumors.
samples from the discovery cohort. Decreased methylation of a probe located immediately 5-prime of MRPS30 was significantly associated with the A allele of the rs7716600 genotype in ER-positive but not ER-negative tumors (P = 0.0004 vs. P = 0.37, Figure 3a). Methylation at this locus and expression of MRPS30 were significantly and inversely related, only in ER-positive tumors (P < 2 × 10⁻¹⁶ vs. P = 0.99, Figure 3b). Levels of methylation in ER-negative tumors were high in all three genotypes.

Since rs7716600 is 60 kB downstream of MRPS30 and yet was more strongly associated with MRPS30 expression than any SNP within one megabase of the MRPS30 transcription start site in both the discovery and validation cohorts, we hypothesized that a trans-acting locus may be affecting MRPS30 expression via chromatin remodeling at or near the MRPS30 promoter. Genomic regions hypersensitive to the enzyme DNase I are free of nucleosomes and often bound by enhancers such as the insulator-binding protein CCCTC-binding factor (CCTF) (Gross and Garrard, 1988; Maurano et al., 2012). Data from the ENCODE consortium indicated that a locus 700 bases from rs7716600 contains a DNase I hypersensitivity cluster, binding sites for numerous transcription factors, and histone marks in Human Mammary Epithelial Cells (HMEC) associated with promoters and enhancers (Supplemental Figure 2) (Dunham et al., 2012).

We performed ChIP-PCR to test whether ER-alpha binding at the MRPS30 promoter region and the DNase hypersensitive region near rs7716600 increased after 12 h of estrogen exposure. We measured a two-fold increase in ER-alpha binding at the MRPS30 promoter after estrogen exposure compared to vehicle (P = 0.055, t test, Figure 4a) and a 42% increase in ER-alpha binding at the DNase locus (P = 0.006, t test, Figure 4b) in MCF-7 cells carrying the CC genotype, but no significant increase in 600MPE cells that carry the AA genotype. It was recently reported that binding of ESR1 and the pioneer factor FOXA1 to rs7716600 significantly increase after short-term estrogen exposure in MCF-7 cells (Cowper-Sal et al., 2012), corroborating these findings. Since ENCODE data suggested that both the MRPS30 promoter and a region approximately 12 kB upstream from the SNP contained CTCF binding sites in immortalized HMEC cells (Supplemental Figure 2), we measured the estrogen-dependent binding of CTCF at these loci. Both the MRPS30 promoter and the CTCF locus bound a significantly greater amount of CTCF in response to estrogen stimulus in MCF-7 cells but not 600MPE cells carrying the AA genotype (P = 0.02 vs. P = 0.43, P = 0.016 vs. P = 0.21, t test, Figure 4c, d).

We performed a formaldehyde-assisted isolation of regulatory elements (FAIRE) experiment (Giresi and Lieb, 2009; Gilfillan et al., 2012) to assess whether chromatin changes

<figure><img src="image_8.png" /><figcaption>Figure 4 – Increased binding of ER & CTCF to *MRPS30* in MCF-7 but not 600MPE cells after estrogen exposure. ER-alpha binding levels at the *MRPS30* promoter (a) and near rs7716600 (b) after 12 h of estrogen exposure in MCF-7 and 600MPE cells, indicating that ER-alpha binding levels were increased at both loci in MCF-7 but not 600MPE cells. CTCF binding levels at the *MRPS30* promoter (c) and a predicted CTCF binding site 12 Kb from rs7716600 (d) after 12 h of estrogen exposure in MCF-7 and 600MPE cells, indicating that CTCF binding was increased only in MCF-7 cells. (e) Results from FAIRE analysis of the *MRPS30* promoter in response to 12 h of estrogen exposure showing a significant decrease in nucleosome availability in MCF-7 cells but no change in 600MPE cells. All error bars indicate 1 s.d.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 5 – A model for control of *MRPS30* gene expression in ER-positive tumors by chromatin remodeling. In the absence of estrogen, the *MRPS30* promoter is methylated. *MRPS30* expression is correlated with the production of mitochondrial ribosomes. In tissues which respond to estrogen and possess the more common rs17716600 *CC* genotype, estrogen stimulation causes ER-alpha and CTCF to bind to *MRPS30* and *MRPS30* transcription is repressed. This is not the case in estrogen-responsive tissues with the susceptibility-linked rs17716600 *A* allele. These tumors lack *MRPS30* promoter methylation and respond to estrogen stimulation with increased expression of *MRPS30*.</figcaption></figure>

were induced by estrogen and whether they were associated with rs7716600 genotype. We found reduced euchromatic conditions at the MRPS30 promoter in MCF-7 cells after 12 h of estrogen treatment (P = 0.018, t test, Figure 4e). In contrast, no significant chromatin changes were observed at the same locus in the 600MPE cell line (P = 0.205). These data are compatible with a model (Figure 5) in which transcription of MRPS30 is affected by methylation and chromatin status at its promoter conditional on the rs7716600 genotype.

## 4. Discussion

We have used eQTL analysis of breast tumors to show the rs7716600 risk allele is associated with elevated MRPS30 expression and decreased methylation at the MRPS30 promoter. We hypothesize that elevated MRPS30 expression increases risk. In this model DNA methylation appears to play a protective role. In ER-positive breast tumor cells that carry only the protective C allele of rs7716600, estrogen stimulus causes increased ER and CTCF binding to MRPS30 and decreased availability of the MRPS30 promoter. We speculate that a chromatin loop mediated by CTCF binding can connect the region near rs7716600 to the MRPS30 promoter (Phillips and Corces, 2009; Splinter et al., 2006), and that this is the

mechanism of the variant's action at a distance. Many ER-responsive genes are affected by ER binding at loci distant from the promoter and interact with the promoter via chromatin loops (Pan et al., 2008). Although CTCF binding sites have been reported to be largely invariant between cell lines (Kim et al., 2007) and CTCF binding is not ubiquitously affected by estrogen treatment in breast cancer cell lines, cell-line specific changes in ER-mediated CTCF binding have been observed (Ross-Innes et al., 2011).

This model, summarized in Figure 5, is compatible with the risk effect being present only in ER-positive tumors, and with the effect on MRPS30 expression not being detectable in normal tissue, which contains a mix of ER-positive and ER-negative cells. Our previous eQTL analysis of normal tissue and matched tumors demonstrated that germline influence on cancer susceptibility mediated by gene expression is altered during tumor progression (Quigley et al., 2011). The present study had modest power to identify eQTL in normal breast tissue; to our knowledge no larger study of normal breast tissue has yet been published. Because we did not detect a MRPS30 eQTL in normal tissue, we hypothesize that this locus is relevant after an ER-positive tumor has been initiated.

The causal variant or variants affecting MRPS30 expression are unknown. A limitation of our study is that MCF-7 and
600MPE cells contain many differences apart from their geno-
type at rs7716600; generating an isogenic MCF-7 cell line con-
taining the MRPS30 region from 600MPE was beyond the scope
of this work. Other groups have reported that SNPs upstream
of the MRPS30 promoter are linked to susceptibility, and the
low recombination rate in this region makes isolation of a nar-
row risk locus more difficult. Although eQTL studies are
frequently used to suggest a functional consequence of dis-
ease risk variants (Emilsson et al., 2008; Michailidou et al.,
2013; Quigley et al., 2009), we did not formally demonstrate a
link between elevated MRPS30 expression and risk. This
work does not address the question of why elevated MRPS30
expression would increase breast cancer risk. Changes in
MRPS30 expression may affect the probability of apoptosis
by altering mitochondrial pathways. The mitochondria are
crucial in the induction of apoptosis, and transgenic expres-
sion of MRPS30 (also called p52 and PDCD9) in mouse fibro-
blasts induces apoptosis (Sun et al., 1998). It is not clear
whether the modest changes in constitutive MRPS30 expres-
sion induced by estrogen in physiologically relevant doses
would have the same effect. A study examining interactions
between 27 breast cancer risk alleles and occupational radia-
tion exposure reported that the only nominally significant
interaction was at the risk allele at 5p12 in MRPS30 (Bhatti
et al., 2010). That study, which was not stratified by ER status,
is consistent with an effect of MRPS30 genotype on radiation-
induced apoptosis and breast cancer risk. Alternatively,
MRPS30 could affect ATP production in a way that stimulates
tumor growth.

Our study was motivated by the hypothesis that some sus-
ceptibility loci function by affecting basal gene expression
levels in tumors, but this is unlikely to be true for all variants.
Our unbiased screen of the genome identified only one GWAS
locus associated with gene expression. Mechanisms which
would not be detected by this approach include variants
which modify gene function, protein localization, or post-
translational modification but not expression levels. Somatic
variations affecting expression which are not associated
with germline variation reduce the proportion of variance
attributable to the germline, increasing the difficulty of
isolating eQTL effects (Quigley et al., 2011). However, when so-
matic variation is allele-specific we will still be able to detect a
germline effect. Normal breast tissue and breast tumors have
distinct genetic architectures, and both tissues must be
analyzed to understand how and when a risk allele is relevant.
This study demonstrates that eQTL studies in tumors should
be integrated with genetic analysis of DNA methylation and
functional assays to identify a candidate mechanism behind
risk variants, and that one way variants can affect the
response to estrogen stimulation is by changing gene pro-
moter availability.

Author's contributions

Performed in vitro work: EF, DAQ. Planned and supervised
in vitro work: AH. Performed statistical analysis: DAQ. Contrib-
uted to analysis: SN. Contributed genotyping data: PVL, GGA,
JT, JO. Contributed expression data: HKV, IRB. Contributed
methylation data: TF. Planned and supervised the study: VK,

ABD, AB. Wrote the manuscript: DAQ. All authors read and
approved the final manuscript.

Competing interests

The authors declare no competing interests.

Database references

GEO: GSE19783; GEO: GSE24117; GEO: GSE18672; GEO: GSE48067

Acknowledgements

DAQ thanks Penguin Computing Inc. for a donation of computer
processing time, Matthew Ritchie for assistance with CRLMM,
Stephen Turner for sharing code to generate Manhattan plots,
and Harry Quigley for comments on the manuscript. This
work was supported by NCI grants CA084244-15 and
CA141455-01 to AB. ABD acknowledges DNK grant 4196283563;
VK acknowledges DNK grant 4196163832; ABD and VK acknowl-
edge NFR-FUGE grant 193387/V50 from the Norwegian Research
Council and Helse Sø-Øst grant 2011042 from the South-Eastern
Norway Regional Health Authority. Thanks to the UNIFOR and
Eckbo legater for supporting the genotyping. PVL is a postdoc-
toral researcher of the Research Foundation – Flanders (FWO).

Appendix A.
Supplementary data

Supplementary data related to this article can be found at
http://dx.doi.org/10.1016/j.molonc.2013.11.008.

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