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1 available sequence data, and linkage and/or association analyses.
2 impacts on downstream bioinformatics and CNV association analyses.
3 e for genotyping in 270 trios and subsequent association analyses.
4 51, as revealed by reporter gene and protein association analyses.
5 pulation structure are known to confound the association analyses.
6 on through the interpretation of genome-wide association analyses.
7 ntly big sample sizes for adequately powered association analyses.
8 nt and burden tests and performs conditional association analyses.
9 ally higher quality data for common and rare association analyses.
10 genotypes to generate additional markers for association analyses.
11 uperior to SNPs and are promising in genetic association analyses.
12 and then regress out these components in the association analyses.
13 austive univariate and epistatic interaction association analyses.
14 production and early necrosis in genome-wide association analyses.
15 the presence of linkage (APL) were used for association analyses.
16 ffects that escape the standard single-locus association analyses.
17 Family studies permit both linkage and association analyses.
18 mily studies, using segregation, linkage and association analyses.
19 nome-wide linkage map and quantitative trait association analyses.
20 ts were genotyped in 1,037 Pima subjects for association analyses.
21 6 full-blooded, nondiabetic Pima Indians for association analyses.
22 and the statistical power of haplotype-based association analyses.
23 de-polymorphism markers for fine mapping and association analyses.
24 on the basis of pooled-sample microarray and association analyses.
25 was the top category for the 10-year change association analyses.
26 or network, overrepresentation, and guilt-by-association analyses.
27 ly 1% for both single-variant and gene-based association analyses.
28 d for population genetic studies and genetic association analyses.
29 ory, and LD-related genomic annotations into association analyses.
30 Each study performed ancestry-specific association analyses.
31 els with propensity adjustment were used for association analyses.
32 linear mixed-effects models were used in the association analyses.
33 enic assay, independent mutants, and allelic association analyses.
34 genome-wide association studies and genetic association analyses.
35 g linear mixed models to perform genome-wide association analyses.
36 ignificant association signals in single SNP association analyses.
37 ht identify this and thus improve downstream association analyses.
38 for subsequent locus-specific genotyping and association analyses.
40 summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in
41 ods are lacking to perform joint multi-locus association analyses across more than one gene/region.
42 ignal at D9S288; furthermore, pedigree-based association analyses also implicated the 9p24 candidate
43 nd, we conducted ethnic-specific genome-wide association analyses among 1235 Hispanic, 706 Asian, 154
46 reference haplotypes to perform genome-wide association analyses and observe 17 genome-wide signific
47 used logistic regression for cross-sectional association analyses and proportional hazards regression
48 rk highlights the value of iPSCs for genetic association analyses and provides a unique resource for
49 hod to increase power in large-scale genomic association analyses and report a novel variant associat
51 kelihood ratio test and permutation test) in association analyses, and all yielded similar results.
52 for climate modelling and trait/environment association analyses, and decentralized participatory im
53 us allelic heterogeneity through conditional association analyses, and epistasis through interaction
54 we show that the powers of both linkage and association analyses are crucially dependent on the prop
56 tation, admixture resolution and genome-wide association analyses are timely and computationally inte
57 addition to evaluating main exposure-disease associations, analyses are also conducted to evaluate wh
59 dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those ba
63 s and performed hypothesis-free phenome-wide association analyses between major depressive disorder (
68 related traits jointly, most of the previous association analyses considered each phenotype separatel
80 framework for power calculations in genetic association analyses for a wide range of genetic effects
82 ity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-deri
84 with FTD and 4308 controls), we did separate association analyses for each FTD subtype (behavioural v
85 ort here genotyping data and type 1 diabetes association analyses for HLA class I loci (A, B, and C)
87 protein-1 (MCP-1), we conducted genome-wide association analyses for MCP-1 in 3 independent cohorts
88 sion profiling and carried out a genome-wide association analyses for more than 100,000 gene expressi
95 we investigate the power of QTL linkage and association analyses for simple random sibship samples,
98 lation genetics, linkage disequilibrium, and association analyses have shown that specific MAPT H1 su
103 CGM method identified 5 SBP trajectories and association analyses identified a genome-wide significan
112 d approach of genetic linkage and positional association analyses implicates tau as a susceptibility
113 erformed SNP and copy number variation (CNV) association analyses in 1,509 children with obesity at t
115 We performed genome-wide genotype imputation association analyses in 1616 non-Hispanic white (NHW) ca
116 isms (SNPs) were selected for genotyping and association analyses in 2 independent validation cohorts
119 uding 30 amino acid changes, and carried out association analyses in a subset of 3,665 subjects from
121 confirms the findings of earlier genome-wide association analyses in African American and Latino subj
123 dus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from C
133 ng debate over the utility of multiple locus association analyses in the identification of genomic re
135 t linkage analyses of additional markers and association analyses in the same region supported the in
137 EHR)-based phenotypes allows for genome-wide association analyses in thousands of traits and has grea
139 aper in this issue) and targeted replication association analyses in up to 18,554 independent partici
143 -based approach that embeds both linkage and association analyses into a unified framework for genera
146 ddition to sib-pair linkage and case-control association analyses, linkage disequilibrium mapping wit
147 o full-length thrombin, followed by clinical association analyses, Mendelian randomization, and funct
148 tabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 i
150 polipoprotein L3 (APOL3), using family-based association analyses of 150 total prostate cancer (PC) c
153 bination of whole-genome selection scans and association analyses of 544 Populus trichocarpa trees to
154 48K transcripts) and carried out genome-wide association analyses of 8370 expression phenotypes.
157 tical evidence for this suggestion came from association analyses of a rare 3'-UTR variant, var321, w
163 NVs to complex phenotypes, we have performed association analyses of expression levels of 14,925 tran
166 oposed methods, we performed genome-wide CNV association analyses of hematological trait, hematocrit,
167 e to HapMap SNPs and independently conducted association analyses of hemostasis measures using an add
171 of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel o
173 Here, we report the results of genome-wide association analyses of multiple phenotypes and two meas
174 ing approaches that involve joint linkage or association analyses of multiple seemingly disparate phe
175 eling offer a flexible framework for genetic association analyses of offspring (child), parent-of-ori
179 rtium has created a resource for genome-wide association analyses of personality traits in more than
184 eport single- and multiple-trait genome-wide association analyses of self-reported sleep duration, in
185 c group may complicate the interpretation of association analyses of such variants, highlighting the
188 In the present study, we extended previous association analyses of the gene cluster to include APOA
190 or wet lab-based HLA typing and thus renders association analyses of the HLA in large cohorts feasibl
199 component-clustered ethnicity, we performed association analyses on ~10 million imputed variants usi
200 with low ICC may be excluded from subsequent association analyses, or extra caution needs to be taken
202 ificance in the Chinese-specific genome-wide association analyses (P = 4.15 x 10(-14) and 4.30 x 10(-
203 We have developed the "Power for Genetic Association analyses" (PGA) package which comprises algo
205 s test, compatibility matrices, and index of association analyses provided substantial evidence that
206 d from uncorrected and genome-wide corrected association analyses (r(2) = 0.141), highlighting that p
208 age, suggesting that genome-wide surveys for association analyses require SNPs every 100-200 bp.
212 weighted MRI (swMRI). Conditional and joint association analyses revealed independent GWAS signals i
226 A from 2738 Amish participants and performed association analyses to determine the effects of the del
228 performing both single-SNP and moving-window association analyses to identify a colon neoplasia risk
231 the need for adequately powered cohorts for association analyses to identify not only genetic determ
232 the species' range and genotype-environment association analyses to identify the genetic signature a
233 a joint and ancestry-stratified genome-wide association analyses to identify variants specifically a
234 the results of applying genetic linkage and association analyses to neuropsychological endophenotype
235 cture are included, this tool can be used in association analyses to provide high-resolution evaluati
239 C, we conducted both genome-wide linkage and association analyses, using approximately 400 microsatel
241 s, followed by family-based and case-control association analyses, using two independent data sets.
249 A variety of univariate and multivariate association analyses were conducted after data quality c
264 ion, imputation analysis and gender-specific association analyses were performed in the two independe
275 s analyses and population-based case-control association analyses were performed, and the P values, f
283 e present the results of large-scale genetic association analyses with 69,054 cases from five differe
284 uman neural stem cell line were selected for association analyses with average cortical thickness.
285 Both single SNP and SNP x SNP interaction association analyses with body mass index (BMI) were eva
286 LD structure in a candidate gene followed by association analyses with both SNPs and haplotypes is an
287 romosomal abnormalities in RDD and performed association analyses with clinical data derived from thi
293 predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61
294 We describe a novel approach to genetic association analyses with proteins sub-divided into biol
297 e reliable, powerful, and convenient genetic association analyses without access to the individual-le
299 sitory Network (UNICORN), a means to perform association analyses without necessitating direct access
300 ogic cohorts and applied directly in disease-association analyses, without relying on self-reported d