2024
Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex
Cote A, Young H, Huckins L. Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex. Human Genetics And Genomics Advances 2024, 5: 100311. PMID: 38773772, PMCID: PMC11214266, DOI: 10.1016/j.xhgg.2024.100311.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGene co-expressionCo-expressionExpression quantitative trait locus methodGenetic variantsComplex trait heritabilityMultiple testing burdenGene-based testsQuantitative trait lociTrans-eQTLsCis-eQTLsRegulatory variationSequencing datasetsTrait lociGene regulationTrait heritabilityGene functionGene modulesReal-data applicationModule genesGenesTesting burdenDorsolateral prefrontal cortexVariantsComparison to prior studiesDissecting the biology of feeding and eating disorders
Huckins L, Brennand K, Bulik C. Dissecting the biology of feeding and eating disorders. Trends In Molecular Medicine 2024, 30: 380-391. PMID: 38431502, DOI: 10.1016/j.molmed.2024.01.009.Peer-Reviewed Original ResearchGenome-wide association studiesVariants to genesGenes to pathwaysSignificant lociFunctional genomicsAssociation studiesGenetic relationshipsIntestinal microbiotaGenetic researchGenomeGenetic correlationsGenesMetabolic contributorsAnorexia nervosaEating disordersPathwayBiologyMetabolic outcomesRisk factorsLociMicrobiotaPhenomicsLethal illnessTraitsFeeding
2023
56. USING HIPSC-NEURONS AND CRISPR TO UNCOVER NON-ADDITIVE EFFECTS OF SCZ RISK GENES
Deans M, Seah C, Johnson J, García-González J, Townsley K, Cao E, Schrode N, Stahl E, O'Reilly P, Huckins L, Brennand K. 56. USING HIPSC-NEURONS AND CRISPR TO UNCOVER NON-ADDITIVE EFFECTS OF SCZ RISK GENES. European Neuropsychopharmacology 2023, 75: s86. DOI: 10.1016/j.euroneuro.2023.08.162.Peer-Reviewed Original ResearchSCZ risk genesNon-additive effectsRisk genesCombinatorial perturbationsTranscriptomic effectsFunctional roleRisk variantsGene expression changesBulk RNA-seqMultiple functional rolesSynaptic functionHigh-throughput imagingFunctional redundancyTranscriptional regulatorsRNA-seqCRISPR activationCellular phenotypesRNA interferenceEGenesGene expressionExpression changesHiPSC neuronsPolygenic risk scoresGenetic studiesGenesSTRESS IN A DISH: MODELING THE IMPACT OF COMMON GENETIC VARIATION ON STRESS RESPONSE IN HIPSC-DERIVED NEURONS IN PTSD
Seah C, Signer R, Young H, Kozik E, Rusielewicz T, Bader H, Xu C, de Pins A, Breen M, Paull D, Yehuda R, Girgenti M, Brennand K, Huckins L. STRESS IN A DISH: MODELING THE IMPACT OF COMMON GENETIC VARIATION ON STRESS RESPONSE IN HIPSC-DERIVED NEURONS IN PTSD. European Neuropsychopharmacology 2023, 75: s40. DOI: 10.1016/j.euroneuro.2023.08.081.Peer-Reviewed Original ResearchCommon genetic variationGenetic variationStress responseCell typesEQTL associationsTranscriptional stress responseGenomic risk lociTissue-specific mannerChIP-seq datasetsCell type deconvolutionCommon genetic variantsPost-mortem brainsGene expression signaturesHiPSC-derived neuronsTranscription factorsSuch lociCatalog genesRisk lociGenetic studiesExpression signaturesGenetic variantsRegulatory activityGenesEQTLsMechanistic understandingSchizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations
Liu D, Meyer D, Fennessy B, Feng C, Cheng E, Johnson J, Park Y, Rieder M, Ascolillo S, de Pins A, Dobbyn A, Lebovitch D, Moya E, Nguyen T, Wilkins L, Hassan A, Burdick K, Buxbaum J, Domenici E, Frangou S, Hartmann A, Laurent-Levinson C, Malhotra D, Pato C, Pato M, Ressler K, Roussos P, Rujescu D, Arango C, Bertolino A, Blasi G, Bocchio-Chiavetto L, Campion D, Carr V, Fullerton J, Gennarelli M, González-Peñas J, Levinson D, Mowry B, Nimgaokar V, Pergola G, Rampino A, Cervilla J, Rivera M, Schwab S, Wildenauer D, Daly M, Neale B, Singh T, O’Donovan M, Owen M, Walters J, Ayub M, Malhotra A, Lencz T, Sullivan P, Sklar P, Stahl E, Huckins L, Charney A. Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations. Nature Genetics 2023, 55: 369-376. PMID: 36914870, PMCID: PMC10011128, DOI: 10.1038/s41588-023-01305-1.Peer-Reviewed Original ResearchConceptsProtein-truncating variantsRare protein-truncating variantsControls of diverse ancestryGenetic architecture of schizophreniaRare variant signalsProtein-coding regionsSchizophrenia risk genesCustom sequencing panelAncestral populationsAllelic spectrumGenetic architectureHuman populationDiverse ancestryHuman geneticsRisk genesVariant signalsGenesSequencing panelSchizophrenia casesSchizophrenia riskChronic mental illnessAncestryGeneticsMental illnessVariants
2022
What next for eating disorder genetics? Replacing myths with facts to sharpen our understanding
Huckins LM, Signer R, Johnson J, Wu YK, Mitchell KS, Bulik CM. What next for eating disorder genetics? Replacing myths with facts to sharpen our understanding. Molecular Psychiatry 2022, 27: 3929-3938. PMID: 35595976, PMCID: PMC9718676, DOI: 10.1038/s41380-022-01601-y.Peer-Reviewed Original ResearchChapter 14 Integration with systems biology approaches and -omics data to characterize risk variation
Young H, Cote A, Huckins L. Chapter 14 Integration with systems biology approaches and -omics data to characterize risk variation. 2022, 289-315. DOI: 10.1016/b978-0-12-819602-1.00017-6.Peer-Reviewed Original ResearchAssociation studiesPatterns of linkage disequilibriumTranscriptome-wide association studyGenome-wide association studiesFunctional genomic annotationsSystems biology approachGenome annotationNoncoding variantsNoncoding regionsLinkage disequilibriumGene regulationRegulatory regionsGene networksGenetic variantsBiology approachFunctional pathwaysRisk variationDevelopmental stagesGenetic riskGenesPathwayUnique considerationsVariantsVariation researchPsychiatric disorders
2020
Massively parallel techniques for cataloguing the regulome of the human brain
Townsley KG, Brennand KJ, Huckins LM. Massively parallel techniques for cataloguing the regulome of the human brain. Nature Neuroscience 2020, 23: 1509-1521. PMID: 33199899, PMCID: PMC8018778, DOI: 10.1038/s41593-020-00740-1.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsRegulatory elementsTarget genesParallel reporter assaysPutative regulatory elementsNon-coding regionsDisease-associated lociSpecific expression patternsCandidate risk lociPluripotent stem cellsHigh-throughput assaysRelevant molecular pathwaysTranscriptional responseRegulatory architectureRisk lociExpression patternsReporter assaysComplex brain disordersMolecular pathwaysRegulomeStem cellsRisk architectureGenetic riskGenesLociGenetic diagnosis
2019
Synergistic effects of common schizophrenia risk variants
Schrode N, Ho SM, Yamamuro K, Dobbyn A, Huckins L, Matos MR, Cheng E, Deans PJM, Flaherty E, Barretto N, Topol A, Alganem K, Abadali S, Gregory J, Hoelzli E, Phatnani H, Singh V, Girish D, Aronow B, Mccullumsmith R, Hoffman GE, Stahl EA, Morishita H, Sklar P, Brennand KJ. Synergistic effects of common schizophrenia risk variants. Nature Genetics 2019, 51: 1475-1485. PMID: 31548722, PMCID: PMC6778520, DOI: 10.1038/s41588-019-0497-5.Peer-Reviewed Original ResearchMeSH KeywordsChloride ChannelsCRISPR-Cas SystemsFemaleFurinGene EditingGene Expression RegulationGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansInduced Pluripotent Stem CellsMaleMonomeric Clathrin Assembly ProteinsPolymorphism, Single NucleotideQuantitative Trait LociSchizophreniaSNARE ProteinsConceptsExpression quantitative trait lociComplex genetic disorderEQTL genesCommon variantsQuantitative trait lociRisk variantsGene expression differencesPsychiatric disease riskCommon risk variantsPluripotent stem cellsSchizophrenia risk variantsGenetic disordersTrait lociGene perturbationsGenetic approachesExpression differencesGene editingStem cellsGeneralizable phenomenonSynaptic functionGenesVariantsCRISPRLociSpecific effectsGenetic Overlap Between Alzheimer’s Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes
Drange O, Smeland O, Shadrin A, Finseth P, Witoelar A, Frei O, Group P, Wang Y, Hassani S, Djurovic S, Dale A, Andreassen O, Stahl E, Breen G, Forstner A, McQuillin A, Ripke S, Trubetskoy V, Mattheisen M, Wang Y, Coleman J, Gaspar H, de Leeuw C, Steinberg S, Pavlides J, Trzaskowski M, Pers T, Holmans P, Abbott L, Agerbo E, Akil H, Albani D, Alliey-Rodriguez N, Als T, Anjorin A, Antilla V, Awasthi S, Badner J, Bækvad-Hansen M, Barchas J, Bass N, Bauer M, Belliveau R, Bergen S, Pedersen C, Bøen E, Boks M, Boocock J, Budde M, Bunney W, Burmeister M, Bybjerg-Grauholm J, Byerley W, Casas M, Cerrato F, Cervantes P, Chambert K, Charney A, Chen D, Churchhouse C, Clarke T, Coryell W, Craig D, Cruceanu C, Curtis D, Czerski P, Dale A, de Jong S, Degenhardt F, Del-Favero J, DePaulo J, Djurovic S, Dobbyn A, Dumont A, Elvsåshagen T, Escott-Price V, Fan C, Fischer S, Flickinger M, Foroud T, Forty L, Frank J, Fraser C, Freimer N, Friseìn L, Gade K, Gage D, Garnham J, Giambartolomei C, Pedersen M, Goldstein J, Gordon S, Gordon-Smith K, Green E, Green M, Greenwood T, Grove J, Guan W, Parra J, Hamshere M, Hautzinger M, Heilbronner U, Herms S, Hipolito M, Hoffmann P, Holland D, Huckins L, Jamain S, Johnson J, Jureìus A, Kandaswamy R, Karlsson R, Kennedy J, Kittel-Schneider S, Knott S, Knowles J, Kogevinas M, Koller A, Kupka R, Lavebratt C, Lawrence J, Lawson W, Leber M, Lee P, Levy S, Li J, Liu C, Lucae S, Maaser A, MacIntyre D, Mahon P, Maier W, Martinsson L, McCarroll S, McGuffin P, McInnis M, McKay J, Medeiros H, Medland S, Meng F, Milani L, Montgomery G, Morris D, Mühleisen T, Mullins N, Nguyen H, Nievergelt C, Adolfsson A, Nwulia E, O’Donovan C, Loohuis L, Ori A, Oruc L, Ösby U, Perlis R, Perry A, Pfennig A, Potash J, Purcell S, Regeer E, Reif A, Reinbold C, Rice J, Rivas F, Rivera M, Roussos P, Ruderfer D, Ryu E, Saìnchez-Mora C, Schatzberg A, Scheftner W, Schork N, Weickert C, Shehktman T, Shilling P, Sigurdsson E, Slaney C, Smeland O, Sobell J, Hansen C, Spijker A, St Clair D, Steffens M, Strauss J, Streit F, Strohmaier J, Szelinger S, Thompson R, Thorgeirsson T, Treutlein J, Vedder H, Wang W, Watson S, Weickert T, Witt S, Xi S, Xu W, Young A, Zandi P, Zhang P, Zollner S, Adolfsson R, Agartz I, Alda M, Backlund L, Baune B, Bellivier F, Berrettini W, Biernacka J, Blackwood D, Boehnke M, Børglum A, Corvin A, Craddock N, Daly M, Dannlowski U, Esko T, Etain B, Frye M, Fullerton J, Gershon E, Gill M, Goes F, Grigoroiu-Serbanescu M, Hauser J, Hougaard D, Hultman C, Jones I, Jones L, Kahn R, Kirov G, Landeìn M, Leboyer M, Lewis C, Li Q, Lissowska J, Martin N, Mayoral F, McElroy S, McIntosh A, McMahon F, Melle I, Metspalu A, Mitchell P, Morken G, Mors O, Mortensen P, Müller-Myhsok B, Myers R, Neale B, Nimgaonkar V, Nordentoft M, Nöthen M, O’Donovan M, Oedegaard K, Owen M, Paciga S, Pato C, Pato M, Posthuma D, Ramos-Quiroga J, Ribaseìs M, Rietschel M, Rouleau G, Schalling M, Schofield P, Schulze T, Serretti A, Smoller J, Stefansson H, Stefansson K, Stordal E, Sullivan P, Turecki G, Vaaler A, Vieta E, Vincent J, Werge T, Nurnberger J, Wray N, Di Florio A, Edenberg H, Cichon S, Ophoff R, Scott L, Andreassen O, Kelsoe J, Sklar P. Genetic Overlap Between Alzheimer’s Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes. Frontiers In Neuroscience 2019, 13: 220. PMID: 30930738, PMCID: PMC6425305, DOI: 10.3389/fnins.2019.00220.Peer-Reviewed Original ResearchCommon genetic variantsNovel lociGenetic variantsGenetic overlapPolygenic overlapGenome-wide associationNovel genomic lociNumerous common genetic variantsGenomic lociComplex traitsWide associationGenesLociInternational GenomicsGenetic originTraitsAlzheimer's diseaseImplicatingVariantsGenomicsOverlapBipolar disorderDistinct featuresFurther studies
2018
Landscape 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 samplesExploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics
Barbeira A, Dickinson S, Bonazzola R, Zheng J, Wheeler H, Torres J, Torstenson E, Shah K, Garcia T, Edwards T, Stahl E, Huckins L, GTEx Consortium, Nicolae D, Cox N, Im H. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nature Communications 2018, 9: 1825. PMID: 29739930, PMCID: PMC5940825, DOI: 10.1038/s41467-018-03621-1.Peer-Reviewed Original ResearchConceptsGene expression variationExpression variationMonogenic disease genesGWAS summary statisticsSpectrum of milder phenotypesGTEx tissuesS-PrediXcanDisease genesTrait etiologyPhenotypic consequencesGenetic variantsHuman phenotypesRegulatory mechanismsMilder phenotypeSummary statisticsSignificant associationPhenotypeGenesTraitsMeta-analysis studySummary dataReference setsPrediXcanIndependent cohortGWASCommon schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection
Pardiñas A, Holmans P, Pocklington A, Escott-Price V, Ripke S, Carrera N, Legge S, Bishop S, Cameron D, Hamshere M, Han J, Hubbard L, Lynham A, Mantripragada K, Rees E, MacCabe J, McCarroll S, Baune B, Breen G, Byrne E, Dannlowski U, Eley T, Hayward C, Martin N, McIntosh A, Plomin R, Porteous D, Wray N, Caballero A, Geschwind D, Huckins L, Ruderfer D, Santiago E, Sklar P, Stahl E, Won H, Agerbo E, Als T, Andreassen O, Bækvad-Hansen M, Mortensen P, Pedersen C, Børglum A, Bybjerg-Grauholm J, Djurovic S, Durmishi N, Pedersen M, Golimbet V, Grove J, Hougaard D, Mattheisen M, Molden E, Mors O, Nordentoft M, Pejovic-Milovancevic M, Sigurdsson E, Silagadze T, Hansen C, Stefansson K, Stefansson H, Steinberg S, Tosato S, Werge T, GERAD1 Consortium, CRESTAR Consortium, Collier D, Rujescu D, Kirov G, Owen M, O’Donovan M, Walters J. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics 2018, 50: 381-389. PMID: 29483656, PMCID: PMC5918692, DOI: 10.1038/s41588-018-0059-2.Peer-Reviewed Original ResearchConceptsMutation-intolerant genesGenetic architecture of schizophreniaGenome-wide association studiesChromosome conformation dataGenome fine mappingVariant association signalsAssociation signalsFine-mappingGenetic architectureCausal genesGenomic studiesAssociation studiesRisk variantsSelection pressureGenesLociBrain expressionAssociated with poor qualityBiologyConformational dataDecreased life expectancyChromosomeDebilitating psychiatric conditionAllelesLife expectancy