ライフサイエンスコーパス: genetic epidemiology

1 Meta-analysis is a vital tool in genetic epidemiology.

2 studies highlight the need to understand the genetic epidemiology.

3 entral concepts and topical issues in modern genetic epidemiology.

4 ies with genome-wide association studies and genetic epidemiology.

5 (and prospective) studies of M. tuberculosis genetic epidemiology.

6 data for testing any multivariate method in genetic epidemiology.

7 ts, both of which are relevant to studies of genetic epidemiology.

8 the evolution of case-control studies and of genetic epidemiology.

9 est known meta-analysis published to date in genetic epidemiology.

10 lying the associations, a major challenge in genetic epidemiology.

11 pted to describe the mode of inheritance and genetic epidemiology.

12 control framework plays an important role in genetic epidemiology.

13 s share common characteristics with those of genetic epidemiology.

14 loys four major research paradigms: 1) basic genetic epidemiology, 2) advanced genetic epidemiology,

15 : 1) basic genetic epidemiology, 2) advanced genetic epidemiology, 3) gene finding methods, and 4) mo

16 erosclerotic Disease, Vascular Function, and Genetic Epidemiology (ADVANCE) study.

17 therosclerotic Disease VAscular functioN and genetiC Epidemiology (ADVANCE) Study.

18 Here I revisit the recent history of genetic epidemiology and argue for retaining statistical

19 and appraisal of published field synopses in genetic epidemiology and assessed their main findings an

20 Although many genetic epidemiology and biomarker studies have been con

21 to influence complex traits is important in genetic epidemiology and disease etiology.

22 Recent findings from genetic epidemiology and from genome-wide association st

23 rmance comparable to classical approaches in genetic epidemiology and have the potential to uncover n

24 obial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows

25 monstrate the utility of Bayesian methods in genetic epidemiology and provide support for their appli

26 xciting new advances in understanding of the genetic epidemiology and treatment of HLA-B27-associated

27 ndividuals provide superlative resources for genetic, epidemiology and other biomedical research.

28 e Giardia genome, but their consequences for genetics, epidemiology and evolution remain unknown.

29 Building on new insights into the genetics, epidemiology and pathogenesis of Parkinson's d

30 of analysing underrepresented populations in genetic epidemiology, and the urgent need for larger gen

31 logy is relatively young and brings together genetics, epidemiology, and biostatistics to identify an

32 Research from genetics, epidemiology, and cell biology all converge, s

33 d to improve our knowledge of the population genetics, epidemiology, and ecology of bacterial pathoge

34 dalities, anatomy and pathology, embryology, genetics, epidemiology, and imaging.

35 Examples are given from ecology, genetics, epidemiology, and immunology.

36 ogress in elaborating the molecular biology, genetics, epidemiology, and management of these inherite

37 in the elaboration of the molecular biology, genetics, epidemiology, and management of these prototyp

38 a public resource for information on cancer genetics, epidemiology, and pathology in genetically def

39 The future challenges facing genetic epidemiology are considerable both in scale and

40 Standard techniques from genetic epidemiology are ill-suited to formally assess t

41 notypes for multiple complex traits in human genetic epidemiology as well as plant and livestock bree

42 Although genetic epidemiology, as a research field, is oriented t

43 However, traditional approaches to genetic epidemiology assume a linear association and thu

44 ch has important implications for social and genetic epidemiology because it substantiates a particul

45 Genetic epidemiology began in 1979, followed by advances

46 athways back to basic biological mechanisms, genetic epidemiology can also provide important etiologi

47 Genetic epidemiology can contribute to establishing the

48 the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) studies.

49 Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland

50 the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading ef

51 first described in this paper, to translate genetic epidemiology data into actionable visual informa

52 the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of g

53 oxidative stress-mediated injury, the use of genetic epidemiology for the study of oxidative stress-r

54 erts in the fields of clinical and molecular genetics, epidemiology, functional assays, and variant i

55 and Cohorts for Heart and Aging Research in Genetic Epidemiology GWAS for parathyroid hormone (n=29,

56 ditional (and much contemporary) research in genetic epidemiology has barely tapped the potential tha

57 Genetic epidemiology has greatly expanded its scope as a

58 he last two decades, the available tools for genetic epidemiology have expanded from a genetic focus

59 e standard research approaches developed for genetic epidemiology, however, are not necessarily appro

60 genotype-predicted mean male alcohol intake (genetic epidemiology-ie, Mendelian randomisation), with

61 lipsed candidate gene association studies in genetic epidemiology in providing robust, unbiased evide

62 It is our view that future genetic epidemiology inquiry will benefit greatly from s

63 s to incorporate the discipline of molecular/genetic epidemiology into the study of cancer prevention

64 candidate areas of interest, with molecular/genetic epidemiology investigations honing in on promisi

65 Genetic epidemiology is a hybrid discipline whose ultima

66 Genetic epidemiology is a rapidly expanding research fie

67 Therefore, defining the genetic epidemiology is also challenging given the overl

68 Genetic epidemiology is an alliance of the 2 fields that

69 The field of genetic epidemiology is relatively young and brings toge

70 ated traits and BMI at the observational and genetic epidemiology level in a cross-sectional populati

71 epresented specialties in bioethics, policy, genetic epidemiology, medical genetics, statistical gene

72 multaneously invoke principles in population genetics, epidemiology, molecular biology and biostatist

73 ile the number of published epidemiology and genetic epidemiology multicenter studies increased by 8-

74 ed our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry s

75 and black families of the NHLBI Hypertension Genetic Epidemiology Network (HyperGEN) study to identif

76 ograms from participants in the Hypertension Genetic Epidemiology Network (HyperGEN) study, a populat

77 In the Hypertension Genetic Epidemiology Network (HyperGEN) Study, a populat

78 As part of the Hypertension Genetic Epidemiology Network (HyperGEN) study, creatinin

79 ypertensive participants in the Hypertension Genetic Epidemiology Network (HyperGEN) Study.

80 udies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN).

81 cases; n = 356,122 controls) from the Asian Genetic Epidemiology Network and GWAS summary statistics

82 analysis of fasting insulin in Hypertension Genetic Epidemiology Network families.

83 s [Women's Health Initiative (WHI), Maywood, Genetic Epidemiology Network of Arteriopathy (GENOA) and

84 ican-Americans and 10 427 Whites) and in the Genetic Epidemiology Network of Arteriopathy (GENOA) sib

85 mericans and 801 European Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) stu

86 ping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) stu

87 cipated in the Family Blood Pressure Project Genetic Epidemiology Network of Arteriopathy (GENOA) stu

88 ed for hypertension collected as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) stu

89 , Multi-Ethnic Study of Atherosclerosis, and Genetic Epidemiology Network of Arteriopathy studies, we

90 sters who did not using the dataset from the Genetic Epidemiology Network of Arteriopathy study, whic

91 In 822 African Americans from the Genetic Epidemiology Network of Arteriopathy, phase 2, s

92 our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

93 this study of families participating in the Genetic Epidemiology Network of Salt Sensitivity (GenSal

94 offspring and/or parents in the Hypertension Genetic Epidemiology Network study were recruited from f

95 can individuals from the TOPMed Hypertension Genetic Epidemiology Network study.

96 -tracking analysis on HyperGEN (Hypertension Genetic Epidemiology Network) study echocardiograms with

97 cohort of participants from the Hypertension Genetic Epidemiology Network.

98 were collected within an examination of the genetic epidemiology of alcoholism.

99 to substantiate available information on the genetic epidemiology of AMD.

100 cribe the progress that has been made in the genetic epidemiology of AS, and in identifying the genes

101 Milieu, Stockholm, Epidemiology (BAMSE); and Genetic Epidemiology of Asthma in Costa Rica Study (GACR

102 a Management Program study (n = 953) and the Genetic Epidemiology of Asthma in Costa Rica Study (n =

103 enome sequencing data for 'NHLBI TOPMed: The Genetic Epidemiology of Asthma in Costa Rica' is availab

104 le-genome sequencing (WGS) data for the 'The Genetic Epidemiology of Asthma in Costa Rica' study (GAC

105 ism and advance current understanding of the genetic epidemiology of autism spectrum conditions.

106 erein, we report on the current state of the genetic epidemiology of birth defects and comment on fut

107 Recent investigations into the genetic epidemiology of BRCA1 have revealed an unexpecte

108 r Head and Neck Cancer Susceptibility Genes, Genetic Epidemiology of Chronic Lymphocytic Leukemia, Im

109 F15 results were replicated in the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Di

110 hods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Di

111 n this secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Di

112 s: Cross-sectional analyses of the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Di

113 Methods: Participants from COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Di

114 s in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Di

115 d former tobacco cigarette users enrolled in Genetic Epidemiology of Chronic Obstructive Pulmonary Di

116 mics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Di

117 The Genetic Epidemiology of Chronic Obstructive Pulmonary Di

118 Gene/Environment Susceptibility), COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Di

119 omography densitometry data in the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Di

120 We apply the Umediation package to the Genetic Epidemiology of Chronic Obstructive Pulmonary Di

121 SNP analysis was performed in the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Di

122 Data are sparse on the genetic epidemiology of CKD and the clinical association

123 al application of the tools for studying the genetic epidemiology of complex disease.

124 ing standard- and low-dose CT scans from the Genetic Epidemiology of COPD (COPDGene) phase II cohort

125 y disease (COPD) enrolled in the prospective Genetic Epidemiology of COPD (COPDGene) study (ClinicalT

126 cular mediator data were identified from the Genetic Epidemiology of COPD (COPDGene) study cohort.

127 ls and MethodsThis retrospective analysis of Genetic Epidemiology of COPD (COPDGene) study participan

128 gham Heart Study (FHS) and the COPD-enriched Genetic Epidemiology of COPD (COPDGene) study.

129 t study included 2 longitudinal cohorts: the Genetic Epidemiology of COPD (COPDGene), which enrolled

130 of patients with a diagnosis of COPD in the Genetic Epidemiology of COPD cohort.

131 previously published PRS(spiro) to research (Genetic Epidemiology of COPD study and Childhood Asthma

132 s This secondary analysis of the prospective Genetic Epidemiology of COPD study evaluated current or

133 Methods: In COPDGene (Genetic Epidemiology of COPD Study) (N = 10,108), airflo

134 In the Genetic Epidemiology of COPD study, the PRS(Asthma) (OR

135 Methods: In COPDGene (Genetic Epidemiology of COPD) (N = 10,132), the severity

136 res in COPD Study) (n = 1,544) and COPDGene (Genetic Epidemiology of COPD) (n = 602) cohorts had 90 p

137 mokers enrolled in the multicenter COPDGene (Genetic Epidemiology of COPD) cohort.

138 Methods: We randomly split 2,569 COPDGene (Genetic Epidemiology of COPD) participants with whole-bl

139 d in a subset of patients from the COPDGene (Genetic Epidemiology of COPD) study (n = 458).

140 ver 5 years of follow-up among the COPDGene (Genetic Epidemiology of COPD) study participants.

141 quencing data (n = 3,618) from the COPDGene (Genetic Epidemiology of COPD) study were analyzed.

142 ve of mortality in a subset of the COPDGene (Genetic Epidemiology of COPD) study, representing 101 de

143 COPDGene (Genetic Epidemiology of COPD), a multicenter, longitudin

144 in participants with COPD from the COPDGene (Genetic Epidemiology of COPD), ECLIPSE (Evaluation of CO

145 redictive Surrogate Endpoints) and COPDGene (Genetic Epidemiology of COPD).

146 alysis included participants enrolled in the Genetic Epidemiology of COPD, or COPDGene, study at base

147 This study defined the genetic epidemiology of dengue viruses (DENV) in two piv

148 al, Amirian et al. present a report from the Genetic Epidemiology of Glioma International Consortium

149 , 2010-2013), a study being conducted by the Genetic Epidemiology of Glioma International Consortium

150 To address this challenge, the Genetic Epidemiology of Glioma International Consortium

151 asting protection to better characterize the genetic epidemiology of HIV-1.

152 er an overview of recent developments in the genetic epidemiology of knee and hip osteoarthritis, wit

153 The genetic epidemiology of late-onset idiopathic Parkinson'

154 cases and 219 cancer-free controls from the Genetic Epidemiology of Lung Cancer Consortium (GELCC) c

155 of relevant data from primary studies of the genetic epidemiology of major depression.

156 ineurs (n = 1,212) from the population-based Genetic Epidemiology of Migraine study.

157 Arab-Berber) from sites participating in the Genetic Epidemiology of Parkinson's Disease Consortium.

158 l replication (p</=0.05) using data from the Genetic Epidemiology of Responses to Antihypertensives (

159 and November 2005 that have investigated the genetic epidemiology of rheumatoid arthritis.

160 d whole-genome sequencing to investigate the genetic epidemiology of SARS-CoV-2 in Bangladesh, with p

161 ion, and geographical locations to infer the genetic epidemiology of the epidemic in the United Kingd

162 his review, we discuss the current status of genetic epidemiology of the most common neurodegenerativ

163 r data provide mechanistic insights into the genetic epidemiology of venous thromboembolism and sugge

164 sk scores (PRS) have ushered in a new era in genetic epidemiology, offering insights into individual

165 ases, both from the Statistical Analysis for Genetic Epidemiology package (Case Western University, C

166 st in various scientific disciplines such as genetic epidemiology, population and conservation geneti

167 Genetic epidemiology provides overwhelming evidence that

168 Genetic epidemiology represents a hybrid of epidemiologi

169 an independent sample with 4464 East Asians (Genetic Epidemiology Research in Adult Health and Aging

170 An independent dataset from the Genetic Epidemiology Research in Adult Health and Aging

171 y (GWAS) of alcohol consumption in the large Genetic Epidemiology Research in Adult Health and Aging

172 d 18 to 100 years from the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging

173 A meta-analysis combining results from Genetic Epidemiology Research on Adult Health and Aging

174 study (GWAS) of EF in 26 638 adults from the Genetic Epidemiology Research on Adult Health and Aging

175 trait) and POAG risk in individuals from the Genetic Epidemiology Research on Adult Health and Aging

176 4,823) were sourced from the UK Biobank, the Genetic Epidemiology Research on Adult Health and Aging

177 rs in European ancestry individuals from the Genetic Epidemiology Research on Adult Health and Aging

178 cord-based (Mass General Brigham Biobank and Genetic Epidemiology Research on Adult Health and Aging

179 itudinal electronic health records on 99,785 Genetic Epidemiology Research on Adult Health and Aging

180 We applied it to the Genetic Epidemiology Research on Adult Health and Aging

181 ,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging

182 Two large-scale cohorts (GERA [Genetic Epidemiology Research on Adult Health and Aging]

183 ed CoMM to analyze 25 traits in NFBC1966 and Genetic Epidemiology Research on Aging (GERA) studies by

184 258 individuals enrolled in the Resource for Genetic Epidemiology Research on Aging Cohort during 200

185 trait loci in our previous work in ~100 000 Genetic Epidemiology Research on Aging study participant

186 tic instrumental variable analyses using the Genetic Epidemiology Research Study on Adult Health and

187 Genetic epidemiology shows that the apparently protectiv

188 t incremental step for clinical genomics and genetic epidemiology since it is the first haplotype mod

189 iation of Genetic Counsellors, International Genetic Epidemiology Society, and US National Society of

190 commentary, we describe 4 different eras of genetic epidemiology, spanning this evolution from theor

191 Recent large-scale genetic epidemiology studies have identified association

192 Future genetic epidemiology studies in severe malaria would ben

193 ed reporting guidelines for observational or genetic epidemiology studies lack key features specific

194 Recent genetic epidemiology studies of parkinsonism in twins an

195 Recent genetic epidemiology studies of some CVD risk factors ha

196 However, appropriately powered genetic epidemiology studies often require recruitment f

197 increased CRP production, but comprehensive genetic epidemiology studies provide no support for a pa

198 In modern genetic epidemiology studies, the association between th

199 uals) levels from the Japanese Consortium of Genetic Epidemiology studies.

200 counted for, can produce spurious results in genetic epidemiology studies.

201 he scientific value of large consortia-based genetic epidemiology studies.

202 he importance of careful stroke subtyping in genetic epidemiology studies.

203 growth is presented from the perspective of genetic epidemiology studies.

204 ordinated studies of human genome scans; (8) genetic epidemiology studies; (9) activities to foster k

205 2 diabetes, from the Veterans Administration Genetic Epidemiology Study (VAGES).

206 Evaluation of the risk of the same genetic epidemiology study by 31 IRBs ranged from minima

207 Review of a protocol for a multicenter genetic epidemiology study by local IRBs was highly vari

208 In a genetic epidemiology study conducted in July 2015, we sy

209 This genetic epidemiology study examined the sex-specific ass

210 he Multi-Institutional Research in Alzheimer Genetic Epidemiology study in order to derive models of

211 Study, an ongoing multi-center family-based genetic epidemiology study of AD.

212 Genetic epidemiology study replication and functional as

213 eparate group of 8951 patients from the COPD Genetic Epidemiology study.

214 onwealth University Experimental Research on Genetic Epidemiology) study collected data on 5278 patie

215 s and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of pati

216 (Multi Institutional Research in Alzheimer's Genetic Epidemiology) Study, and from 168 AD cases, 336

217 Genetic epidemiology suggested that alcohol consumption

218 he UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are

219 SD, MDD, and the anxiety disorders including genetic epidemiology, the role of common genetic variati

220 d other advances will allow the potential of genetic epidemiology to be revealed over the next few ye

221 volution of genetic association studies from genetic epidemiology to contemporary large-scale genome-

222 netic diversity and exploit the potential of genetic epidemiology to identify important variants, mul

223 Here, we use casual inference approaches in genetic epidemiology to investigate whether adult, tissu

224 Multidisciplinary approaches integrating genetic epidemiology to systems biology will be required

225 lished in 2002 describing the basic tools of genetic epidemiology used to study nonsyndromic structur

226 Rodriguez et al. have applied genetic epidemiology using predetermined phenotype data

227 approaches involving murine models and human genetic epidemiology, we show here the importance of the

228 This work brings together genetic epidemiology with scRNA-seq to uncover drivers o

229 As part of a Human Genetic Epidemiology workshop convened by the Centers fo