ライフサイエンスコーパス: QTL

1 QTL analysis identified 11 loci for CT distributed on ei

2 QTL associated with summer-dormancy related traits in ta

3 QTL from these four datasets identified a region in chro

4 QTL mapping identified two additive and three epistatic

5 QTL regions contained several genes with known or predic

6 QTL were identified for depth of seed dormancy and seedl

7 of soybean germplasms harboring the qSw17-1 QTL for the big-seeded phenotype indicated that reduced

8 ty and/or transcript abundance, including 10 QTL hotspots where genetic variation at a single locus c

9 np(2) QTL also provides a tool for constructing a predictive m

10 Based on LVODE parameters, np(2) QTL constructs a bidirectional, signed and weighted netw

11 Furthermore, 21 QTL for six investigated phenotypic traits were detected

12 Among them, 273 QTLs were delimited to <=1.0-Mb intervals and 7 of them

13 GWA with the whole panel identified 29 QTL for height and disease resistance with allelic varia

14 Identified SNPs were close to 31 QTLs for milk yield and its components, body weight, and

15 More than 40 QTLs associated with 14 stress-related, quality and agro

16 Haplotype analysis using SNPs within the 5A2 QTL applied to the GTWC identified novel haplotypes and

17 ggesting the expression stability of the 5A2 QTL in various genetic backgrounds.

18 The 5A2 QTL was confirmed in the derivatives, suggesting the exp

19 e phenotypic difference between parents in a QTL mapping cross, a scenario that is common in crop gen

20 ns showed significant evidence of linkage; a QTL on chromosome 1p influencing corpus callosum volume

21 Here, we present the results of a QTL mapping study for the most important behavioral isol

22 the expression of RKS1, a gene underlying a QTL conferring quantitative and broad-spectrum resistanc

23 entification of candidate genes underlying a QTL, solidifying the foundation for large-scale QTL fine

24 We found that m(6)A QTLs are largely independent of expression and splicing

25 wide association studies, we show that m(6)A QTLs contribute to the heritability of various immune an

26 By leveraging m(6)A QTLs in a transcriptome-wide association study framework

27 ne to characterize additive and non-additive QTL in whole genome sequence data, which complements cur

28 genetic basis (few, predominantly additive, QTLs of moderate to large effect), as well as little evi

29 Lines carrying AG1 + AG2 QTLs showed higher alpha-amylase activity, leading to ra

30 Introgression of AG2 and AG1 + AG2 QTLs with seed pretreatment showed 101-153% higher emerg

31 Introgression of AG1 and AG2 QTLs associated with tolerance of flooding during germin

32 All QTLs showed an effect when survival across all screening

33 istic pleiotropy (few trait correlations and QTL colocalization, particularly between traits of diffe

34 Therefore, this bin-map and QTL associated with TSWV resistance made it possible for

35 ree overlap in both individual trait QTL and QTL for principal component scores (PCA QTL), may have b

36 to search for positional candidate genes and QTLs within strong LD genomic regions around the signifi

37 Genetic approaches such as QTL mapping have been successfully used to identify the

38 al QTLs by association, allele-specific (AS) QTLs are a powerful measure of cis-regulation that are c

39 omoeologous recombination and map associated QTLs resulting from deviations in normal pairing in allo

40 n qualitative trait, we have identified both QTL and genes of small effect.

41 thin five causal variants, compared to 2% by QTL-based fine mapping, and a 6.9-fold overall reduction

42 phological traits tended to be controlled by QTL of larger effect.

43 and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of h

44 d intercross with a combination of classical QTL mapping of red colouration as a quantitative trait a

45 Notably, comparative QTL mapping revealed little evidence for shared underlyi

46 Consequently, QTLs driven by dominance and other non-additive effects

47 ogrammes in rice and other crops considering QTLs and genes associated with complex traits in natural

48 experiment analysis to identify constitutive QTLs and candidate genes associated with the grain Mn co

49 reater power at 50 samples than conventional QTL-based fine mapping at 500 samples, with more than 17

50 g smaller introgression in the corresponding QTL interval or by analysis of lines from an independent

51 Here, we couple QTL for divergence in visual preference behaviours with

52 Using DO QTL analysis, we show that MHC-IB reactive NK cells exer

53 t where ADDO identified significant dominant QTL that were not detectable by an additive model.

54 All the putative summer dormant QTL regions in male map showed pleiotropic responses and

55 t environments, evaluated the effect of each QTL on SOC, and analyzed selection in QTL regions during

56 We refined each QTL by combining information inferred from the ancestry

57 The genome location of the large-effect QTL on A02 is rich in genes encoding TIR-NBS-LRR protein

58 idate gene located inside the largest effect QTL and of two other ribosomal genes RPL5A and RPL5B est

59 However, as we divided the largest-effect QTL using stable introgression strains, we found evidenc

60 several variable QTLs, but two large-effect QTLs which we have named Prr1 and Prr2 were consistently

61 gant analysis (BSA) to identify large-effect QTLs.

62 y contrast, there were numerous small-effect QTLs at southern sites, indicating a genotype-by-environ

63 detection of closely linked and small-effect QTLs.

64 The study enables QTL mapping analysis to be conducted in autotetraploid s

65 Significantly, epistatic QTLs were detected in all datasets.

66 d show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specific

67 of which are associated with mRNA expression QTLs.

68 are enriched in enhancers or near expression QTLs.

69 splicing QTLs and roughly half of expression QTLs.

70 licing to identify novel platelet expression QTLs and splice QTLs.

71 ncluding known and novel platelet expression QTLs.

72 esting a mechanism for long-range expression QTLs.

73 Few QTLs for stigma position have been described and only on

74 The present study reports the first QTLs associated with Phytophthora crown rot resistance i

75 s revealed that beneficial biomass (fitness) QTL generally incur minimal costs when transplanted to o

76 present a novel likelihood-based method for QTL mapping in outbred segregating populations of autote

77 d statistical properties and is powerful for QTL detection.

78 e demonstrate that the nNILs can be used for QTL mapping and allelic testing.

79 At the 31K SNPs level, we detected four QTL on three chromosomes (Omy1, Omy12 and Omy20).

80 nimum sample size required to detect a given QTL with a certain statistical power or calculate the st

81 e 5A: 5A1 was co-located with a plant height QTL, and 5A2 with a major maturity QTL.

82 The identified QTL regions suggest candidate meiotic genes that could b

83 of seed weight on root traits and identified QTLs that control seed weight, root architecture, shoot

84 ated that the phenotypes of these identified QTLs were highly predictable.

85 ith previous mapping populations to identify QTL for lodging resistance.

86 use of a high-density SNP array to identify QTL which were integrated with whole genome sequence sig

87 f each QTL on SOC, and analyzed selection in QTL regions during breeding.

88 s that have high identity with rice genes in QTLs affecting similar traits.

89 ape of genetic effects exerted by individual QTLs, as well as their interactions with N-induced signa

90 To improve informative QTL in soybean, a mapping population from a cross betwee

91 ping), a fine-mapping method that integrates QTL and asQTL information to improve accuracy.

92 ow a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously

93 d genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-intera

94 These results show that introgressed QTL can greatly improve broad-spectrum disease resistanc

95 , and Vrn-D1) that have been cloned or known QTLs (TaGA2ox8, APO1, TaSus1-7B, and Rht12) that were pr

96 WUE(plant) for each of the delta(13) C(leaf) QTL allele classes was negatively correlated with delta(

97 y pleiotropic QTL or multiple tightly linked QTL.

98 Thus, almost all the small-effect and linked QTLs are included in a multi-locus model.

99 In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold.

100 Here we use quantitative trait loci (QTL) analyses of crosses between H. melpomene races from

101 m exhumed seeds and quantitative trait loci (QTL) analyses on a mapping population from crossing the

102 Quantitative trait loci (QTL) analysis is an important approach to investigate th

103 , and a total of 49 quantitative trait loci (QTL) and 24 pairs of epistatic interactions related to y

104 ication of valuable quantitative trait loci (QTL) and candidate genes responsible for the concentrati

105 Using a quantitative trait loci (QTL) approach, we investigated the genetic basis of plas

106 Quantitative trait loci (QTL) conferring resistance to multiple fungal pathogens

107 Three quantitative trait loci (QTL) for delta(13) C(leaf) were found and co-localized w

108 of observations of quantitative trait loci (QTL) for lodging resistance have been reported by indepe

109 nsortium (BCAC) and quantitative trait loci (QTL) from the Genotype-Tissue Expression (GTEx) project

110 ausative genes from quantitative trait loci (QTL) is challenging for complex agronomically important

111 two FHB resistance quantitative trait loci (QTL) on chromosome 5A: 5A1 was co-located with a plant h

112 tudies can identify quantitative trait loci (QTL) putatively underlying traits of interest, and neste

113 icantly enriched in quantitative trait loci (QTL) related to yield traits, such as spikelet number an

114 we identified five quantitative trait loci (QTL) significantly associated with six traits.

115 To identify quantitative trait loci (QTL) that contribute to phenotypic variance of brain str

116 Three quantitative trait loci (QTL) were identified that contributed to the control of

117 ently controlled by Quantitative Trait Loci (QTL), and often involves differential regulation of Defe

118 oth qualitative and quantitative trait loci (QTL), and this technique is referred to as BSA-Seq here.

119 etic susceptibility quantitative trait loci (QTL).

120 dies on oil related quantitative trait loci (QTL).

121 is study was to map quantitative trait loci (QTLs) associated with CT using thermal infrared imaging

122 udy was to identify quantitative trait loci (QTLs) associated with resistance to Phytophthora crown r

123 We identify quantitative trait loci (QTLs) associated with the level and directionality of eR

124 Several quantitative trait loci (QTLs) associated with the trait have been identified in

125 ework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait varia

126 me-wide significant quantitative trait loci (QTLs) associated with weight and length were detected on

127 ng and detected 395 quantitative trait loci (QTLs) for 12 traits under 7 environments.

128 tified thousands of quantitative trait loci (QTLs) for 38 traits.

129 results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, an

130 respectively mapped quantitative trait loci (QTLs) for r and K in each environment.

131 Mapping quantitative trait loci (QTLs) is now a routine practice in diploid species but i

132 an characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex trai

133 tudy, we mapped the quantitative trait loci (QTLs) of m(6)A peaks in 60 Yoruba (YRI) lymphoblastoid c

134 e found 30 distinct quantitative trait loci (QTLs) that control chronological life span (CLS) in calo

135 We mapped reliable quantitative trait loci (QTLs) that control SOC in eight environments, evaluated

136 ing to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass a

137 ve been used to map quantitative trait loci (QTLs) underlying complex traits.

138 Two major quantitative trait loci (QTLs), pc1 and pc10 that affect chlorophyll content in t

139 ocusing on a set of quantitative trait loci (QTLs), we provide evidence supporting that distinct phas

140 d recognition of 17 quantitative trait loci (QTLs).

141 y genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy.

142 genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs).

143 out a genome-wide quantitative trait locus (QTL) analysis of preference behaviors between these spec

144 pin and conducting quantitative trait locus (QTL) analysis of yield under well-watered (WW) and water

145 Using DO quantitative trait locus (QTL) analysis, we mapped the QTL that influences both to

146 Although quantitative trait locus (QTL) associations have been identified for many molecula

147 the most important quantitative trait locus (QTL) for FHB resistance.

148 We conducted quantitative trait locus (QTL) mapping across 10 sites, ranging from Texas to Sout

149 a unified model of quantitative trait locus (QTL) mapping based on an open-pollinated design composed

150 3 yr and conducted quantitative trait locus (QTL) mapping for rust progression.

151 Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites

152 Here, via quantitative trait locus (QTL) mapping, we uncover the genetic basis underlying di

153 and resolution of Quantitative Trait Locus (QTL) mapping.

154 round identified a quantitative trait locus (QTL) on chromosome 7, which had a synergistic effect on

155 for each putative quantitative trait locus (QTL) were separately scanned so that a negative logarith

156 a high-confidence Quantitative Trait Locus (QTL) with a single variant of MMS21 associated with incr

157 and domestication quantitative trait locus (QTL).

158 nvironment and across both sites, three main QTL hotspots were found in chromosomal bins 2.02, 2.05-2

159 A major QTL detected by the static model constantly affects the

160 A major QTL for fruit firmness, named qP-FF4.1, that had not pre

161 ified Vitreous endosperm 1 (Ven1) as a major QTL influencing this process.

162 e were previously unidentified while a major QTL on chromosome 5 in the BTx623/BTx642 RIL population

163 In contrast, we discovered no major QTL for plasticity itself, including the Adh locus, rega

164 One major QTL on BnaA9 contributed between 32 and 58% of the obser

165 Genomic analyses show that major QTL control these traits, and they differ between breed.

166 Based on the 5.2 M SNP dataset, major QTLs were located on chromosomes 3 and 7 for Mn containi

167 We tracked the two major QTLs to the cell wall glycoprotein genes FLO11 and HPF1

168 iation with bipolar disorder (n = 24) to map QTL influencing regional measures of brain volume and co

169 To characterize Mo2s, we map QTLs to chromosomes 1, 6, 7, and 9 using an F(2) populat

170 the proximal region of the previously mapped QTL, ranging from 47.4 to 64.4 megabases (Mb) on chromos

171 me, transcriptome, and microbiome, we mapped QTL and correlated the abundance of cecal messenger RNA,

172 nt height QTL, and 5A2 with a major maturity QTL.

173 ecessary to detect a biologically meaningful QTL, say explaining 5% of the phenotypic variance.

174 ignificant cis-eQTL and metabolomic-QTL (met-QTL), 92% demonstrated colocalization between these sign

175 sing a "truth" set of causal genes at 61 met-QTLs, the sensitivity was high (67%), but the positive p

176 estricted to colocalized associations at met-QTLs) involved true causal genes.

177 reported and extensively curated metabolite QTLs.

178 l was a significant cis-eQTL and metabolomic-QTL (met-QTL), 92% demonstrated colocalization between t

179 SNP p-value-based tests for detecting minor QTL (heritability of 5-10%) and is competitive with rega

180 oci, we found additional but different minor QTLs in the 0 and 14% alcohol environments.

181 On average, miR-QTLs explain approximately 60% of population differences

182 control of miRNA expression variability (miR-QTLs) and the lower occurrence of gene-environment inter

183 dentified across experiments, whereas new Mn QTLs were identified that were not found in individual e

184 ially expressed genes that coincide with Mo2 QTLs, suggesting a potential role in vitreous endosperm

185 and versatile tool for annotating molecular QTLs.

186 ntinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reli

187 identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their

188 ) suggests that colocalizations of molecular QTLs and causal complex trait associations are widesprea

189 liding windows allowed the detection of more QTL than traditional single-SNP GWAA.

190 by domestication and improvement, with more QTLs selected for during improvement.

191 dditive effects, on the assumption that most QTL act additively.

192 at flower traits were controlled by multiple QTL of small effect, while leaf physiological and morpho

193 resent the most thorough mapping of multiple QTL types in a highly disease-relevant primary cultured

194 No QTL effects were observed under aerobic conditions.

195 ical power of QTL detection, and accuracy of QTL location, as demonstrated by an intensive simulation

196 rofiling extinction-driven BLA expression of QTL-linked genes, we nominated Ppid (peptidylprolyl isom

197 Hundreds of QTL for 23 agronomic traits are uncovered with 14 millio

198 Introgression of QTL from disease-resistant lines strongly shifted the re

199 idirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the

200 Positions of QTL detected on chromosome 3 matched those for survival

201 perimental data, in the statistical power of QTL detection, and accuracy of QTL location, as demonstr

202 is shown to interfere with the assignment of QTLs.

203 The identification of QTLs and causal genes for anaerobic germination will fac

204 s often complicated by incomplete overlap of QTLs in multi-SNP models; and (D) using a "truth" set of

205 gh-resolution analyses of these two types of QTLs reveal distinct positions of enrichment at the cent

206 mmary statistics with multiple sets of omics QTL summary statistics from different cellular condition

207 One QTL associated with variation in the flg22 response was

208 One QTL had the largest effect of 36.51% to the phenotypic v

209 c model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf a

210 Our QTL results initially suggested that differences in male

211 interval 10 Mbp shorter than the bi-parental QTL mapping interval.

212 and QTL for principal component scores (PCA QTL), may have been critical for evolutionary divergence

213 vironments could be explained by pleiotropic QTL or multiple tightly linked QTL.

214 Two previous QTLs on chromosome 3 were identified across experiments,

215 Previously, QTLs controlling nematode resistance were identified on

216 The principle QTL determining SET (SET1: dormancy cycling) is physical

217 e QTL, five summer dormancy related putative QTL were identified in R43-64 linkage groups (LGs) 4, 5,

218 s, cumulatively detected 88% of the putative QTL.

219 detected a higher proportion of the putative QTL.

220 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using t

221 ecurrent selection for domestication-related QTL and associated genomic regions, spontaneous interspe

222 In contrast, fruit taste-related QTLs were successively selected for by domestication and

223 per knowledge of valuable lodging resistance QTL in soybean, and these QTL could be used to increase

224 Analysis of a major resistance QTL in RIL LA3952 on chromosome 8 revealed that the pres

225 o discovered a diversity of minor resistance QTL, not detected using p-value-based tests, some of whi

226 square mean trait data, four TLS resistance QTL were identified, two in each population.

227 e CAD-associated FN1 gene through a response QTL that modulates both chromatin accessibility and chro

228 h other summer dormant and stress responsive QTL regions for plant height, new leaf and dry biomass w

229 , solidifying the foundation for large-scale QTL fine mapping, candidate gene validation, and develop

230 kb and 347 kb respectively, with the second QTL explaining up to 14% of the total genetic variance o

231 We show that SET1 and two other SET QTLs each contain a candidate gene (AHG1, ANAC060, PDF1

232 Seventy-seven QTL were identified in the male and 46 in the female par

233 We identified several QTL linked to host benefit, supporting the feasibility o

234 D measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes

235 Significant QTLs correlated with both length and weight were detecte

236 A number of significant QTLs controlling total biomass and 100-seed weight under

237 effects of the seven genome-wide significant QTLs accounted for approximately one-third of the total

238 activity was largely influenced by a single QTL encompassing the Adh-coding gene and its known regul

239 gly, we identify rs6128 as a platelet splice QTL and define an rs6128-dependent association between S

240 rs6128 is a platelet splice QTL that alters SELP exon 14 skipping and soluble versus

241 fy novel platelet expression QTLs and splice QTLs.

242 rgely independent of expression and splicing QTLs and are enriched with binding sites of RNA-binding

243 Alu insertions are responsible for splicing QTLs (sQTL).

244 ated traits at levels comparable to splicing QTLs and roughly half of expression QTLs.

245 We mapped the sub-QTL for body weight in the proximal region of the previo

246 behavior, in this case, is more complex than QTL mapping suggested, highlighting potential challenges

247 Among all the QTL, five summer dormancy related putative QTL were iden

248 The phenotypic variability explained by the QTL ranged between 9.91 and 32.67%.

249 In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosai

250 emented to determine the causal genes in the QTL regions based on multi-omic datasets.

251 ve trait locus (QTL) analysis, we mapped the QTL that influences both total IgG and IgG2(a/b/c) Ab re

252 er evaluated the genetic contribution of the QTL to cIMT by resequencing.

253 een bulks differing for pc1, showed that the QTL affects multiple photosynthesis and oxidation-reduct

254 The flanking markers related to the QTL reported in this study will be useful to improve tal

255 Comparing its performance to the QTL sign test-an existing test of selection that require

256 an families with evidence for linkage to the QTL.

257 enome-wide polymorphism information with the QTL mapping and expression profiling data led to identif

258 We sequenced all exons within the QTL and genomic regions of PRiMA1, FOXN3 and CCDC88C in

259 uding two candidate genes located within the QTL associated with C16:0 content, showed differential e

260 The QTLs identified through this study will allow the improv

261 The QTLs were in the background of two popular varieties PSB

262 lues for additive effects observed among the QTLs for most traits indicated that the phenotypes of th

263 Joint analysis of the QTLs of m(6)A and related molecular traits suggests that

264 Together, the QTLs explained 39 to 55% of the phenotypic variation for

265 However, the genes underlying the QTLs and their functions remain largely unknown.

266 lodging resistance QTL in soybean, and these QTL could be used to increase lodging resistance.

267 and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate ge

268 Combining the results of these QTL in each environment and across both sites, three mai

269 Candidate genes underlying these QTL suggest pathogen effector recognition and plant prot

270 We examine the overlap of these QTLs and their relationship to smooth muscle-specific ge

271 er, the functional characterization of these QTLs has been limited by the heterogeneous cellular comp

272 he high frequency of the BJ1 allele of these QTLs will enhance the robustness of germination under an

273 h leaf allometry, under the control of these QTLs, varies as a response to environment change.

274 we use multiple analyses to show that these QTLs are highly associated with CAD GWAS loci and correl

275 Several markers linked to these QTLs are potential targets for MAS against Phytophthora

276 orter, OsMOT1;1, as the causal gene for this QTL.

277 The ontogenetic model identified this QTL plus a few other QTLs that determine developmental t

278 SNPs linked to this QTL were used to develop Kompetitive allele specific PCR

279 Within this QTL, we detected three putative candidate genes, fgfa8,

280 The existence of three QTL hotspots associated with various traits across multi

281 Three QTLs significantly (P < 0.05) associated with resistance

282 ve candidate genes were identified for three QTLs that enhance spike seed setting and grain size usin

283 We identify three QTLs that together explain a large proportion (approxima

284 tion in median credible set size compared to QTL-based fine mapping when applied to H3K27AC ChIP-seq

285 In addition to traditional QTLs by association, allele-specific (AS) QTLs are a pow

286 ulation that are concordant with traditional QTLs but typically less susceptible to technical/environ

287 high degree overlap in both individual trait QTL and QTL for principal component scores (PCA QTL), ma

288 cted for most shape and size-related traits, QTL on chromosomes 1 and 12.

289 identified by likelihood ratio test for true QTL identification.

290 n, we narrowed down the positions of the two QTL detected on Omy1 to 96 kb and 347 kb respectively, w

291 A pair of homologous genes, BnPMT6s, in two QTLs were identified and experimentally demonstrated to

292 Association analysis within the two QTLs identified three SNPs correlated with the brain mea

293 n #394-1-27-12 (R) and Butter Bush (S) using QTL-seq bulk segregant analysis.

294 e used to characterize, dissect and validate QTL, but the development of NILs is costly.

295 We mapped several variable QTLs, but two large-effect QTLs which we have named Prr1

296 cient tool to detect, classify and visualize QTLs with additive and non-additive effects.

297 1 to a distal region of 5AL colocalized with QTL for number of spikelets per spike, kernel weight, ke

298 on of transcriptome data in combination with QTL information has been applied in many crops to study

299 correlate these expression differences with QTLs in this population, which would help identify the r

300 rounding the DEP1 locus, a major grain yield QTL in cultivated rice, from four Oryza polyploids of va