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 notype measures, which can benefit from such joint analysis.

3 nts also showed genomewide significance in a joint analysis.

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

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

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

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

8 Joint analysis across populations enables the detection

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

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

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

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

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

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

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

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

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

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

19 The joint analysis enabled determination of transient distan

20 Such joint analysis essentially leads to a multivariate analo

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

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

23 Our joint analysis identifies new ESCC susceptibility loci o

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

25 Through joint analysis including the genome-wide association stu

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

41 The joint analysis of brain atrophy measured with magnetic r

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

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

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

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

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

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

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

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

50 In a joint analysis of European American and Hispanic America

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

52 Joint analysis of five polymorphisms in three FU pathway

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

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

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

56 This joint analysis of genotype and DNAm demonstrates the pot

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

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

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

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

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

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

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

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

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

66 Joint analysis of multiple biparental families offers an

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

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

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

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

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

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

73 Additionally, joint analysis of multiple transcripts by multivariate r

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

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

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

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

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

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

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

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

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

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

84 70 cases and 286 913 controls, followed by a joint analysis of the discovery and replication stages.

85 The joint analysis of the experimental band structure and th

86 Joint analysis of the four smoking-related diseases reve

87 The joint analysis of the two AA samples demonstrated highly

88 A joint analysis of the two experiments offers some scope

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

90 Joint analysis of these two variants (rs1051730 and rs48

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

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

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

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

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

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

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

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

99 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

115 A joint analysis with smoking suggested that smoking and c