ライフサイエンスコーパス: disease gene

1 , a known split-hand/split-foot malformation disease gene.

2 guous truncating mutations of an established disease gene.

3 tients, suggesting that HPSE2 is a candidate disease gene.

4 ultiple DSBR pathways at a single endogenous disease gene.

5 encing is a useful technique for finding the disease gene.

6 , which encodes titin, is also a major human disease gene.

7 catalytic subunit of DNA primase as a novel disease gene.

8 ecific, patient-derived alleles of candidate disease genes.

9 inding, and allowed interrogation of posited disease genes.

10 riants from background missense variation in disease genes.

11 lung, including hundreds of drug targets and disease genes.

12 latory regions controlling cell identity and disease genes.

13 exts facilitates identification of candidate disease genes.

14 ified causative mutations in currently known disease genes.

15 ients who lack exonic mutations in the known disease genes.

16 variants identified among a growing list of disease genes.

17 egulates the transcription of several cystic disease genes.

18 ot equate to the discovery of 40 Alzheimer's disease genes.

19 n-disease-associated variants) from 22 human disease genes.

20 rch and prioritising candidate mitochondrial disease genes.

21 effects was associated with known metabolic disease genes.

22 use models that express familial Alzheimer's disease genes.

23 r amino acid changing variants and Mendelian disease genes.

24 nd broadened the clinical phenotype of known disease genes.

25 unction and identify new pathway members and disease genes.

26 d effectively in the prediction of candidate disease genes.

27 ol by heart-specific enhancers, and putative disease genes.

28 trol through mapping of and visualization of disease genes.

29 diseases, even if they do not share primary disease genes.

30 ined by mutations in known ECM or glomerular disease genes.

31 n metabolites and diseases through annotated disease genes.

32 xpected presentations for mutations in known disease genes.

33 arly useful for determining causal Mendelian disease genes.

34 complete and biased toward some well-studied disease genes.

35 ork and demonstrated its ability to retrieve disease genes.

36 on data, we functionally annotated 46 kidney disease genes.

37 ification of variations in known or putative disease genes.

38 lidation strategy and the definition of seed disease genes.

39 tool to assist in the diagnosis of Mendelian disease genes.

40 ses and give a good prioritization for known disease genes.

41 process, and help to identify new candidate disease genes.

42 ntification of 45 novel variants in 43 known disease genes, 41 candidate genes, and CNVs in 10 famili

43 ritize both dominant and recessive Mendelian disease genes(5), that outperforms missense constraint m

44 ession proximity between candidate and known disease genes allowed for further understanding of genet

45 selected tissues, the expression patterns of disease genes alone cannot explain the observed tissue s

46 The Parkinson's disease gene alpha-synuclein (SNCA) is selectively expre

47 PLEKHM2 joins LAMP-2 and BAG3 as a disease gene altering autophagy resulting in an isolated

48 the human brain that affects the Alzheimer's disease gene, amyloid precursor protein (APP).

49 derstand the role of EMP2 in the etiology of disease, gene analysis was performed to show transcripts

50 om 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological process

51 ociated deglycase) is known as a Parkinson's disease gene and an oncogene.

52 entify NONO as a possible neurodevelopmental disease gene and highlight the key role of the DBHS prot

53 regulation, demonstrating that the Parkinson disease gene and tumor suppressor Parkin bound and ubiqu

54 of pathogenic variants in autosomal dominant disease genes and 51.7% (15 of 29) of pathogenic variant

55 ntrality, a measure of communication between disease genes and differentially expressed genes.

56 uggests its usefulness in discovery of novel disease genes and dissection of disease pathways.

57 nriched with regulatory and signaling genes, disease genes and drug targets.

58 to the identification of many new causative disease genes and functional studies have clarified thei

59 lized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs u

60 o be in close proximity to known Parkinson's disease genes and lysosomal-related genes.

61 solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the s

62 analysis is very useful in identifying novel disease genes and potential drug targets.

63 Finally, disease genes and protein complexes have the tendency to

64 ell types that are directly affected by lung disease genes and respiratory viruses.

65 use [essential genes (EGs)] are enriched for disease genes and under strong purifying selection relat

66 iants that may unmask dispensable regions of disease genes and unrecognized false positives in diagno

67 variant and discover previously unrecognized disease genes and variants while keeping up to 99.7% of

68 ng protein-protein interaction, drug-target, disease-gene and signaling pathways among various cells

69 sing an interactome-based extension of known disease-genes and introduce several measures of function

70 We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three

71 more useful hypotheses of associations among diseases, genes and chemicals, which may guide the devel

72 ification of new risk factors, new candidate disease genes, and a better understanding of the molecul

73 ignificant connections between gene sets and disease genes, and apply it to several gene sets related

74 s, functionally annotate genetic variants of disease genes, and inform clinical trials.

75 enetically-defined cohorts to validate novel disease genes, and provide much-needed genotype-phenotyp

76 TASTPM', transgenic for familial Alzheimer's disease genes APP/PSEN1).

77 or genes associated to diseases, finds novel disease genes applying various network-based prioritizat

78 as more cardiomyopathy and congenital heart disease genes are discovered, giving researchers a power

79 In addition, we show that human disease genes are enriched for essential genes, thus pro

80 Although some disease genes are expressed only in selected tissues, th

81 terogeneous, and many, possibly most, of the disease genes are still unknown.

82 ed with essential, regulatory, signaling and disease genes as well as drug targets, indicating their

83 types that can be associated with a specific disease gene, as well as the complexity of the pathogene

84 of cell differentiation markers and retinal disease genes, as well as in mRNA alternative splicing.

85 Of 1305 human disease genes assayed, 20 genes exhibited strong toxicit

86 Given a set of disease genes associated with a disease, neighbourhood-b

87 nding of the range of disease phenotypes and disease genes associated with deficiencies of the GPI-an

88 er parameters utilizing historical Mendelian disease-gene association discovery data.

89 s than in other types of study (for example, disease-gene association studies).

90 as an intuitive and versatile method to aid disease-gene association, which naturally lends itself t

91 ene predictions, suggesting that many of the disease gene associations are now captured directly in h

92 hod, which we have called Cardigan (ChARting DIsease Gene AssociatioNs), uses semi-supervised learnin

93 We report state-of-the-art results on 12 disease-gene associations and on a time-stamped benchmar

94 0 provides an expanded comprehensive list of disease-gene associations based on manual curation from

95 ed methodology is extended to discover novel disease-gene associations by including valuable domain k

96 the number of diseases from 72 to 88 and the disease-gene associations from 216 201 to 679 602.

97 es and 22 human tissues and incorporated the disease-gene associations from DisGeNET.

98 The identification of disease-gene associations is a task of fundamental impor

99 ues and tissue-specific networks to identify disease-gene associations more accurately than GWAS alon

100 ther identify candidate gene lists for which disease-gene associations were not studied previously.

101 < 0.05) is observable for ~67% of 4,549 OMIM disease-gene associations, using a combination of high q

102 tion, with scores reflecting the strength of disease-gene associations.

103 ng roles of genes across tissues and uncover disease-gene associations.

104 ex traits increased the number of associated disease genes at a 5% false discovery rate by an average

105 As in other common diseases, genes at COPD GWAS loci were not differentiall

106 xample by prioritizing among candidate human disease genes based on their network properties or by fi

107 thods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms enter

108 nes; however, the framework is applicable to disease genes beyond epilepsy.

109 15 of 29) of pathogenic variants in X-linked disease genes; both variants were de novo in 44.7% (17 o

110 cation (p.Trp1088Leufs*16) confirmed AHI1 as disease gene, but based on a more N-terminal missense mu

111 first reported skeletal muscle K(+) channel disease gene, but the requirement for KCNE3 in skeletal

112 creased the number of variants identified in disease genes, but the diagnostic utility is limited by

113 ental rare variants in established Mendelian disease genes, but the frequency of related clinical phe

114 se of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data.

115 ods have been developed to predict potential disease genes by utilizing the disease similarity networ

116 Application to the major ALS disease gene C9ORF72 identified high-quality antibodies

117 RDEN can be applied to the neurodegenerative disease genes C9orf72 and APP, and methylation can be in

118 Many new disease genes can be identified through high-throughput

119 ork-based proximity between drug targets and disease genes can provide novel insights regarding the r

120 hort of young adult premanifest Huntington's disease gene carriers (preHD) far from predicted clinica

121 cohort of 12 early premanifest Huntington's disease gene carriers with a mean estimated 90% probabil

122 DE10A expression in premanifest Huntington's disease gene carriers, which are associated with the pro

123 survival and improve outcome in Huntington's disease gene carriers.

124 conversion in early premanifest Huntington's disease gene carriers.

125 Our study therefore identifies POPDC1 as a disease gene causing a very rare autosomal recessive car

126 n Online Mendelian Inheritance in Man (OMIM) disease genes compared with Africans, Latinos, South Asi

127 sive and compound heterozygous genotypes and disease genes, controlling for confounding effects, such

128 ncogenesis and genome engineering, including disease gene correction.

129 nosed Diseases Program, nominating 60% of 30 disease genes determined to be diagnostic by a standard

130 one of two autosomal dominant cystic kidney disease genes, did not show increased risk of developmen

131 as an active area of research for both novel disease gene discovery and drug repositioning.

132 and drug-specific mechanisms that may guide disease gene discovery and drug repurposing.

133 using WES data; this tool can be useful for disease gene discovery efforts and clinical WES analyses

134 ome a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying

135 nts in time to replicate the dynamics of the disease gene discovery process, prove that Cardigan is a

136 erpretation of human genomes and can advance disease gene discovery(1-4).

137 Here, we review recent developments in disease gene discovery, functional characterization, and

138 ilarities to improve the prediction power of disease gene discovery.

139 of applications in population inference and disease gene discovery.

140 e propose FUSIL as an efficient approach for disease gene discovery.

141 neration sequencing (NGS) projects for novel disease-gene discovery or differential diagnostics of Me

142 Using data from Mendelian disease-gene discovery projects, we show that ALoFT can

143 plain the phenotype, and could lead to novel disease-gene discovery.

144 endogenous reporter gene, the X-chromosomal disease gene encoding hypoxanthine phosphoribosyltransfe

145 error rate and power, depends on underlying disease-gene-environment associations, estimates of whic

146 risk factors and obtaining insight into the disease-gene-environment relationship.

147 e of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals.

148 vel missense variants in recently discovered disease genes exhibiting genetic heterogeneity, by combi

149 Disease gene expression changes correlated instead with

150 We report that drug patterns for reverting disease gene expression follow the cell-specificity of t

151 Disease gene expression profiles can be utilized as biom

152 p-learning-based reference tissue selection, disease gene expression signature creation, drug reversa

153 mouse model expressing the full human ATXN3 disease gene, expression of this alternatively spliced t

154 In conclusion, our data establish PCYT2 as a disease gene for a new complex hereditary spastic parapl

155 We therefore propose AHR to be a novel disease gene for a new, recessively inherited disorder i

156 on with gene expression implicated FLCN as a disease gene for diabetic retinopathy.

157 These results define LMOD1 as a disease gene for MMIHS and suggest its role in establish

158 sibling pair, targeted testing of the known disease gene for Roifman syndrome (RNU4ATAC) provided a

159 rmin network was significantly enriched with disease genes for both T2D and cancer, and that the netw

160 PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecul

161 Variants in SCN5A and KCNH2, disease genes for long QT and Brugada syndromes, were as

162 that Cardigan is able to accurately predict disease genes for molecularly uncharacterized diseases.

163 fy putative pathogenic variants in candidate disease genes for tooth agenesis in 10 multiplex Turkish

164 rameshift deletion in CR1, a top Alzheimer's disease gene, found in disease cases but not in controls

165 d phenotype information may help to identify disease genes from human whole-genome and whole-exome se

166 s have been applied successfully to identify disease genes, genetic modules and drug targets.

167 More than one-third of these disease genes have been characterized in the past 5 year

168 Although tools to prioritize candidate disease genes have been developed, the great majority of

169 Although a number of disease genes have been identified for CMT, the gene dis

170 ion underlie FSGS; however, highly penetrant disease genes have been identified in a small fraction o

171 A total of 17 FA disease genes have been reported, all of which act in a

172 y result in biases, showing that age-related disease genes have faster molecular evolution rates and

173 The CAG repeat expansion in the Huntington's disease gene HTT extends a polyglutamine tract in mutant

174 ng, gene and variant filtering strategies on disease gene identification.

175 This complicates disease-gene identification and efforts to understand th

176 Disease-gene identification is a challenging process tha

177 Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throu

178 -based functional system to screen candidate disease genes identified from Congenital Heart Disease (

179 n vivo validation system to screen candidate disease genes identified from patients.

180 Diagnostic analysis of established disease genes identified pathogenic variants in 21.8% of

181 es from transgenic mice expressing the human disease gene, identified the atypical antipsychotic arip

182 ental mRNA-abundance profiles and neuropathy disease genes illustrates the utility of this resource.

183 disease modules will provide novel candidate disease genes, improve interpretation of candidate genes

184 We aim to identify the disease gene in a large 3-generation family (n=25) with

185 and a repair template to induce repair of a disease gene in adult animals.

186 l stem cell (NSC) fate and a bona fide human disease gene in congenital hydrocephalus (CH).

187 nt in animals, but whether Piwi is an actual disease gene in human infertility remains unknown.

188 ALG9 is a novel disease gene in the genetically heterogeneous ADPKD spec

189 screened mutations in over 200 known retinal disease genes in a cohort of 12 unrelated Uyghur IRD pro

190 diffusion approach, starting from the known disease genes in a heterogenous network constructed from

191 To empower the search for Mendelian-disease genes in family-based sequencing studies, we imp

192 ted pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of

193 a powerful tool for rapid analysis of known disease genes in large patient cohorts.

194 ological connections between aging genes and disease genes in over three thousand subnetworks corresp

195 candidate genes by their similarity to known disease genes in protein-protein interaction networks an

196 is a valuable tool to help prioritize novel disease genes in sequence interpretation.

197 We sequenced HCM disease genes in Singaporean patients (n=224) and Singap

198 unction (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD

199 signed, which incorporated 195 known retinal disease genes, including 61 known RP genes.

200 -1beta regulates the transcription of cystic disease genes, including Pkd2 and Pkhd1.

201 sociation studies-identified coronary artery disease genes, including PPAP2B, participate in mechanot

202 How FTD-causing disease genes interact is largely unknown.

203 ease annotations, placing numerous candidate disease genes into a cellular framework.

204 fied by en masse transformation of the human disease genes into a pool of 4653 homozygous diploid yea

205 xt-generation sequencing and categorized the disease genes into functional gene groups.

206 vel genes and non-coding RNAs, a Parkinson's disease gene involved in dopamine regulation, PARK2, is

207 tribute to a more complete identification of disease genes involved in cardiomyopathy.

208 Accurately prioritizing candidate disease genes is an important and challenging problem.

209 zygous variants on chromosome X in two known disease genes, L1CAM and PAK3, and in two novel candidat

210 Parkinson's disease gene leucine-rich repeat kinase 2 (LRRK2) has be

211 We found that the Parkinson's disease gene, leucine-rich repeat kinase 2 (LRRK2), has

212 a tensor factorization to accurately predict disease-gene links.

213 y (critical for the delivery of large ocular disease genes) make their further development a research

214 s' genomes is useful in studies ranging from disease gene mapping to speciation genetics.

215 y evolving loci, and be an important tool in disease gene mapping.

216 signatures of natural selection, and may aid disease gene mapping.

217 3 M patients from the Medicare database with disease-gene maps that we derived from several resources

218 As clinically significant disease genes may be subject to negative selection, we d

219 ls and patients, this principle assumes that disease genes more likely undergo rewiring in patients,

220 duals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008-11).

221 ation sequencing assay for detecting cardiac disease gene mutations with improved accuracy, flexibili

222 1, SPG11), and the identification of a novel disease gene (n = 1; NSL1).

223 ilico analysis of the architecture of the PH disease gene network coupled with molecular experimentat

224 s based on shared systems of disease and non-disease gene networks may have broad implications for fu

225 d the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T

226 y of using phenotypic data to focus on known disease genes or genomic elements interacting with them.

227 Most notably, Mendelian disease genes, particularly those associated with develo

228 The retinal disease gene peripherin 2 (PRPH2) is essential for the f

229 etic defects such as loss of the Parkinson's disease genes Pink or Parkin in Drosophila.

230 Knockout of the polycystic kidney disease genes PKD1 or PKD2 induces cyst formation from k

231 d-2 (homologs of the human polycystic kidney disease genes, PKD1 and PKD2), which are expressed in ma

232 in the autosomal recessive polycystic kidney disease gene PKHD1, indicating that adult PKHD1 carriers

233 filing from healthy human brain to develop a disease gene prediction model and this generic methodolo

234 hich was a widely used phenotype database in disease gene prediction studies.

235 application to protein function annotation, disease gene prediction, and drug discovery.

236 otein interactions does not markedly improve disease gene predictions, suggesting that many of the di

237 d the impact of mutations affecting AP1S3, a disease gene previously identified by our group and vali

238 vealed de novo dominant mutations, validated disease genes previously described in isolated families,

239 are our methods with 7 popular network-based disease gene prioritization algorithms on diseases from

240 sue-specific molecular networks for studying disease gene prioritization and show the superiority of

241 ed inference traditionally used in candidate disease gene prioritization applications with experiment

242 d be used to generate testable hypotheses on disease gene prioritization of brain disorders.

243 there is a need for strategies that advance disease gene prioritization(1,2).

244 applications in protein function prediction, disease gene prioritization, and patient stratification.

245 applications include functional annotation, disease gene prioritization, comparative analysis of bio

246 grate tissue-specific molecular networks for disease gene prioritization.

247 ncing has accelerated the discovery of human disease genes, progress has been largely limited to the

248 in mice also display altered splicing of the disease gene, promoting expression of an alternative iso

249 named entities including Anatomy, Chemical, Disease, Gene/Protein and Species.

250 volutionized the use of genetics to identify disease genes, provide insights into human evolution, an

251 ods that screen on the basis of the marginal disease-gene relationship are more robust to exposure mi

252 of the positive cases harbored mutations in disease genes reported since 2011.

253 nal testing of this model on two known human disease genes revealed discrete cis amino acid residues

254 ison of aging-related genes with age-related disease genes reveals species-specific effects with stro

255 nvestigating the regulatory landscape of the disease gene Scn1a, we discovered enhancers selective fo

256 Given a disease gene set, BTSC seeks a balanced solution that ma

257 d measure of closeness between the query and disease gene sets capable of detecting associations unde

258 When applied to real disease gene sets, our algorithms not only identified kn

259 ST, and AP3B1), X-linked lymphoproliferative disease genes (SH2D1A and XIAP), and others such as NLRC

260 The aging gene signatures and complex disease genes show a complex overlapping pattern and onl

261 ay the downstream genes of drug targets with disease gene signatures to display the potential therape

262 To develop broader clinical relevance from disease gene signatures, Inkeles et al. demonstrate how

263 y and decreased expression of several cystic disease genes, some of which we identified as novel Hnf1

264 an ENS due to a mutation in the Hirschsprung disease gene, sox10, develop microbiota-dependent inflam

265 on of linkage analyses and WGS to search for disease genes still remains a fruitful strategy for comp

266 richment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our p

267 se of dominantly inherited neurodegenerative diseases, gene suppression strategies can target the und

268 factor MEF2C and the human congenital heart disease gene TDGF1.

269 netically heterogeneous, with >100 purported disease genes tested in clinical laboratories.

270 t have been implicated in stillbirth and six disease genes that are good candidates for phenotypic ex

271 PE Problem which seeks to identify candidate disease genes that may be associated with a phenotype su

272 Moreover, the disease genes that overlap these two innate immunity pat

273 To identify Alzheimer's disease genes, these loci need to be mapped to variants

274 The oculome design covers 429 known eye disease genes; these are subdivided into 5 overlapping v

275 nd has been implicated as a congenital heart disease gene through an ill-defined function at centriol

276 Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both i

277 ly of computational techniques for inferring disease genes through a set of training genes and carefu

278 To promote the identification of disease genes through confirmation of previously describ

279 By mapping neuropsychiatric disease genes to cell types, we implicate dysregulation

280 h that is discovering the mechanisms linking disease genes to epilepsy syndromes.

281 We used next-generation sequencing for renal disease genes to genotype cohorts of patients with suspe

282 ovo mutations in three previously identified disease genes (TUBA1A (n=2), SCN8A (n=1) and KDM5C (n=1)

283 ment to the state of the art for curation of disease-gene-variant relationships.

284 We extract disease-gene-variant triplets from all abstracts in PubM

285 rning approach to automate the extraction of disease-gene-variant triplets from biomedical literature

286 iseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in Un

287 To identify new disease genes, we performed whole-exome sequencing of 26

288 Eighty-nine cardiac disease genes were evaluated.

289 Mutations in non-cancer-related Mendelian disease genes were seen in 55 of 1566 cases (3.5%; 95% C

290 ified analysis) was similar to that in known disease genes, which indicates that the genetic cause of

291 ecision >90% for variants from all 99 tested disease genes while approaching 100% accuracy for some g

292 l for creating a targeted panel of potential disease genes while supporting different forms of input.

293 The X-chromosome harbors hundreds of disease genes whose associated diseases predominantly af

294 leterious and whether they occurred in known disease genes whose clinical spectrum overlaps CP.

295 e receptor RET represents the most important disease gene, whose mutations account for half of the fa

296 ases, there is compelling evidence that many disease genes will map to a much smaller number of biolo

297 to identify 253 candidate neurodevelopmental disease genes with an excess of missense and/or likely g

298 oci and to find additional variants in known disease genes with potential clinical impact.

299 Our search focused on new candidate disease genes within 19 deleted de novo CNVs, which did

300 stematic study of the expression patterns of disease genes within the human interactome.