A systematic eqtl study of cis–trans epistasis in 210 hapmap individuals

ABSTRACT We aimed at identifying transcripts whose expression is regulated by a SNP–SNP interaction. Out of 47 294 expression phenotypes we used 3107 transcripts that survived an extensive

quality control and 86 613 linkage disequilibrium-pruned SNP markers that have been genotyped in 210 individuals. For each transcript we defined _cis_-SNPs, tested them for epistasis with

all _trans_-SNPs, and corrected all observed _cis_–_trans_-regulated expression effects for multiple testing. We determined that the expression of about 15% of all included transcripts is

regulated by a significant two-locus interaction, which is more than expected (_P_=2.86 × 10−144). Our findings suggest further that _cis_-markers with so called ‘marginal effects’ are more

likely to be involved in two-locus gene regulation than expected (_P_=8.27 × 10−05), although the majority of interacting _cis_-markers showed no one-locus regulation. Furthermore, we found

evidence that gene-mediated _trans_-effects are not a major source of epistasis, as no enrichment of genes has been found in close vicinity of _trans_-SNPs. In addition, our data support the

notion that neither chromosomal regions nor cellular processes are enriched in epistatic interactions. Finally, some of the _cis_–_trans_ regulated genes have been found in genome-wide

association studies, which might be interesting for follow-up studies of the corresponding disorders. In summary, our results provide novel insights into the complex genome-transcriptome

regulation. SIMILAR CONTENT BEING VIEWED BY OTHERS DISENTANGLING GENETIC EFFECTS ON TRANSCRIPTIONAL AND POST-TRANSCRIPTIONAL GENE REGULATION THROUGH INTEGRATING EXON AND INTRON EXPRESSION

QTLS Article Open access 06 May 2024 MULTIMODAL ANALYSIS OF RNA SEQUENCING DATA POWERS DISCOVERY OF COMPLEX TRAIT GENETICS Article Open access 29 November 2024 ADJUSTING FOR GENETIC

CONFOUNDERS IN TRANSCRIPTOME-WIDE ASSOCIATION STUDIES IMPROVES DISCOVERY OF RISK GENES OF COMPLEX TRAITS Article Open access 26 January 2024 INTRODUCTION Mapping studies of gene expression

phenotypes have successfully lead to the identification of regulatory variants and networks across the genome.1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 In these expression quantitative trait locus

(eQTL) analyses, genes have been identified whose expression are regulated by SNP markers, which are either in close proximity to (_cis_-acting SNPs) or at greater distances from the gene

locus (_trans_-acting SNPs).12 Although the nature of _cis_-regulation is influenced by factors such as 5′ promoter- or 3′ transcript-variants, the mechanisms involved in _trans_-regulation

include gene-mediated (eg, transcription factors) or sterical interactions such as ‘chromosome cross-talk’.13, 14, 15, 16 However, at many gene loci it must be assumed that both, _cis_- and

_trans_-effects are involved simultaneously in the regulation of expression. Furthermore, it is possible that expression at certain gene loci is regulated by a more complex process that

involves epistasis (eg, _cis_–_trans_ interaction). Unfortunately, these regulatory effects are not detected in one-locus eQTL studies where genetic variants are examined solely. There are

two main reasons why two-locus or interaction eQTL mappings have not been applied to existing data. First, potential two-locus effects are difficult to identify and interpret, as substantial

correction for multiple testing is required if the interaction was analyzed in a genome-wide fashion. In a genome-wide 100K SNP set, for example, the _P_-value of an observed interaction

would have to be in the range of _P_=5 × 10−12 per transcript before being considered significant. Second, systematic two-locus eQTL mappings require substantial computational resources,

although this limitation has recently been overcome by the introduction of novel biostatistical methods.17, 18, 19 In the present study we tried to circumvent some of the limitations

associated to interaction scans and performed a systematic two-locus eQTL study for epistasis. Out of three possible two-locus interaction models (ie, _cis_–_cis_, _cis_–_trans_,

_trans–trans_), we restricted our analysis only to _cis_–_trans_ epistasis. We used the expression data of 3107 high-quality transcripts and 86 613 linkage disequilibrium (LD)-pruned SNP

markers obtained from 210 HapMap founders. For each transcript, we tested whether expression levels showed statistical epistasis between a locus-specific _cis_- and an interacting

_trans_-SNP located elsewhere in the genome. Although other interaction effects may be involved in gene regulation, _cis_–_trans_ interacting effects were investigated as these may be easier

to interpret. For example, it is difficult to control for intermarker LD in _cis_–_cis_ or for multiple testing in _trans_–_trans_ interaction studies. A further aim of the study was to

characterize identified _cis_–_trans_ interaction effects, for example, to determine whether SNP markers involved in epistatic gene regulation also represent significant one-locus eQTLs.

MATERIALS AND METHODS EXPRESSION DATA AND STUDY SAMPLE For our genome-transcriptome eQTL analysis we used the expression phenotypes that have been generated by The Wellcome Trust Sanger

Institute Cambridge (GENEVAR, http://ftp://ftp.sanger.ac.uk/pub/genevar/) from human lymphoblastoid cell lines (LCLs) of all 210 founders in the four International HapMap II populations

(http://snp.cshl.org/).8, 9 The sample includes 60 Caucasian individuals (CEU, of northern and western European ancestry), 90 Asian individuals (45 Han Chinese, CHB; and 45 Japanese, JPT),

as well as 60 African individuals (YRI, from Nigeria). Although this strategy cannot detect interaction effects on gene regulation that are restricted to one particular population, use of

the combined sample provides improved statistical power for the detection of epistasis and has been successfully used in previous one-locus eQTL studies.8, 9 In this sample, we used only

expression phenotypes for transcripts that were filtered through a detailed and extensive quality control. Of the 47 294 transcripts analyzed using Illumina's human whole genome

expression (WG-6 version 1) array (Illumina Inc., San Diego, CA, USA), only those probes that have shown an Illumina detection score of >0.99 in each of the four hybridization experiments

conducted across all 210 HapMap individuals were used. These scores were obtained from the Sanger Institute website (‘gene_profile-files’ at http://ftp://ftp.sanger.ac.uk/pub/genevar/) and

reduced the number of transcripts included in the present study to 7978 probes. The respective transcripts could be expected to be robustly expressed in human LCLs. In a subsequent step, the

presence of SNPs in the hybridization probes was excluded using the web-based program ReMOAT (version March 2009, http://www.compbio.group.cam.ac.uk/Resources/Annotation/index.html)20 and

the dbSNP 126 database (http://www.ncbi.nlm.nih.gov/projects/SNP/). Although there is a current debate in the field as to whether this step is necessary and other studies have included

SNP-containing probes, we decided to exclude them as they possibly might influence the true expression quantity. However, the removal of probes with known coding SNPs did not substantially

reduce the number of included transcripts to 6226 probes. Furthermore, we used ReMOAT for the inclusion of probes that are located on autosomes only and mapped over the full length (50 bp)

to a contiguous genomic location (ie, no intron-spanning probes). We decided to use exon-specific probes only in order to avoid any inaccurate expression signals, which could be caused by

insufficient hybridization to different isoforms of the gene (eg, due to exon-skipping or -incorporation). This step reduced the number of included probes to 5237. Next, the uniqueness of

genomic hits for each probe was determined using nuID (https://prod.bioinformatics.northwestern.edu/nuID/), which represents a probe identifier for microarray experiments. This reduced the

number of included probes further to 4418 showing a nuID uniqueness score of 100. Only these probes could be specifically mapped to a single Entrez GeneID. Entrez Gene is a repository from

the National Center for Biotechnology Information (NCBI) for gene-specific information. In final steps, we filtered for probes whose corresponding transcripts were annotated as ‘reviewed’ or

‘validated’ using NM_N_=3124). The RefSeq database provides a collection of annotated sequences including transcripts. When multiple probes hybridized to the same RefSeq NM_ transcript,

only one randomly selected probe was included in the analyses. In the final filtering step, the UCSC Browser version HG18 (http://genome.ucsc.edu/cgi-bin/hgGateway) was used to identify

probes with defined transcription start and end sites. Exact matches were found for a total of 3107 transcripts, and these were included in the two-locus eQTL analysis. The expression data

for each of these 3107 probes were subjected to inverse quantile normalization according to the procedure described by Veyrieras _et al_10 and the normalized data were saved as PLINK21

alternate phenotype files. PLINK represents the program that was used for the interaction analysis (see below). GENOTYPING DATA SNP genotypes of each of the 210 founder individuals were

obtained from HapMap release 23 using PLINK.21 A total of 3.95 million SNPs were available for each individual after exclusion of SNPs with Mendel errors. The Mendel check was performed in

the 30 CEU and 30 YRI trios analyzed in the HapMap Project. Next, only SNPs were selected, which were located on autosomes, which had no HWE deviation (_P_>0.05), and which had allele

frequencies between 0.2–0.8 as well as a per-SNP genotyping missingness cutoff of 0.02. Although this filtering procedure was done in each of the four populations separately, an LD-pruning

step was restricted to the YRI acknowledging the lowest LD structure in this population. Here, a pairwise SNP-SNP-r2 of 0.8 was used as a pruning criterion. The filtering process resulted in

_N_=86 613 SNPs, which were saved as PLINK binary file for inclusion in the analyses. INTERACTION ANALYSIS The two-locus interaction eQTL analysis was performed using the PLINK --epistasis

command. For every transcript that corresponded to an included probe, _cis_-SNPs were defined as being variants located within the transcript or <1 Mb apart from the transcription start

and end site. Each _cis_-SNP of a transcript was then tested for epistasis with all remaining SNPs, which were defined as _trans_-SNPs (ie, 86 613 SNPs minus the number of _cis_-SNPs per

transcript). For the interaction eQTL mapping, the four different HapMap populations were used as categorical co-variates. To determine the significance of our findings, we finally corrected

for each transcript all _cis_–_trans_ interaction results by multiplying the number of analyzed _cis_-variants with the number of included _trans_-SNPs. This resulted in transcript-wise

Bonferroni-adjusted _P_-values between 5.77 × 10−07 (1 _cis-_SNP and 86 612 _trans_-SNPs for _DNAJA2_, _NETO2_ and _ORC6L_) and 2.84 × 10−09 (204 _cis_-SNPs and 86 409 _trans_-SNPs for

_CHD8_ and _SUPT16H_). Under the null hypothesis of no enrichment for transcripts showing _cis_–_trans_ interactions 0.05*3107=155 transcripts would be expected to have at least one

significant _cis_–_trans_ interaction following a transcript-wise Bonferroni's correction. The applied correction procedure is also given in detail in Supplementary Table 1. RESULTS Of

all 3107 included probes we identified 440 transcripts whose expression was – transcript-wise Bonferroni-adjusted – regulated by a _cis_–_trans_ interaction (Supplementary Table 2). The

significant two-locus eQTL _P_-values ranged between 4.69 × 10−08 and 2.82 × 10−12. The observed interactions showed a significant (_P_=2.86 × 10−144) and almost threefold enrichment

compared with the number of SNP pairs expected under the null hypothesis, ie 5% of all probes (_N_=155) would be associated by chance. Table 1 lists the top-16 interaction findings, which

were all associated with _P_-values of <10−10. Importantly, as an LD-pruning step was applied, all of the 440 _cis_–_trans_ SNP combinations were independent and not the result of LD

between _cis_- or _trans_-markers. To elucidate the nature of the epistasis, an analysis was performed to determine whether SNPs, which are involved in gene regulation via one-locus eQTL

effects, mainly contributed to the interactions. At present there is no consensus on whether SNPs with so-called ‘marginal effects’ are more likely to be involved in epistasis and should be

prioritized for SNP–SNP interaction scans. An analysis was therefore performed to determine whether the 440 _cis_- and _trans_-SNPs involved in epistasis also have regulatory effects on gene

expression without their interacting markers, that is, in a one-locus fashion. This proved to be true for the _cis_-markers: a total of 40 of the 440 _cis_-SNPs (9.09%) also showed

regulatory effects in the one-locus analysis at an uncorrected significance level of _P_≤0.05. This was significant compared with the expected number of SNPs with marginal effects (_N_=22,

_P_=8.27 × 10−05) (Supplementary Table 3). However, it is notable that the majority of _cis_-markers (> 90%) were not involved in gene regulation at the one-locus level. In contrast, only

16 of the 440 two-locus _trans_-SNPs (3.63%) were involved in gene regulation on the one-locus level. This was not significant compared with the number of expected markers (_N_=22,

_P_=0.187, Supplementary Table 3) and points to more independent mechanisms involved in the one- and two-locus regulation. As the mechanisms involved in _trans_-regulation and -epistasis are

complex and not well understood, we tried to characterize them in more detail. We analyzed whether the _trans_-epistasis is gene or pathway mediated rather than the result of other

regulatory mechanisms and tested at each trans-locus if there are more genes in close vicinity to the marker than expected. Of all 440 _trans_-markers, 198 SNPs (45.10%) were closely located

to at least one gene according to the program SNPper (http://snpper.chip.org/bio/snpper-enter), that is, the SNP is located within a distance of ≤10 kb to a corresponding gene

(Supplementary Table 2). However, the number of observed genes involved in _trans_-epistasis was not significantly increased compared with the number of all potentially involved genes tagged

by all included _trans_-SNPs using SNPper (_N_=35 731, 41.35%, _P_=0.112). Previous one-locus eQTL studies have reported an enrichment of certain chromosomal regions involved in the

regulation of gene expression. We adapted the approach of Morley _et al_6 and analyzed our data for evidence for so-called ‘master regulator’ SNP-regions on a two-locus interaction level.

Master regulator-regions are chromosomal regions that contain more SNPs involved in epistasis than expected by chance. All 86 613 SNPs were used, and the entire autosomal genome was divided

into 444 non-overlapping bins, each containing 200 neighboring SNPs. We estimated that a bin, which comprises more than 4 of the 440 _trans_-SNPs, would be a master regulator region.

However, correcting this number by a factor of 444, which corresponds to the number of analyzed bins, more than six _trans_-SNPs per bin are necessary for defining a significant master

regulator region. Only for bins at the end of chromosomes did we adapt our approach to account for the number of SNPs within these regions. For example, if 100 neighboring SNPs were located

within the last bin of a chromosome, more than three _trans_-SNPs were necessary to fulfill the criterion of a significant master regulator region. Although we found 8 out of the 444 bins

harboring four _trans_-SNPs, which are nominally significant (_P_=0.019), no bin fulfilled the criterion of a significant master regulator region after the correction procedure. In addition,

our data provide no evidence for superordinated mechanisms involved in epistasis by analyzing whether certain chromosomal ‘hotspot’ regions harbor more regulated transcripts than expected.

We used all 3107 transcripts, divided the autosomal genome into 321 bins, each containing 10 neighboring transcripts, and estimated that a bin with more than 6 of the 440 identified

transcripts would be a significant hit. After a correction for the number of analyzed bins (factor 321) no hotspot could be identified, although one bin harbored six transcripts and 12

further bins harbored four transcripts (uncorrected _P_=0.001 and _P_=0.041, respectively). On the functional level, we tested whether certain cellular processes are particularly regulated

by epistatic effects. We used all 440 genes that were identified as being _cis_–_trans_ regulated and performed an analysis for enriched cellular functions using Ingenuity Pathways Analysis

(IPA, version 8.6, http://www.ingenuity.com). IPA is a web-based interface that provides computational algorithms to identify biological processes and networks on the basis of functional

annotation and molecular interactions. The top biological category was ‘gene expression’, including 69 transcripts. However, the most enriched subcategory ‘transcription of chromosome

components’ (_P_=0.046 after Benjamini–Hochberg correction) was defined by only 4 of all 440 included transcripts (_CREBBP_, _EP300_, _SRC_ and _TBP_). Finally, an analysis was performed to

determine whether any of the two-locus regulated genes are implicated in complex disorders. Complex disorders were considered, as genome-wide association studies (GWAS) of a number of

diseases have failed to identify any one-locus variants, which are associated with a strong genetic effect size. Two-locus regulation may therefore have an impact on the respective

phenotypes. Furthermore, the functional consequence of many top GWAS-SNPs is unknown, which suggests that expression differences may be disease-relevant mechanisms. In total, we identified

25 _cis_–_trans_ regulated genes that have been implicated in complex disorders using the web tool GWAS Catalog (http://genome.gov/26525384). For example (Table 2), we identified a two-locus

interaction between a _trans_-SNP 5.9 kb upstream of _CCL4_ (MIM 182284) and a _cis_-SNP of _BLK_ (MIM 191305) influencing its expression. _BLK_ is one of the strongest risk genes for

rheumatoid arthritis and systemic lupus erythematosus and _CCL4_ encodes a chemokine ligand involved in immune activation.22, 23, 24, 25, 26 However, the connection between _BLK_ and _CCL4_

remains speculative, as it is unclear whether the close proximity of the _trans_-SNP to _CCL4_ reflects a gene- or pathway-mediated mechanism, or whether other interaction mechanisms that do

not involve _CCL4_ exist. Unfortunately, we could not test the effect of the _trans_-SNP on the expression of _CCL4_ because no probe for _CCL4_ has been included in our analysis. Another

interesting finding concerns _STAT2_ (MIM 600556). Its expression was found to be _cis–trans_ regulated, and the corresponding _trans_-SNP is located 31.1 kb upstream of _IL23R_ (MIM 607562)

(Table 2). Again, we could not test whether this SNP is involved in the expression of _IL23R_ due to a missing probe, but it is noteworthy that both genes have an important role in the

innate immune system and have been implicated in the development of psoriasis in a recently published GWAS.27, 28, 29 DISCUSSION Genes function through a complex mechanism that involves

multiple genetic factors. These effects are missed if genetic factors are examined in isolation without taking potential interactions with other genetic factors into account. The aim of the

present study was to elucidate the genetic architecture of gene expression through the performance of a systematic _cis–trans_ interaction analysis. Out of 47 294 expression phenotypes, we

used 3107 transcripts that survived a stringent quality control procedure and 86 613 LD-pruned SNP markers, which were in linkage equilibrium and have been genotyped in the 210 HapMap

founder individuals. Using a conservative correction procedure, we identified that the expression of about 15% of all included transcripts (_N_=440) is regulated by a two-locus interaction,

which is far more than expected by chance (_P_=2.86 × 10−144). The results of the present study confirm that epistasis has an important role in the genetic architecture of complex phenotypes

and imply that this approach may be of relevance to other eQTL and GWAS data sets. Such studies could also benefit from samples that are ethnically more homogeneous. Although we have used

four different populations as categorical co-variates, we cannot completely rule out that our results are to a certain degree inflated by the heterogeneity of the present sample. The present

findings also indicate that regulatory one-locus _cis_-markers are more likely to be involved in two-locus gene regulation than would be expected by chance alone (_P_=8.27 × 10−05). This

suggests that there is a correlation between the mechanisms, which underlie one- and two-locus gene regulation. However, as the majority of _cis_-markers involved in epistasis showed no

‘marginal effects’, our findings imply that most epistasis effects would be missed if interaction studies were focused on _cis-_markers with marginal effects only. Furthermore, the present

results indicate that gene- or pathway-mediated _trans_-effects were not the major source of epistasis, as _trans_-SNPs were not more likely to be located in or in close proximity to an

annotated gene or transcript (_P_=0.112). Therefore, other regulatory mechanisms, such as non-coding sequence-mediated effects (eg, RNA) and intra- or interchromosomal cross-talk, seem to be

of equal importance in _trans_-epistatic regulation. Our analyses as to whether particular chromosomal regions are involved in epistasis produced negative results (_P_>0.05 for master

regulators and hotspots). This implies that _cis_–_trans_ epistasis is not ‘topographically’ organized throughout the genome. In addition, the IPA analysis revealed that only one functional

category (involving only four transcripts) was enriched for epistatic effects (_P_=0.046 for the subcategory ‘transcription of chromosome components’ within the high-level category ‘gene

expression’). This suggests that multiple cellular processes are regulated by two-locus interactions rather than specific ones. Furthermore, 25 of all _cis_–_trans_-regulated genes have been

found to be associated with complex diseases through GWAS. The _trans_-markers and -genes identified in the present study may therefore represent interesting candidates for epistatic tests

in the respective GWAS data. In conclusion, the present _cis_–_trans_ interaction approach identified transcripts, which are potentially influenced by a two-locus epistasis, and yielded

certain characteristics of the complex process of genome-transcriptome regulation. Furthermore, the approach may represent a solution for overcoming the problem of multiple testing in

interaction scans, and it may thus be worthwhile to apply this approach to other eQTL data. A limitation of this approach, however, is that it is only able to detect _cis_–_trans_ epistasis

and cannot be used to detect other regulation mechanisms such as _cis_–_cis_, _trans_–_trans_ or higher-order interactions. REFERENCES * Dimas AS, Deutsch S, Stranger BE _et al_: Common

regulatory variation impacts gene expression in a cell type-dependent manner. _Science_ 2009; 325: 1246–1250. Article  CAS  Google Scholar  * Dixon AL, Liang L, Moffatt MF _et al_: A

genome-wide association study of global gene expression. _Nat Genet_ 2007; 39: 1202–1207. Article  CAS  Google Scholar  * Dombroski BA, Nayak RR, Ewens KG, Ankener W, Cheung VG, Spielman RS

: Gene expression and genetic variation in response to endoplasmic reticulum stress in human cells. _Am J Hum Genet_ 2010; 86: 719–729. Article  CAS  Google Scholar  * Goring HH, Curran JE,

Johnson MP _et al_: Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. _Nat Genet_ 2007; 39: 1208–1216. Article  Google Scholar  * Idaghdour Y,

Czika W, Shianna KV _et al_: Geographical genomics of human leukocyte gene expression variation in southern Morocco. _Nat Genet_ 2010; 42: 62–67. Article  CAS  Google Scholar  * Morley M,

Molony CM, Weber TM _et al_: Genetic analysis of genome-wide variation in human gene expression. _Nature_ 2004; 430: 743–747. Article  CAS  Google Scholar  * Myers AJ, Gibbs JR, Webster JA

_et al_: A survey of genetic human cortical gene expression. _Nat Genet_ 2007; 39: 1494–1499. Article  CAS  Google Scholar  * Stranger BE, Forrest MS, Dunning M _et al_: Relative impact of

nucleotide and copy number variation on gene expression phenotypes. _Science_ 2007; 315: 848–853. Article  CAS  Google Scholar  * Stranger BE, Nica AC, Forrest MS _et al_: Population

genomics of human gene expression. _Nat Genet_ 2007; 39: 1217–1224. Article  CAS  Google Scholar  * Veyrieras JB, Kudaravalli S, Kim SY _et al_: High-resolution mapping of expression-QTLs

yields insight into human gene regulation. _PLoS Genet_ 2008; 4: e1000214. Article  Google Scholar  * Cheung VG, Nayak RR, Wang IX _et al_: Polymorphic cis- and trans-regulation of human

gene expression. _PLoS Bio_ 2010; 8. Article  Google Scholar  * Cheung VG, Spielman RS : Genetics of human gene expression: mapping DNA variants that influence gene expression. _Nat rev_

2009; 10: 595–604. Article  CAS  Google Scholar  * Fraser P, Bickmore W : Nuclear organization of the genome and the potential for gene regulation. _Nature_ 2007; 447: 413–417. Article  CAS

  Google Scholar  * Gondor A, Ohlsson R : Chromosome crosstalk in three dimensions. _Nature_ 2009; 461: 212–217. Article  Google Scholar  * Spilianakis CG, Lalioti MD, Town T, Lee GR,

Flavell RA : Interchromosomal associations between alternatively expressed loci. _Nature_ 2005; 435: 637–645. Article  CAS  Google Scholar  * Williams A, Spilianakis CG, Flavell RA :

Interchromosomal association and gene regulation in trans. _Trends Genet_ 2010; 26: 188–197. Article  CAS  Google Scholar  * Herold C, Steffens M, Brockschmidt FF, Baur MP, Becker T :

INTERSNP: genome-wide interaction analysis guided by _a priori_ information. _Bioinformatics_ 2009; 25: 3275–3281. Article  CAS  Google Scholar  * Steffens M, Becker T, Sander T _et al_:

Feasible and successful: genome-wide interaction analysis involving all 1.9 × 10(11) pair-wise interaction tests. _Hum Hered_ 2010; 69: 268–284. Article  CAS  Google Scholar  * Wan X, Yang

C, Yang Q _et al_: BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies. _Am J Hum Genet_ 2010; 87: 325–340. Article  CAS  Google Scholar  *

Barbosa-Morais NL, Dunning MJ, Samarajiwa SA _et al_: A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. _Nucleic Acids Res_ 2010; 38:

e17. Article  Google Scholar  * Purcell S, Neale B, Todd-Brown K _et al_: PLINK: a tool set for whole-genome association and population-based linkage analyses. _Am J Hum Genet_ 2007; 81:

559–575. Article  CAS  Google Scholar  * Graham RR, Cotsapas C, Davies L _et al_: Genetic variants near TNFAIP3 on 6q23 are associated with systemic lupus erythematosus. _Nat Genet_ 2008;

40: 1059–1061. Article  CAS  Google Scholar  * Gregersen PK, Amos CI, Lee AT _et al_: REL, encoding a member of the NF-kappaB family of transcription factors, is a newly defined risk locus

for rheumatoid arthritis. _Nat Genet_ 2009; 41: 820–823. Article  CAS  Google Scholar  * Han JW, Zheng HF, Cui Y _et al_: Genome-wide association study in a Chinese Han population identifies

nine new susceptibility loci for systemic lupus erythematosus. _Nat Genet_ 2009; 41: 1234–1237. Article  CAS  Google Scholar  * Hom G, Graham RR, Modrek B _et al_: Association of systemic

lupus erythematosus with C8orf13-BLK and ITGAM-ITGAX. _N Engl J Med_ 2008; 358: 900–909. Article  CAS  Google Scholar  * Marroni F, Pfeufer A, Aulchenko YS _et al_: A genome-wide association

scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project. _Circ Cardiovasc Genet_ 2009; 2: 322–328. Article  CAS  Google Scholar  * Nair RP,

Duffin KC, Helms C _et al_: Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappaB pathways. _Nat Genet_ 2009; 41: 199–204. Article  CAS  Google Scholar  * Zhernakova A,

van Diemen CC, Wijmenga C : Detecting shared pathogenesis from the shared genetics of immune-related diseases. _Nat Rev_ 2009; 10: 43–55. Article  CAS  Google Scholar  * Cordell HJ :

Detecting gene-gene interactions that underlie human diseases. _Nat Rev_ 2009; 10: 392–404. Article  CAS  Google Scholar  * Cirulli ET, Kasperaviciute D, Attix DK _et al_: Common genetic

variation and performance on standardized cognitive tests. _Eur J Hum Genet_ 2010; 18: 815–820. Article  Google Scholar  * Schymick JC, Scholz SW, Fung HC _et al_: Genome-wide genotyping in

amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. _Lancet Neurol_ 2007; 6: 322–328. Article  CAS  Google Scholar  * Need AC,

Attix DK, McEvoy JM _et al_: A genome-wide study of common SNPs and CNVs in cognitive performance in the CANTAB. _Hum Mol Genet_ 2009; 18: 4650–4661. Article  CAS  Google Scholar  *

Stefansson H, Ophoff RA, Steinberg S _et al_: Common variants conferring risk of schizophrenia. _Nature_ 2009; 460: 744–747. CAS  PubMed  PubMed Central  Google Scholar  * Levy D, Larson MG,

Benjamin EJ _et al_: Framingham Heart Study 100K Project: genome-wide associations for blood pressure and arterial stiffness. _BMC Med Genet_ 2007; 8 (Suppl 1): S3. Article  Google Scholar

  * Soranzo N, Rivadeneira F, Chinappen-Horsley U _et al_: Meta-analysis of genome-wide scans for human adult stature identifies novel Loci and associations with measures of skeletal frame

size. _PLoS Genet_ 2009; 5: e1000445. Article  Google Scholar  * Weedon MN, Lango H, Lindgren CM _et al_: Genome-wide association analysis identifies 20 loci that influence adult height.

_Nat Genet_ 2008; 40: 575–583. Article  CAS  Google Scholar  * Asano K, Matsushita T, Umeno J _et al_: A genome-wide association study identifies three new susceptibility loci for ulcerative

colitis in the Japanese population. _Nat Genet_ 2009; 41: 1325–1329. Article  CAS  Google Scholar  * Barrett JC, Hansoul S, Nicolae DL _et al_: Genome-wide association defines more than 30

distinct susceptibility loci for Crohn's disease. _Nat Genet_ 2008; 40: 955–962. Article  CAS  Google Scholar  * McArdle PF, Parsa A, Chang YP _et al_: Association of a common

nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish. _Arthritis Rheum_ 2008; 58: 2874–2881. Article  Google Scholar  * Wallace C, Newhouse SJ, Braund P _et al_:

Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. _Am J Hum Genet_ 2008; 82: 139–149. Article  CAS  Google Scholar  *

Crowther-Swanepoel D, Broderick P, Di Bernardo MC _et al_: Common variants at 2q37.3, 8q24.21, 15q21.3 and 16q24.1 influence chronic lymphocytic leukemia risk. _Nat Genet_ 2010; 42: 132–136.

Article  CAS  Google Scholar  * Shrestha S, Irvin MR, Taylor KD _et al_: A genome-wide association study of carotid atherosclerosis in HIV-infected men. _Aids_ 2010; 24: 583–592. Article 

CAS  Google Scholar  * Daly AK, Donaldson PT, Bhatnagar P _et al_: HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. _Nat Genet_ 2009; 41:

816–819. Article  CAS  Google Scholar  * Gudbjartsson DF, Walters GB, Thorleifsson G _et al_: Many sequence variants affecting diversity of adult human height. _Nat Genet_ 2008; 40: 609–615.

Article  CAS  Google Scholar  * Kim JJ, Lee HI, Park T _et al_: Identification of 15 loci influencing height in a Korean population. _J Hum Genet_ 2010; 55: 27–31. Article  Google Scholar 

* Gudbjartsson DF, Bjornsdottir US, Halapi E _et al_: Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. _Nat Genet_ 2009; 41: 342–347. Article 

CAS  Google Scholar  * Ganesh SK, Zakai NA, van Rooij FJ _et al_: Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium. _Nat Genet_ 2009; 41: 1191–1198. Article  CAS 

Google Scholar  * Vasan RS, Glazer NL, Felix JF _et al_: Genetic variants associated with cardiac structure and function: a meta-analysis and replication of genome-wide association data.

_JAMA_ 2009; 302: 168–178. Article  CAS  Google Scholar  * MS-consortium: Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. _Nat

Genet_ 2009; 41: 824–828. Article  Google Scholar  * Easton DF, Pooley KA, Dunning AM _et al_: Genome-wide association study identifies novel breast cancer susceptibility loci. _Nature_

2007; 447: 1087–1093. Article  CAS  Google Scholar  * Kathiresan S, Voight BF, Purcell S _et al_: Genome-wide association of early-onset myocardial infarction with single nucleotide

polymorphisms and copy number variants. _Nat Genet_ 2009; 41: 334–341. Article  CAS  Google Scholar  * Kathiresan S, Melander O, Guiducci C _et al_: Six new loci associated with blood

low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. _Nat Genet_ 2008; 40: 189–197. Article  CAS  Google Scholar  * Sabatti C, Service SK,

Hartikainen AL _et al_: Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. _Nat Genet_ 2009; 41: 35–46. Article  CAS  Google Scholar  * Willer

CJ, Sanna S, Jackson AU _et al_: Newly identified loci that influence lipid concentrations and risk of coronary artery disease. _Nat Genet_ 2008; 40: 161–169. Article  CAS  Google Scholar 

Download references ACKNOWLEDGEMENTS JS was supported by a NIH/DFG Research Career Transition Award, and MMN was supported by the Alfried Krupp von Bohlen und Halbach-Stiftung. We are

grateful to all of the scientists at The Wellcome Trust Sanger Institute in Cambridge who were involved in generating the expression data, and to all of the scientists from the HapMap

Consortium who were involved in generating the genotypic data used in the present study. AUTHOR INFORMATION Author notes * Jessica Becker and Jens R Wendland: These authors contributed

equally to this work. AUTHORS AND AFFILIATIONS * Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany Jessica Becker, Britta Haenisch & Markus M Nöthen *

Institute of Human Genetics, University of Bonn, Bonn, Germany Jessica Becker, Britta Haenisch, Markus M Nöthen & Johannes Schumacher * Pharma Research and Early Development, F.

Hoffmann-La Roche Ltd, Basel, Switzerland Jens R Wendland Authors * Jessica Becker View author publications You can also search for this author inPubMed Google Scholar * Jens R Wendland View

author publications You can also search for this author inPubMed Google Scholar * Britta Haenisch View author publications You can also search for this author inPubMed Google Scholar *

Markus M Nöthen View author publications You can also search for this author inPubMed Google Scholar * Johannes Schumacher View author publications You can also search for this author

inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Johannes Schumacher. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no conflict of interest. ADDITIONAL

INFORMATION Supplementary Information accompanies the paper on European Journal of Human Genetics website SUPPLEMENTARY INFORMATION SUPPLEMENTARY TABLES 1–3 (DOC 1150 KB) RIGHTS AND

PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Becker, J., Wendland, J., Haenisch, B. _et al._ A systematic eQTL study of _cis_–_trans_ epistasis in 210 HapMap

individuals. _Eur J Hum Genet_ 20, 97–101 (2012). https://doi.org/10.1038/ejhg.2011.156 Download citation * Received: 15 February 2011 * Revised: 30 May 2011 * Accepted: 07 July 2011 *

Published: 17 August 2011 * Issue Date: January 2012 * DOI: https://doi.org/10.1038/ejhg.2011.156 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative

KEYWORDS * eQTLs * epistasis * interaction * _cis_-regulation * _trans_-regulation