ライフサイエンスコーパス: imputation

1 s computationally tractable for whole-genome imputation.

2 ility of large reference panels for genotype imputation.

3 structure between phenotypes to perform the imputation.

4 ion region variance, and naive missing value imputation.

5 es, assessed with all available data without imputation.

6 relation structure of the phenotypes used in imputation.

7 iple-imputation models, and nearest-neighbor imputation.

8 Missing data were replaced using multiple imputation.

9 Missing data were replaced using multiple imputation.

10 participants were accounted for by multiple imputation.

11 e variants, respectively, can be captured by imputation.

12 te more accurately than the classic one-step imputation.

13 linear/log-linear (0.80; 95% CI, 0.76-0.83) imputation.

14 and easily handle missing values without any imputation.

15 stand what factors influence the accuracy of imputation.

16 ues from the available measurements, without imputation.

17 ified intention to treat, with non-responder imputation.

18 10(-8)) by direct genotyping and ADO-EGR2 by imputation.

19 Reference Consortium panel as the basis for imputation.

20 Missing data were handled using multiple imputations.

21 After imputation, 1,844,133 genetic variants were analyzed in

22 After quality control filters and GWAS imputation, 285 patients and 1,006 controls remained in

23 Missing data were managed using multiple imputation, accounting for multilevel data structure.

24 eference panels are needed to assure optimal imputation accuracy and allele frequency estimation.

25 uted using various methods, (2) evaluate the imputation accuracy and post-imputation quality associat

26 irectly using allele read counts can improve imputation accuracy and reduce computation compared with

27 We show that large increases in imputation accuracy can be achieved by re-phasing WGS re

28 t Phase 3 reference panel can yield improved imputation accuracy compared with Phase 1, particularly

29 peline, Eagle prephasing improved downstream imputation accuracy in comparison to prephasing in batch

30 Genomes Project, hold promise for improving imputation accuracy in genetically diverse populations s

31 sample level, the Phase 3 reference improved imputation accuracy in Hispanic/Latino samples from the

32 Here, we sought to empirically evaluate imputation accuracy when imputing to a 1000 Genomes Phas

33 ipt of vasopressors were not associated with imputation accuracy.

34 correlated omics datasets for improving the imputation accuracy.

35 s purpose, while achieving various levels of imputation accuracy.

36 ontrols, both of Indian descent, followed by imputation across the genome.

37 Most existing imputation algorithms are not well suited for this situa

38 and SDA models with four different types of imputation, all of which are common approaches in the fi

39 More efficient imputation allows geneticists to locate and test effects

40 Imputation analysis also supported the association betwe

41 We also performed imputation analysis in the region, with 562 imputed SNPs

42 Besides the complete case analysis, multiple imputation analysis was performed to address for missing

43 After multiple imputation analysis, infants undergoing delayed clamping

44 enotype data extended to 7174392 variants by imputation analysis.

45 both for complete case analysis and multiple imputation analysis.

46 on the intention-to-treat principle without imputation and all serious adverse events were investiga

47 ook within-family association analysis after imputation and assessed candidate variants for evidence

48 long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in

49 Here, we use regional imputation and bioinformatics analysis of the 12p12.1 lo

50 n studies can be empowered by sequence-based imputation and by studying founder populations.

51 ean ancestry by refined genotype calling and imputation and by the addition of 5,033 cases and 5,707

52 Through high-density imputation and conditional analyses, we identified seven

53 We used imputation and conditional logistic regression analyses

54 An essential property for missing data imputation and detection of outliers is that the uncorru

55 These studies used HapMap imputation and did not examine the X-chromosome.

56 in complex disease etiology via large-scale imputation and exome and whole-genome sequence-based ass

57 Imputation and fine-mapping analyses were performed in t

58 were genotyped, and single-SNP association, imputation and gender-specific association analyses were

59 naffected individuals) subsequently informed imputation and haplotype analyses.

60 After imputation and HLA allele prediction, 9 380 034 single n

61 Genome-wide imputation and meta-analysis identified five new risk lo

62 Through imputation and mixed-model association analysis in 12,57

63 esources that allow researchers to carry out imputation and phasing consistently and efficiently.

64 WGS freeze 3 dataset in which joint calling, imputation and phasing of over 5300 whole genome samples

65 NL and Rice supercomputers were used for the imputation and phasing step.

66 mputation methods in multi-tissue expression imputation and that incorporating imputed expression dat

67 Multiple imputation and weighting were used to account for missin

68 Genotyping, imputation, and genome-wide association study were perfo

69 d SNPs to be imputed in the add-one two-step imputation, and lastly how the clusters of imputed genot

70 mented GWAS1 and GWAS2 controls, genome-wide imputation, and meta-analysis of all three GWAS, followe

71 Missing data were handled with multiple imputation, and we estimated hazard ratios by means of C

72 The imputation approach confirmed the four QTLs found in the

73 ybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic archi

74 on about half of the population only and an imputation approach using SNP and DArT markers on the en

75 We use a WGS-based imputation approach utilizing 10,422 reference haplotype

76 Icelandic whole-genome sequence data and an imputation approach we searched for rare sequence varian

77 that hidden Markov models and random forest imputation are more suitable to studies that aim analyse

78 We performed genome-wide genotype imputation association analyses in 1616 non-Hispanic whi

79 ing this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% an

80 ture application for GeneImp is whole-genome imputation based on the off-target reads from deep whole

81 Here we apply genotype imputation based on whole-genome sequence data from the

82 Here we describe a method (STITCH) for imputation based only on sequencing read data, without r

83 an population panels (directly sequenced and imputation based).

84 Imputation-based fine-mapping across the class II MHC re

85 xamined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European

86 those from a 2,000-fold larger, traditional imputation-based fine-mapping study.

87 id, allowing easy haplotype construction and imputation-based genotype calling, even without the avai

88 In such cases, imputation-based methods are biased.

89 Using the idea of multiple imputation by chained equations (MICE), we investigate t

90 ast, accurate, and memory-efficient genotype imputation by restricting the probability model to marke

91 ally, our analysis further demonstrates that imputation can be used to exploit GWAS data to identify

92 Deep sequence-based imputation can enhance the discovery power of genome-wid

93 er, missing values are common in MS data and imputation can impact between-biospecimen correlation an

94 Linear and log-linear imputations cannot be recommended.

95 reference panels and cohorts makes genotype imputation computationally challenging for moderately si

96 identified using whole-genome sequencing and imputation data (based on 1000 Genomes Project and Haplo

97 dividuals through the UK10K program and deep-imputation data from 39,655 individuals genotyped genome

98 The imputation dataset contained 1635 individuals.

99 Our findings can inform imputation design for other genome-wide association stud

100 r each imputation method, we calculated both imputation error and the area under the curve for patien

101 of our method, consistently in terms of the imputation error and the recovery of mRNA-miRNA network

102 more biological information to minimize the imputation error and thus can improve the performance of

103 rence panel, the AGRP substantially improved imputation, especially for rarer variants.

104 nformatic approaches, including 1000 Genomes imputation, expression quantitative trait locus analyses

105 ation methods, specified within the multiple imputation FCS method, for selected predictors for each

106 als plus 250 with HD array genotypes to test imputation, findhap used 7 hours, 10 processors and 50 G

107 and 23,731 controls) population by using HLA imputation, followed by a multi-ethnic validation study

108 iminary success on whole genome SNP genotype imputation for genotyped animals via a series of stairca

109 nts in reference panels may result in better imputation for genotypes in some ethnic groups.

110 s, is flexible to be applied to missing data imputation for large meta-analysis with different cohort

111 sis was modified intention-to-treat (without imputation for losses to follow-up) accounting for withi

112 nalysis by intention to treat, with multiple imputation for missing data.

113 to treat with all available data and without imputation for missing data.

114 x proportional hazards model, using multiple imputation for missing data.

115 sion analyses were performed, using multiple imputation for missing data.

116 ion with the use of baseline-carried-forward imputation for missing data.A total of 153 participants

117 rements were summarized after using multiple imputation for missing measurements.

118 e in the intervention arm, and used multiple imputation for missing or incomplete primary outcome dat

119 Data were analysed with and without imputation for missing values of anti-JCV antibody statu

120 pe reference panels allow efficient genotype imputation for this purpose.

121 analysis (SDA) with k-nearest neighbors for imputation for varying mechanism and amount of missing d

122 o much larger reference panels by performing imputation from a simulated reference panel having 5 mil

123 calling from low-coverage sequence data and imputation from array genotypes of various densities is

124 t read depths of 2x to 4x gave more accurate imputation from array genotypes.

125 tion at the HP locus in detail and, by using imputation from flanking SNP genotype data, shows that i

126 We leverage expression imputation from genetic data to perform a transcriptome-

127 validation, dendrogram production, genotype imputation from sequence data in linkage studies, and ad

128 thus far, we describe a way to analyze it by imputation from SNP haplotypes and find among 22,288 ind

129 Nonlinear imputation had consistently lower error than other techn

130 High-density genotyping and imputation identified 100,000 variants within each link

131 nit beta gene [HBB] variant) on the basis of imputation in 12,226 adult Hispanics/Latinos grouped acc

132 iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of E

133 riants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to

134 leotide polymorphisms and performed genotype imputation in 760 acute kidney injury cases and 669 cont

135 expanded the sample size via genotyping and imputation in a further 111,548 subjects.

136 evaluation of 5 commonly used approaches for imputation in samples of (typically) unrelated subjects.

137 For rare variants with 0.01% < MAF </= 0.5%, imputation in the Framingham Heart Study with the combin

138 f different ethnic populations, we conducted imputations in the Framingham Heart Study and the North

139 Genotype imputation is a key component of genetic association stu

140 Genotype imputation is a key step in the analysis of genome-wide

141 Genotype imputation is a powerful strategy for achieving the larg

142 Imputation is commonly used in genome-wide association s

143 Genotype imputation is computationally demanding and, with curren

144 cing technologies, whole genome SNP genotype imputation is often used as an alternative for obtaining

145 contain large amounts of missing values, and imputation is often used to create complete data sets fo

146 ngness of these phenotypes, even if multiple imputation is used.

147 useful family-based analysis tools, such as imputation, linkage, and association tools, have yet to

148 Here we describe improvements to imputation machinery that reduce computational requireme

149 Through genotyping and dense imputation mapping from whole-genome sequencing, we test

150 We propose an imputation method based on multivariate mixed models usi

151 We concluded that our proposed imputation method can utilize more biological informatio

152 hese methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of ge

153 work linking the HLA to AD, we used a robust imputation method on two separate case-control cohorts t

154 We showed that the use of any imputation method outperforms the omission of the cohort

155 We present a genotype imputation method that scales to millions of reference s

156 lly conditional specification (FCS) multiple imputation method to establish complete datasets for all

157 ies, we found the amount of missing data and imputation method to substantially change the between-ma

158 In this study, a novel multi-omics imputation method was proposed to integrate multiple cor

159 No one imputation method was universally the best, but the simp

160 eneous, the last-observation-carried-forward imputation method was used in 73% of trials, and publica

161 GBS, should be handled carefully because the imputation method will impact downstream analysis.

162 The imputation method, based on the Li and Stephens model an

163 For each imputation method, we calculated both imputation error a

164 hout reported data using a two-step multiple imputation method.

165 prediction were not observed by changing the imputation method.

166 eritability of traits can be affected by the imputation method.

167 ppresant use (544 patients) using a multiple imputation method.

168 The employed imputation methodology implies that variation of DNAm le

169 We investigated the effects of seven imputation methods (half minimum substitution, mean subs

170 other two KNN procedures as well as simpler imputation methods based on substituting missing values

171 All imputation methods better identified moderate-severe acu

172 Several existing genotype imputation methods can be efficient for this purpose, wh

173 Statistical imputation methods developed for other complex loci (e.g

174 We identified significant differences across imputation methods in a dataset missing 20 % of the geno

175 hat the proposed methods outperform existing imputation methods in multi-tissue expression imputation

176 mpare our MICE methods with several existing imputation methods in simulation studies.

177 Current imputation methods mainly focus on using single omics da

178 In this work, we developed multi-tissue imputation methods to impute gene expression in uncollec

179 We compared our method with five imputation methods using single omics data at different

180 The imputation methods we evaluated were as follows: multiva

181 egion, differences in sequencing and allelic imputation methods, and diversity across ethnic populati

182 ed regression-based predictive mean matching imputation methods, specified within the multiple imputa

183 than 6 million sites, using standard genomic imputation methods.

184 eased with the level of missing data for all imputation methods.

185 Multiple imputation (MI) has been widely used for handling missin

186 Multiple imputation (MI) is increasingly being used to handle mis

187 rvation carried forward (LOCF), and multiple imputation (MI), in a setting where time-dependent covar

188 h the incomplete outcome was included in the imputation model.

189 sing-data complete-case analysis, 2 multiple-imputation models, and nearest-neighbor imputation.

190 been used as a natural strategy for building imputation models, but limited research has been conduct

191 Coverage was increased by imputation of >25 million common SNPs, using the 1000 Ge

192 Imputation of classic HLA alleles identified two in high

193 Imputation of classical HLA alleles, amino acids and SNP

194 ts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-res

195 m Illumina iSelect array (iCOGS) followed by imputation of genotypes for 3,134 SNPs in more than 89,0

196 By means of imputation of GWAS data and subsequent validation SNP ge

197 Imputation of GWAS data was performed using the 1000 Gen

198 Imputation of indels increased 9.9% power and identifies

199 Imputation of individual level genotypes at untyped mark

200 We present KIR *IMP, a method for imputation of KIR copy number.

201 Results After imputation of missing data, mean cumulative costs were -

202 After imputation of missing data, point prevalence was 1.0 cas

203 predicting case-control status that included imputation of missing data.

204 g inverse probability weighting and multiple imputation of missing data.

205 and mental health confounders, with multiple imputation of missing data.

206 (OR, 2.06; 95% CI, 1.27-3.37; P = .004, with imputation of missing data; absolute difference, 17.2%;

207 Our framework can also perform imputation of missing or low quality data in existing se

208 es were performed to determine the impact of imputation of missing peak VO2 data.

209 [95% CI, -12.3 to -6.4]) and after multiple imputation of missing results.

210 analyses showed our findings to be robust to imputation of missing TB-related cost components, and us

211 was to validate the superiority of nonlinear imputation of PaO2/FIO2 among mechanically ventilated pa

212 Retrospective studies suggest that nonlinear imputation of PaO2/FIO2 from SpO2/FIO2 is accurate, but

213 mechanically ventilated patients, nonlinear imputation of PaO2/FIO2 from SpO2/FIO2 seems accurate, e

214 variants underlying complex phenotypes, but imputation of rare variants remains problematic.

215 For the North Chinese samples, imputation of rare variants with 0.01% < MAF </= 0.5% wi

216 Imputation of such intermediate phenotypes represents a

217 We therefore develop direct imputation of summary statistics allowing covariates (DI

218 Direct imputation of summary statistics can also be valuable, f

219 similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from

220 onstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

221 Imputation of the midpoint of intervals was used in some

222 tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx t

223 was performed using microarrays, followed by imputation of unobserved SNPs.

224 oritizing variants assuming a disease model, imputation of untyped variants, and linkage and associat

225 Our analyses demonstrate that multiple imputation offers a principled approach by which to inco

226 The effects of imputation on multiple biological matrix analyses have n

227 w method (LASER 2.0), combined with genotype imputation on the reference individuals, can substantial

228 in case of segregation distortion; (iv) data imputation on VCF files using a new approach, called LB-

229 ub-regions containing the top ranked SNPs by imputation P-value revealed a 30 bp insertion/deletion (

230 We show that modern imputation panels (sets of reference genotypes from whic

231 f including population-specific sequences in imputation panels and exemplify the power gains afforded

232 We also observed that, when used within an imputation pipeline, Eagle prephasing improved downstrea

233 A multiple imputation procedure was used for missing data.

234 ETHOD: Following uniform quality control and imputation procedures using the 1000 Genomes Project (ph

235 1000 Genomes imputation provides better coverage of uncommon variants

236 2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3

237 Imputation quality improved with inclusion of small amou

238 t and the Haplotype Consortium, will improve imputation quality of rare and less common variants, but

239 panel increased well-imputed genotypes (with imputation quality score >/=0.4) from 62.9% to 76.1% whe

240 riants with minor allele frequencies >1% and imputation quality scores >0.6.

241 We show that GeneImp achieves imputation quality very close to that of BEAGLE, using o

242 To evaluate imputation quality with a relatively larger reference pa

243 find associated loci, we assembled a custom imputation reference panel from whole-genome-sequenced p

244 ale for whole-genome sequencing and improved imputation reference panels to study the genetic archite

245 ress syndrome (PaO2/FIO2 </= 150); nonlinear imputation remained superior (p < 0.001).

246 ed genotype are pieced together as the final imputation result.

247 We compare imputation results based on KNN-TN with results from oth

248 pulation of European descent, and subsequent imputation revealed 660,238 single nucleotide polymorphi

249 s, including weighting adjustments, multiple imputation, selection models, and pattern-mixture models

250 e Sanger Imputation Service and the Michigan Imputation Server.

251 s freely available to researchers through an imputation server.

252 ailable for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.

253 tigen (HLA) region, and classical HLA allele imputation showed a protective association with the clas

254 und detection, and intelligent missing value imputation steps to the conventional informatic workflow

255 nd use of sensitivity analyses using an MNAR imputation strategy for longitudinal studies when missin

256 sis, this effect was highly sensitive to the imputation strategy for peak VO2 among patients who died

257 Here, we present results from a genome-wide imputation study of nsCL/P in which, after adding replic

258 explain why there was less of an increase in imputation success in the North Chinese samples.

259 GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project,

260 ack some proteomic features, we developed an imputation technique to fill such missing features.

261 coverage of genetic markers, we implemented imputation techniques to extend the number of tested mak

262 We describe GeneImp, a program for genotype imputation that does not require prephasing and is compu

263 We also describe a new web-based service for imputation that facilitates access to new reference pane

264 m a proper correction for the uncertainty in imputation through the variance estimator using the jack

265 After imputation to 1000 Genomes Project data, we assessed ass

266 We address the task of genotype imputation to a dense reference panel given genotype lik

267 the first practical choice for whole-genome imputation to a dense reference panel when prephasing ca

268 ing to the US population, and using multiple imputation to address loss to follow-up.

269 We applied multiple imputation to address missing data and performed binary

270 We used multiple imputation to address potentially biased estimates resul

271 e with a custom Illumina Omni2.5M array, and imputation to approximately 20 million single-nucleotide

272 entified anti-HCV+ persons by using multiple imputation to assign anti-HCV results to untested patien

273 sign and data quality control (QC), genotype imputation to augment available sequence data, and linka

274 y representative US survey, we used multiple imputation to impute ABP-defined hypertension status for

275 full sequence data validated the use of SNP imputation to predict the optimal variants for capturing

276 Using imputation to the 1000 genomes (1000G) reference set, we

277 o loss of accuracy in comparison to standard imputation tools.

278 t improved still further when pedigree-based imputation using larger pedigrees was also added.

279 Comparable results were observed through imputation using SNP2HLA with another reference panel fr

280 Imputation using the 1000 Genomes haplotype reference pa

281 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference

282 Genotyping and imputation was performed in fluticasone furoate (FF) or

283 Multiple imputation was used for intention-to-treat analysis.

284 ndary outcome measures at 9 months; multiple imputation was used for missing data.

285 Direct typing and imputation was used to fine-map the human leukocyte anti

286 Multiple imputation was used to impute tumor markers for those mi

287 Through replication genotyping and imputation we found that a predicted protein-truncating

288 Using the two-step piecemeal imputation, we present some preliminary success on whole

289 he sensitivity and specificity for nonlinear imputation were 0.87 (95% CI, 0.83-0.90) and 0.91 (95% C

290 ned to the DHA group, 291 (49.1% by multiple imputation) were classified as having physiological bron

291 ch for large scale joint variant calling and imputation which can scale up to over 10,000 samples whi

292 nt computational challenges for missing data imputation, while the advances of genotype technologies

293 lthough it is still unclear exactly how well imputation will work for rare variants.

294 Multiple imputation with delta adjustment provides a flexible and

295 ts were generally larger than those based on imputation with earlier reference panels, consistent wit

296 t the missing variable (benchmark), multiple imputation with fixed or random intercepts for cohorts w

297 selected 23 CLOCK gene SNPS was obtained by imputation with IMPUTE2 software and reference phase dat

298 ovel pipeline, SOILoCo (Scaffold Ordering by Imputation with Low Coverage), to detect heterozygous re

299 Multiple imputation with predictive mean matching was used for mi

300 >/=0.4) from 62.9% to 76.1% when compared to imputation with the 1000 Genomes.

301 new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optim