ライフサイエンスコーパス: GWAS

1 GWAS cannot identify functional SNPs (fSNP) from disease

2 GWAS data are discoverable from the perspective of genet

3 GWAS have several important limitations.

4 GWAS have successfully contributed to the characterisati

5 GWAS identified 101 marker-trait associations (MTAs), so

6 GWAS identified a HLA region associated with NMO, led by

7 GWAS loci explain 32% of disease risk in East Asians and

8 GWAS of >71,000 individuals across 27 cancer types sugge

9 GWAS of fasting and postprandial serum TG at 150 minutes

10 GWAS revealed that grain chalk and hyperspectral variati

11 GWAS reveals associations with KSHV antibody response in

12 GWAS, performed with the recently developed multiple loc

13 GWASs have revealed multiple loci associated with atopic

14 s also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes

15 number of AF cases compared with AFGen 2017 GWAS.

16 : (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association

17 evant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes).

18 genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF ca

19 A GWAS of 6,382 high-quality DArTseq SNPs revealed 15 sign

20 We conducted a GWAS for SUA in 6,881 Korean individuals, calculated pol

21 inal information can significantly enhance a GWAS's power to comprehend the genetic architecture of c

22 For example, a GWAS signal mapped to the gene encoding uromodulin has b

23 We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930

24 In this study, we used FUMA, a GWAS annotation tool, to pinpoint potential causal varia

25 r to establishing causal relationships: if a GWAS trait and a gene's expression share the same associ

26 Here, we report the results of a GWAS of mood instability as a trait in a large populatio

27 A PRS based on a GWAS of neuroticism (n = 390,278) was positively associa

28 We performed a GWAS of 35,839 participants in the UKB with high intake

29 ic susceptibility to obesity, we performed a GWAS on metabolically healthy thin individuals (lowest 6

30 We performed a GWAS on the genotyped cohort, limiting the cases to eith

31 traction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclas

32 demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 ind

33 99% credible set of Alzheimer's disease (AD) GWAS variants identified at the clusterin (CLU) locus to

34 tional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects

35 associated at the level of P < 1e-05 in all GWAS data sets and that SNPs with P-values above 0.2 wer

36 ell-type specific data are needed to map all GWAS loci.

37 ltering variants in 13 genes outside the AMD-GWAS loci in three or more families.

38 ovel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and

39 In the genome-wide association analysis (GWAS), we detected for most shape and size-related trait

40 mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits.

41 By integrating RNA-seq data and GWAS summary statistics, novel computational methods all

42 Analysis of local eQTL and GWAS data prioritised 13 likely causal genes for differe

43 and admixture on gene expression, eQTLs, and GWAS colocalization.

44 a-analyses for smoking status in the MVP and GWAS & Sequencing Consortium of Alcohol and Nicotine use

45 ing a combination of metabolic profiling and GWAS, we identified FADS3 to be essential for forming De

46 These PRS and GWAS results from this large Peruvian cohort advance gen

47 Standard procedures for PRSs and GWAS were used along with extra steps to rule out confou

48 TL data across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits.

49 dium homeostasis, and the effects of another GWAS signal were mediated by endothelin.

50 scenarios in high-dimensional data, such as GWAS, gene expression, eQTL and structural/functional ne

51 lian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation qu

52 nes have not been identified in ADHD and ASD GWASs before.

53 of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-ext

54 tissues, genes in kidney function-associated GWAS loci were enriched in kidney (P=9.1E-8 for eGFR; P=

55 To make full use of widely available GWASs summary statistics, we extend TisCoMM to use summa

56 WAS of birthweight and a large biobank-based GWAS of atrial fibrillation.

57 disease alleles analyzed by SNP array-based GWASs.

58 causal variants and genes from the latest BD GWAS findings, and performed integrative analyses, inclu

59 Hence, beyond GWASs, this meeting focused on gene regulatory mechanism

60 s arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies

61 Both GWAS and EWAS gene-based associations were then meta-ana

62 , a large fraction of 92 genes identified by GWAS studies as associated with ADHD in humans are signi

63 of periodontitis previously investigated by GWAS and bioinformatics studies.

64 Most loci identified by GWASs have been found in populations of European ancestr

65 d at least nominal significance in classical GWAS.

66 Collaborative GWAS on large cohorts of patients across multiple instit

67 We collected GWAS summary statistics data for a wide range of human t

68 data from Global Lipids Genetics Consortium GWAS across multiple lipid traits.

69 ta from the Early Growth Genetics Consortium GWAS of birthweight and a large biobank-based GWAS of at

70 P) analysis approach adopted in most current GWAS can be limited in that it is usually biologically s

71 In a full dataset GWAS, 13 further loci for resting Tpe, 1 for Tpe respons

72 puted for major depression (MD) at different GWAS p value thresholds using genetic data obtained from

73 ECs are enriched for coronary artery disease GWAS hits.

74 Three new Alzheimer's disease GWAS published in 2018 and 2019, which used larger sampl

75 LDtrait searches the NHGRI-EBI GWAS Catalog to identify susceptibility loci in linkage

76 e that developing an efficient and effective GWAS method to detect epistasis will be a key for discov

77 ly, most methods cannot be applied to either GWAS/eQTL summary statistics or cases with more than two

78 nd renal disease in two independent European GWAS (Pcohort1 = 2.44e-08; Pcohort2 = 0.00205) and a sig

79 tudies to be performed post hoc for existing GWAS, reduces multiple testing burden, and can prioritiz

80 Manhattan plot for displaying and exploring GWAS data.

81 This first GWAS of serum uric acid in continental Africans identifi

82 re likely to overlap OMIM genes (2.94-fold), GWAS loci (1.52-fold), and non-coding RNAs (1.44-fold),

83 r example, 690 loci for LT-FH versus 423 for GWAS); relative improvements were similar when applying

84 ch as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies

85 Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users.

86 ity of a Bayesian framework is promising for GWAS, but current approaches can benefit from more infor

87 However, ORR has never been used for GWAS due to its severe shrinkage on the estimated effect

88 cluding the imputed genotype matrix used for GWAS, are easily downloadable via the respective databas

89 with MDD and demonstrate NPDR's utility for GWAS and continuous outcomes.

90 A critical challenge for GWASs has been the dependence on individual-level data t

91 al and analytical approach of the foundation GWAS together with the ethnic characteristics of that co

92 We used four GWASs to test the performance of GRS both cross validati

93 roxy, which was meta-analysed with data from GWAS of clinical Alzheimer's disease to attain sample si

94 Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites t

95 erappreciated biological theme emerging from GWAS: the role of glycosylation in schizophrenia.

96 rence tissue(s) enriched with the genes from GWAS.

97 can be used to gain additional insights from GWAS data.

98 ion mapping (RAM) to supplement results from GWAS.

99 important in identifying implicated TFs from GWAS SNPs.

100 phenotypes and suggest priorities for future GWASs.

101 n-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeline usi

102 lysis with Pre- and Post-Integration (HAPPI) GWAS, an open-source GWAS tool able to perform pre-GWAS,

103 d questionnaire GWAS and that family history GWAS has better power to detect genetic associations for

104 We also show that family history GWAS using cases ascertained on family history of diseas

105 However, GWAS techniques for detecting epistasis, the interaction

106 fic pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and infla

107 i-SNP models for identifying causal genes in GWAS data for 46 circulating metabolites.

108 in a joint multiple-SNP regression model in GWAS.

109 h tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS trait

110 hybrid approach of ascertaining variants in GWAS but reestimating effect sizes in siblings reduces b

111 17) near MAD1L1 was previously identified in GWASs of bipolar disorder and schizophrenia.

112 nd the limited power of some of the included GWASs, potentially leading to possible type II error.

113 urther replicated 50 genes in an independent GWAS, including one of the two novel loci.

114 for suicidality, including four independent GWAS, have not reproduced each other's top implicated ge

115 Here we demonstrate that two independent GWAS signals at RSPO3, which are associated with increas

116 nd summary statistics of two recent insomnia GWAS and 13 significant loci were identified.

117 dentify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, i

118 story of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a simila

119 in the form of SNP effect sizes from a large GWAS cohort.

120 We took advantage of the largest GWAS meta-analysis available for this disorder consistin

121 This is the largest GWAS of anxiety traits to date.

122 further applied BWG-TWAS to individual-level GWAS data (N = ~3.3K), which identified significant asso

123 mmary statistics instead of individual-level GWAS data.

124 our methods in applications to summary-level GWASs data of 33 complex traits.

125 cked up by correlation-based techniques like GWAS.

126 re, HiGwas, designed to analyze longitudinal GWAS datasets.

127 ion patterns at genomic sites linked to meta-GWAS hits may disrupt established genetic signals in sur

128 ad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive di

129 sis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from

130 s for improving the performance of microbial GWAS and advance our understanding of the genetic basis

131 We performed a linear mixed model GWAS on standardized log-transformed 25OHD, adjusting fo

132 We propose an approach that models GWAS summary data for one trait in two populations to es

133 Integration of multi-trait models-GWAS and SEM-GWAS identified six significant SNPs for SC

134 However, most GWAS focus on additive genetic effects while ignoring no

135 put machine learning approach for multilocus GWAS using longitudinal traits by coupling Empirical Bay

136 At multiple GWAS-implicated SNPs within pG4 UTR sequences, we find r

137 susceptibility genes/variants from multiple GWAS loci.

138 tions of single variants into a multivariate GWAS setting.

139 that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular

140 Bmp4-mediated repression of Pax9 Analyses of GWAS data revealed a genome-wide significant association

141 Our candidate-gene association analyses of GWAS datasets suggested an increased risk of breast canc

142 ill become routine in downstream analyses of GWAS.

143 ing the Functional Mapping and Annotation of GWAS platform, and did colocalisation analyses using the

144 Integration of GWAS signals with epigenomic annotations has demonstrate

145 powerful than the trait-specific maximum of GWAS and GWAX, based on the number of independent genome

146 Through the use of GWAS and subsequent sequencing of a PRA case, we have id

147 Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics.

148 le that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS

149 3p24.1 that were suggestive in the original GWAS with additional genotyping.

150 Our GWAS results identified one genome-wide significant locu

151 We experimentally confirm our GWAS results and demonstrate that RplD G70D and other ma

152 that passed genome-wide significance in our GWAS using the Functional Mapping and Annotation of GWAS

153 e following covariate values to these pairs: GWAS statistics from genetically correlated bipolar diso

154 and highlights the importance of performing GWAS in non-European populations.

155 tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeline using the command

156 ttern matched in simulations of well powered GWAS.

157 omprehensive pipeline that includes both pre-GWAS analysis, such as outlier removal, data transformat

158 an open-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeli

159 lts were combined with those from a previous GWAS including 42,274 Europeans.

160 ng two novel loci not implicated in previous GWAS.

161 ciated with lung cancer risk in our previous GWAS.

162 between the GAD-2 score results and previous GWASs for anxiety (r(g)=0.75), depression (r(g)=0.81), a

163 The CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine r

164 polygenic risk scores (PRS(cis-eQTL) and PRS(GWAS)), and tested for predicting brain disorders or pat

165 ria, from two separate cohorts: the 2011 PSP GWAS cohort, from brain banks based at the Mayo Clinic (

166 he summary statistics of two major published GWASs for anxiety, and also found evidence of significan

167 h combined hospital record and questionnaire GWAS and that family history GWAS has better power to de

168 Rationale: GWAS (Genome-Wide Association Studies) have identified h

169 n discovery and replication samples reaching GWAS significance in the combined meta-analysis (beta =

170 methods, and application of plasso to a real GWAS dataset gains new additional insights into the gene

171 ts for 380 association signals from a recent GWAS meta-analysis of type 2 diabetes (T2D) in Europeans

172 Applied to traits studied in recent GWAS studies, we find that CAUSE detects causal relation

173 Biobank (UKBB) and 596 from other resources (GWAS Catalog and literature mining), totaling 5019 uniqu

174 vidually have small effects on disease risk, GWAS provide a powerful opportunity to explore pathways

175 This large-scale GWAS in a Japanese population provides insights into the

176 he strongest coding variant in schizophrenia GWAS is a missense mutation in the manganese transporter

177 ther comparable methods to the schizophrenia GWAS data and type 2 diabetes (T2D) GWAS meta-analysis s

178 Selected SCZ GWAS association P values play the role of the primary d

179 We performed a secondary GWAS in the 1986 Northern Finland Birth Cohort (NFBC1986

180 tegration of multi-trait models-GWAS and SEM-GWAS identified six significant SNPs for SCS, and quanti

181 r enrichment in genes within the significant GWAS loci reported by the Psychiatric Genomic Consortium

182 Simulated GWAS datasets are also packaged with the pipeline for us

183 A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step

184 ect of residual stratification, we simulated GWAS under realistic models of demographic history.

185 ost-Integration (HAPPI) GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS a

186 orrection, our meta-analysis of sex-specific GWAS identified 1 variant at chromosome 6q11.1 (rs112894

187 We performed sex-specific GWAS of BE/EA in 3 separate studies and then used fixed-

188 entified in genome-wide association studies (GWAS) analyses.

189 nfounder in genome-wide association studies (GWAS) and can lead to false-positive associations.

190 s involving genome-wide association studies (GWAS) and meta-analysis have discovered numerous genomic

191 ) integrate genome-wide association studies (GWAS) and transcriptomic data to showcase their improved

192 entified in genome-wide association studies (GWAS) are often not specific enough to reveal complex un

193 ied through genome-wide association studies (GWAS) are predominantly non-coding and typically attribu

194 Genome-wide association studies (GWAS) are still the primary steps toward gene discovery.

195 Bacterial genome-wide association studies (GWAS) can identify novel resistance genes but must contr

196 thm (Pi) of genome wide association studies (GWAS) data and a broad screen of epigenetic inhibitors,

197 Genome wide association studies (GWAS) facilitated recognition of 17 quantitative trait l

198 s) based on genome-wide association studies (GWAS) for 54 diseases and complex traits coupled with mu

199 to conduct genome-wide association studies (GWAS) for identifying genes underlying root growth varia

200 es based on genome-wide association studies (GWAS) for phenotypic prediction.

201 Genome-wide association studies (GWAS) have discovered thousands of significant genetic e

202 Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms

203 Genome-wide association studies (GWAS) have identified 41 risk variants across 41 loci.

204 Genome-wide association studies (GWAS) have identified more than 20 susceptibility loci f

205 Genome-wide association studies (GWAS) have identified numerous genetic loci underlying h

206 Genome-wide association studies (GWAS) have identified numerous genetic variants that are

207 Genome-wide association studies (GWAS) have identified over 400 signals robustly associat

208 Genome-wide association studies (GWAS) have identified thousands of genetic variants asso

209 Genome-wide association studies (GWAS) have linked IGF2BP2 single-nucleotide polymorphism

210 sis such as genome-wide association studies (GWAS) have played an important role in identifying disea

211 Genome-wide association studies (GWAS) have reported dozens of loci associated with serum

212 Genome Wide Association Studies (GWAS) have successfully identified thousands of loci ass

213 nd existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the ob

214 Recent genome-wide association studies (GWAS) identified DUSP8, encoding a dual-specificity phos

215 analysis of genome-wide association studies (GWAS) identified eight loci that are associated with hea

216 Genome-wide association studies (GWAS) link >60 loci with SLE risk, but the causal varian

217 diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification err

218 analyses of genome-wide association studies (GWAS) of ADHD (n = 53,293) and lifetime cannabis use (n

219 founding on genome-wide association studies (GWAS) of complex human traits in the UK Biobank has not

220 ed in prior genome-wide association studies (GWAS) of human temperament.

221 e conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, pr

222 rformed two genome-wide association studies (GWAS) on a subsample of 1139 individuals classified as f

223 we conduct genome-wide association studies (GWAS) on lifetime self-harm ideation and self-harm behav

224 Genome-wide association studies (GWAS) provide an unbiased approach to identify loci infl

225 richment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a success

226 om multiple genome-wide association studies (GWAS) revealed that genes with an intrinsic sex differen

227 entified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait

228 informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of inte

229 analysis of genome-wide association studies (GWAS) that involved 542,934 European participants and id

230 to perform genome-wide association studies (GWAS) to identify genetic variants associated with diver

231 e leveraged genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs)

232 independent genome-wide association studies (GWAS) with a case-control study design.

233 Genome-wide association studies (GWAS), particularly designed with thousands and thousand

234 mapping and genome-wide association studies (GWAS), we unveiled the genetic determinants of tocochrom

235 results of genome-wide association studies (GWAS).

236 c traits in genome-wide association studies (GWAS).

237 lenging for genome-wide association studies (GWAS).

238 g number of genome-wide association studies (GWAS).

239 east cancer genome-wide association studies (GWASs) have identified 150 genomic risk regions containi

240 (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 6

241 Genome-wide association studies (GWASs) seek to identify genetic variants associated with

242 ance in the genome-wide association studies (GWASs) that aim to detect genetic variance significantly

243 ied through genome-wide association studies (GWASs).

244 ng non-coding genome-wide association study (GWAS) association findings.

245 s index (BMI) genome-wide association study (GWAS) data from >457,000 individuals.

246 f large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the

247 e conducted a genome-wide association study (GWAS) for circulating FGF21 and FGF23 concentrations to

248 e performed a genome-wide association study (GWAS) for infant mental and motor ability at two years o

249 eport a large genome-wide association study (GWAS) for longitudinal smoking phenotypes in 286,118 ind

250 e we report a genome-wide association study (GWAS) for primary MN in 3,782 cases and 9,038 controls o

251 e performed a genome-wide association study (GWAS) for root Na(+) /K(+) ratio in a population consist

252 Our genome-wide association study (GWAS) identified one novel genomic risk locus on chromos

253 analysis and genome-wide association study (GWAS) in 4365 individuals from an African population coh

254 e performed a genome-wide association study (GWAS) in cohorts of European ancestry (n = 527).

255 lesterol in a genome-wide association study (GWAS) meta-analysis (N <=196 475) were used to proxy the

256 data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and

257 e conducted a genome wide association study (GWAS) of 146 maize genotypes comprising of landraces, in

258 A recent genome-wide association study (GWAS) of 59 cerebrospinal fluid (CSF) proteins with a co

259 s performed a genome-wide association study (GWAS) of a continuous trait for anxiety (based on score

260 A Genome-Wide Association Study (GWAS) of DT696 derivative lines from 72 crosses based on

261 rmed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive r

262 carried out a genome-wide association study (GWAS) on 2274 dyslexia cases and 6272 controls, testing

263 eat through a genome-wide association study (GWAS) on a set of 179 pre-breeding lines (PBLs).

264 dent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic ris

265 A genome-wide association study (GWAS) revealed variants in the fatty acid desaturase 3 (

266 sociated with genome-wide association study (GWAS) signals of complex traits or diseases.

267 carried out a genome-wide association study (GWAS) using a panel of 5130 clones developed at the Inte

268 his two stage genome-wide association study (GWAS), we included individuals with PSP, diagnosed accor

269 ublished PCOS genome-wide association study (GWAS), we investigated whether there were reproducible p

270 y undertook a genome-wide association study (GWAS), which identified a 1.3 Mb disease-associated regi

271 rough a large genome-wide association study (GWAS).

272 e included, the sample sizes for the subtype GWAS were small, and the GWAS findings were not replicat

273 ponse showed enrichment among the suggestive GWAS signals.

274 ese variants on PTC risk by using summarized GWAS results to build polygenic risk score (PRS) models

275 Pleiotropy in sub-surface GWAS significance strata can be explored in a sectional

276 ophrenia GWAS data and type 2 diabetes (T2D) GWAS meta-analysis summary data.

277 y can disentangle disease mechanisms at T2DM GWAS signals.

278 sal variant and effector transcripts at T2DM GWAS susceptibility loci.

279 standard error (s.e.) 6%) more powerful than GWAS and 36% (s.e. 4%) more powerful than the trait-spec

280 Our results indicate that GWAS-associated variants within the FAM13A locus alter a

281 The GWAS Central resource provides a toolkit for integrative

282 The GWAS of lipids and apolipoproteins in the UKBB included

283 zes for the subtype GWAS were small, and the GWAS findings were not replicated.

284 me on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci

285 of the SNP mediated through the gene on the GWAS trait.

286 tional research must be done to validate the GWAS and genomic prediction results reported in our stud

287 These GWAS comprised 21 168 Han Chinese individuals, of whom 1

288 Through GWAS, we uncovered a number of genes previously not know

289 ments were similar when applying BOLT-LMM to GWAS, GWAX and LT-FH phenotypes.

290 e useful to improve the power of traditional GWAS, which might be particularly useful for rare traits

291 permeability assay, we performed an unbiased GWAS screen (using 156 strains from the Drosophila Genet

292 FERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory

293 A total of 4,201 participants underwent GWAS analysis.

294 and literature mining), totaling 5019 unique GWAS data sets and 15 770 trait-associated gene sets.

295 ncy is more obvious in the big data era when GWAS are conducted simultaneously for thousand traits, e

296 CYAP1R1 gene was extracted from genome-wide (GWAS) analyses.

297 Combining this meta-analysis with GWAS of nevus count and hair color, and transcriptome as

298 WAS, and likelihood of being associated with GWAS traits.

299 Integration of the predicted PAIs with GWAS data highlight interactions among 601 DNAm sites as

300 o prioritize candidate effector genes within GWAS loci and to find additional variants in known disea