ライフサイエンスコーパス: eQTL

1 eQTL analyses identify mencRNAs whose expression is asso

2 expression quantitative trait loci: with 10 eQTLs involving SNPs in promoter regions or transcriptio

3 ndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for wais

4 to be the potential causal variant for 3522 eQTLs and 3717 sQTLs.

5 es under genetic regulation, including 4,541 eQTLs detected only in the retina.

6 diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes wit

7 Using 1 702 612 eQTLs discovered by the Genotype-Tissue Expression (GTEx

8 gression provides more reliable and accurate eQTL mapping than conventional linear models.

9 cript/exon-level and cis versus trans-acting eQTL, across 10 regions of the human brain.

10 of infection-specific cis- and trans-acting eQTLs in the DGRP, including one common non-coding varia

11 An eQTL database screen revealed that rs2242367 is associat

12 ns between two subgenomes, highlighted by an eQTL hotspot (Hot216) that established a genome-wide gen

13 We introduce ReQTL, an eQTL modification which substitutes the DNA allele count

14 e accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and

15 discovery, differential expression analysis, eQTL prioritization, and pathway enrichment analysis.

16 nd enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17

17 method that uses GWAS summary statistics and eQTL to infer differential gene expression and interroga

18 ional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the inte

19 RK2, analysis of the PSP survival signal and eQTLs for LINC02555 in the eQTLGen blood dataset reveale

20 indicated that rs6967330 and other SNPs are eQTLs for CDHR3.

21 nt in the GWAS compared to those reported as eQTL in only one tissue type.

22 icate that the SNP's eQTL status, as well as eQTL density in the adjacent region are positively assoc

23 Pan-tissue eQTLs (those reported as eQTLs in multiple tissues) tended to be more significant

24 We found that SNPs reported as eQTLs were more significant in GWAS (higher -log(10)p) r

25 Tissue specificity analysis of associated eQTLs provide additional evidence of the distinct roles

26 The pMEI-associated eQTLs and sQTLs show a high level of tissue specificity,

27 The skin cell-type associated eQTLs colocalize with skin diseases, indicating that var

28 Integrative analysis of publicly available eQTL, DNaseI, and chromatin conformation data identified

29 s enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological signi

30 and our own study of colocalization between eQTLs and loci associated with CAD using SMR/HEIDI appro

31 nstrated the close spatial proximity between eQTLs and their target genes among multiple human primar

32 By integrating sex-biased eQTLs with genome-wide association study data, we identi

33 cohorts with: RNAseq data (n = 50) and blood eQTL dataset (n = 31,684).

34 Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used

35 Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTL

36 58413 with DCSTAMP were further supported by eQTL data from other tissue types.

37 es and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification

38 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared wit

39 Cis-eQTL analysis identified 10,474 genes under genetic regu

40 cis-eQTL for 587 Matsuda index-correlated genes were identif

41 velopment of human brain and suggested a cis-eQTL effect for rs2535629 and rs3617 on ITIH3 in the hip

42 fecting circulating resistin levels as a cis-eQTL increasing RETN expression.

43 AEC cis-eQTL analysis indicated that rs6967330 and other SNPs ar

44 association, co-expression networks, and cis-eQTL discovery.

45 for 42 traits (average N = 323,000) and cis-eQTL summary statistics for 48 tissues from the Genotype

46 ted with Matsuda index, and regulated by cis-eQTL, rs34844369 being the top cis-eSNP in AAGMEx.

47 a single haplotype as a potential causal cis-eQTL for CXorf21.

48 as been shown to accurately discriminate cis-eQTL SNVs from non-eQTL SNVs and perform favorably to ot

49 es a data-driven nonparametric prior for cis-eQTL effect sizes.

50 nments through comparison with data from cis-eQTL enrichment, functional fine-mapping, RNA co-express

51 We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than

52 nd 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets,

53 signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome

54 cis-expression quantitative trait loci (cis-eQTL) analyses for 294 GWAS-identified variants for six

55 ting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery

56 cis expression quantitative trait loci (cis-eQTL) mapping for this 2 megabase genomic region using p

57 d by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association

58 orroborates four colocalizing melanocyte cis-eQTL genes.

59 end the use of BAGEA for the analysis of cis-eQTL data to reveal annotations relevant to expression b

60 desirable to characterize the impact of cis-eQTL SNVs in a context-specific manner.

61 d, methods for annotating and predicting cis-eQTL SNVs are under-developed.

62 into distinct polygenic risk scores (PRS(cis-eQTL) and PRS(GWAS)), and tested for predicting brain di

63 n the expression model was a significant cis-eQTL and metabolomic-QTL (met-QTL), 92% demonstrated col

64 We identify sixty-nine significant cis-eQTL effects for eBMD GWAS variants after correction for

65 SNP, rs73227498, acted as a significant cis-eQTL for expression of EPB41L4A, rs17134155 was a signif

66 cision trees, to predict tissue-specific cis-eQTL SNVs.

67 increase MX2 levels, consistent with the cis-eQTL of MX2 in primary human melanocytes.

68 variation in expression than did the top cis-eQTL (median 2-fold improvement); (B) predicted expressi

69 this effect was mediated by a transient cis-eQTL present only in early LPS response and lost before

70 mong all non-coding regulatory variants, cis-eQTL single nucleotide variants (SNVs) are of particular

71 We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adj

72 Cis-eQTLs are largely specific to cell type or stimulation,

73 We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx t

74 electively constrained, and enriched for cis-eQTLs and RNA-binding protein (RBP) interactions.

75 and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, i

76 , a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations.

77 We find multiple cis-eQTLs embedded in a risk haplotype that progressively do

78 tion between condition-specific neonatal cis-eQTLs and variants associated with immune-mediated disea

79 ate samples with 117 SNPs revealed novel cis-eQTLs and trans-eQTLs.

80 iated with human disease are depleted of cis-eQTLs (cis-expression quantitative trait loci), suggesti

81 en queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and s

82 ary and conditionally distinct secondary cis-eQTLs, including some across ancestries.

83 3) and rs9515201 (13q34) are significant cis-eQTLs for PMF1 (P = 1 x 10-4 in tibial nerve), NBEAL1, F

84 undance of a gene and its proximal SNPs (cis-eQTLs) are now readily identified, identification of hig

85 n to tissue specificity than bulk tissue cis-eQTLs.

86 Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less

87 imulation, and 31% and 52% of genes with cis-eQTLs have response eQTLs (reQTLs) in myeloid cells and

88 , UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs.

89 ts had the highest percentage of colocalized eQTLs (15% and 14%, respectively).

90 Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in

91 Conducting eQTL analyses for GTEx liver and skin samples using cell

92 ene-based test of association that considers eQTL from multiple tissues, we identify seven (and four)

93 for disease association beyond conventional eQTL mapping.

94 dge gap by performing a large-scale in-depth eQTL mapping study on 1,032 African Americans (AA) and 8

95 Characterisation of distal eQTL revealed unequal genetic regulation patterns betwee

96 6706 and 4628 significant local- and distant-eQTL associations, respectively.

97 We also found nonlinear dynamic eQTLs, which affect only intermediate stages of differen

98 We identified hundreds of dynamic eQTLs that change over time, with enrichment in enhancer

99 By incorporating empirical eQTL of the disease relevant tissue, GIGSEA naturally ac

100 sible and can identify a subset of expressed eQTL loci.

101 amic methylation (methQTL), gene expression (eQTL) and behaviour.

102 uantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs).

103 nsional data, such as GWAS, gene expression, eQTL and structural/functional neuroimage studies for ca

104 f ancestry and admixture on gene expression, eQTLs, and GWAS colocalization.

105 Five eQTLs were associated with regulatory motif alterations

106 ith diabetes revealed an association of FLCN eQTLs with diabetic retinopathy.

107 distributed errors in linear regression for eQTL detection, which results in increased Type I or Typ

108 e association study on income with data from eQTL studies and chromatin interactions, 24 genes are pr

109 s article we present a method for functional eQTL discovery and provide insights into relevance of no

110 genome sequences of donors, enabling future eQTL studies.

111 ut genomic intervals, combined with grouping eQTLs by the pathways or gene sets that their target gen

112 expression, RefSeq Functional elements, GTEx eQTLs, CRISPR Guides, SNPpedia and created a 30-way prim

113 nt a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to

114 ost methods cannot be applied to either GWAS/eQTL summary statistics or cases with more than two poss

115 g the two samples via meta-analysis, 895 GxE eQTLs are identified.

116 tasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset

117 monstrates an important use of our brain GxE eQTLs.

118 However, eQTL mapping is usually confronted with the analysis cha

119 We previously identified eQTL in subcutaneous adipose tissue from 770 participant

120 Identified eQTLs produced a co-ordinated regulatory action between

121 ression Project (GTEx) were used to identify eQTL SNPs.

122 llowed us to identify functionally important eQTLs and show mechanisms that explain their cell-type r

123 short of pinpointing functionally important eQTLs.

124 w that the use of local ancestry can improve eQTL mapping in admixed and multiethnic populations, res

125 and dropouts, and it significantly improves eQTL mapping.

126 for a number of features including being in eQTLs in blood and the frontal cortex, CpG islands and s

127 eGenes in AA tend to harbor more independent eQTLs than eGenes in EA, suggesting potentially diverse

128 target gene promoters (promoter-interacting eQTLs, pieQTLs) in five common immune cell types (Databa

129 in silico saturation mutagenesis, interpret eQTLs, make predictions for structural variants and prob

130 Analysis of local eQTL and GWAS data prioritised 13 likely causal genes fo

131 y effect to only 10% of these detected local-eQTLs.

132 c, expression quantitative trait loci (local-eQTLs) with infection-specific ones located in regions e

133 additional analyses indicate that many local-eQTLs may act in trans instead.

134 Expression quantitative loci (eQTL) are being used widely to annotate and interpret GW

135 n silico expression quantitative trait loci (eQTL) analyses for biological function using the BRAINEA

136 tion and expression quantitative trait loci (eQTL) analyses to identify IR-correlated cis-regulated t

137 ng 7,962 expression quantitative trait loci (eQTL) and 4,635 spliceQTL (sQTL), including several thou

138 -related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals.

139 pes with expression quantitative trait loci (eQTL) and phenotype associations.

140 egrating expression quantitative trait loci (eQTL) colocalization, fine-mapping, and human rare-disea

141 i-tissue expression quantitative trait loci (eQTL) data from the GTEx (v.8) suggests that colocalizat

142 and the expression quantitative trait loci (eQTL) dataset.

143 with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci.

144 specific expression quantitative trait loci (eQTL) information to help annotate a set of genomic inte

145 inently, expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admi

146 specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-

147 previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underl

148 ssed the expression quantitative trait loci (eQTL) profile of variants that passed genome-wide signif

149 d, using expression quantitative trait loci (eQTL) results, with a decrease in gene expression much m

150 and the expression quantitative trait loci (eQTL) status of the SNP.

151 nt trans-expression quantitative trait loci (eQTL) that are known to explain important expression var

152 nome and expression quantitative trait loci (eQTL) to identify susceptibility genes/variants from mul

153 ng brain expression quantitative trait loci (eQTL), gene coexpression network, differential gene expr

154 d 15 330 expression quantitative trait loci (eQTL).

155 n of CTS expression Quantitative Trait Loci (eQTL).

156 anization with expression quantitative loci (eQTLs) analysis, using CoDeS3D, to identify the function

157 onnected expression quantitative trait loci (eQTLs) (IRT), to predict the regulatory targets of non-c

158 ation of expression quantitative trait loci (eQTLs) and identification of long-range chromatin intera

159 sociated expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) in 4

160 inferred expression quantitative trait loci (eQTLs) and then identify expression-mediated genetic eff

161 sands of expression quantitative trait loci (eQTLs) at all ranges of effect sizes not detected by the

162 pping of expression quantitative trait loci (eQTLs) facilitates interpretation of the regulatory path

163 ority of expression quantitative trait loci (eQTLs) for the gene expression traits in the two environ

164 veraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissue

165 specific expression quantitative trait loci (eQTLs) in 20 genes, including four autoimmune disease ge

166 lity and expression quantitative trait loci (eQTLs) in humans and chimpanzees, using gene expression

167 , we map expression quantitative trait loci (eQTLs) in resting myeloid cells and CD4(+) T cells from

168 sociated expression quantitative trait loci (eQTLs) likely regulate genes upstream of read-in genes.

169 4160 are expression quantitative trait loci (eQTLs) of CRKL.

170 Expression quantitative trait loci (eQTLs) of the glucose response genes were tested for ass

171 Expression quantitative trait loci (eQTLs) studies provide associations of genetic variants

172 ntegrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWA

173 e mapped expression quantitative trait loci (eQTLs) throughout differentiation to elucidate the dynam

174 tify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize

175 lap with expression quantitative trait loci (eQTLs), but it remains unclear whether this overlap is d

176 ealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific eff

177 .e., GxE expression quantitative trait loci (eQTLs).

178 on as SZ expression quantitative trait loci (eQTLs).

179 specific expression quantitative trait loci (eQTLs).

180 are gene expression quantitative trait loci (eQTLs).

181 ription [expression quantitative trait loci (eQTLs)] are implicated in complex diseases through unkno

182 levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding h

183 Expression quantitative trait locus (eQTL) analysis detects hotspots harboring master regulat

184 ium cis-expression quantitative trait locus (eQTL) analysis of CDHR3 was performed, followed by assoc

185 Expression quantitative trait locus (eQTL) association meta-analysis on 496 prostate tumor an

186 eoclast expression quantitative trait locus (eQTL) dataset.

187 xisting expression quantitative trait locus (eQTL) mapping studies have been focused on individuals o

188 a robust model, quantile regression, to map eQTLs for genes with high degree of overdispersion or la

189 Here, using H3K27ac HiChIP assays, we mapped eQTLs overlapping active cis-regulatory elements that in

190 gulation-associated loci including missense, eQTL and sQTL variants of critical complement and coagul

191 Our analysis identifies more eQTLs than existing approaches, consistent with improved

192 e risk measured based on single and multiple eQTL.

193 Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independ

194 in the expression of genes with top nominal eQTL association p-values < 10-7.

195 curately discriminate cis-eQTL SNVs from non-eQTL SNVs and perform favorably to other methods by obta

196 We highlight one example of a nonlinear eQTL that is associated with body mass index.

197 ffects ~40% of samples and leads to numerous eQTL assignments in inappropriate tissues among these 18

198 ncestry can both impede the dissemination of eQTL mapping results that would otherwise benefit indivi

199 Integration of eQTL and sQTL with genome-wide association studies (GWAS

200 Finally, we identify a small subset of eQTL-associated variants highly correlated with local an

201 In a third of eQTL, we find that there is a correlation between gene e

202 ercentage (an increase of 18.7% to 47.2%) of eQTLs identified by T-GEN are inferred to be functional

203 critically depends on the identification of eQTLs, which may not be functional in the corresponding

204 count is important for the interpretation of eQTLs in systems where transcription termination is bloc

205 g the presence or the absence of millions of eQTLs in a set of input genomic intervals, combined with

206 non-coding variants identified in studies of eQTLs.

207 icant colocalization is called only with one eQTL ancestry adjustment method.

208 , such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be

209 co-localisation of eBMD GWAS and osteoclast eQTL association signals for 21 of the 69 loci, implicat

210 ion analysis of the eBMD GWAS and osteoclast eQTL datasets identifies significant associations for 53

211 sk variants among high-confidence osteoclast eQTL across multiple GWAS P value thresholds.

212 We utilise a unique osteoclast eQTL dataset to identify a number of potential effector

213 gly, we concluded that Hi-C loop outperforms eQTL in explaining neurological GWAS results, revealing

214 r regulatory analysis reveals one particular eQTL that significantly decreases the binding affinity f

215 We perform eQTL analysis for 406 breast cancer-related genes to tra

216 Analysis of available cross-population eQTLs and massively parallel reporter assay data show th

217 Lack of large-scale well-powered eQTL mapping studies in populations with African ancestr

218 europsychiatric disease lies within prenatal eQTL and sQTL.

219 Compared to primary eQTL signals, secondary eQTL signals were located furthe

220 nalysis and takes advantage of probabilistic eQTL annotations to delineate and tackle the unique chal

221 that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity th

222 regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most

223 We experimentally revealed that BD-related eQTL SNPs rs10865973, rs12635140, and rs4687644 regulate

224 nd 52% of genes with cis-eQTLs have response eQTLs (reQTLs) in myeloid cells and T cells, respectivel

225 We report potential epilepsy-risk eQTLs, some of which are specific to tissue from patient

226 esults of this study indicate that the SNP's eQTL status, as well as eQTL density in the adjacent reg

227 deserves more attention for the large-scale eQTL mapping.

228 ne which has shown to be unreliable; second, eQTL allows us to provide the regulatory annotation unde

229 Compared to primary eQTL signals, secondary eQTL signals were located further from transcription sta

230 four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1.

231 m a real data analysis, the most significant eQTL discoveries differ between quantile regression and

232 nal analyses, 90% were dominated by a single eQTL SNP; (C) among the 35% of associations where a SNP

233 tly constructed a unique osteoclast-specific eQTL resource using cells differentiated in vitro from 1

234 ve designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

235 ion on chromosome 17 plus two brain-specific eQTLs.

236 ffects, analysis of the environment-specific eQTLs reveals enrichment of binding sites for two transc

237 t nutcracker is linked to infection-specific eQTLs that correlate with its expression level and to en

238 iption factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain

239 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional.

240 relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-

241 on by using summary statistics from standard eQTL analyses.

242 and identifies convergence downstream of SZ eQTL gene perturbations.

243 ession technologies to study one putative SZ eQTL (FURIN rs4702) and four top-ranked SZ eQTL genes (F

244 Z eQTL (FURIN rs4702) and four top-ranked SZ eQTL genes (FURIN, SNAP91, TSNARE1 and CLCN3), our platf

245 We found that eQTL harboring genes (eGenes) are enriched in metabolic

246 The eQTL associations observed for rs4294134 with STMP1, and

247 lustrate the power of PTWAS by analyzing the eQTL data across 49 tissues from GTEx (v8) and GWAS summ

248 leles for an upstream LD block tagged by the eQTL rs9397171.

249 Newly implicated genes identified in the eQTL analysis include those encoding proteins that make

250 European haplotype specifically includes the eQTL intronic SNP rs62436463 that must have arisen after

251 regardless of the tissue specificity of the eQTL.

252 Only the eQTL block containing the rs6967330 SNP showed a signifi

253 We detected an enrichment of the eQTLs from the glucose response genes among small associ

254 metabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulat

255 results demonstrate that assayed bulk tissue eQTLs, although disease relevant, cannot explain the maj

256 Pan-tissue eQTLs (those reported as eQTLs in multiple tissues) tend

257 We also devised an approach to eQTL discovery that relies on HiChIP-based promoter inte

258 We apply NPDR to eQTL data to identify potentially interacting variants t

259 By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 ge

260 S) method that leverages both cis- and trans-eQTL information for a TWAS.

261 , which not only accounts for cis- and trans-eQTL of the target gene but also enables efficient compu

262 -8)) which was driven by both cis- and trans-eQTL.

263 isting TWAS methods that do not assess trans-eQTL information.

264 for ZC3H12B were completely driven by trans-eQTL.

265 Four of the top driven trans-eQTL of ZC3H12B are located within APOC1, a known major

266 Fs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways.

267 on data with five methods as traits in trans-eQTL analysis to limit multiple testing and improve inte

268 ed associations, we discovered a novel trans-eQTL near SLC39A8 regulating a module of metallothionein

269 ls biological insights when applied to trans-eQTL (expression quantitative trait loci) identification

270 at most heritability is driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral

271 Immune genes associated with trans-eQTL were numerous and spread throughout the genome.

272 117 SNPs revealed novel cis-eQTLs and trans-eQTLs.

273 n of high-quality distal associations (trans-eQTLs) has been limited by a heavy multiple testing burd

274 dulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect size

275 ne(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies.

276 ped a computational method to identify trans-eQTLs that are mediated by multiple mediators.

277 ng expression quantitative trait loci (trans-eQTLs) can directly reveal cellular processes modulated

278 Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait

279 had increased power to detect mediated trans-eQTLs, especially in large samples.

280 is-eQTLs, the regulatory mechanisms of trans-eQTLs are less known.

281 e identification and prioritisation of trans-eQTLs when applied to emerging cell-type-specific datase

282 Motivated from the observation that trans-eQTLs are more likely to associate with more than one ci

283 ng sample genotypes for SNPs, identified two eQTL for salmonid alphavirus load.

284 sis showed that, depending on the underlying eQTL data used, the directed genomic annotations could p

285 to be one of the main mechanisms underlying eQTLs, most evidence came from studies of cell lines and

286 We assemble datasets using eQTL results from the Genotype-Tissue Expression (GTEx)

287 enome-wide association studies (GWASs) using eQTL information, and establishes a framework for identi

288 wcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.

289 We utilize eQTL and chromosome conformation datasets to link differ

290 By combining genes predicted via eQTL analysis, as well as those predicted from base-pair

291 An intronic SNP rs79237970 in the WDR92 (eQTL for PPP3R1) was significantly associated with bette

292 derstand their relationship, such as whether eQTLs regulate their target genes through physical chrom

293 integrate directed genomic annotations with eQTL summary statistics from tissues of various origins.

294 ronic apaQTLs are negatively correlated with eQTL effect sizes.

295 that TOA scores can be directly coupled with eQTL colocalization to further resolve effector transcri

296 ificant increase in two 118A haplotypes with eQTL SNPs associated with drug addiction (rs510769) and

297 e confirm that several pMEIs associated with eQTLs and sQTLs can alter gene expression levels and iso

298 overlap of orthologous genes associated with eQTLs in both species.

299 ronment interactions, in stark contrast with eQTLs that are largely context-dependent.

300 Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported lo

301 ing colocalization for 180 disease loci with eQTLs.