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Co-regulatory expression quantitative trait loci mapping: method and application to endometrial cancer

BMC Medical Genomics20114:6

DOI: 10.1186/1755-8794-4-6

Received: 23 July 2010

Accepted: 12 January 2011

Published: 12 January 2011

Abstract

Background

Expression quantitative trait loci (eQTL) studies have helped identify the genetic determinants of gene expression. Understanding the potential interacting mechanisms underlying such findings, however, is challenging.

Methods

We describe a method to identify the trans-acting drivers of multiple gene co-expression, which reflects the action of regulatory molecules. This method-termed co-regulatory expression quantitative trait locus (creQTL) mapping-allows for evaluation of a more focused set of phenotypes within a clear biological context than conventional eQTL mapping.

Results

Applying this method to a study of endometrial cancer revealed regulatory mechanisms supported by the literature: a creQTL between a locus upstream of STARD13/DLC2 and a group of seven IFNβ-induced genes. This suggests that the Rho-GTPase encoded by STARD13 regulates IFNβ-induced genes and the DNA damage response.

Conclusions

Because of the importance of IFNβ in cancer, our results suggest that creQTL may provide a finer picture of gene regulation and may reveal additional molecular targets for intervention. An open source R implementation of the method is available at http://sites.google.com/site/kenkompass/.

Background

Expression Quantitative Trait Locus (eQTL) mapping searches across the genome for markers associated with individual transcripts to identify loci containing regulatory elements [1, 2]. Although cis regulators are easily interpreted, assigning function to trans regulators is more difficult. At the transcriptional level, genes in trans are often co-regulated by transcription factors binding the same regulatory elements in the noncoding sequences of multiple genes [3]. When looking genome-wide, however, genes across many ontologies acting upstream of transcription factors participate in the co-regulation of genes [4, 5]. Because of the difficulty in predicting the trans-regulators, the majority of transcriptional regulatory proteins remain unknown.

Identification of genetic components of gene co-regulation is important because there is compelling evidence that aberrant gene co-regulation participates in human disease [6, 7]. Investigations into the genetic basis of gene co-regulation have used Bayesian networks to identify cis- and trans-acting factors controlling modules of co-regulated genes [810] or gene clusters. A key result here was the identification of associations that would have been missed when genes were tested individually, as in traditional eQTL. Biologically, this is very compelling because genes typically do not perform their functions in isolation but rather in coordinated groups. The existing Bayesian methods have focused primarily on the identification of yeast regulatory programs where other sources of information, such as sequence conservation, transcription factor binding site (TFBS) data, or protein interaction data are readily available and serve as prior information [8, 10]. Extension of these methods to human genetics with data from HapMap subjects has shown that sequence conservation and cis-regulatory information were the most useful prior data [8].

Studies in other human cohorts and mice have used directed Bayesian networks or undirected weighted gene coexpression networks to incorporate existing marker and phenotype data into models that have made biologically validated predictions [1116]. These network-based methods and their alternatives (e.g. [1721]) show great promise but often have high computational complexity, making them most practical for smaller datasets with limited numbers of traits. Furthermore, prior information is generally not readily available in humans. For example, most TF binding sites remain unknown and even within the same tissue, the vast majority of TFBS appear divergent between human and mouse [22]. This suggests that relying on sequence conservation or the presence of conserved TF binding motifs may miss some key associations and that agnostic, complementary methods should be developed.

Many algorithms that search for the determinants of gene co-regulation assign each gene to a single cluster (e.g. [4, 9, 23]), which is limiting, because genes can belong to different clusters under different biological conditions [24]. More recent network approaches overcome this problem by examining differential canonical correlation between multiple states, such as healthy and diseased, or with a reference [11]; these approaches, however, may rely on methods that are not robust to non-normal data to find correlated genes. This may be a problem for gene microarray expression data, which is often not normally distributed. Alternatively, robust, "mega-clustering" methods have been developed to provide improved estimates of co-regulation for microarray data [25]. One such algorithm-the 'Gene Recommender'-has successfully predicted previously unknown interactions that were verified experimentally in a multicellular organism [26]. A key property of the Gene Recommender is the categorization of genes into clusters under different conditions (i.e., allowing for "biclusters") where different samples' contributions to the given cluster may vary. Since inexpensive genotyping platforms can presently interrogate >1 million SNPs and we are rapidly shifting into the era of whole genome sequencing, existing genetics and systems biology methods would be nicely complemented by computationally feasible, agnostic approaches to the detection of trans-acting factors that regulate groups of genes.

Therefore, here we extend the Gene Recommender with an approach that can systematically identify trans loci controlling gene co-regulation. We broadly refer to the identification of gene co-expression trait loci as 'co-regulatory expression quantitative trait loci' (creQTL) mapping. Unlike trans-eQTL analysis, our method does not consider individual transcripts but rather focuses on multiple co-regulated transcripts. We provide a genome-wide implementation of the Gene Recommender and a statistical framework for association testing. The key steps of the creQTL approach are: gene clustering; calculation of each sample's similarity to each cluster; and statistical testing of how well genotype explains the similarity. We applied creQTL to a study of germline variants and tumor expression in endometrial cancer [27] and identified many loci significantly associated with gene co-regulation. These loci were commonly in noncoding regions closely in cis with genes encoding proteins required for transcription, signaling, cell adhesion, and development. Our results suggest that associating genetic variants with co-regulation via creQTL mapping provides an efficient and agnostic avenue for detecting biological factors important in the coordinate regulation of groups of genes.

Methods

creQTL Approach

To identify trans regulatory associations, creQTL mapping tests for the association between genetic variants spanning the genome and clusters of co-regulated genes. The variant data can be obtained, for example, from single nucleotide polymorphism (SNP) arrays. Gene expression data can be obtained from standard gene expression arrays or other high-throughput methods for mRNA quantification.

Gene clustering is based on a recursive, heuristic implementation of the Gene Recommender (GR) [26], automated for genome-wide application. GR is a rank-based biclustering algorithm with an existing, open source R implementation [28]. Here we outline relevant portions of the GR. First, gene expression data are normalized to a uniform distribution with mean zero and variance 1/3. A suggested gene list, based on prior knowledge, of at least two putative co-regulated genes is input to GR. For each putative cluster input, each sample's relative score, the "ZE(j)", is computed, as in Equation 1.
Z E ( j ) = k j Y ¯ Q , j V ^ Q , j + 1 3 p 2
(1)
where k j is the number of gene expression values in sample j, p is the number of samples, and Y ¯ Q , j is the median gene cluster expression value in sample j. Note that the original implementation of Gene Recommender used the mean gene cluster expression value in the numerator of ZE(j) [26]; later versions of the algorithm used the median [28]. Here we used the latest version. V ^ Q , j is the sample variance, given by
V ^ Q , j = k j 1 k j var ( Y Q , j )
(2)

where var(Y Q, j ) is the variance of the genes within the gene cluster in sample j (both equations, ref. [26]). ZE(j) has an approximate Student's t null distribution and larger values indicate tighter co-regulation of clustered genes within sample j. Once ZE(j) has been computed, an incremental approach is used to compute the correlation coefficient, s.g.i., with a scoring function that minimizes the number of nonquery genes scoring higher than those in the query. The final s.g.i., which is proportional to the Euclidean distance, is then based on the most informative experiments.

It is not computationally feasible to compute all possible gene clusters from the dataset with GR, so heuristics are necessary to find tightly co-regulated gene clusters. Our extensions permit genome-wide use of the GR by automatically selecting putative co-regulated genes, running the algorithm, and then recursively modifying the query using leave-one-out cross validation (LOOCV) as a scoring function until the cluster converges at a point where all query genes contribute approximately equally to the cluster. The procedure is as follows. First, using each gene as a seed for a potential cluster, initial predictions of co-regulated genes are made using Spearman's rank correlation. If the number of highly correlated genes is less than 5 or more than 20, the putative cluster is expanded or trimmed to the most correlated 5 or 20 genes, respectively. Next, the Gene Recommender algorithm is run with this initial gene cluster, and then rerun using the top hits (by s.g.i, the Gene Recommender normalized correlation metric) from the initial run. If the seed gene scores highly after the second run (i.e., within the top 50 genes most correlated with the putative cluster) LOOCV is used to trim the cluster to only the highest scoring hits (regardless of gene set size). Once a tightly regulated cluster has been found, the next-highest scoring genes are added incrementally while stringently keeping LOOCV scores low. Ultimately, all genes within a predicted cluster will have an approximately equivalent contribution to the cluster, as determined by LOOCV.

Once gene clusters were assigned, for creQTL association testing, we computed a modified version of ZE(j), called Z2E(j), to describe how tightly regulated the gene cluster was within a given sample. The modified version replaces the numerator of ZE(j) with the mean of all genes' contributions to the cluster and adds a small positive constant, s0, to the denominator in order to moderate variance and avoid extremely large values of Z2E(j). s0 was chosen by minimizing the coefficient of variation of the denominator of ZE(j) across moving windows of data, akin to the strategy of Tusher et al. [29] for the moderation of t statistics. The modified statistic for association testing is
Z 2 E ( j ) = k j m e a n ( Y ¯ Q , j Y Q , j ) V ^ Q , j + 1 3 p 2 + s 0
(3)

In place of the median expression value in the numerator of the original ZE(j), we multiplied the sample's median gene expression value with the expression values for each individual gene within the cluster, and computed the mean of the resulting vector. Although this strategy will produce a higher Z2E(j) in the case of a cluster with strongly driven outliers, it will also produce a lower Z2E(j) in the presence of weakly driven outliers, even when the cluster is otherwise strongly driven. Thus, a larger priority was given to penalizing strongly driven clusters with weakly driven outliers than the opposite. Overall, this produced smaller values of Z2E(j) and gave a slightly greater dispersion of clusters with outliers when compared to taking the median gene expression value (not shown). Our software implementation provides a choice of either calculation for the numerator.

The association between genotypes and the Z2E(j) statistic was then evaluated with Bartlett's k-sample variance test [30], using the 'bartlett.test' function in R to find significant differences in the variance of Z2E(j) across each genotype using additive coding (i.e. AA, AB, BB). Resulting p-values were then adjusted for FDR using the 'p.adjust' function in R and the method of Benjamini and Hochberg [31].

Application of creQTL to Endometrial Cancer

We applied creQTL mapping to a study of 52 endometrial tumor samples with genotype and expression data available (NCBI GEO 14860; [27]). We downloaded the raw genotype and normalized microarray data from GSE14860. Genotype data were from Affymetrix 100K SNP chips, and calls were made with the 'crlmm' function from the 'oligo' library (v1.10.0) [32] for Bioconductor (v2.5; [33]) and R (v2.10.0; [34]). Prior to association testing, SNP markers were prefiltered to satisfy the following stringent conditions for each marker: a call confidence value of at least 95% in 51/52 samples, no significant deviations from Hardy-Weinberg equilibrium at p = 0.05 (with 'snpMatrix' v1.8.0 [32]), and at least two different genotypes observed with at least 5 samples for each genotype. This was analogous to requiring a SNP minor allele frequency of 5% if two genotypes were observed, and 14% if all three genotypes were seen. To enrich the microarray data for abundant and strongly-hybridized transcripts, we calculated the variance of each probe and excluded the lowest 50% from our analyses. Microarray data were ranked and normalized to mean 0 and variance 1/3 across each gene, the default for the Gene Recommender algorithm. SNP r2 values were calculated with 'snpMatrix.' Because all 52 samples were from a single county in Norway [27], we did not perform any adjustments for population stratification.

For comparison with the creQTL results, we also undertook an eQTL analysis of the EC dataset. Here, the microarray data were normalized to have a mean of zero and variance equal to one within each individual sample. cis eQTL analysis evaluated all SNPs within 5 MB of a gene using an analysis of variance (ANOVA) of expression level on genotype and including covariates identified with surrogate variable analysis [35]. trans eQTL was done similarly, focusing on the remaining SNPs > 5 MB from any protein-coding gene. For cis and trans eQTL analyses, p-values were adjusted separately for FDR [31].

Finally, we evaluated whether associated genes were overrepresented in broad gene ontology categories for creQTL. We organized all Gene Ontology [36] annotations into Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) subcategories (Affymetrix, Santa Clara CA). SNPs were assigned to genes, as for cis-eQTL, using the minimum q-value for association across all tested SNPs within a 5 MB distance cutoff (to generate per-gene q-values). For all GO subcategories with at least 10 genes, the two-sample Kolmogorov-Smirnov test (one-tailed) was used to test for enrichment of significant genes in each Gene Ontology category. Specifically, to compute exact p-values for each category, we compared the observed q-value for a given gene set to those calculated from 10,000 same-sized sets of randomly drawn q-values. Because of the large number of categories, many of which contained overlapping genes, resulting p-values (for each GO category) were then adjusted for FDR [31].

Results

The key steps in our creQTL approach and application to identify loci controlling co-regulation of genes are outlined in Figure 1. Because of the overall unreliability of low-expression measurements in gene microarray expression data and our interest only in genes with dynamic expression patterns, we prefiltered those data to exclude the bottom 50% of probes by variance, leaving 8,543 for inclusion in our analysis of gene clustering and QTL testing. 68,523 SNPs from the Affymetrix 100K chip met cutoffs for inclusion in our analysis.
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-6/MediaObjects/12920_2010_Article_203_Fig1_HTML.jpg
Figure 1

Overview of analysis.

From the expression data, we identified 282 gene clusters with at least 3 co-regulated genes. The distribution of gene cluster sizes is shown in Figure 2. Many genes present within the same clusters had overlapping, known biological functions, and genes within individual clusters often were syntenic, such as the IFNβ-induced genes IFI44, IFI44L, and IFIT1, -2, and -3, on chromosomal regions 1p31.1 and 10q23-26, contained within a cluster significantly associated with a SNP/locus upstream of RFC3 and STARD13.
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-6/MediaObjects/12920_2010_Article_203_Fig2_HTML.jpg
Figure 2

Histogram of gene cluster sizes identified from the EC gene expression data.

We found 453 associations between genotype and Z2E(j) with q-values < = 0.005 (Additional File 1 contains annotated associations) after 19,323,486 variance tests of 282 gene clusters and 68,523 SNPs. Note that a FDR q-value of 0.005 corresponded to an unadjusted p-value of approximately 1 × 10-7. Table 1 and Figure 3 highlight two of the most significant creQTLs that were within 5 MB of a protein-coding gene. As shown in Figure 3, for statistically significant creQTLs, the SNP genotypes exhibit varying contributions to the clusters. Moreover, samples that contribute most to a given cluster have more extreme, more tightly-clustered expression values; samples that contribute little to a given cluster have expression values (and contributions) closer to zero with greater variability. Additional File 2 shows the q-value density for associations as a function of gene cluster size. As can be seen in the figure, there was no relationship between q-values and gene cluster size.
Table 1

Two representative, significant creQTL associations.

creQTL 1

SNP Annotation

Locus

dbSNP

Location (bp)

Position

Gene Symbol

Gene Title

chr13q12.3-q13

rs9315220

304602

upstream

RFC3

replication factor C (activator 1) 3, 38 kDa

chr13q12-q13

rs9315220

227702

upstream

STARD13

StAR-related lipid transfer (START) domain containing 13

Gene Cluster Annotation

Locus

Entrez ID

q creQTL

p eQTL

Gene Symbol

Gene Title

chr1p31.1

10561

1.13 × 10-4

0.21

IFI44

interferon-induced protein 44

chr1p31.1

10964

 

0.28

IFI44L

interferon-induced protein 44-like

chr10q23-q25

3433

 

0.21

IFIT2

interferon-induced protein with tetratricopeptide repeats 2

chr10q24

3437

 

0.49

IFIT3

interferon-induced protein with tetratricopeptide repeats 3

chr10q25-q26

3434

 

0.19

IFIT1

interferon-induced protein with tetratricopeptide repeats 1

chr12q24.1

4938

 

0.52

OAS1

2',5'-oligoadenylate synthetase 1, 40/46 kDa

chr21q22.3

4599

 

0.56

MX1

myxovirus (influenza virus) resistance 1, interferon-inducible protein p78 (mouse)

creQTL 2

SNP Annotation

Locus

dbSNP

Location (bp)

Position

Gene Symbol

Gene Title

chr1p31.1

rs2296697

0

intron

LPHN2

latrophilin 2

Gene Cluster Annotation

Locus

Entrez ID

q creQTL

p eQTL

Gene Symbol

Gene Title

chr2p25

6664

5.40 × 10-4

0.92

SOX11

SRY (sex determining region Y)-box 11

chr3p14.3

57408

 

0.38

LRTM1

leucine-rich repeats and transmembrane domains 1

chr11q22.3

4317

 

0.10

MMP8

matrix metallopeptidase 8 (neutrophil collagenase)

chr14q11.2

9362

 

0.38

CPNE6

copine VI (neuronal)

chr21q22.3

54058

 

0.64

C21orf58

chromosome 21 open reading frame 58

NA

NA

 

0.35

NA

NA

For each of the two creQTLs shown in this table (labeled 'creQTL 1' and 'creQTL 2'), the associated SNP (labeled 'SNP Annotation') is first shown with annotation for the nearest coding genes in cis. Beneath the SNP annotation is annotation for the significantly associated, co-regulated gene cluster (labeled 'Gene Cluster Annotation'). These two significant associations correspond to the two loci shown in Figure 3. 'Position' indicates the relative location of the SNP relative to the nearest coding gene; 'Location (bp)' indicates the physical distance, in base pairs, between the SNP and the nearest coding genes; p eQTL indicates unadjusted significance of standard eQTL analysis.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-6/MediaObjects/12920_2010_Article_203_Fig3_HTML.jpg
Figure 3

creQTL1 (left panel) and creQTL2 (right panel) from Table 1, with SNP IDs rs9315220 and rs2296697, respectively. For each association, co-regulation of a given cluster and a SNP is shown. The 52 endometrial carcinoma samples are color coded by genotype on the x-axis (upper panel), with every third sample labeled beneath the axis. On the y-axis (upper panel), normalized gene cluster expression values are plotted. The median, 25th/75th quantiles, minimum, and maximum gene values across all genes in the cluster are shown for each sample. The bottom panel of each figure shows the Z2E(j) statistic for each sample and the given cluster.

For the association between rs9315220 and the cluster noted above, several other nearby SNPs (rs9315219, rs9315215, rs7981602, rs4943110) were also significantly associated with this cluster (see Additional File 1), reflecting the high linkage disequilibrium (LD) among them (the pairwise r2 across all five SNPs ranged from 0.92 to 0.96 in these 52 samples). The smallest q-value was nearest to the STARD13 locus (q-value = 1.13 × 10-4). Another strong association (q = 5.40 × 10-4) shown in Figure 2 was between the intronic SNP rs2296697 in LPHN2 (latrophilin 2) and a cluster of genes including C21orf58, CPNE6, SOX11, MMP8, LRTM1, and one unannotated gene. The genes within this cluster have established roles in Ca2+-dependent signaling and cancer [3741] (Discussion). Interestingly, a conventional trans-eQTL analysis between the SNPs and individual expression values of these co-regulated genes did not detect any associations (see Table 1); these results were typical of other clusters (data not shown).

The strongest regulatory loci for creQTL (q = 5.50 × 10-4) are shown in Table 2. We calculated pairwise r2 between all 453 SNPs from creQTLs with q < 0.005 and eliminated those with r2 values above 0.5, leaving 338 creQTLs. Of the 338 SNPs remaining, 190 were in noncoding regions; 145 were intronic; two were in coding sequences; and one was in the 3' untranslated region. Interestingly, many of the associated creQTLs contained SNPs in noncoding sequences >500 kb from the nearest gene. After assigning the 68,523 SNPs to 8,398 protein-coding genes in cis (Methods), we plotted the resulting 8,398 q-values versus cis-location relative to the nearest gene in Figure 4. Consistent with the pattern for the highest-scoring hits at q < 0.005, associations appeared most enriched in the intronic regions, and within the non-intronic, noncoding regions, significant associations appeared most in regions >10 KB away from the coding sequence.
Table 2

Top scoring creQTLs at a significance threshold of q = 5.50 × 10-4.

Locus

dbSNP

Location (bp)

Position

Gene Symbol

Gene Title

qcreQTL

chr6q16

rs959186

125994

downstream

RNGTT

RNA guanylyltransferase and 5'-phosphatase

1.13 × 10-4

chr13q12.3-q13

rs9315215

348455

upstream

RFC3

replication factor C (activator 1) 3, 38 kDa

1.13 × 10-4

chr13q12-q13

rs9315215

183849

upstream

STARD13

StAR-related lipid transfer (START) domain containing 13

1.13 × 10-4

chrXq21.33

rs4969656

695921

upstream

DIAPH2

diaphanous homolog 2 (Drosophila)

1.93 × 10-4

chrXq21.3-q22

rs4969656

2315179

upstream

NAP1L3

nucleosome assembly protein 1-like 3

1.93 × 10-4

chr3q13.1

rs4856121

2246645

upstream

ALCAM

activated leukocyte cell adhesion molecule

1.93 × 10-4

chr5q31-q32

rs319217

0

intron

PPP2R2B

protein phosphatase 2 (formerly 2A), regulatory subunit B, beta isoform

1.13 × 10-4

chr1p31.1

rs2296697

0

intron

LPHN2

latrophilin 2

5.40 × 10-4

chr2p21

rs222471

31099

downstream

KCNG3

potassium voltage-gated channel, subfamily G, member 3

4.91 × 10-4

chr2p21

rs222471

49701

upstream

COX7A2L

cytochrome c oxidase subunit VIIa polypeptide 2 like

4.91 × 10-4

chr4q21

rs1992489

59023

upstream

CXCL13

chemokine (C-X-C motif) ligand 13

5.19 × 10-4

chr4q21.1

rs1992489

282671

downstream

CCNG2

cyclin G2

5.19 × 10-4

chr14q13-q21

rs1951319

680177

downstream

RPL10L

ribosomal protein L10-like

5.48 × 10-4

chr14q21.2

rs1951319

717439

upstream

C14orf106

chromosome 14 open reading frame 106

5.48 × 10-4

chr8p23.2

rs1714757

0

intron

CSMD1

CUB and Sushi multiple domains 1

1.47 × 10-4

chr16p13.12

rs1159167

147599

upstream

ERCC4

excision repair cross-complementing rodent repair deficiency, complementation group 4

4.91 × 10-4

chr16p13.12

rs1159167

968670

upstream

CPPED1

calcineurin-like phosphoesterase domain containing 1

4.91 × 10-4

chr22q12.1

rs10483151

0

intron

TTC28

tetratricopeptide repeat domain 28

1.81 × 10-4

Associated gene clusters have been omitted for brevity and are listed in Additional File 1. 'Position' and 'Location (bp)' as for Table 1; q creQTL ' indicates FDR-adjusted significance.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-6/MediaObjects/12920_2010_Article_203_Fig4_HTML.jpg
Figure 4

Genewise genomic locations of SNP q-values. After assigning 68,523 SNPs to 8,398 genes based on the minimum association q-value within 5 MB of the nearest gene's coding sequence, q-values were binned into categories based on their relative position to the nearest gene. Note that the q-values do not reach the bottom of the plot as they were chosen as the minimum value within 5 MB of the nearest gene.

We further analyzed the creQTL results with Gene Ontology in order to determine if any broad categories of genes participated in the regulation of gene clusters. There were 648, 176, and 310 Gene Ontology Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) annotations, respectively, for gene set enrichment testing that were represented on the SNP arrays after assigning the 68,523 SNPs to 8,398 protein-coding genes in cis, using an arbitrary minimum cutoff of 10 genes for a category to be tested. Top hits are shown in Table 3 at a more relaxed q = 0.05. The most significant ontologies were related to cell adhesion, potassium- and calcium ion transport, binding, channel activity, development, and the nervous system, particularly development and plasticity. For Biological Process and Molecular Function, as expected, some of the most enriched categories were related to transcription factors and other aspects of transcriptional regulation. For Molecular Function, categories '0003700' (transcription factor activity) and '0003714' (transcription corepressor activity), with 497 and 74 genes, respectively, were just beyond the FDR significance cutoff with adjusted p-values of 0.056 and 0.078 (Additional File 3). For Cellular Component, nearly all significant categories were directly related to various aspects or cell-type specific (e.g. neuronal) specializations of the cellular membrane (9 of 13 hits at q = 0.05).
Table 3

Gene Ontology enrichment analysis for creQTL at q = 0.05.

Gene Ontology Biological Process

Category ID

Category Size

Category Title

qKS

7214

18

gamma-aminobutyric acid signaling pathway

< 0.008

7612

21

learning

< 0.008

7411

62

axon guidance

< 0.008

7156

69

homophilic cell adhesion

< 0.008

7417

80

central nervous system development

< 0.008

7155

320

cell adhesion

< 0.008

45944

203

positive regulation of transcription from RNA polymerase II promoter

0.008

7399

225

nervous system development

0.008

45595

12

regulation of cell differentiation

0.014

48754

19

branching morphogenesis of a tube

0.018

30900

42

forebrain development

0.018

45165

29

cell fate commitment

0.027

43065

69

positive regulation of apoptosis

0.04

7224

16

smoothened signaling pathway

0.043

7420

74

brain development

0.043

30326

35

embryonic limb morphogenesis

0.043

7169

56

transmembrane receptor protein tyrosine kinase signaling pathway

0.043

6813

94

potassium ion transport

0.043

Gene Ontology Cellular Component

Category ID

Category Size

Category Title

q KS

5913

18

cell-cell adherens junction

< 0.004

45202

156

synapse

< 0.004

30054

231

cell junction

< 0.004

14069

49

postsynaptic density

0.004

45211

79

postsynaptic membrane

0.004

30424

83

axon

0.006

781

13

chromosome, telomeric region

0.013

5912

27

adherens junction

0.013

34707

36

chloride channel complex

0.016

5923

40

tight junction

0.016

5886

1504

plasma membrane

0.037

42734

21

presynaptic membrane

0.038

5578

174

proteinaceous extracellular matrix

0.038

Gene Ontology Molecular Function

Category ID

Category Size

Category Title

q KS

8066

11

glutamate receptor activity

< 0.008

5216

187

ion channel activity

< 0.008

5509

491

calcium ion binding

< 0.008

4993

11

serotonin receptor activity

< 0.008

4970

12

ionotropic glutamate receptor activity

0.009

5234

12

extracellular-glutamate-gated ion channel activity

0.009

43565

273

sequence-specific DNA binding

0.009

5244

85

voltage-gated ion channel activity

0.016

4890

17

GABA-A receptor activity

0.017

5246

11

calcium channel regulator activity

0.019

31404

49

chloride ion binding

0.023

30594

18

neurotransmitter receptor activity

0.028

30165

32

PDZ domain binding

0.029

5001

13

transmembrane receptor protein tyrosine phosphatase activity

0.029

5267

53

potassium channel activity

0.029

5230

24

extracellular ligand-gated ion channel activity

0.037

5254

48

chloride channel activity

0.041

8146

26

sulfotransferase activity

0.041

16455

11

RNA polymerase II transcription mediator activity

0.042

Results are presented for Biological Process, Cellular Component, and Molecular Function, respectively. Category IDs are presented minus leading zeroes. 'q KS ' indicates FDR-adjusted significance of the category.

eQTL analysis identified no significant cis-eQTLs (q = 0.05); the smallest FDR-adjusted significance level for cis-eQTLs was q = 0.098 (not shown). There were 63 significant trans-eQTLs (q = 0.05; Additional File 4), and the most significant are shown in Table 4. A raw p-value of approximately 6 × 10-9 corresponded to q = 0.05. Even with a high 50% FDR cutoff, there were only 1,293 cis-eQTLs (of 1,468,327), or 0.09%, and 290,625 (of 507,010,655), or 0.06% trans-eQTLs. Although marginally significant (q = 0.049), the association between ZNF639 and TCF12 is included in Table 4 due to association of 3q26 with survival in this dataset [27] (Discussion). We did not perform gene ontology analysis of the eQTL experiment since there were no clear cis-eQTLs detected.
Table 4

The most significant associations from trans-eQTL analysis at q = 0.05.

SNP Locus

dbSNP

Location (bp)

Position

SNP Gene Symbol

mRNA Locus

Entrez ID

mRNA Gene Symbol

qeQTL

chrXp22.11

rs4828879

23770

upstream

PRDX4

chr11p15.5

7140

TNNT3

0.003

chr7q32.3

rs277491

0

intron

PLXNA4

chr3p26-p25

7862

BRPF1

0.003

chr4q32.3

rs10517754

0

intron

FSTL5

chr22q12.1

25770

C22orf31

0.005

chr7q34

rs2363830

0

intron

DENND2A

chr11q23-q24

56

ACRV1

0.016

chr3q22.1

rs938243

0

intron

CPNE4

chr16q23.3

93517

SDR42E1

0.018

chr2q13

rs10496425

0

intron

CCDC138

chr7q11.23

7461

CLIP2

0.019

chr2q13

rs7591305

0

intron

CCDC138

chr7q11.23

7461

CLIP2

0.019

chr10q21.1

rs10509024

0

intron

PCDH15

chrXp11.23

778

CACNA1F

0.029

chr3p24.3

rs964910

561192

upstream

SGOL1

chr1q44

114548

NLRP3

0.029

chr21q21.1

rs2826728

0

intron

NCAM2

chr11p15.5

7140

TNNT3

0.029

chr21q21.1

rs2155798

0

intron

NCAM2

chr11p15.5

7140

TNNT3

0.029

chr1q43

rs2790645

135107

upstream

CHRM3

chr5q31-q32

5521

PPP2R2B

0.029

chr11q14.1

rs62388

0

intron

DLG2

chr7q34

154790

CLEC2L

0.029

chr3q26.33

rs9290675

34917

upstream

ZNF639

chr15q21

6938

TCF12

0.049

'Position' indicates SNP genomic location; ' q eQTL ' indicates FDR-adjusted significance. Annotation is shown for SNPs in the first 5 columns, then for mRNA expression; e.g. for the first (non-header) row of this table, the mRNA expression levels for gene TNNT3 were significantly associated with genotype at rs4828879, which is 23,770 base pairs upstream of the PRDX4 locus.

Discussion

creQTL provides an agnostic framework for predicting trans regulators of clusters of genes. It does not require any one particular biclustering method or statistical test and can be extended to any species for which genetic markers (including those derived from linkage) and gene expression data can be obtained; for example, creQTL mapping in mice would allow the study of gene regulation in many disease models and in normal biological processes such as development, which are highly relevant to human disease. Like eQTLs, creQTLs may be incorporated into additional analyses. As recent studies have used eQTL data to assemble genetic loci into directed, Bayesian networks to predict causal relationships (e.g. [16, 42]), future studies could assemble creQTLs into directed or undirected gene networks. Because creQTL mapping groups co-regulated genes into a smaller number of modules, network construction using creQTL information in place of eQTL information should be computationally more efficient.

In our application to endometrial cancer, there was an abundance of creQTLs in noncoding regions and introns, and relatively very few in coding regions. Polymorphisms in coding regions, although observed less frequently, would likely generate more pronounced effects, and for signaling molecules, these could result in substantial downstream effects important for carcinogenesis due to a change in function of the protein. However, these alleles, which are likely to be under strong negative selection and very rare, would not be covered by the genotyping platform used in the present study. Unlike the results of eQTL studies, our strongest hits were not typically located in cis regions very close to the coding sequences of individual genes, but rather in more distant, noncoding regions located over 10 KB from the nearest gene. This suggests that regions that regulate clusters of genes, unlike those regulating individual genes, are located farther from coding sequences and may possibly function as general enhancers. However, without careful experimentation, such as promoter reporter gene assays that would specifically test the enhancer activity of these sequences, it is difficult to predict their actual roles. Future studies could address the relationship between location, allele frequency, and other empirical aspects of creQTLs in larger cohorts and healthy tissue. Collapsing SNPs into a dominant model would permit testing of less common SNPs, as requiring five observations in each genotype essentially restricted our analysis to more common variants.

Our results for creQTL from the Gene Ontology enrichment analysis indicated a regulatory hierarchy, consistent with data from wet-lab studies, where signal transduction molecules receive signals at the plasma membrane and transduce them through various intracellular intermediates to the nucleus to activate specific transcription factors, other DNA-binding proteins, and other transcriptional regulatory proteins. The relative abundances of DNA-binding proteins at the promoter region may control gene expression [43]. Because of the enrichment of cell adhesion and signaling ontologies at the cell junction and membrane at the highest significance levels, the major steps of gene regulation in endometrial cancer may occur not within the nucleus but rather at the cellular membrane. In addition, GO enrichment in genes required for telomere maintenance suggests additional target genes and loci for further investigation, as telomerase has long been considered an attractive target for therapy in endometrial carcinoma [44] and more recently, a mouse model of endometrial cancer showed that telomere length affected initiation of type II carcinogenesis [45].

Gene Ontology analysis is not without its own pitfalls. Besides the obvious effects of overlapping annotations for the majority of genes and annotation bias, the categories themselves often do not describe real biological phenomena. Given that transcription factors are very cell-type specific, a category that includes all of them is not particularly useful. In light of this, the q-values for transcription factor-related categories are likely to be overly conservative, and would probably be smaller if those TFs not expressed in this tissue were excluded. Future studies could address this problem using array and wet lab expression data.

For the association shown in Figure 2, the nearest coding sequence to rs9315220 is STARD13, a.k.a. DLC2 (deleted in liver cancer 2). All genes of this cluster but one (OAS1), including MX1, IFI44, IFIT2, IFIT3, IFI44L, and IFIT1, are known to be induced by IFNβ in humans[46]. IFNβ, besides its role in the treatment of multiple sclerosis (MS), is anti-tumorigenic [4750]. Similarly, many genes from this cluster also have established or putative roles in cancer; IFI44 was part of a small set of genes independently validated for recurrence status in non-small cell lung carcinoma [37], is anti-proliferative in melanoma cell lines [38], and is upregulated in squamous cell carcinoma [39]. IFIT2 inhibits migration and proliferation in squamous cell carcinoma cultures [41]. IFIT1 was one of a small group of genes that predict carcinogenesis after training on DNA-damage response [40], while STARD13 is upregulated in human lymphocytes following gamma-irradiation [51], suggesting that IFIT1 may be a downstream target of STARD13 following DNA damage.

STARD13/DLC2 encodes a Rho GTPase activating protein and was identified from a region of chromosome 13 exhibiting a loss of heterozygosity in hepatocellular carcinoma [52]. It is a tumor suppressor that antagonizes Rho expression [53], and its downregulation or deletion has been reported in multiple cancers [54, 55]. Supporting its role as a regulator of IFNβ-induced genes, an intronic SNP in STARD13 was amongst eighteen that were replicated in a study of responders to IFNβ therapy in MS patients [56]. Further, SNPs in OAS1 have been associated with MS susceptibility [57]. Therefore, this particular gene cluster and its predicted regulatory partner, STARD13, may be interacting determinants of the response to various negative stimuli. This also proposes the hypothesis that the role of STARD13 as a tumor suppressor is from its induction in response to DNA damage, and that deletion of this region in at least ten different cancers is pathogenic possibly due to hyper-activation of Rho, which promotes migration, proliferation, and invasion [58]. In this case, beyond improvement of the total lack of significant association from the individual eQTLs, creQTL proposed a more complete picture of the biological pathway.

We detected a strong association of rs2296697, within an intron of LPHN2 (latrophilin 2), with a cluster of genes including C21orf58, CPNE6, SOX11, MMP8, and LRTM1. LPHN2 belongs to the latrophilin subfamily of G-protein coupled receptors, with known roles in cell adhesion and signal transduction [59]. Cell adhesion was among the most enriched categories from Gene Ontology analysis of creQTL. CPNE6 (copine VI), is a member of the ubiquitous copine family, which bind phospholipid in Ca2+-dependent manner and have roles in cellular division and growth [60]. Although very little is known regarding CPNE6, other members of the copine family have been shown to promote tumor cell migration [61] and repress NF-KB transcription [62]. SOX11 is a TF with important roles in brain development that is strongly upregulated in lymphoma [63] and malignant glioma, where it may affect tumorigenesis [64, 65]. SOX11 may also be a prognostic marker for recurrence-free survival in another uterine neoplasia, epithelial ovarian cancer [66]. MMP8 (human neutrophil collagenase) is a member of the matrix metallopeptidase family. Recent work has shown that a SNP in MMP8 may be a predictor of lung cancer risk [67], and that higher plasma levels of MMP8 may protect against lymph node metastasis in breast cancer [68]. Consistent with these findings, somatic mutations of MMP8 were common in melanomas, and mutated MMP8 failed to inhibit tumor formation in vivo [69], suggesting widespread roles for MMP8 in cancer progression. Three members of this gene cluster have no known roles, yet the functions of the other cluster members suggest they could contribute to cancer progression. In this case, the association of this cluster of genes with LPHN2 has suggested that LPHN2 may be a regulatory point for the modulation of genes with important roles in cancer, including MMP8 and SOX11.

eQTL analysis was not done in the original paper presenting this dataset [27], and the general lack of findings from the eQTL analysis may be from a lack of power, given that there were only 52 samples. This is not surprising: an eQTL study of 60 samples from the CEU cohort in the HapMap data identified only 10 cis-eQTLs and 94 in trans [70], although there may have been some technical issues here [71].

However, in the present study, creQTL appeared to identify interactions that were supported by recent work in the literature. A very compelling benefit of creQTL over trans-eQTL is greater computational efficiency and a reduced multiple testing burden, as there was an approximate 25-fold reduction in the number of statistical tests. By organizing genes into co-regulated clusters prior to statistical testing, the problem becomes more computationally feasible and the resulting output more biologically interpretable. The identification of more significant hits after adjustment was likely not simply due to a lighter multiple testing penalty; cis-eQTL required 1,468,327 tests, far less than the 19,323,486 tests for creQTL, and produced no statistically significant results even at the more liberal q-value cutoff of 0.05. Overall, these results suggest that individual SNP-gene interactions are more difficult to detect (as in eQTL) when compared to relative changes in the expression levels of tightly co-regulated genes, in agreement with previous work in yeast [9]. creQTL may be better-suited to the identification of the strongest trans drivers of gene expression because it better explains the data; i.e., genes do not work individually to exert their biological effects, but rather in tightly coordinated groups. However, our results regarding the location of these drivers of co-regulation relative to coding sequences suggests that they might be disjoint from cis drivers of individual gene expression.

Some trans-eQTL hits were significant and may merit further investigation based on previously established biological roles for associated loci, indicating complementary utility of eQTL and that the two methods may be most useful when applied side-by-side. Copy number analysis of this EC dataset by the original authors revealed two regions of gain that were predictive of survival - 3q26.32 and 12p12.1 [27]. Deletions were not considered. Because PIK3CA is located in this region, the authors proposed a role for the PI3-kinase pathway, and found supporting evidence for this theory based on indirect, bioinformatic analysis of existing, in vitro data. Located 200 kb from PIK3CA is ZNF639, a.k.a. ZASC1, which is often contained within the same amplicon in cancer [72, 73]. ZNF639 was originally identified in squamous cell carcinoma as a Kruppel-like transcription factor with mRNA expression levels prognostic for survival and metastasis [72, 73]. trans-eQTL identified a SNP less than 35 kb upstream of ZNF639 (SNP_A-1686963) driving expression of the TCF12 gene at 15q21. TCF12, a.k.a. HTF4 or ME1, is a basic helix-loop-helix TF with key roles in development [74]. In a mouse experiment, TCF12 was associated with obesity [14], a key prognostic factor for endometrial cancer [75]. Because the stoichiometric concentration of TFs within the nucleus may control gene expression [43], this association proposes a link between obesity and TCF12 through ZNF639. Because ZNF639 regulates anchoring of E-cadherin to the cytoskeleton through alpha N-catenin [76], given the importance of cell adhesion proteins in cancer [77], future studies of endometrial cancer might focus on ZNF639.

Conclusions

With the advent of low-cost SNP and expression arrays, human genetics has become a common tool for the dissection of gene regulation. Genetic approaches to the study of transcriptional regulation are compelling because they can detect regulatory molecules at all stages of gene regulation. Our approach expands upon previous methods and uses genetic variation to help identify transcriptional regulatory mechanisms, providing a biologically intuitive approach for detecting potential links between genotype and gene co-regulation.

List of Abbreviations

ANOVA: 

analysis of variance

BP: 

gene ontology biological process

CC: 

gene ontology cellular component

creQTL: 

co-regulatory expression quantitative trait locus

EC: 

endometrial cancer

EDF: 

empirical distribution-free

eQTL: 

expression quantitative trait locus

FDR: 

false discovery rate

GO: 

gene ontology

KS: 

Kolmogorov-Smirnov test

LD: 

linkage disequilibrium

LOOCV: 

leave-one-out cross-validation

MF: 

gene ontology molecular function

mRNA: 

messenger ribonucleic acid

MS: 

multiple sclerosis

SNP: 

single nucleotide polymorphism

TF: 

transcription factor

TFBS: 

transcription factor binding site.

Declarations

Acknowledgements

We appreciate helpful feedback from Joel Mefford, Jun Song, Sung Kim, Jill Hardin, Mike Baldwin, and Niall Cardin. This work was supported by grant CA88164 from the National Institutes of Health. The funding body had no role in the design, collection, analysis, interpretation of data, writing, or decision to submit the manuscript for publication. The authors have no competing interests, financial or otherwise.

Authors’ Affiliations

(1)
Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco

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