2025
Identifying Genetic Variants for Brain Connectivity Using Ball Covariance Ranking and Aggregation
Dai W, Zhang H. Identifying Genetic Variants for Brain Connectivity Using Ball Covariance Ranking and Aggregation. Journal Of The American Statistical Association 2025, ahead-of-print: 1-19. DOI: 10.1080/01621459.2025.2450837.Peer-Reviewed Original ResearchSingle nucleotide polymorphismsDetect single nucleotide polymorphismsGene-based analysisControlling false discovery rateControlling false discoveriesSNP setsFunctional connectivityFalse discovery rateGenetic architectureNovel genesGenetic basisNucleotide polymorphismsGenetic variantsUK Biobank dataPsychiatric disordersDiscovery rateBrain functionFalse discoveriesBiobank dataCorrelations of neural activityBrain regionsBiological etiologyBrain connectivityEQTLNeural activityAlternative Splicing Alterations in Patients With Amyotrophic Lateral Sclerosis: Link to the Disruption of TAR DNA‐Binding Protein 43 kDa Functions
Miwa T, Takeuchi E, Ogawa K, Abdelhamid R, Morita J, Hiraki Y, Yasumizu Y, Nakamura Y, Ohkura N, Saito Y, Murayama S, Nagai Y, Mochizuki H, Nagano S. Alternative Splicing Alterations in Patients With Amyotrophic Lateral Sclerosis: Link to the Disruption of TAR DNA‐Binding Protein 43 kDa Functions. Neurology And Clinical Neuroscience 2025, 13: 187-194. DOI: 10.1111/ncn3.12880.Peer-Reviewed Original ResearchAlternative splicing alterationsAlternative splicingTDP-43Splicing changesSplicing alterationsAmyotrophic lateral sclerosis pathologyAmyotrophic lateral sclerosisDNA-binding proteinsDysregulation of alternative splicingTAR DNA-binding proteinAberrant alternative splicingTAR DNA-binding protein 43 kDaSH-SY5Y cellsRNA metabolismDNA-binding protein 43 kDaNovel genesSplicing patternsNeurons of patientsRNA sequencingSplicingLateral sclerosisMotor neuronsTreatment of amyotrophic lateral sclerosisPolymerase chain reaction analysisMotor neurons of patients
2023
Multi‐omics cannot replace sample size in genome‐wide association studies
Baranger D, Hatoum A, Polimanti R, Gelernter J, Edenberg H, Bogdan R, Agrawal A. Multi‐omics cannot replace sample size in genome‐wide association studies. Genes Brain & Behavior 2023, 22: e12846. PMID: 36977197, PMCID: PMC10733567, DOI: 10.1111/gbb.12846.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesLarge genome-wide association studiesNovel genesMulti-omics dataMulti-omics informationAssociation studiesGenome-wide significant lociSmall genome-wide association studyBrain-related traitsGWAS sample sizesEarly genome-wide association studiesNovel gene discoveryGene discoverySignificant lociAdditional genesPositional mappingHeritable traitVariant discoverySimilar traitsGenesNovel variant discoveryTraitsDisease biologyLociDiscoveryConvergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies
Liao C, Moyses-Oliveira M, De Esch C, Bhavsar R, Nuttle X, Li A, Yu A, Burt N, Erdin S, Fu J, Wang M, Morley T, Han L, Consortium C, Dion P, Rouleau G, Zhang B, Brennand K, Talkowski M, Ruderfer D. Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies. Cell Genomics 2023, 3: 100277. PMID: 37082147, PMCID: PMC10112287, DOI: 10.1016/j.xgen.2023.100277.Peer-Reviewed Original ResearchRisk genesNovel risk genesProtein-altering variantsLarge-scale genetic studiesASD risk genesHeritable neurodevelopmental disorderAutism-associated genesCRISPR perturbationsConvergent genesNovel genesTranscriptional consequencesFunctional mutationsGenetic studiesCoexpression patternsDifferential expressionGenesHuman neuronsASD-associationHuman postmortem brainRare variationCoexpressionASD brainNeurodevelopmental disordersPostmortem brainsMutationsKidneyNetwork: using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease
Boulogne F, Claus L, Wiersma H, Oelen R, Schukking F, de Klein N, Li S, Westra H, van der Zwaag B, van Reekum F, Sierks D, Schönauer R, Li Z, Bijlsma E, Bos W, Halbritter J, Knoers N, Besse W, Deelen P, Franke L, van Eerde A. KidneyNetwork: using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease. European Journal Of Human Genetics 2023, 31: 1300-1308. PMID: 36807342, PMCID: PMC10620423, DOI: 10.1038/s41431-023-01296-x.Peer-Reviewed Original ResearchConceptsCo-expression networkTissue-specific expressionCandidate genesGene functionPhenotypic consequences of genetic variationPathogenic variantsConsequences of genetic variationInterpretation of genetic variantsGenetic causeRare variantsGene-phenotype associationsHereditary kidney diseaseExome sequencing dataDisease-associated genesGene expression dataPlausible candidate genesCandidate gene prioritizationKidney disease phenotypesUnbiased mannerCystic kidneysNovel genesGenetic variationPhenotypic consequencesGene prioritizationSequence dataGenetic Variants in ARHGEF6 Cause Congenital Anomalies of the Kidneys and Urinary Tract in Humans, Mice, and Frogs
Klämbt V, Buerger F, Wang C, Naert T, Richter K, Nauth T, Weiss A, Sieckmann T, Lai E, Connaughton D, Seltzsam S, Mann N, Majmundar A, Wu C, Onuchic-Whitford A, Shril S, Schneider S, Schierbaum L, Dai R, Bekheirnia M, Joosten M, Shlomovitz O, Vivante A, Banne E, Mane S, Lifton R, Kirschner K, Kispert A, Rosenberger G, Fischer K, Lienkamp S, Zegers M, Hildebrandt F. Genetic Variants in ARHGEF6 Cause Congenital Anomalies of the Kidneys and Urinary Tract in Humans, Mice, and Frogs. Journal Of The American Society Of Nephrology 2023, 34: 273-290. PMID: 36414417, PMCID: PMC10103091, DOI: 10.1681/asn.2022010050.Peer-Reviewed Original ResearchConceptsIntegrin-linked kinaseFocal adhesion proteinsThree-dimensional (3D) MadinCdc42/Rac1Genetic variantsRac1/Cdc42Loss of interactionFrog modelPolarity defectsExchange factorNovel genesFocal adhesionsLamellipodia formationARHGEF6Adhesion proteinsDisease genesDeleterious variantsCell spreadingLumen formationCell migrationGenesProteinHemizygous variantKidney cellsExome sequencing
2022
Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases
Chen M, Zhang Y, Adams T, Ji D, Jiang W, Wain LV, Cho M, Kaminski N, Zhao H. Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases. Thorax 2022, 78: 792-798. PMID: 36216496, PMCID: PMC10083187, DOI: 10.1136/thorax-2021-217703.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisLocal genetic correlationsSingle-cell expression dataCandidate genesTranscription factorsIntegrative analysisGenomic regionsGenetic correlationsExpression dataTF target genesComplex genetic architectureTF binding sitesWide association studyPower of GWASSpecific DEGsGenetic architectureNew genesNovel genesCausal genesTarget genesGenetic basisEnrichment analysisAssociation studiesRegulatory roleAssociation analysisA retrospective cohort analysis of the Yale pediatric genomics discovery program
Al‐Ali S, Jeffries L, Faustino EVS, Ji W, Mis E, Konstantino M, Zerillo C, Jiang Y, Spencer‐Manzon M, Bale A, Zhang H, McGlynn J, McGrath JM, Tremblay T, Brodsky NN, Lucas CL, Pierce R, Deniz E, Khokha MK, Lakhani SA. A retrospective cohort analysis of the Yale pediatric genomics discovery program. American Journal Of Medical Genetics Part A 2022, 188: 2869-2878. PMID: 35899841, PMCID: PMC9474639, DOI: 10.1002/ajmg.a.62918.Peer-Reviewed Original ResearchConceptsRetrospective cohort analysisNext-generation sequencingCohort analysisSystem abnormalitiesImmune system abnormalitiesCardiovascular system abnormalitiesFunctional molecular analysesNovel genesPrecise molecular diagnosisClinical characteristicsFurther genetic evaluationDiscovery programsComplex patientsMultisystem diseaseDisease genesPediatric providersRare genetic diseaseNew diagnosisPhenotype relationshipsPatientsGenetic diseasesMolecular analysisDiagnosisParticipant demographicsNGS resultsTranscriptional Divergence Underpinning Sexual Development in the Fungal Class Sordariomycetes
Kim W, Wang Z, Kim H, Pham K, Tu Y, Townsend JP, Trail F. Transcriptional Divergence Underpinning Sexual Development in the Fungal Class Sordariomycetes. MBio 2022, 13: e01100-22. PMID: 35638737, PMCID: PMC9239162, DOI: 10.1128/mbio.01100-22.Peer-Reviewed Original ResearchConceptsSingle-copy orthologous genesFungal class SordariomycetesTranscriptional divergenceOrthologous genesHypothetical proteinsNovel genesPerithecial developmentTranscriptional activationClass SordariomycetesGene expressionRich genomic resourceGene expression divergenceMulticellular fruiting bodiesBody developmentSuccessful sexual reproductionKey developmental genesSpecies-specific functionsFunctional protein domainsKnockout of genesRecent common ancestorGene expression levelsOrthologous counterpartsSordariomycetes speciesDevelopmental transcriptomeExpression divergence
2021
Genome-wide association study of stimulant dependence
Cox J, Sherva R, Wetherill L, Foroud T, Edenberg HJ, Kranzler HR, Gelernter J, Farrer LA. Genome-wide association study of stimulant dependence. Translational Psychiatry 2021, 11: 363. PMID: 34226506, PMCID: PMC8257618, DOI: 10.1038/s41398-021-01440-5.Peer-Reviewed Original ResearchConceptsStimulant dependenceAssociation studiesVoltage-gated channel proteinsGenome-wide association studiesNicotinic acetylcholine receptor genesTop GWAS signalsUse disordersAcetylcholine receptor genesWide association studyGWAS signalsSubstance use disordersEuropean ancestry subjectsNovel genesCocaine use disorderUsers of cocaineChannel proteinsGenetic studiesSpecific genetic factorsGenetics of AlcoholismSignificant pleiotropyWhole-exome sequencing identifies genes associated with Tourette’s disorder in multiplex families
Cao X, Zhang Y, Abdulkadir M, Deng L, Fernandez TV, Garcia-Delgar B, Hagstrøm J, Hoekstra PJ, King RA, Koesterich J, Kuperman S, Morer A, Nasello C, Plessen KJ, Thackray JK, Zhou L, Dietrich A, Tischfield J, Heiman G, Xing J. Whole-exome sequencing identifies genes associated with Tourette’s disorder in multiplex families. Molecular Psychiatry 2021, 26: 6937-6951. PMID: 33837273, PMCID: PMC8501157, DOI: 10.1038/s41380-021-01094-1.Peer-Reviewed Original ResearchConceptsCandidate genesProtein-protein interaction networkGene ontology categoriesHigh-throughput sequencingStrong candidate geneCandidate gene expressionFamily member 1Heritable neurodevelopmental disorderIdentifies genesNovel genesOntology categoriesNeurodevelopmental disordersMultiplex familiesInteraction networksPolygenic natureBiological insightsGene expressionFunction predictionWhole-exome sequencingGenesGenetic variantsSegregation patternsGenetic heterogeneitySegregation informationMember 1
2020
Non-AUG start codons: Expanding and regulating the small and alternative ORFeome
Cao X, Slavoff SA. Non-AUG start codons: Expanding and regulating the small and alternative ORFeome. Experimental Cell Research 2020, 391: 111973. PMID: 32209305, PMCID: PMC7256928, DOI: 10.1016/j.yexcr.2020.111973.Peer-Reviewed Original ResearchConceptsSmall open reading framesFunctional small open reading framesStart codonClasses of genesImportant biological processesNon-AUG start codonsOpen reading frameNon-AUG codonsStart codon usageAUG start codonEukaryotic genomesGenomic toolsRibosome profilingNovel genesCodon usageReading frameProteomic studiesBiological processesSequence propertiesCodonGenesPresence of thousandsEukaryotesProkaryotes
2019
International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci
Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Dalvie S, Duncan LE, Gelernter J, Levey DF, Logue MW, Polimanti R, Provost AC, Ratanatharathorn A, Stein MB, Torres K, Aiello AE, Almli LM, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker DG, Beckham JC, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Børglum AD, Bradley B, Brashear M, Breen G, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Calabrese JR, Caldas- de- Almeida J, Dale AM, Daly MJ, Daskalakis NP, Deckert J, Delahanty DL, Dennis MF, Disner SG, Domschke K, Dzubur-Kulenovic A, Erbes CR, Evans A, Farrer LA, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Geuze E, Gillespie C, Uka AG, Gordon SD, Guffanti G, Hammamieh R, Harnal S, Hauser MA, Heath AC, Hemmings SMJ, Hougaard DM, Jakovljevic M, Jett M, Johnson EO, Jones I, Jovanovic T, Qin XJ, Junglen AG, Karstoft KI, Kaufman ML, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kranzler HR, Kremen WS, Lawford BR, Lebois LAM, Lewis CE, Linnstaedt SD, Lori A, Lugonja B, Luykx JJ, Lyons MJ, Maples-Keller J, Marmar C, Martin AR, Martin NG, Maurer D, Mavissakalian MR, McFarlane A, McGlinchey RE, McLaughlin KA, McLean SA, McLeay S, Mehta D, Milberg WP, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Neale BM, Nelson EC, Nordentoft M, Norman SB, O’Donnell M, Orcutt HK, Panizzon MS, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Rice JP, Ripke S, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero K, Rung A, Rutten BPF, Saccone NL, Sanchez SE, Schijven D, Seedat S, Seligowski AV, Seng JS, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Trapido E, Uddin M, Ursano RJ, van den Heuvel LL, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Werge T, Williams MA, Williamson DE, Winternitz S, Wolf C, Wolf EJ, Wolff JD, Yehuda R, Young RM, Young KA, Zhao H, Zoellner LA, Liberzon I, Ressler KJ, Haas M, Koenen KC. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications 2019, 10: 4558. PMID: 31594949, PMCID: PMC6783435, DOI: 10.1038/s41467-019-12576-w.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesDisease genesAssociation studiesGenome-wide significant lociAfrican-ancestry analysesNon-coding RNAsGenetic risk lociParkinson's disease genesEuropean ancestry populationsNovel genesSignificant lociGenetic variationSpecific lociRisk lociAdditional lociLociAncestry populationsCommon variantsHeritability estimatesGenesGWASRNABiologySNPsPARK2
2018
Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk
Consortium I, Mitrovič M, Patsopoulos N, Beecham A, Dankowski T, Goris A, Dubois B, D’hooghe M, Lemmens R, Van Damme P, Søndergaard H, Sellebjerg F, Sorensen P, Ullum H, Thørner L, Werge T, Saarela J, Cournu-Rebeix I, Damotte V, Fontaine B, Guillot-Noel L, Lathrop M, Vukusik S, Gourraud P, Andlauer T, Pongratz V, Buck D, Gasperi C, Bayas A, Heesen C, Kümpfel T, Linker R, Paul F, Stangel M, Tackenberg B, Bergh F, Warnke C, Wiendl H, Wildemann B, Zettl U, Ziemann U, Tumani H, Gold R, Grummel V, Hemmer B, Knier B, Lill C, Luessi F, Dardiotis E, Agliardi C, Barizzone N, Mascia E, Bernardinelli L, Comi G, Cusi D, Esposito F, Ferrè L, Comi C, Galimberti D, Leone M, Sorosina M, Mescheriakova J, Hintzen R, van Duijn C, Theunissen C, Bos S, Myhr K, Celius E, Lie B, Spurkland A, Comabella M, Montalban X, Alfredsson L, Stridh P, Hillert J, Jagodic M, Piehl F, Jelčić I, Martin R, Sospedra M, Ban M, Hawkins C, Hysi P, Kalra S, Karpe F, Khadake J, Lachance G, Neville M, Santaniello A, Caillier S, Calabresi P, Cree B, Cross A, Davis M, Haines J, de Bakker P, Delgado S, Dembele M, Edwards K, Fitzgerald K, Hakonarson H, Konidari I, Lathi E, Manrique C, Pericak-Vance M, Piccio L, Schaefer C, McCabe C, Weiner H, Goldstein J, Olsson T, Hadjigeorgiou G, Taylor B, Tajouri L, Charlesworth J, Booth D, Harbo H, Ivinson A, Hauser S, Compston A, Stewart G, Zipp F, Barcellos L, Baranzini S, Martinelli-Boneschi F, D’Alfonso S, Ziegler A, Oturai A, McCauley J, Sawcer S, Oksenberg J, De Jager P, Kockum I, Hafler D, Cotsapas C. Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk. Cell 2018, 175: 1679-1687.e7. PMID: 30343897, PMCID: PMC6269166, DOI: 10.1016/j.cell.2018.09.049.Peer-Reviewed Original ResearchConceptsRare coding variationsGenome-wide association studiesNon-coding variationCommon variant signalsSubstantial linkage disequilibriumLow-frequency variantsNovel genesCell homeostasisAssociation studiesComplex neurological diseasesLinkage disequilibriumGenetic variantsCommon variantsHeritabilityRich resourceGenesVariantsKey pathogenic roleIndividual familiesEpistasisAdditive effectBiologyHomeostasisMutationsNeurological diseasesLandscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS
Dobbyn A, Huckins L, Boocock J, Sloofman L, Glicksberg B, Giambartolomei C, Hoffman G, Perumal T, Girdhar K, Jiang Y, Raj T, Ruderfer D, Kramer R, Pinto D, Akbarian S, Roussos P, Domenici E, Devlin B, Sklar P, Stahl E, Sieberts S, Sklar P, Buxbaum J, Devlin B, Lewis D, Gur R, Hahn C, Hirai K, Toyoshiba H, Domenici E, Essioux L, Mangravite L, Peters M, Lehner T, Lipska B, Cicek A, Lu C, Roeder K, Xie L, Talbot K, Hemby S, Essioux L, Browne A, Chess A, Topol A, Charney A, Dobbyn A, Readhead B, Zhang B, Pinto D, Bennett D, Kavanagh D, Ruderfer D, Stahl E, Schadt E, Hoffman G, Shah H, Zhu J, Johnson J, Fullard J, Dudley J, Girdhar K, Brennand K, Sloofman L, Huckins L, Fromer M, Mahajan M, Roussos P, Akbarian S, Purcell S, Hamamsy T, Raj T, Haroutunian V, Wang Y, Gümüş Z, Senthil G, Kramer R, Logsdon B, Derry J, Dang K, Sieberts S, Perumal T, Visintainer R, Shinobu L, Sullivan P, Klei L. Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS. American Journal Of Human Genetics 2018, 102: 1169-1184. PMID: 29805045, PMCID: PMC5993513, DOI: 10.1016/j.ajhg.2018.04.011.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociConditional expression quantitative trait lociCommonMind ConsortiumEQTL signalsGenome-wide association study (GWAS) lociSchizophrenia GWASContext-specific regulationQuantitative trait lociCo-localization analysisGene expression levelsGWAS associationsNovel genesTrait lociStudy lociCausal genesEQTL dataFine mappingGenomic featuresGWAS statisticsGene expressionGenesGWASLociExpression levelsHuman brain samplesSystematic reconstruction of autism biology from massive genetic mutation profiles
Luo W, Zhang C, Jiang YH, Brouwer CR. Systematic reconstruction of autism biology from massive genetic mutation profiles. Science Advances 2018, 4: e1701799. PMID: 29651456, PMCID: PMC5895441, DOI: 10.1126/sciadv.1701799.Peer-Reviewed Original ResearchConceptsComplex genetic diseasesWhole-exome studiesHundreds of variantsGene functionNovel genesSubpathway levelGene groupsSame geneCanonical pathwaysPathway levelAutism-related mutationsSecond messenger systemsGenesGenetic diseasesASD biologyCAMP second messenger systemBiologyGenetic associationMutationsMultiple independent analysesMost variantsPathwayVariant levelsSynaptic functionGenetic mutation profilesDstac is required for normal circadian activity rhythms in Drosophila
Hsu IU, Linsley JW, Varineau JE, Shafer OT, Kuwada JY. Dstac is required for normal circadian activity rhythms in Drosophila. Chronobiology International 2018, 35: 1016-1026. PMID: 29621409, PMCID: PMC6103890, DOI: 10.1080/07420528.2018.1454937.Peer-Reviewed Original ResearchConceptsPigment Dispersing FactorS-LNSmall ventrolateral neuronsBrain of DrosophilaL-type voltage-gated CaCircadian activityNovel genesAdaptor proteinCircadian locomotionVoltage-gated CaActivity rhythmsDrosophilaDmca1DGenesVentrolateral neuronsCircadian activity rhythmsVertebratesSTAC3ProteinBindsCACHActivityImpact of Transcriptomics on Our Understanding of Pulmonary Fibrosis
Vukmirovic M, Kaminski N. Impact of Transcriptomics on Our Understanding of Pulmonary Fibrosis. Frontiers In Medicine 2018, 5: 87. PMID: 29670881, PMCID: PMC5894436, DOI: 10.3389/fmed.2018.00087.Peer-Reviewed Original ResearchTranscriptomic studiesImpact of transcriptomicsGenome-scale profilingSingle-cell RNAseqRole of microRNAsIdiopathic pulmonary fibrosisNovel genesTranscriptomic analysisEpithelial genesIPF lungsRNA transcriptsDevelopmental pathwaysWnt pathwayBulk tissueMolecular analysisPulmonary fibrosisSpatial heterogeneityAnimal modelsTranscriptomicsGenesLethal fibrotic lung diseaseHuman IPF lungsImpact of lungPathwayFibrotic lung disease
2016
Acute blockade of the Caenorhabditis elegans dopamine transporter DAT-1 by the mammalian norepinephrine transporter inhibitor nisoxetine reveals the influence of genetic modifications of dopamine signaling in vivo
Bermingham DP, Hardaway JA, Snarrenberg CL, Robinson SB, Folkes OM, Salimando GJ, Jinnah H, Blakely RD. Acute blockade of the Caenorhabditis elegans dopamine transporter DAT-1 by the mammalian norepinephrine transporter inhibitor nisoxetine reveals the influence of genetic modifications of dopamine signaling in vivo. Neurochemistry International 2016, 98: 122-128. PMID: 26850478, PMCID: PMC4969213, DOI: 10.1016/j.neuint.2016.01.008.Peer-Reviewed Original ResearchConceptsDAT-1Caenorhabditis elegansSwimming-Induced ParalysisNematode Caenorhabditis elegansGenetic mutationsPresynaptic DA transporterNovel genesHeterologous expressionDA releaseFunction mutationsGenetic modificationIon channelsElegansMutationsPresynaptic DA receptorsModulation of neurotransmissionGenesSWIPPhenotypeDA receptorsAcute blockadeDA transporterDA uptakeDA signalingExpression
2015
Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios
Zhu X, Petrovski S, Xie P, Ruzzo EK, Lu YF, McSweeney KM, Ben-Zeev B, Nissenkorn A, Anikster Y, Oz-Levi D, Dhindsa RS, Hitomi Y, Schoch K, Spillmann RC, Heimer G, Marek-Yagel D, Tzadok M, Han Y, Worley G, Goldstein J, Jiang YH, Lancet D, Pras E, Shashi V, McHale D, Need AC, Goldstein DB. Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios. Genetics In Medicine 2015, 17: 774-781. PMID: 25590979, PMCID: PMC4791490, DOI: 10.1038/gim.2014.191.Peer-Reviewed Original ResearchConceptsDisease genesWhole-exome sequencingDamaging de novo mutationsNovel bioinformatics approachNovel disease genesAppropriate bioinformatics analysisNew gene-disease associationsClinical sequence dataGene-disease associationsDisease-causing genesNovel genesIntolerant genesBioinformatics approachSequence dataBioinformatics analysisDe novo mutationsGenomic interpretationPattern of genotypesSimilar phenotypeGenesGenetic diseasesDiagnostic genotypesUndiagnosed genetic diseasesNovo mutationsCandidate genotypes
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