ライフサイエンスコーパス: eQTL

1 eQTLs and eGenes provide great supporting evidence for G

2 At 49 of these 140 eQTLs, gene expression was nominally associated (p < 0.0

3 ent cis-eQTLs at 109 GWAS loci, including 93 eQTLs not previously described.

4 We identified cis-acting and trans-acting eQTL for key immune and metabolic response genes and sep

5 portion of genetic variance explained by all eQTL (hCOJO(2)) was 31% (0.060/0.192), meaning that 69%

6 candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study

7 fy loci where autoimmune-disease risk and an eQTL are driven by a single shared genetic effect.

8 This variant was reported to be an eQTL of the ZXDC gene, biologically linked to monocyte I

9 We conducted an eQTL investigation of microarray-based gene and exon exp

10 These observations suggest that an eQTL analysis that includes disease-affected brain tissu

11 nctional analysis, gene fusion detection and eQTL mapping.

12 visualization for linkage disequilibrium and eQTL data, and an ontology search for phenotypes, traits

13 us (SLE) through the integration of GWAS and eQTL data from the TwinsUK microarray and RNA-Seq cohort

14 plex-disease susceptibility loci by GWAS and eQTL integration have predominantly employed microarrays

15 ilitating the understanding of both GWAS and eQTL results and functional genomics data.

16 p a framework for the integration of PPI and eQTL into a heterogenous network model, enabling efficie

17 tified novel associations between asthma and eQTLs for 4 genes related to nucleotide synthesis/signal

18 ve maps of cis-acting hippocampal meQTLs and eQTLs provide a link between disease-associated SNPs and

19 Cis-acting SNPs of hippocampal meQTLs and eQTLs significantly overlap schizophrenia-associated SNP

20 d meta-analysis of all published Arabidopsis eQTL datasets.

21 he number of GWAS catalog SNPs identified as eQTL in the conditional analyses increases with 24% as c

22 e-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across

23 y than in standard unconditional whole blood eQTL databases.

24 and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Ge

25 functional annotation data, especially brain eQTL, methylation QTL, brain expression featured in deep

26 3, P=3.0 x 10(-6)) and the independent Brain eQTL Almanac (N=134, P=0.028).

27 s of the transcription factor CTCF and brain eQTLs.

28 ico functional analyses for these 11 SNPs by eQTL analysis, two of which, PTPN2 SNPs rs2847297 and rs

29 these, for 37, the association was driven by eQTLs located in established risk loci for allergic dise

30 Candidate eQTL analyses in in LCLs in the Hutterites suggest that

31 d the greatest frequency of candidate-causal eQTLs using exon-level RNA-Seq, and identified novel SLE

32 analysis showed that 35% of genes with a cis eQTL have at least two independent cis eQTLs; for severa

33 We provide an online conditional cis eQTL mapping catalog for whole blood, which can be used

34 Also, 12% (671) of the independent cis eQTLs identified in conditional analyses were not signif

35 ; for several genes up to 13 independent cis eQTLs were identified.

36 a cis eQTL have at least two independent cis eQTLs; for several genes up to 13 independent cis eQTLs

37 Associations between cis-eQTL markers and host response phenotypes using 383 pigs

38 These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONF

39 FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a

40 c curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different anc

41 nts, which are highly enriched for islet cis-eQTL.

42 ting expression quantitative trait loci (cis-eQTL) for 869 expressed genes (qval < 0.05).

43 ting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing p

44 cis-expression quantitative trait loci (cis-eQTL) maps.

45 cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggestin

46 cis-expression quantitative trait locus (cis-eQTL) variants in linkage disequilibrium with the index

47 FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types.

48 We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not

49 A set of 145 cis-eQTLs depended on type I interferon signaling.

50 QTLs at 14,118 CpG methylation sites and cis-eQTLs for 302 3'-mRNA transcripts of 288 genes.

51 variants are difficult to identify, and cis-eQTLs occur frequently, it remains challenging to identi

52 lic traits, we identified 140 coincident cis-eQTLs at 109 GWAS loci, including 93 eQTLs not previousl

53 eQTLs) impact expression of local genes (cis-eQTLs).

54 We identified cis-eQTLs for 12,400 genes at a 1% false-discovery rate.

55 We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, target

56 Although detecting local, cis-eQTLs is now routine, trans-eQTLs, which are distant fro

57 cis-expression quantitative trait loci (cis-eQTLs) and/or splicing eQTLs.

58 cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predicti

59 We identify numerous cis-eQTLs that contribute to the marked differences in immun

60 unit for quantifying the effect size of cis-eQTLs and a mathematically convenient approach for syste

61 other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and sign

62 We also identified copy-number variant (CNV) eQTLs, including some that appear to affect gene express

63 r studies, and two loci exhibited coincident eQTLs (P < 1 x 10-5) in subcutaneous adipose tissue for

64 After adjusting for common eQTLs and the major axes of gene expression covariance,

65 ene-by-environment interactions by comparing eQTLs under different conditions.

66 Conditional eQTL analysis allows a distinction between dependent and

67 We performed conditional eQTL analysis in 4,896 peripheral blood microarray gene

68 ronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases.

69 mber of them were unexplored in conventional eQTL mapping.

70 ical power) to detect eQTLs over the current eQTL mapping approaches.

71 0.05), 2,743 (12%) showed context-dependent eQTL effects.

72 systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not req

73 We show that our RA patient cohort derived eQTL network is more informative for studying RA than th

74 ease of approximately 50% in power to detect eQTL.

75 ce (in terms of statistical power) to detect eQTLs over the current eQTL mapping approaches.

76 sion remain unclear, particularly for distal eQTLs, which these studies are not well powered to detec

77 Distant eQTL formed 125 significant distant eQTL hotspots with t

78 Distant eQTL formed 125 significant distant eQTL hotspots with their targets significantly enriched

79 Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy

80 We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epi

81 In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue e

82 re significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger

83 normal hippocampal eQTLs than with epilepsy-eQTLs.

84 covered with a larger blood-based expression eQTL resource.

85 DNA methylation (meQTL) and gene expression (eQTL) in 110 human hippocampal biopsies.

86 ssue Expression (GTEx) data are used to find eQTLs and eGenes.

87 s many tissues to increase power for finding eQTLs and eGenes.

88 There was no evidence for eQTL effects for WNT4.

89 icularly Negative-Binomial-based) models for eQTL mapping.

90 thods and fits within existing pipelines for eQTL discovery.

91 we introduce a new haplotype-based test for eQTL studies that looks for haplotypic effects on expres

92 For eQTLs, we observed a strong correlation between sample s

93 hat aFC estimates independently derived from eQTL and allelic expression data are highly consistent,

94 n genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable fo

95 e of RNA-Seq and performing integrative GWAS-eQTL analysis against gene, exon, and splice-junction qu

96 epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies.

97 a) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs.

98 proportion can be attributed to identifiable eQTL of large effect, typically in cis.

99 As an example, our method identifies eQTLs by leveraging methylated CpG sites in a LIM homeob

100 al adult human brains, our method identifies eQTLs that were undetected using standard tissue-by-tiss

101 same confounding factor methods to identify eQTL that replicate between matched twin pair datasets i

102 ank), which provides an easy way to identify eQTLs that are associated with the conditional quantile

103 iFORM/eQTL can particularly discern the role of cis-QTLs, tran

104 This platform, named iFORM/eQTL, was assembled by forward-selection-based procedure

105 d genetic and genomic data set through iFORM/eQTL gain new discoveries on the genetic origin of gene

106 to identify and replicate a few broad impact eQTL although the overall number was small even when app

107 or confounding factors to model broad impact eQTL as non-genetic variation.

108 ower of the analysis or produce broad impact eQTL false positives, and the tendency of methods that a

109 factor methods when considering broad impact eQTL recovery from synthetic data.

110 f these results, we discuss the broad impact eQTL that have been previously reported from the analysi

111 tify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used

112 able the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimatio

113 cting issues when searching for broad impact eQTL: the need to account for non-genetic confounding fa

114 s for association mapping is able to improve eQTL mapping accuracy, and inferring individual and grou

115 In eQTL catalogs, gene expression is often strongly associa

116 tecting significant genes (called eGenes) in eQTL studies and analyzing the effect sizes of cis-SNPs

117 th gene expression levels, are identified in eQTL mapping studies.

118 of all loci with identified AH is 4%-23% in eQTLs, 35% in GWASs of high-density lipoprotein (HDL), a

119 tic variant and were primarily identified in eQTLs derived from pathophysiologically relevant tissues

120 both cis- and trans-eQTLs; (3) incorporates eQTLs identified in different tissues; and (4) uses simu

121 istinction between dependent and independent eQTLs.

122 By integrating eQTL, Hi-C and ChIP-seq data, we show that the pleiotrop

123 ative analysis combining CKD GWAS and kidney eQTL results can identify candidate genes for CKD.

124 issing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attrib

125 s) with WGS and RNA-seq, and found that lead eQTL variants called with WGS were more likely to be cau

126 We found that local eQTL were more frequently mapped to adjacent genes, disp

127 apping studies routinely identify many local eQTLs, the molecular mechanisms by which genetic variant

128 The genetic architecture of local eQTLs linked to the expressed genes was particularly com

129 screen a physical region for specific local eQTLs that could harbour candidate genes for phenotypic

130 Expression quantitative loci (eQTL) analysis is a vital aid for the identification and

131 ts using expression quantitative trait loci (eQTL) analysis and clinical phenotypes.

132 Expression quantitative trait loci (eQTL) analysis is a method to identify genetic variation

133 Using expression quantitative trait loci (eQTL) analysis, we found that the C7 rs6876739 CC genoty

134 ,587 cis-expression quantitative trait loci (eQTL) and 6,695 trans-eQTL associated with the 433 signi

135 ncluding expression quantitative trait loci (eQTL) and disease-associated mutations.

136 to both expression quantitative trait loci (eQTL) and genome-wide association study (GWAS) signals.

137 using an expression quantitative trait loci (eQTL) approach.

138 ependent expression quantitative trait loci (eQTL) by using linear mixed models to perform genome-wid

139 ysis and expression quantitative trait loci (eQTL) data showed that this locus contributes to MICALL2

140 and four expression quantitative trait loci (eQTL) datasets.

141 h as for expression quantitative trait loci (eQTL) detection, require perfect matching between both d

142 d 25 660 expression quantitative trait loci (eQTL) for 17 311 genes, capturing an unprecedented range

143 udies of expression quantitative trait loci (eQTL) have shown that regulatory variants play a crucial

144 udies of expression quantitative trait loci (eQTL) indicate that genetic variation frequently alters

145 such as expression quantitative trait loci (eQTL) or Hi-C genome conformation data and reports the m

146 ify 5311 expression quantitative trait loci (eQTL) regulating the expression of 4105 genes, including

147 ome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights

148 numerous expression Quantitative Trait Loci (eQTL) studies.

149 entioned expression quantitative trait loci (eQTL) using a statistically disciplined approach that jo

150 ation of expression quantitative trait loci (eQTL), the genetic determinants of variation in gene exp

151 Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with vary

152 rlapping expression quantitative trait loci (eQTL)-associated single nucleotide polymorphisms of mess

153 i-tissue expression quantitative trait loci (eQTL).

154 AA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are locate

155 Expression quantitative trait loci (eQTLs) across all annotated transcripts were mapped and

156 d 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs

157 pping of expression quantitative trait loci (eQTLs) and allele-specific expression (ASE).

158 ncluding expression quantitative trait loci (eQTLs) and DNase I sensitivity quantitative trait loci (

159 tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene express

160 Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression.

161 ponds to expression quantitative trait loci (eQTLs) at these loci.

162 nd trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated

163 udies of expression quantitative trait loci (eQTLs) from a broad range of tissues by using a Mendelia

164 Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underly

165 of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs).

166 pped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-n

167 talog of expression quantitative trait loci (eQTLs) in a nonhuman primate model.

168 files to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we i

169 coded by expression quantitative trait loci (eQTLs) is a key to construct the genotype-phenotype map

170 k across expression quantitative trait loci (eQTLs) of a gene and use this approach to identify asthm

171 lysis of expression Quantitative Trait Loci (eQTLs) provides modest support for altered regulation of

172 pping of expression quantitative trait loci (eQTLs) revealed that genetic factors had a stronger effe

173 e to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes.

174 or these expression quantitative trait loci (eQTLs) that disrupt transcription factor binding and val

175 pping of expression quantitative trait loci (eQTLs) with WGS and RNA-seq, and found that lead eQTL va

176 ne, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants infl

177 Expression quantitative trait loci (eQTLs), genetic variants associated with gene expression

178 known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role

179 ks using expression quantitative trait loci (eQTLs), methylation quantitative trait loci (meQTLs), ch

180 s act as expression quantitative trait loci (eQTLs), suggesting that modulation of transcript abundan

181 iched in expression quantitative trait loci (eQTLs), thus suggesting that most risk variants alter ge

182 ing many expression quantitative trait loci (eQTLs).

183 f 20 206 expression quantitative trait loci (eQTLs).

184 such as expression quantitative trait loci (eQTLs).

185 apped as expression quantitative trait loci (eQTLs); however, a major limitation of eQTLs is their lo

186 rformed expression quantitative trait locus (eQTL) analyses by using abdominal subcutaneous adipose t

187 Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in whi

188 Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highl

189 Expression quantitative trait locus (eQTL) analysis in multiple cell types of the melanocytic

190 Expression quantitative trait locus (eQTL) analysis showed suggestive eQTL signals at rs14466

191 WAS and expression quantitative trait locus (eQTL) data with RNA-seq data from the RISK study, an inc

192 4 as an expression quantitative trait locus (eQTL) for IFITM3, with the risk allele associated with l

193 -acting expression quantitative trait locus (eQTL).

194 for whole blood, which can be used to lookup eQTLs more accurately than in standard unconditional who

195 We identified many eQTL genes (eGenes) not observed in the comparably sized

196 Many eQTLs operate in a tissue- and condition-specific manner

197 ts of post-transcriptional events in mapping eQTL.

198 of DNA (in this case, CpG sites) on mapping eQTLs.

199 While most eQTL studies focus on identifying mean effects on gene e

200 In recent years, multiple eQTL (expression quantitative trait loci) catalogs have

201 Detecting multiple eQTLs simultaneously in a population based on paired DNA

202 nts the existing methods, and identifies new eQTLs with heterogeneous effects across different quanti

203 the expression of 4105 genes, including nine eQTLs regulating genes associated with flavonoid biosynt

204 E scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated are

205 x populations of different ancestry from non-eQTL SNVs with an AUC of 0.939.

206 approach for investigating novel aspects of eQTL data sets.

207 ntify technical and biological correlates of eQTL effect size.

208 Our dissection of eQTL effects may be used to distinguish genes whose asso

209 While the majority of eQTL identified in genome-wide analyses impact a single

210 formation available to maximize the power of eQTL mapping.

211 estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimat

212 loci (eQTLs); however, a major limitation of eQTLs is their low resolution, which precludes investiga

213 tial to discover a more comprehensive set of eQTLs and illuminate the underlying molecular consequenc

214 t that determines the nature and strength of eQTLs may help identify cell types relevant to pathophys

215 ative individual peaks for networks based on eQTL, mQTL or pQTL information.

216 that most haplotypes associated with GWAS or eQTL phenotypes are located outside of DNase-seq footpri

217 own as expression quantitative trait loci or eQTLs) is important in unravelling the genetic basis of

218 ore enriched in GWAS associations than other eQTLs.

219 We illustrate the value of our eQTL database in the context of a recent GWAS meta-analy

220 at rs715212 may influence AREG expression (P eQTL = 0.035), although further functional studies are n

221 attributed to the presence of 'third-party' eQTLs that influence the gene expression mean in a fract

222 eated with interferon (IFN)-beta and perform eQTL analysis on 23 pooled samples.

223 clinical strains-which allowed us to perform eQTL mapping with 50-fold higher resolution than previou

224 We performed eQTL analysis by correlating genotype with RNA-seq-based

225 Furthermore, we performed eQTL and co-expression analyses in lung tissue.

226 Finally, our results suggest the placental eQTLs may mediate the function of GWAS loci on postnatal

227 nstrate it by analyzing the cross-population eQTL data from the GEUVADIS project and the multi-tissue

228 dentified ceruloplasmin (Cp) as a positional eQTL in macrophages but not in serum.

229 The small sample sizes of some previous eQTL studies have limited their statistical power.

230 cis- and/or trans-eQTLs across 16 published eQTL studies.

231 SMR analyses performed with expression QTL (eQTL) data.

232 ntegration with genome-wide expression QTLs (eQTLs) from the same BC population identified ceruloplas

233 antification type to detect disease relevant eQTLs and eGenes.

234 iological inferences based on these reported eQTL.

235 lts demonstrate the power of high-resolution eQTL mapping in understanding the molecular mechanisms o

236 stimuli and provide a time-resolved response eQTL map.

237 trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions.

238 These response eQTLs were enriched in disease-associated variants, part

239 Although large-scale eQTL mapping studies routinely identify many local eQTLs

240 S SNPs, 48% are identified to be significant eQTLs in our study.

241 sible loci and identify a set of significant eQTLs modulating differentiation and function of gene ex

242 atalog contains more genome-wide significant eQTLs per sample than comparable human resources and ide

243 s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the unde

244 ative trait loci (cis-eQTLs) and/or splicing eQTLs.

245 ing applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform us

246 actor analysis methods when identifying such eQTL.

247 rait locus (eQTL) analysis showed suggestive eQTL signals at rs1446669, rs699664 and rs1078004 for CA

248 Our results reveal that TE-eQTL are involved in population-specific gene regulation

249 also more likely to be tissue specific than eQTLs identified by linear regression.

250 lated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopat

251 relevance of these genes is supported by the eQTL analyses and co-expression of PTTG1lP with vimentin

252 pecific transcription factors binding to the eQTL SNPs.

253 The eQTLs are predominantly under negative selection, partic

254 The eQTLs that regulate the expression levels of Egfr betwee

255 Notably, we show that the eQTLs identified by QRank but missed by linear regressio

256 We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular c

257 Many of these eQTLs were found to influence the expression of several

258 nd computational analyses revealed that this eQTL is linked to an unannotated alternate MFN2 start si

259 sis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes und

260 ng standard tissue-by-tissue or joint tissue eQTL mapping techniques.

261 om the GEUVADIS project and the multi-tissue eQTL data from the GTEx project.

262 Current multi-tissue eQTL mapping techniques are limited to only exploiting g

263 Traditional eQTL mapping is to associate one transcript with a singl

264 ive Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds o

265 polyTE loci correspond to both cis and trans eQTL, and their regulatory effects are directly related

266 In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes,

267 antitative trait loci (eQTL) and 6,695 trans-eQTL associated with the 433 significant insulin-associa

268 Of note, we identified a trans-eQTL (rs592423), where the A allele was associated with

269 analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better

270 the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

271 attributed to all identified cis- and trans-eQTL.

272 Four trans-eQTL hotspots coincided with GLS disease QTLs mapped in

273 tion testing suggested that for 35% of trans-eQTL-trans-eGene pairs in different chromosomes and 90%

274 n and between populations and a strong trans-eQTL hotspot at TLR1 that decreases expression of pro-in

275 These trans-eQTL signals confirmed and extended the previously repor

276 We discovered 3218 cis- and 35 trans-eQTLs at </=10% false discovery rate in human placentas.

277 We identified 2,350 trans-eQTLs (at p < 10(-7)); more than 80% of them were found

278 tatistics; (2) considers both cis- and trans-eQTLs; (3) incorporates eQTLs identified in different ti

279 One common trans-eQTLs mechanism is "mediation" by a local (cis) transcri

280 articular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eG

281 of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms.

282 s-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with

283 ultaneously and found that hundreds of trans-eQTLs each affect hundreds of transcripts in lymphoblast

284 ranscripts of a high-confidence set of trans-eQTLs encode proteins that interact more frequently than

285 tify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 2

286 ranscripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation

287 es that were found to have cis- and/or trans-eQTLs across 16 published eQTL studies.

288 cting local, cis-eQTLs is now routine, trans-eQTLs, which are distant from the genes of origin, are f

289 Some trans-eQTLs point toward novel mechanistic explanations for th

290 We hypothesized that some trans-eQTLs regulate expression of distant genes by altering t

291 These trans-eQTLs target the same genes across the three populations

292 gests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susc

293 498 genes) was enriched for genes with trans-eQTLs in hotspots coinciding with GLS resistance QTLs on

294 Y-s module was enriched for genes with trans-eQTLs in hotspots on chromosomes 9 and 10, which also co

295 Ex and show that in nearly all cases the two eQTLs act independently in regulating gene expression.

296 We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two

297 Using eQTLs from three major immune subpopulations, we found s

298 tions, several studies investigated variance eQTLs.

299 These enriched loci, along with eQTL associations, were unexpectedly present in non-neur

300 ther combined DXME gene expression data with eQTL data from the GTEx project and with allele frequenc