ライフサイエンスコーパス: summary statistics

1 have been proposed for imputing association summary statistics.

2 consortium to combine otherwise incompatible summary statistics.

3 dated to process more variants and calculate summary statistics.

4 a may suggest different conclusions from the summary statistics.

5 iation analysis of multiple traits with GWAS summary statistics.

6 based imputation, which cannot be applied to summary statistics.

7 ain exhibits strong sensitivity to low-level summary statistics.

8 l is computationally efficient and uses only summary statistics.

9 rations and numerically compute the relevant summary statistics.

10 the full data are replaced with a vector of summary statistics.

11 confidence intervals (CIs) were computed as summary statistics.

12 reatly affects the expected distributions of summary statistics.

13 ion rates from population genetic data using summary statistics.

14 tion, and surgical approach was denoted with summary statistics.

15 s of population parameters based on multiple summary statistics.

16 Analysis included frequencies and summary statistics.

17 computational efficiency of methods based on summary statistics.

18 yesian statistical inference on the basis of summary statistics.

19 n criteria, the authors derived quantitative summary statistics.

20 trait inheritance, but use different genetic summary statistics.

21 have been revealed by conventional tables of summary statistics.

22 to colocalizing genetic risk variants using summary statistics.

23 a may suggest different conclusions from the summary statistics.

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

25 genetic covariance between traits using GWAS summary statistics.

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

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

28 cal imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmenta

29 es at the transcript level as well as obtain summary statistics across genes and individuals.

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

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

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

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

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

35 ccepts original allele size data rather than summary statistics and allows the incorporation of prior

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

37 We describe a tool that produces summary statistics and basic quality assessments for gen

38 Summary statistics and compressed-in-time images are gen

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

40 Careful interpretation of useful summary statistics and graphical data displays can minim

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

42 genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap.

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

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

45 ough the ALLPATHS-LG algorithm gave the best summary statistics and most accurate rDNA operon number

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

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

48 our inference is more powerful than previous summary statistics and robust to realistic levels of rec

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

50 istory consistent with the observed array of summary statistics and then to correct the likelihood wi

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

52 s of the haplotype frequency distribution as summary statistics and use simulations to obtain rejecti

53 This program can calculate various summary statistics, and perform association mapping and

54 n of simulated parameter values on simulated summary statistics, and then substituting the observed s

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

56 Summary statistics are also provided in graphical or tex

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

58 Detailed coverage and error summary statistics are outputted.

59 me that the correlations between association summary statistics are the same as the correlations betw

60 ces an elevated rate of false positives when summary statistics are used to test for deviations from

61 an variation have discovered three trends in summary statistics as a function of increasing geographi

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

63 ur lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have b

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

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

66 The multilocus analysis, based on summary statistics (average and variance of Tajima's D a

67 Here, we propose an approach for choosing summary statistics based on boosting, a technique from t

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

69 a combination of instrumental variables and summary statistics-based genetic risk scores to test the

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

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

72 Current summary statistics-based methods rely on global 'best gu

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

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

75 Summary statistics, bivariate analyses, and mixed-effect

76 ne, we derived two physiologically plausible summary statistics by spatially pooling local contrast f

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

78 ion causes order of magnitude differences in summary statistics calculated over short time periods or

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

80 a wide range of conditions the power of all summary statistics can be greatly increased by incorpora

81 According to our criteria, ABC with summary statistics chosen locally via boosting with the

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

83 m, the obtaining of confidence intervals for summary statistics corresponding to measured quantities,

84 Summary statistics derived from such simulated data are

85 Direct Imputation of summary STatistics (DIST) imputes the summary statistics

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

87 approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being co

88 ever, because of their intrinsic reliance on summary statistics, distance-matrix methods are assumed

89 teria, which were assessed for quality using summary statistics (e.g. number of contigs, N50) and in

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

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

92 The server generates summary statistics (error rates and success rates target

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

94 s: determining cohesive subgraphs, computing summary statistics, fitting mathematical models to the d

95 The database also includes summary statistics for 702 measures with statistical com

96 Summary statistics for absolute differences relative to

97 Publication of summary statistics for approximately 10 000 African Amer

98 For 13 variants, summary statistics for associations with BMI were meta-a

99 Summary statistics for both groups were generated for ag

100 ssion-based strategy for choosing subsets of summary statistics for coding data, and show that this a

101 he main results page with a table containing summary statistics for each motif.

102 We devised simple multilocus summary statistics for estimating gene-conversion rates

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

104 We initiate study of structure theory and summary statistics for general processes in the class.

105 Using GWAS summary statistics for over 50 000 individuals from thre

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

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

108 ol genes from the Ensembl database, collates summary statistics for three frequency-spectrum-based ne

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

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

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

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

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

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

115 enic risk scores constructed using published summary statistics from genome-wide association meta-ana

116 Using summary statistics from genome-wide association studies

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

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

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

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

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

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

123 Our visual system can extract summary statistics from large collections of similar obj

124 In addition, for publicly available summary statistics from large meta-analyses of four lipi

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

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

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

128 METHODS AND Summary statistics from several genome-wide association

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

130 Summary statistics from the 3 centers were analyzed usin

131 tions include writing traces, histograms and summary statistics from the data collectors in addition

132 Summary statistics from the ENGAGE Telomere Consortium w

133 The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatr

134 We used summary statistics from the GEFOS consortium for lumbar

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

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

137 ted these analyses with previously published summary statistics from the International Consortium on

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

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

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

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

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

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

144 ed health, nutrition, and household surveys; summary statistics from WHO's Vitamin and Mineral Nutrit

145 However, existing methods either use summary statistics (gene trees) to carry out estimation,

146 Using the ADIPOQ summary statistics genetic risk scores, we found no evid

147 Thus, recent genome-wide analyses based on summary statistics have sparked controversy about the po

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

149 e these two data sets and contrast two basic summary statistics, heterozygosity and F(ST), as well as

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

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

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

153 This test evaluates a pair of summary statistics in a two-dimensional field.

154 to infer recombination properties from such summary statistics in bacterial genomes.

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

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

157 ical fractal scaling--in addition to several summary statistics, including the mean clustering coeffi

158 atistics, and then substituting the observed summary statistics into the regression equation.

159 The choice of summary statistics is a crucial step in approximate Baye

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

161 of covariates, correlation among association summary statistics is indeed the same as that among the

162 ikelihood methods--challenging the idea that summary statistics lead to suboptimal analyses.

163 cy, we also propose an approach for choosing summary statistics locally, in the putative neighborhood

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

165 Although most studies report all univariate summary statistics, many of them limit the access to sub

166 Summary statistics, mean differences, and effect sizes w

167 Summary statistics, migration analyses and the genealogy

168 ts were on levodopa or dopaminomimetics, and summary statistics needed for computation of effect size

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

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

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

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

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

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

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

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

177 corporated into meta-analyses that estimated summary statistics of aggregate familial risk and herita

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

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

180 Summary statistics of each study underwent meta-analysis

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

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

183 Summary statistics of FASTA (and QUAL) or FASTQ files ar

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

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

186 I-core predictors, together with time-series summary statistics of minute-by-minute mean arterial pre

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

188 It also has functions for computing related summary statistics of probe sets and Gene Ontology terms

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

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

191 Summary statistics of RIDE/RISE baseline characteristics

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

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

194 ally presented to the biological end-user as summary statistics of spot pixel data, such as the spot

195 Summary statistics of the associations of the five SNPs

196 Summary statistics of the metadata are also presented on

197 bution of the Euclidean distance between the summary statistics of the observed and simulated dataset

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

199 ion of summary STatistics (DIST) imputes the summary statistics of untyped variants without first imp

200 Plots and summary statistics offer a picture of how an expression

201 gulatory element annotation system) provides summary statistics on ChIP enrichment in important genom

202 Summary statistics on iron deficiency anemia, night blin

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

204 Plots visualising summary statistics or individual patient data over discr

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

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

207 Using summary statistics (P values and odds ratios) from genom

208 Integration of GWAS summary statistics (P-values) and functional genomic dat

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

210 Using summary statistics (P-values) from large recent genome-w

211 nt-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample

212 e main domains of function: data management, summary statistics, population stratification, associati

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

214 We used summary statistics publicly available from the discovery

215 data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure

216 Consideration of summary statistics rather than entire nucleotide sequenc

217 Gaussian imputation from summary statistics recovers 95% (105%) of the effective

218 rmula were evaluated against TER-CF by using summary statistics, regression analysis, and residual pl

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

220 nd Mineral Nutrition Information System; and summary statistics reported by other national and intern

221 Summary statistics, risk ratios, and CIs were calculated

222 y control tool is demonstrated using several summary statistics, selected to identify different poten

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

224 ibutions compared using four genealogy-based summary statistics sensitive to nonneutral evolution.

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

226 Summary statistics, Spearman rank correlation coefficien

227 hood-based methods, methods based on certain summary statistics (specifically, the sample homozygosit

228 tests are conducted based on study-specific summary statistics, specifically score statistics for ea

229 in as an individual data point for computing summary statistics such as mean and variance in the spik

230 nces on the population genealogy than simple summary statistics such as the average pairwise sequence

231 global alignment is presented online at with summary statistics, such as basepair frequency tables, a

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

233 y analysis of the data considers descriptive summary statistics, such as the mean and the range.

234 lore the effect of bias on the commonly used summary statistics Tajima's D, Fu and Li's D, and Fay an

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

236 Such data make it vital to study summary statistics that offer enough compression to be t

237 Summary statistics, the chi(2) test, rank correlation, m

238 Estimation is based on two simple summary statistics, the proportion infected by the natur

239 t in contrast to current estimates from GWAS summary statistics, the variance-component approach part

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

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

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

243 ition, algebraic expressions for some of the summary statistics used in the approximate Bayesian comp

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

245 Descriptive summary statistics were calculated for central zone imag

246 Summary statistics were calculated using Mantel-Haenszel

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

248 phenotypes in the 2 cohorts were tabulated, summary statistics were determined, and comparisons were

249 GWAS summary statistics were examined for enrichment of funct

250 Summary statistics were obtained for conservative treatm

251 Summary statistics were obtained from the International

252 Basic summary statistics were produced for hot flash score and

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

254 Descriptive and summary statistics were used for assessment by the senio

255 Summary statistics were used to describe results, and th

256 s the gene expression data, instead of using summary statistics, when synthesizing studies.

257 a statistical package to calculate relevant summary statistics, which, for the first time allows use

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

259 Thus, imputation of summary statistics will be a valuable tool in future fun

260 f inferences obtained using methods based on summary statistics with those obtained directly from the

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

262 it is helpful to develop methods that impute summary statistics without going through the interim ste

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

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