ライフサイエンスコーパス: summary statistics

1 in the form of Genome Wide Association Study summary statistics.

2 t based on individual-level genotypes and/or summary statistics.

3 g the selection-related parameter alpha from summary statistics.

4 ference (SMD) and 95% confidence interval as summary statistics.

5 espective of genetic correlation, using GWAS summary statistics.

6 enable enrichment analyses using genome-wide summary statistics.

7 strategies to quantify enrichment using GWAS summary statistics.

8 Data are presented using summary statistics.

9 obtain a very high accuracy imputation from summary statistics.

10 genetic covariance between traits using GWAS summary statistics.

11 iation analysis of multiple traits with GWAS summary statistics.

12 to colocalizing genetic risk variants using summary statistics.

13 a may suggest different conclusions from the summary statistics.

14 did random-effects meta-analyses to estimate summary statistics.

15 nual occupational doses were described using summary statistics.

16 ich visualization and informative scores and summary statistics.

17 henotype across 65 studies and meta-analysed summary statistics.

18 have been proposed for imputing association summary statistics.

19 consortium to combine otherwise incompatible summary statistics.

20 dated to process more variants and calculate summary statistics.

21 a may suggest different conclusions from the summary statistics.

22 tter predictive results compared with simple summary statistics.

23 stinct from CoMM, CoMM-S2 requires only GWAS summary statistics.

24 e traditionally emphasized the use of simple summary statistics.

25 ffectiveness of their parameter estimates as summary statistics.

26 nnot fully make use of widely available GWAS summary statistics.

27 r (ADHD) using genome-wide association study summary statistics.

28 ise through the integration of time-averaged summary statistics.

29 called EUGENE that (1) is applicable to GWAS summary statistics; (2) considers both cis- and trans-eQ

30 t of differential privacy (DP) while sharing summary statistics about genomic data.

31 tions) for inference of driver variants from summary statistics across multiple traits using hundreds

32 ing pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits.

33 Using genome-wide association study summary statistics across six cancer types based on a to

34 Policies were evaluated for summary statistics across the following 5 categories of

35 We therefore develop direct imputation of summary statistics allowing covariates (DISSCO).

36 y mode, which also supports retrieval of the summary statistics, an overhead in the compression rate

37 s functionalities that enable visualization, summary statistics analysis and fast queries from the co

38 o a public data repository for GWAS data and summary statistics and already includes published data a

39 rphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibriu

40 tal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenoty

41 selection of the relevant components of the summary statistics and bypassing the derivation of the a

42 ent Analysis), a novel method that uses GWAS summary statistics and eQTL to infer differential gene e

43 rait-relevant cell types and genes from GWAS summary statistics and gene expression data.

44 am (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an ind

45 d critical evaluation of the data behind the summary statistics and may be valuable for promoting tra

46 experimental metadata checklists, experiment summary statistics and more advanced searching tools.

47 to datasets for which we have access only to summary statistics and not to the raw genotypes.

48 set of genes, visualize results and provide summary statistics and other reports using a single comm

49 Crucially, our method requires only GWAS summary statistics and remains accurate when SNP correla

50 Using genome-wide association studies summary statistics and shared polygenic pleiotropy-infor

51 PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359

52 ased on biological function and measurement, summary statistics and unsupervised clustering.

53 s that consider combinations of conventional summary statistics and/or richer features derived from i

54 person using previously published meta-GWAS summary statistics, and were tested for association with

55 Finally, the VarMatch software provides summary statistics, annotations and visualizations that

56 single cell distributions whose ground truth summary statistics are known.

57 and an online simulator that illustrates why summary statistics are meaningful only when there are en

58 Summary statistics are provided with a commentary on imp

59 then remove the correlation structure across summary statistics arising due to linkage disequilibrium

60 Importantly, iDEA uses only DE summary statistics as input, enabling effective data mod

61 quantified through experimentally accessible summary statistics, as well as by the tissue recoil afte

62 providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconci

63 ly GWAS summary statistics to (i) impute the summary statistics at unmeasured eQTLs and (ii) test for

64 this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and

65 oduce metaCCA, a computational framework for summary statistics-based analysis of a single or multipl

66 ibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-

67 was identified using gene-based analysis and summary statistics-based Mendelian randomization analysi

68 s of local genetic correlation structure for summary statistics-based methods in arbitrary population

69 r method by measuring the performance of two summary statistics-based methods: imputation and joint-t

70 of covariates, correlation among association summary statistics becomes the partial correlation of th

71 framework for joint analysis of association summary statistics between multiple rare variants and di

72 a useful framework for the analysis of GWAS summary statistics by utilizing SNP prior information, a

73 Direct imputation of summary statistics can also be valuable, for example in

74 are facilitated through diagnostic plots and summary statistics computed over regions of the genome w

75 ic stroke and intracerebral hemorrhage using summary statistics data for 34,217 ischemic stroke cases

76 We collected GWAS summary statistics data for a wide range of human traits

77 We used summary statistics data for single-nucleotide polymorphi

78 format proposed as a community standard for summary statistics data representation.

79 access, an improved web interface and a new summary statistics database.

80 New content includes 284 full P-value summary statistics datasets for genome-wide and new targ

81 r value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association

82 uting SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their i

83 in terms of robustness to the choice of the summary statistics, does not depend on any type of toler

84 ting an association between repertoire-level summary statistics (e.g., diversity) and patient outcome

85 ed dust pesticide concentrations reported as summary statistics [e.g., geometric means (GM)].

86 Although methods for the imputation of summary statistics exist, they lack precision for geneti

87 We used summary statistics extracted from genome wide associatio

88 By computing a distance between the summary statistics extracted from the input and each sim

89 over, integration of these results with GWAS summary statistics for 13 brain-associated traits reveal

90 his information with genome-wide-association summary statistics for 17 metabolic and anthropometric t

91 We applied MESC to GWAS summary statistics for 42 traits (average N = 323,000) a

92 42 traits (average N = 323,000) and cis-eQTL summary statistics for 48 tissues from the Genotype-Tiss

93 BedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph or

94 e threshold for a whole-genome scan; utilize summary statistics for a meta-analysis; incorporate func

95 We analyzed independent GWAS summary statistics for ADHD (19,099 cases and 34,194 con

96 ed independent genome-wide association study summary statistics for ADHD (19,099 cases and 34,194 con

97 Publication of summary statistics for approximately 10 000 African Amer

98 MetaLAFFA also generates summary statistics for each step in the pipeline so that

99 We applied mtCOJO to summary statistics for five psychiatric disorders from t

100 ndividuals of European ancestry, we obtained summary statistics for four independent single nucleotid

101 Summary statistics for genetic variants strongly associa

102 we combine existing genome-wide association summary statistics for healthspan, parental lifespan, an

103 Leveraging large-scale summary statistics for migraine (N(cases)/N(controls) =

104 of either single GWAS or meta-analyzed GWAS summary statistics for single SNPs.

105 to have highly adaptive tests applicable to summary statistics for single SNPs.

106 We obtained summary statistics for the association of these SNPs wit

107 Summary statistics for the base phenotype were derived f

108 We apply MTAR to rare-variant summary statistics for three lipid traits in the Global

109 ta across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits.

110 y predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls

111 were extracted from 22 studies, encompassing summary statistics from 18,611 unique participants.

112 Meta-analysis of the summary statistics from 19 cohorts identified in CYP2R1

113 Using summary statistics from 24 genome-wide association studi

114 We obtained summary statistics from 27 published genome-wide associa

115 y applying stratified LD score regression to summary statistics from 41 independent diseases and comp

116 Here we analyzed summary statistics from 56 complex traits (average N = 1

117 We obtained summary statistics from 7 studies that included 396 pati

118 or height how polygenic risk scores based on summary statistics from a European-based genome-wide ass

119 d in estimating both stratified and marginal summary statistics from a joint model of gene-environmen

120 performed gene-set enrichment analysis using summary statistics from a large-scale genome-wide associ

121 known expression quantitative trait loci and summary statistics from a PAH genome-wide association st

122 This analysis used summary statistics from a prior genome-wide association

123 atform that facilitates the dissemination of summary statistics from biobanks to the scientific and c

124 been performed, it may be challenging to get summary statistics from both exposure-stratified and mar

125 Using GW-summary statistics from both toenail and blood Se, we ob

126 y statistics with multiple sets of omics QTL summary statistics from different cellular conditions or

127 , integration with rheumatoid arthritis (RA) summary statistics from European (N = 38,242) and East A

128 We assembled genome-wide association study summary statistics from European-derived participants re

129 ciated with BMI and T2D by incorporating the summary statistics from existing GWASs of these two trai

130 f genomic SEM, including a joint analysis of summary statistics from five psychiatric traits.

131 Multi-trait analyses using public summary statistics from genome-wide association studies

132 Using summary statistics from genome-wide association studies

133 n developed for fine-mapping with the use of summary statistics from genome-wide association studies

134 tional efficiency, most methods use as input summary statistics from genome-wide association studies

135 We analyzed summary statistics from genome-wide association studies

136 mmary-level analyses, MR was performed using summary statistics from genome-wide association studies

137 Summary statistics from genome-wide association studies

138 We used summary statistics from genome-wide association studies

139 By using summary statistics from genome-wide association studies

140 its simplicity and effectiveness, where only summary statistics from genome-wide association studies

141 heritability and genetic correlations using summary statistics from genome-wide association studies.

142 work for assessing heritability models using summary statistics from genome-wide association studies.

143 sk of VTE and ischemic stroke subtypes using summary statistics from genome-wide association studies.

144 al annotation categories of the genome using summary statistics from genome-wide association studies.

145 ich received widespread interest for sharing summary statistics from genomic datasets while protectin

146 To optimize power, we used summary statistics from GWAS consortia and tested the as

147 a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.

148 that such analysis can be executed with the summary statistics from GWASs.

149 rovide novel insights from already published summary statistics from high-throughput phenotyping tech

150 We used summary statistics from large meta-analyses of plasma co

151 ble genome-wide association studies (GWASes) summary statistics from MR Base and LD-hub.

152 ally, we discuss the calculation of relevant summary statistics from participating studies, the const

153 e of a polygenic risk score for CAD based on summary statistics from published genome-wide associatio

154 wo-sample Mendelian randomization (MR) using summary statistics from recent genome-wide association s

155 We used summary statistics from recently published breast and lu

156 disequilibrium score regression, exploiting summary statistics from relevant genome-wide association

157 tudies analyzed the thickness by calculating summary statistics from retinal thickness maps of the ma

158 oftware enables meta-analysis of association summary statistics from SCOPA across GWAS.

159 METHODS AND Summary statistics from several genome-wide association

160 e of our method is that it can be applied to summary statistics from single markers, and so can be qu

161 also enables efficient computation by using summary statistics from standard eQTL analyses.

162 ary exposure and in the marginal model using summary statistics from the "joint" model.

163 Summary statistics from the 3 centers were analyzed usin

164 ree types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole

165 Summary statistics from the conducted GWASs are often av

166 Polygenic risk scores were calculated from summary statistics from the current largest genome-wide

167 Summary statistics from the ENGAGE Telomere Consortium w

168 We used summary statistics from the GEFOS consortium for lumbar

169 Employing a two-sample MR approach, we used summary statistics from the Genetic Investigation of Ant

170 It does so by extracting summary statistics from the input.

171 oking on MS susceptibility as measured using summary statistics from the International Multiple Scler

172 of Alzheimer's disease were calculated using summary statistics from the largest Alzheimer's disease

173 s instrumental variables and applied them to summary statistics from the largest available genome-wid

174 In this study, we used summary statistics from the largest available meta-analy

175 tified with the TWAS FUSION method, based on summary statistics from the largest genome-wide associat

176 Here, we analyzed summary statistics from the largest genome-wide associat

177 Summary statistics from the largest GWAS to date on depr

178 The basis of this study was summary statistics from the largest schizophrenia genome

179 Using summary statistics from the meta-analysis, we constructe

180 Here, we leveraged summary statistics from the most recent genome-wide asso

181 our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Gen

182 We used genome-wide association study (GWAS) summary statistics from the Psychiatric Genetics Consort

183 from the samples with African ancestry with summary statistics from the Psychiatric Genomics Consort

184 ped tool by analysing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consort

185 presenting variant data; however, generating summary statistics from these files is not always straig

186 able genome-wide association studies (GWASs) summary statistics from three sources: published GWASs,

187 grate directed genomic annotations with eQTL summary statistics from tissues of various origins.

188 Taking as input summary statistics from two GWASs-a target GWAS from an

189 ROI-level measures used in these studies are summary statistics from voxelwise measures in the region

190 Methods for analysis of GWAS summary statistics have encouraged data sharing and demo

191 te Gaussian distribution for the association summary statistics, have been proposed for imputing asso

192 ckage providing functionality for collecting summary statistics, identifying shifts in variation, dis

193 been limited to focusing on repertoire-level summary statistics, ignoring the vast amounts of informa

194 AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we e

195 Obtaining summary statistics in a given region is a fundamental ta

196 we developed a simple framework to estimate summary statistics in each stratum of a binary exposure

197 g LD information to more efficiently exploit summary statistics in genetics research.

198 the unobserved bias introduced by the use of summary statistics in our MR analyses.

199 We start with summary statistics in the form of SNP effect sizes from

200 verages transcriptome information using only summary statistics information from GWAS data are requir

201 at genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data

202 Our framework condenses these summary statistics into PRSs using various approaches su

203 t meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of i

204 s derived from genome-wide association study summary statistics is not yet on a par with APOE e4, a b

205 ed memory-trace model that counts occurrence summary statistics is sufficient to replicate honey bees

206 trate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoM

207 Still, SNP-level summary statistics made available here afford the best-a

208 Summary statistics (mean and standard deviation of regio

209 Summary statistics, mean differences, and effect sizes w

210 redicts stability using physically motivated summary statistics measured in integrations of the first

211 riants with reduced statistics, we show that summary statistics modulate the correlations between fre

212 and perform a GWAS meta-analysis with public summary statistics, more than doubling the sample size o

213 must be prioritized in genetic studies, and summary statistics must be publically disseminated to en

214 By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow un

215 complex traits with publicly accessible GWAS summary statistics (Ntotal approximately 4.5 million), w

216 randomization analyses were conducted using summary statistics obtained for 423 genetic variants ide

217 The analyses were performed using summary statistics obtained for single-nucleotide polymo

218 f traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or

219 or type 2 diabetes and CHD were derived from summary statistics of 2 separate genome-wide association

220 reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide marker

221 We used summary statistics of 81 well-powered GWASs of cognitive

222 e applied RiVIERA to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizoph

223 endelian randomization (GSMR) analysis using summary statistics of a genome-wide association study me

224 S participants as the 4 iodine concentration summary statistics of a similar TDS food and used these,

225 covery rate (cFDR) method was applied on two summary statistics of CAD and BP from existing GWASs.

226 from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., populat

227 hierarchical cluster analysis, performed on summary statistics of each individual across their recor

228 ability of 111 genome-wide association study summary statistics of European (average n ~ 189,000) and

229 The tests can be constructed with summary statistics of existing dispersion and burden tes

230 , the uploading of private genotype data and summary statistics of existing GWAS, as well as advanced

231 riants and fine-mapping causal variants from summary statistics of genome-wide association studies ar

232 lymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples wit

233 riants found within IBD regions and observed summary statistics of local sharing of IBD segments to c

234 ygenic risk scores were constructed from the summary statistics of LV genome-wide association studies

235 that can integrate association evidence from summary statistics of multiple traits, either correlated

236 l and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of ge

237 Applying this approach to the GWAS summary statistics of putamen volume in the ENIGMA cohor

238 type as a weighted linear combination of the summary statistics of related phenotypes.

239 Summary statistics of RIDE/RISE baseline characteristics

240 an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the Inter

241 trees and uses this information to calculate summary statistics of spatial spread and to visualize di

242 Summary statistics of the associations of the five SNPs

243 ate the likelihood via simulation either use summary statistics of the data or are at risk of produci

244 Summary statistics of the metadata are also presented on

245 The authors replicated these findings in the summary statistics of two major published GWASs for anxi

246 large-scale meta-analysis of our results and summary statistics of two recent insomnia GWAS and 13 si

247 of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnici

248 BM and non-GBM risk in conjunction with GWAS summary statistics on 12,488 glioma cases (6,183 GBM and

249 Those approaches superimpose the summary statistics on the nodes in the network, followed

250 proximate analytic estimation solution using summary statistics only.

251 genomic data browsing resources provide only summary statistics or aggregate allele frequencies.

252 ethods cannot be applied to either GWAS/eQTL summary statistics or cases with more than two possibly

253 Plots visualising summary statistics or individual patient data over discr

254 multi-trait GWAS methods that exploit either summary statistics or individual-level data have been de

255 ded to incorporate multiple population-level summary statistics or other domain knowledge.

256 mic and proteomic data, eNetXplorer provides summary statistics, output tables, and visualizations to

257 Using GWAS summary statistics (P-values) for SNPs along with refere

258 These issues make methods based on SNP-level summary statistics particularly appealing.

259 of tuning curves, instead of matching a few summary statistics picked a priori by the user, resultin

260 res subject-level genetic data, which unlike summary statistics provided by virtually all studies, is

261 Summary statistics provision is supported by a new forma

262 with published genome-wide association study summary statistics replicated established risk loci and

263 We leveraged the summary statistics reported in two studies: UK Biobank (

264 rely on compressing genomic information into summary statistics, resulting in the loss of information

265 assess the performance of several methods of summary statistics selection.

266 Summary statistics showed higher median (range) age-adju

267 ates errors both in the inference and in any summary statistics, such as lag times, and allows interp

268 ubject-level responses are quantified by two summary statistics that describe the quality of an indiv

269 d automatically generates visualizations and summary statistics that reflect the degree of numeric ch

270 ave shown the potential of combining genetic summary statistics that represent the mutational burden

271 , a novel software tool which uses only GWAS summary statistics to (i) impute the summary statistics

272 of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores fo

273 d how the auditory system might encode these summary statistics to create internal representations of

274 e build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set

275 analysis vary from simply comparing network summary statistics to sophisticated but computationally

276 f the parameter space tested, independent of summary statistics used.

277 68,440 individuals that included these three summary statistics was used as final outcome.

278 Using IOP genome-wide association study summary statistics, we developed a PRS derived solely fr

279 To make full use of widely available GWASs summary statistics, we extend TisCoMM to use summary-lev

280 1 immune diseases with available genome-wide summary statistics, we observed genetic correlation betw

281 pically highest in the population from which summary statistics were derived.

282 GWAS summary statistics were examined for enrichment of funct

283 Using FUSION software, ASD GWAS summary statistics were integrated with predictors of ge

284 Summary statistics were obtained for conservative treatm

285 Summary statistics were obtained for these SNPs from a G

286 Summary statistics were obtained from the International

287 ed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17

288 Summary statistics were used to describe patient demogra

289 Summary statistics were used to describe results, and th

290 ration of these annotations with association summary statistics, which together provide a new and exc

291 ion, for partitioning heritability from GWAS summary statistics while accounting for linked markers.

292 Fast simulation of GWAS summary statistics will enable more complete and rapid e

293 , the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functi

294 hod, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summa

295 Standard descriptive analyses included summary statistics with percentages and means.

296 Here, we integrate the ASD GWAS summary statistics with summary-level gene expression da

297 ization analysis, of publicly available GWAS summary statistics with the cytokine network association

298 Second, it can leverage summary statistics without accessing the individual geno

299 ith a univariate trait to the case with GWAS summary statistics without individual-level genotype and

300 and conducted a meta-analysis with published summary statistics, yielding a total sample size of 59,9