ライフサイエンスコーパス: joint analysis

1 association (P=9.33x10(-15) at rs6939340 for joint analysis).

2 nd develop an R package JAMIE to perform the joint analysis.

3 Monte Carlo (MCMC) algorithms for performing joint analysis.

4 th additional subjects, for replication or a joint analysis.

5 ta into a common latent representation for a joint analysis.

6 er results of scRNA-seq data integration and joint analysis.

7 ht the need to integrate multiple slices for joint analysis.

8 generation methods present a challenge to a joint analysis.

9 notype measures, which can benefit from such joint analysis.

10 nts also showed genomewide significance in a joint analysis.

11 stuzumab-containing arms to be combined in a joint analysis.

12 r opt for simply merging the datasets during joint analysis?

13 Joint analysis across populations enables the detection

14 integrating multiple datasets and performing joint analysis across studies.

15 e single-cell data provide opportunities for joint analysis across tissues and modalities.

16 Each SNP remained significant in a joint analysis after adjusting for the other (rs1447295

17 tive overall survival (OS) results from this joint analysis along with updates on the disease-free su

18 Over a variety of parameter combinations, joint analysis also led to moderate (5%-10%) increases i

19 analysis, including iClusterBayes, Bayesian joint analysis and JIVE.

20 Both joint analysis and meta-analysis approaches were used.

21 We discuss methods for the joint analysis and normalization of data from the HumanM

22 gies and applications, for both marginal and joint analysis, and for addressing model mis-specificati

23 The benefit of this joint analysis approach is demonstrated by both simulati

24 A joint analysis approach, with an initial genome-wide ass

25 f the computational approaches developed for joint analysis are based on summary statistics, the join

26 LUMPY), while providing fast and affordable joint analysis at the scale of >=100 000 genomes.

27 nalysis are based on summary statistics, the joint analysis based on individual-level data with consi

28 posed methods are essentially as powerful as joint analysis by directly pooling individual level geno

29 Joint analysis by DR-SC produces accurate (spatial) clus

30 In these settings, true joint analysis can be stymied using conventional statist

31 The studies were amended to include a joint analysis comparing groups 1 and A (the control gro

32 Joint analysis confirmed that self-reported recipient AA

33 tiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mix

34 The joint analysis enabled determination of transient distan

35 Such joint analysis essentially leads to a multivariate analo

36 a panel of 173 D. rotundata accessions using joint analysis for 23 morphological traits and 136,429 S

37 We recommend joint analysis for all two-stage genome-wide association

38 However, joint analysis for the phenotypic and genotypic data pro

39 Joint analysis for the phenotypic and molecular informat

40 For both marginal and joint analysis, for data without and with missingness, f

41 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (

42 Multi-trait-based conditional and joint analysis identified single-nucleotide polymorphism

43 Multi-trait-based conditional and joint analysis identified three SNPs that may contribute

44 Our joint analysis identifies new ESCC susceptibility loci o

45 epidemiological data, we conducted the first joint analysis in which both data types were used to fit

46 Through joint analysis including the genome-wide association stu

47 Our simulation studies suggest that joint analysis increases the power to detect linkage of

48 r side, when samples are pooled together for joint analysis, individual-level statistical differences

49 that, in the context of flexible design, the joint analysis is generally more powerful than the repli

50 en segregation analysis is used; P=.006 when joint analysis is used) between a codominant major gene

51 However, a joint analysis method designed to integrate DNMs from mu

52 We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistic

53 analysis (n=185 656) in 20 h and an 18-trait joint analysis (n=104 264) in 53 h with an 80 GB memory

54 variants, MendelIHT completed a three-trait joint analysis (n=185 656) in 20 h and an 18-trait joint

55 By joint analysis of 108 TFs in four human cell types, we f

56 Joint analysis of 14 large-scale transcriptome datasets

57 ng approaches through simulation studies and joint analysis of 18 GWAS datasets.

58 A joint analysis of 2 large studies of children with verti

59 ied 17 novel risk loci (P < 5 x 10(-8)) in a joint analysis of 26,035 cases and 403,190 controls.

60 -seq, a scalable, base resolution method for joint analysis of 5mC and 5hmC from thousands of single

61 Through joint analysis of a data set including both the cognate

62 epidemic in Martinique in 2015-2016 from the joint analysis of a household transmission study (n = 68

63 imation methods, however, are limited to the joint analysis of a small number of genotypes; in fact,

64 tion of infections being detected, using the joint analysis of age-stratified seroprevalence, hospita

65 In a joint analysis of all 4 cohorts, IL-1 receptor 2 (IL1R2)

66 The joint analysis of all data allows us to focus on general

67 Joint analysis of all screens revealed that genetic alte

68 an independent dataset (P = 0.035) and in a joint analysis of all the data (P = 0.001).

69 mework may not be the same as those from the joint analysis of all traits, leading to spurious linkag

70 In the joint analysis of all white early-onset CAD cases (N=332

71 A joint analysis of all-atom molecular dynamics (MD) calcu

72 variant associations (MTAR), a framework for joint analysis of association summary statistics between

73 Joint analysis of behavioural error patterns and neural

74 Joint analysis of binarized BLQ flags and the correspond

75 We further demonstrate that a joint analysis of both common and low-frequency variants

76 The joint analysis of both data has showed its superiority i

77 the discovery sample (P=9.02e-07) and in the joint analysis of both stages (P=9.7e-03).

78 Hence, the joint analysis of both, functional and non-functional DN

79 The joint analysis of brain atrophy measured with magnetic r

80 We conducted a joint analysis of canagliflozin and renal events in diab

81 Our analyses demonstrate that joint analysis of cancer cell fraction estimates across

82 The joint analysis of carriage and CPIs showed that CPI path

83 tional Inference, a framework for end-to-end joint analysis of CITE-seq data that probabilistically r

84 thdrawal from Hungary in 1242 CE, based on a joint analysis of climatic, environmental, and historica

85 e set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell

86 pecific Protein-RNA Interaction) that uses a joint analysis of CLIP-seq (cross-linking and immunoprec

87 the iCluster algorithm using two examples of joint analysis of copy number and gene expression data,

88 , interspecies genomic differences limit the joint analysis of cross-species datasets to homologous g

89 Instead, joint analysis of CUT&Tag with RNA-seq highlighted L1CAM

90 A joint analysis of data from 12,360 subjects was performe

91 Our approach allows for joint analysis of data from both triad and case-control

92 However, joint analysis of datasets generated by independent labs

93 egions linked to the dichotomous trait, then joint analysis of dichotomous and quantitative traits sh

94 al properties that are further reinforced by joint analysis of different RNA modalities.

95 an Integrative System), which allows for the joint analysis of different types of genomic aberrations

96 ndent pore-water pressure feedback through a joint analysis of displacement and hydrometeorological m

97 Joint analysis of eBird and BBS data improved precision

98 boembolism, results are first presented from joint analysis of estrogen clinical trial and observatio

99 In a joint analysis of European American and Hispanic America

100 sideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to mode

101 f the diastereomers is achieved based on the joint analysis of experimental and computational data.

102 Joint analysis of five polymorphisms in three FU pathway

103 ain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes.

104 include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with

105 This system provides a model for the joint analysis of generational and chronological age in

106 We used a seascape genetics approach (the joint analysis of genetic data and oceanographic connect

107 Our results have implications for joint analysis of genetic variation and DNA methylation

108 This joint analysis of genotype and DNAm demonstrates the pot

109 Existing methods for joint analysis of GWAS data tend to miss causal SNPs tha

110 Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows

111 This study demonstrates the power of joint analysis of historical DNA and large datasets gene

112 ol of the type I error and is as powerful as joint analysis of individual participant data.

113 culated summary statistics is as powerful as joint analysis of individual-participant data.

114 Novel transcript discovery enabled by joint analysis of large collections of RNA-seq data sets

115 The aggregation and joint analysis of large numbers of exome sequences has r

116 Joint analysis of LOAD, ALS, and other traits highlights

117 stories on the Chinese Loess Plateau through joint analysis of loess/red clay magnetic parameters wit

118 pose to extend our previous approach for the joint analysis of marginal summary statistics to incorpo

119 Joint analysis of mass spectrometry images (MS images) a

120 ter define this relationship, we conducted a joint analysis of methylation sensitive PCR digital (MSd

121 Joint analysis of molecular cell types and molecular tis

122 maging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruc

123 A joint analysis of MSX1 and TGFB3 suggested that there ma

124 However, the joint analysis of multi-omics data remains challenging b

125 metric Bayesian factor model is proposed for joint analysis of multi-platform genomics data.

126 component analysis, an effective method for joint analysis of multimodal imaging data.

127 Joint analysis of multiple biparental families offers an

128 In practice, joint analysis of multiple cancer types usually has a la

129 BaalChIP allows the joint analysis of multiple ChIP-seq samples across a sin

130 k to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits.

131 y risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging p

132 opy to develop new statistical approaches to joint analysis of multiple GWAS.

133 Many follow-up investigations involve joint analysis of multiple independently generated GWAS

134 n methodology and software developed for the joint analysis of multiple longitudinal outcomes and tim

135 ns or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating

136 Joint analysis of multiple phenotypes can increase the o

137 In genome-wide association studies (GWAS), joint analysis of multiple phenotypes could have increas

138 There is an increasing interest in joint analysis of multiple phenotypes for genome-wide as

139 aptive Fisher's Combination (AFC) method for joint analysis of multiple phenotypes in association stu

140 There is increasing interest in the joint analysis of multiple phenotypes in genome-wide ass

141 Thus, one challenge in joint analysis of multiple phenotypes is to construct a

142 s for many widely used association tests for joint analysis of multiple phenotypes.

143 cing (NGS) data; however none of these allow joint analysis of multiple same-patient samples.

144 However, current software can make joint analysis of multiple samples challenging and, for

145 scTSS enables joint analysis of multiple single-cell samples, starting

146 Joint analysis of multiple traits can result in the iden

147 Additionally, joint analysis of multiple transcripts by multivariate r

148 , GRASS emerges as an ideal solution for the joint analysis of multislice ST data.

149 One of these reports, the joint analysis of North Central Cancer Treatment Group N

150 allenges, we developed Paired-Damage-seq for joint analysis of oxidative and single-stranded DNA dama

151 The joint analysis of phase velocity and autospectrum gradie

152 SNPs showing association at P < 10(-4) in a joint analysis of phases 1 and 2 in 4,287 CRC cases and

153 Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (

154 measurements and highlight the advantage of joint analysis of population-based samples and phenotypi

155 me courses and protein complexes inferred by joint analysis of protein co-expression and protein-prot

156 d the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), mi

157 A joint analysis of R-loops and chromatin-bound RNA bindin

158 The joint analysis of radial and transverse components of re

159 ential of this approach to monitor hail with joint analysis of seismic intensity and independent prec

160 Our joint analysis of seven datasets confirms ACE2 upregulat

161 In a joint analysis of simulation and experiment we explore t

162 ons as cell lineage markers, identified from joint analysis of single-cell and bulk DNA sequencing by

163 To address this issue, we performed a joint analysis of single-cell and LFP responses during a

164 mproves cell type identification accuracy by joint analysis of single-cell gene expression and chroma

165 Joint analysis of single-cell genomics data from disease

166 tion from different modalities to facilitate joint analysis of single-cell multi-omics data.

167 Specifically, we coupled a joint analysis of small-angle x-ray and neutron scatteri

168 probabilistic, latent variable modeling for joint analysis of spatial information and gene expressio

169 We demonstrate that a joint analysis of state-trait neural variations and feat

170 ditional information can be inferred via the joint analysis of such genetic sequence data and epidemi

171 ral applications of genomic SEM, including a joint analysis of summary statistics from five psychiatr

172 Here, from the joint analysis of surveillance data and holiday timing i

173 itative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs)

174 In a joint analysis of the combined GWAS and replication resu

175 In this joint analysis of the CREDENCE and DAPA-CKD trials, the

176 application of different HSI techniques and joint analysis of the data.

177 70 cases and 286 913 controls, followed by a joint analysis of the discovery and replication stages.

178 The joint analysis of the experimental band structure and th

179 Joint analysis of the four smoking-related diseases reve

180 The joint analysis of the genome, epigenome, transcriptome,

181 We performed a joint analysis of the gravitational-wave event GW170817

182 Transcriptome profiling followed by joint analysis of the most robust signatures across muta

183 Joint analysis of the QTLs of m(6)A and related molecula

184 ibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation

185 The joint analysis of the two AA samples demonstrated highly

186 A joint analysis of the two experiments offers some scope

187 How to perform a joint analysis of these data to gain new biological insi

188 genetic correlation estimates, we find that joint analysis of these phenotypes results in substantia

189 We then conduct a joint analysis of these SNPs and brain structural connec

190 Joint analysis of these two variants (rs1051730 and rs48

191 Joint analysis of TOMM40/APOE variants revealed the TOMM

192 arallel across thousands of nuclei, enabling joint analysis of transcription factor (TF) levels and g

193 We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate

194 the increased information available from the joint analysis of trios of individuals, integrating this

195 We conducted a joint analysis of two genome-wide association studies of

196 We performed a joint analysis of two genomewide association studies of

197 However, joint analysis of two modalities without properly handli

198 rk we develop a statistical approach for the joint analysis of two or more loci.

199 We demonstrate that joint analysis of V1 and S1 profiles outputs interpretab

200 proaches: collapsed aggregate statistics and joint analysis of variants using the sequence kernel ass

201 power of high-throughput sequencing for the joint analysis of variation in transcription, splicing a

202 and Coupled-Clustering) as a method for the joint analysis of various bulk and single-cell data such

203 Here, we present the first joint analysis of whole genome sequencing data of UDN pa

204 A joint analysis of ~170 crystallographic datasets probing

205 gions reached genome-wide significance after joint analysis over all three data sets.

206 Finally, joint analysis over the above features defined clone-spe

207 nd 2, meta-analysis P (P(M)) = 1.7 x 10(-9), joint analysis P (P(J)) = 1.7 x 10(-9); stages 1, 2 and

208 similar to the other patient groups (overall joint analysis P = 1.0 x 10(-6)).

209 merican patients with anti-dsDNA antibodies (joint analysis P = 4.1 x 10(-5) in anti-dsDNA-positive p

210 We show that this joint analysis performs better than sample-by-sample met

211 ensities produce different sample genotypes, joint analysis reduces genotype errors and identifies no

212 nomic and clinical data available, but their joint analysis remains a challenge.

213 In this paper, we employ a joint analysis scheme of experimental data and computati

214 tant predictor of gene expression and that a joint analysis significantly enhanced the prediction of

215 nificantly associated with birth length in a joint analysis (Stages 1 + 2; beta = 0.046, SE = 0.008,

216 In the joint analysis, the odds ratio of CRC for individuals wi

217 d its proximity to the trait locus, we found joint analysis to be as much as 70% more efficient than

218 obtained for these 99 missense variants in a joint analysis to generate the likelihood of pathogenici

219 ing strategy is investigated, which adopts a joint analysis to integrate information from pathologica

220 replication with the stage II data only or a joint analysis using information from both stages.

221 -corrected threshold for significance in the joint analysis was p=2.20x10(-7)

222 In the combined joint analysis, we confirmed three previously reported l

223 In the joint analysis, we replicated 19 previously identified l

224 In the joint analysis, which included samples from 4312 patient

225 In a joint analysis with a bipolar disorder sample (16,374 af

226 tion studies of ulcerative colitis and their joint analysis with a previously published scan, compris

227 lso did a multi-trait analysis of GWAS, in a joint analysis with a study of cerebral white matter hyp

228 Joint analysis with aged human datasets identified two u

229 Joint analysis with signatures from 15 leading studies p

230 Although joint analysis with single-cell RNA sequencing can allev

231 A joint analysis with smoking suggested that smoking and c