ライフサイエンスコーパス: complex trait

1 Alcohol consumption is a heritable complex trait.

2 ights about the genetic architecture of this complex trait.

3 tition may be important contributors to this complex trait.

4 erational establishment and inheritance of a complex trait.

5 each set controls a biological process or a complex trait.

6 plications to summary-level GWASs data of 33 complex traits.

7 to associations between genetic variants and complex traits.

8 ic architecture of prostate cancer and other complex traits.

9 n (across 48 different tissue types) and 395 complex traits.

10 g aims to identify causal variants impacting complex traits.

11 ene expression and genetic data for studying complex traits.

12 ants with identifiable functional effects on complex traits.

13 Ss) to prioritize candidate target genes for complex traits.

14 t for a significant fraction of variation in complex traits.

15 as a valuable resource for future studies of complex traits.

16 tically identify cell types underlying brain complex traits.

17 oach to studying the genetic architecture of complex traits.

18 adout and the application to the genetics of complex traits.

19 ine for the first time their contribution to complex traits.

20 ence of gene expression on susceptibility to complex traits.

21 ed insights into the genetic architecture of complex traits.

22 ustrate the detailed genetic architecture of complex traits.

23 anced the discovery of genetic risk loci for complex traits.

24 of the transcript-level colocalisations with complex traits.

25 analysing the joint genetic architecture of complex traits.

26 standing key features of the architecture of complex traits.

27 d mechanisms that lead to sex differences in complex traits.

28 tools to dissect the genetic architecture of complex traits.

29 ying the impact of rare genetic variation on complex traits.

30 the genetic overlap with other brain-related complex traits.

31 nt (G x E) interaction is important for many complex traits.

32 scores are a popular tool for prediction of complex traits.

33 s to study associations between variants and complex traits.

34 e mechanistic role of associated variants in complex traits.

35 ty is a fundamental quantity in the study of complex traits.

36 e and are associated with many brain-related complex traits.

37 the tiny effect sizes typical of genetically complex traits.

38 ifying many genetic variants associated with complex traits.

39 stimate alpha for 25 UK Biobank diseases and complex traits.

40 nt regulators and potential consequences for complex traits.

41 same rigorous standards as studies of other complex traits.

42 e identification of variants associated with complex traits.

43 ive trait nucleotides (QTNs) associated with complex traits.

44 enetic variation involved in the etiology of complex traits.

45 sights into the shared genetic basis of many complex traits.

46 ess and can facilitate the evolution of more complex traits.

47 dentifying genetic variants underlying human complex traits.

48 functionally validated as being relevant for complex traits.

49 nding the shared etiology among diseases and complex traits.

50 t MPB is less genetically complex than other complex traits.

51 for fine mapping and association mapping of complex traits.

52 ve TF sets governing biological processes or complex traits.

53 ommon variants on cell types contributing to complex traits.

54 eals the underlying connections across human complex traits.

55 ed with the onset and progression of several complex traits.

56 r characterizing the genetic architecture of complex traits.

57 mportant in unravelling the genetic basis of complex traits.

58 uired to dissect the genetic architecture of complex traits.

59 identifying specific gene regions affecting complex traits.

60 entangled, or whether they have an impact on complex traits.

61 e-specific role of candidate target genes in complex traits.

62 entify the causal features and mechanisms of complex traits.

63 itional insights into the genetic control of complex traits.

64 pportunity to identify genetic components of complex traits.

65 Ex (v8) and GWAS summary statistics from 114 complex traits.

66 lters gene expression and shapes genetically complex traits.

67 imilar patterns to the heritability of other complex traits.

68 tifying causal genes at loci associated with complex traits.

69 ions among 601 DNAm sites associated with 15 complex traits.

70 ic framework to investigate the evolution of complex traits.

71 de polygenic risk score (PRS) analyses on 67 complex traits.

72 chnique has potential to be applied to other complex traits.

73 for investigating evolutionary pressures on complex traits.

74 issues are more likely to influence multiple complex traits.

75 in a joint analysis of multiple diseases and complex traits.

76 standing shared genetic architecture between complex traits.

77 ap quantitative trait loci (QTLs) underlying complex traits.

78 ry, RCCs enhance discovery-based genetics of complex traits.

79 latent variables can be used for predicting complex traits.

80 9 human couples of European ancestry for 105 complex traits.

81 l relationships between gene expressions and complex traits.

82 r understanding of the environment linked to complex traits.

83 provide an alternative mechanism underlying complex traits.

84 lly revealed biological insights for several complex traits.

85 ical power to identify genes associated with complex traits, a number of transcriptome-wide associati

86 onius butterflies is a classic case study of complex trait adaptation via selection on a few large ef

87 Fracture is a complex trait, affected by both genetic and environmenta

88 Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pre

89 ount for both interaction and correlation in complex trait analyses.

90 Using the genome-wide complex trait analysis method, we estimated the IHPS SNP

91 To test this, we conduct a genome-wide complex trait analysis of individual-level turnout.

92 WUE(plant) is a complex trait and an efficient phenotyping method that r

93 ential SNPs are correlated with a particular complex trait and are important to the prediction of the

94 However, it is also a complex trait and uncovering the molecular genetic mecha

95 ic correlation between gene expression and a complex trait and utilize it to estimate the genetic cor

96 supergenes - clusters of genes that control complex traits and are inherited together.

97 the utility of S. viridis for dissection of complex traits and biotechnological improvement of panic

98 Complex traits and common diseases are extremely polygen

99 ing on genomic regions associated with human complex traits and contextualize the relationship betwee

100 ct genetic variants that contribute to human complex traits and disease are typically identified usin

101 Nearly all human complex traits and disease phenotypes exhibit some degre

102 how genetic variants exert their effects on complex traits and disease.

103 lation may explain some of the components of complex traits and disease.

104 s implications for the study of higher-order complex traits and disease.

105 We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank

106 ork modules were tested for association with complex traits and diseases using a unique collection of

107 er to comprehend the genetic architecture of complex traits and diseases, an additional challenge is

108 For complex traits and diseases, assessing the risk due to g

109 ave identified loci that are associated with complex traits and diseases, but index variants are ofte

110 ssociated thousands of genetic variants with complex traits and diseases, but pinpointing the causal

111 rogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged,

112 4 of which harbored robust associations with complex traits and diseases.

113 (PRS) have shown promise in predicting human complex traits and diseases.

114 ally regulated expression is associated with complex traits and diseases.

115 e deleterious variants, potentially underlie complex traits and diseases.

116 will shed light on molecular fundamentals of complex traits and diseases.

117 housands of genetic variants associated with complex traits and diseases.

118 of gene expression in the susceptibility of complex traits and diseases.

119 phenotypic consequences of such variation on complex traits and diseases.

120 a to gain insights into the genetic basis of complex traits and diseases.

121 tain genetic variants that increase risk for complex traits and diseases.

122 assess potential pleiotropic effects on 700 complex traits and diseases.

123 nge of genetic variants related to a host of complex traits and disorders.

124 ession, to identify variants associated with complex traits and DNA methylation.

125 Our DC estimates for complex traits and gene expression are consistent with a

126 ariants pleiotropically associated with both complex traits and gene expression, to identify variants

127 pe data to discover genetic contributions to complex traits and infer relationships between those tra

128 association studies (GWAS) allow to dissect complex traits and map genetic variants, which often exp

129 correlated with genetic associations between complex traits and regional measures of SES, health and

130 tal crosses enable the genetic dissection of complex traits and support modern plant breeding.

131 Our analysis of 4,091 complex traits and the multi-tissue expression quantitat

132 netic architecture of gene expression and of complex traits and the suitability of Mendelian randomiz

133 y to better characterize the heritability of complex traits and to more accurately map genetic associ

134 ntify novel genetic variants associated with complex traits and to shed new insights on underlying bi

135 al to unraveling the genetic architecture of complex traits and to understanding the mechanisms of di

136 ed our understanding of the genetic basis of complex traits and transformed breeding practices.

137 stantially contributes to the variability of complex traits and unmasks additional genetic susceptibi

138 insights into the genetic correlation among complex traits and will facilitate future soybean functi

139 racterization of functional genes underlying complex traits, and the sequencing and assembly of the f

140 ctional follow-up and provides insights into complex trait architectures.

141 However, little is known about how these complex traits are assembled and diversified.

142 n and for which molecular endophenotypes and complex traits are assessed on the same genotypes.

143 As the majority of mutations associated with complex traits are located outside the exome, it is cruc

144 rmal model in which genetic contributions to complex traits are partitioned into direct effects from

145 Common and complex traits are the consequence of the interaction an

146 n genome-wide association studies (GWASs) of complex traits are thought to act by affecting gene regu

147 Complex traits arise from the interplay between genetic

148 In plants, water use efficiency (WUE) is a complex trait arising from numerous physiological and de

149 y deleterious alleles with a large effect on complex traits as such alleles are mostly rare.

150 sed in many different cell types to identify complex trait associated regulatory elements.

151 capture low-frequency and rare variation in complex trait association studies.

152 colocalizations of molecular QTLs and causal complex trait associations are widespread.

153 derappreciated molecular mechanism mediating complex trait associations in a context-specific manner.

154 lti-ethnic populations to drive discovery of complex trait associations of large effect and to identi

155 Resistance to enteric pathogens is a complex trait at the crossroads of multiple biological p

156 ypothesis is that genetic variants influence complex traits at the organismal level via affecting cel

157 statistics from 41 independent diseases and complex traits (average N = 320K) and meta-analyzing res

158 enicity scores across 41 common diseases and complex traits (average N = 320K).

159 ified LD score regression to 42 diseases and complex traits (average N = 323K) to identify enriched a

160 Analyzing 33 complex traits (average N = 361k), we determined that he

161 on under a classical infinitesimal model for complex traits because of large shifts in allele frequen

162 as proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and

163 ta to perform accurate genetic prediction of complex traits can facilitate genomic selection in anima

164 ts into GWAS transferability, we developed a complex trait coalescent-based simulation framework cons

165 sociation studies (GWAS) for 54 diseases and complex traits coupled with multiomic profiling and foun

166 he contribution of low-frequency variants in complex traits, demonstrate the advantage of including p

167 associated with GWAS variants for a range of complex traits, demonstrating the utility of this approa

168 Many neuropsychiatric illnesses are complex trait disorders with etiologic factors convergin

169 iploid lines provided a resource that allows complex trait dissection within this grass model species

170 ation for investigations into the biology of complex traits, drug development and clinical guidelines

171 precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to ma

172 ics are producing powerful DNA predictors of complex traits, especially cognitive abilities.

173 py plays a central role in debates about how complex traits evolve and whether biological systems are

174 pical to temperate biomes sheds light on how complex traits evolve in the light of climate changes.

175 search into the genomic basis of disease and complex traits exemplifies the importance of statistical

176 e increasing proportion of variance in human complex traits explained by polygenic scores, along with

177 een genetic variants in the human genome and complex traits for more than a decade.

178 ially in non-model species and when studying complex traits for which little prior genetic and bioche

179 ctories were genetically correlated with 209 complex traits, for 33 of which smoking was either a cau

180 e that common, polygenic factors of relevant complex traits frequently contribute to variable express

181 low of biological information that underlies complex traits from genotype to phenotype.

182 perspective and highlight the usefulness of complex trait genetic studies in isolated populations.

183 Rodent complex trait genetic studies involving a cross between

184 s analysis has emerged as a powerful tool in complex trait genetics, existing methods for fitting var

185 ith larger effect sizes, become the focus of complex trait genetics, more diverse rural cohorts may b

186 In complex trait genetics, the ability to predict phenotype

187 overies it has facilitated in population and complex-trait genetics, the biology of diseases, and tra

188 Because most complex traits have a polygenic architecture, accurate g

189 dely reported to be enriched for disease and complex trait heritability.

190 lial), and cell type-specific enrichments of complex trait heritability.

191 oil content (SOC) is a highly important and complex trait in oil crops.

192 genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,6

193 ory and evolutionary signals contributing to complex traits in a different mammalian model is needed.

194 cally examine the role of DC in humans in 20 complex traits in a sample of more than 450,000 individu

195 ng of common genetic variants that influence complex traits in a sample of ~450,000 individuals from

196 ed yield insights into genes responsible for complex traits in all populations.

197 Genetic studies of complex traits in animals have been hindered by the need

198 -effective, and will accelerate the study of complex traits in animals.

199 The importance of complex traits in biology and medicine has motivated div

200 genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas ca

201 s populations and environments for different complex traits in coffee.

202 icting the number of alleles associated with complex traits in each locus.

203 allele sharing may be useful for studies of complex traits in founder populations, where hidden rela

204 (STRs) have been implicated in a variety of complex traits in humans.

205 We analyze nine complex traits in individuals of East Asian and European

206 profiles such as DNA methylation (DNAm) and complex traits in large cohorts.

207 ll prove useful in future genetic studies of complex traits in large population cohorts.

208 ecome an important approach for the study of complex traits in large populations.

209 el variance of genetic values of gametes for complex traits in large populations.

210 ever, many sequence variants associated with complex traits in maize have small effects and low repea

211 n and influenced the genetic architecture of complex traits in Mexican Americans.

212 s considering QTLs and genes associated with complex traits in natural populations.

213 It is central to the evolution of many complex traits in nature, including growth and virulence

214 Our work contributes to the study of complex traits in nonmodel plant species by identifying

215 n used to estimate the heritability of human complex traits in recent years.

216 mer disease, may potentially influence other complex traits in the opposite direction.

217 provide estimates of SNP-heritability for 22 complex traits in the UK Biobank and show that, consiste

218 ns and partially overlapping with a range of complex traits including smoking, education, personality

219 ys a central role in phenotypic variation in complex traits including the risk of developing disease.

220 have recently been implicated in a range of complex traits, including gene expression and cancer ris

221 tudy signals and implicate specific eSTRs in complex traits, including height, schizophrenia, inflamm

222 that are associated with gene expression and complex traits, including their locations relative to eG

223 associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obes

224 een LeafCutter intron quantifications and 40 complex traits increased the number of associated diseas

225 Growth is a complex trait influenced by multiple genes that act at d

226 ochastic diversification models that include complex trait interactions alongside hidden states enhan

227 Our model enables the tests of how a complex trait is affected differently by DNA-based effec

228 modern biology, and tracing the evolution of complex traits is an open problem.

229 ting the genetic and genomic architecture of complex traits is essential to understand the forces mai

230 ever, the contribution of non-coding RNAs to complex traits is not clear.

231 This shows that the environment linked to complex traits is partially explained by the genotype of

232 the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related m

233 particularly challenging compared with other complex traits is the difficulty of accessing the releva

234 a large amount of phenotypic variation in a complex trait like growth.

235 However, understanding how complex traits like cold acclimation evolve remains a ma

236 Complex traits like limbs, brains, or eyes form through

237 rse populations can increase power to detect complex trait loci when the underlying causal variants a

238 ve analysis of the genomic architecture of a complex trait locus is a powerful tool for identificatio

239 In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA

240 o the surprising discovery that, for typical complex traits, most of the heritability is due to huge

241 with larger and larger GWAS on more and more complex traits, most of the significant associations had

242 As a complex trait, NTSR is driven by complex evolutionary pr

243 PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the U

244 ecific variation in floral scent, which is a complex trait of documented importance for mutualistic a

245 erpret the underlying mechanism regulating a complex trait of interest in each discovered locus, rese

246 ships between the collected datasets and the complex trait of interest would be through the analysis

247 catabolic pathways that are relevant to the complex traits of interest.

248 in evolutionary biology is to understand how complex traits of multiple functions have diversified an

249 ions between genetic variations and multiple complex traits or diagnoses.

250 ct associations between genetic variants and complex traits or diseases by comparing populations of c

251 tion and thus of the genetic architecture of complex traits or diseases.

252 ome-wide association study (GWAS) signals of complex traits or diseases.

253 to genome-wide association study signals of complex traits or diseases.

254 Understanding how novel complex traits originate is a foundational challenge in

255 Although genetic correlations between complex traits provide valuable insights into epidemiolo

256 to elucidate the genetic factors influencing complex traits related to health and disease among minor

257 However, we hypothesize that for most complex traits, relatively few genes and loci are critic

258 mechanistic links between these variants and complex traits remain elusive.

259 covery of the genetic elements that regulate complex traits remains a challenge.

260 nks that underlie how genetic variants cause complex traits remains elusive.

261 ics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis.

262 s imply that studies of the genetic basis of complex traits should be expanded to include mechanisms

263 Analyses of genetic associations with 87 complex traits show a contribution from cell type-intera

264 e, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect

265 e individual component traits that make up a complex trait such as a flower evolve in a coordinated f

266 rease the genetic gain in wheat breeding for complex traits such as grain and biomass yield.

267 gical traits such as water-use efficiency or complex traits such as leaf morphology, for which we wer

268 Genome-wide association studies (GWAS) of complex traits, such as alcohol use disorders (AUD), usu

269 questions about the genetic architecture of complex traits, such as allele frequency and effect size

270 rmatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma

271 C(4) photosynthesis is a complex trait that boosts productivity in warm environme

272 WUE is a physiologically and genetically complex trait that can be defined at a range of scales.

273 We build a polygenic model for complex traits that distinguishes candidate trait-releva

274 Our results suggest that for most complex traits, the genes and loci with the most critica

275 understanding of the genetic architecture of complex traits, the mechanistic links that underlie how

276 Like most complex traits, the microbiome is under genetic regulati

277 elations reported in the literature for many complex traits, the non-transferability of polygenic ris

278 of genetic resources and apply increasingly complex traits to crop improvement.

279 nalyze data from 41 independent diseases and complex traits to draw three conclusions.

280 (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments.

281 titioned the heritability of 49 diseases and complex traits using stratified linkage disequilibrium (

282 ty to predict differences in disease risk or complex trait values between siblings is a strong test o

283 ge analysis is a useful strategy to identify complex trait variants.

284 al factors associated with human disease and complex trait variation that could help to expand our un

285 d provides insights into the role of PAIs in complex trait variation.

286 y-based whole-genome sequence (WGS) data for complex traits, we developed a rare variant (RV) non-par

287 on of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrat

288 Applying T-GEN to 207 complex traits, we were able to identify more trait-asso

289 riginally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametri

290 understanding of the genetic architecture of complex traits, which enables more efficient analysis of

291 However, behaviours are complex traits, which have been shown to be influenced b

292 Persistent high BP, or hypertension, is a complex trait with both genetic and environmental intera

293 a is characterized by low skeletal muscle, a complex trait with high heritability.

294 scovery of fundamental genetic components of complex traits with a limited number of samples.

295 ied unique biologically informed periodontal complex traits with distinct microbial communities and i

296 association of the genetic susceptibility to complex traits with human lifespan in collaboration with

297 Among 50 complex traits with publicly accessible GWAS summary sta

298 This method is generalizable to all complex traits with relevant annotation data and is made

299 and fetal brain) to prioritize genes for >40 complex traits with robust GWAS data and found considera

300 ary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygen