2025
High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties
Zhang J, Rajendran B, Desai S, Gibson J, DiPalermo J, LoRusso P, Kong Y, Zhao H, Cecchini M, Schalper K. High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties. Journal For ImmunoTherapy Of Cancer 2025, 13: e011653. PMID: 40935566, DOI: 10.1136/jitc-2025-011653.Peer-Reviewed Original ResearchThis study shows that high MGMT expression in colorectal cancer is linked to aggressive behavior, distinct genomic features, immune evasion, and shorter survival, highlighting its potential as a biomarker for prognosis and therapeutic targeting.scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Wang G, Zhao J, Lin Y, Liu T, Zhao Y, Zhao H. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nature Communications 2025, 16: 4994. PMID: 40442129, PMCID: PMC12122792, DOI: 10.1038/s41467-025-60333-z.Peer-Reviewed Original ResearchConceptsDeep learning frameworkSingle-cell multi-omics researchSingle-cell multi-omics dataLearning frameworkMulti-omics dataGenerative adversarial networkSingle-cell technologiesData alignmentSingle-cell resolutionMulti-omics researchDownstream analysisCellular statesOmics datasetsAdversarial networkNeural networkProteomic profilingCorrelated featuresBiological informationOmics perspectiveDiverse datasetsFeature topologyDisease mechanismsCell embeddingData resourcesRelationship inferenceGenomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes
Sierant M, Jin S, Bilguvar K, Morton S, Dong W, Jiang W, Lu Z, Li B, López-Giráldez F, Tikhonova I, Zeng X, Lu Q, Choi J, Zhang J, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Sedore S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, King E, Wagner M, Srivastava D, Shen Y, Bernstein D, Porter G, Newburger J, Seidman J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Chung W, Gelb B, Seidman C, Brueckner M, Lifton R. Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2420343122. PMID: 40127276, PMCID: PMC12002227, DOI: 10.1073/pnas.2420343122.Peer-Reviewed Original ResearchConceptsCongenital heart disease genesCongenital heart diseaseDamaging variantsMissense variantsAnalyzing de novo mutationsCHD probandsEpidermal growth factor (EGF)-like domainsNeurodevelopmental delayLoss of function variantsParent-offspring triosSyndromic congenital heart diseaseHeart disease genesDisease genesGenomic analysisCongenital heart disease subtypesAssociated with neurodevelopmental delayTetralogy of FallotFunctional variantsIncomplete penetranceCHD phenotypesGenesAssociated with developmentGenetic testingMolecular diagnosticsExtracardiac abnormalitiesThe human and non-human primate developmental GTEx projects
Bell T, Blanchard T, Hernandez R, Linn R, Taylor D, VonDran M, Ahooyi T, Beitra D, Bernieh A, Delaney M, Faith M, Fattahi E, Footer D, Gilbert M, Guambaña S, Gulino S, Hanson J, Hattrell E, Heinemann C, Kreeb J, Leino D, Mcdevitt L, Palmieri A, Pfeiffer M, Pryhuber G, Rossi C, Rasool I, Roberts R, Salehi A, Savannah E, Stachowicz K, Stokes D, Suplee L, Van Hoose P, Wilkins B, Williams-Taylor S, Zhang S, Ardlie K, Getz G, Lappalainen T, Montgomery S, Aguet F, Anderson L, Bernstein B, Choudhary A, Domenech L, Gaskell E, Johnson M, Liu Q, Marderstein A, Nedzel J, Okonda J, Padhi E, Rosano M, Russell A, Walker B, Sestan N, Gerstein M, Milosavljevic A, Borsari B, Cho H, Clarke D, Deveau A, Galeev T, Gobeske K, Hameed I, Huttner A, Jensen M, Jiang Y, Li J, Liu J, Liu Y, Ma J, Mane S, Meng R, Nadkarni A, Ni P, Park S, Petrosyan V, Pochareddy S, Salamon I, Xia Y, Yates C, Zhang M, Zhao H, Conrad D, Feng G, Brady F, Boucher M, Carbone L, Castro J, del Rosario R, Held M, Hennebold J, Lacey A, Lewis A, Lima A, Mahyari E, Moore S, Okhovat M, Roberts V, de Castro S, Wessel B, Zaniewski H, Zhang Q, Arguello A, Baroch J, Dayal J, Felsenfeld A, Ilekis J, Jose S, Lockhart N, Miller D, Minear M, Parisi M, Price A, Ramos E, Zou S. The human and non-human primate developmental GTEx projects. Nature 2025, 637: 557-564. PMID: 39815096, PMCID: PMC12013525, DOI: 10.1038/s41586-024-08244-9.Peer-Reviewed Original ResearchConceptsChromatin accessibility dataFunctional genomic studiesWhole-genome sequencingEffects of genetic variationSpatial gene expression profilesNon-human primatesGenotype-Tissue ExpressionGene expression profilesGenomic studiesGene regulationGenetic dataGenetic variationGenomic researchDonor diversityCommunity engagementHuman evolutionEarly developmental defectsGene expressionCell statesDevelopmental programmeHuman diseasesExpression profilesAdult tissuesDevelopmental defectsSingle-cell
2024
SANTO: a coarse-to-fine alignment and stitching method for spatial omics
Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nature Communications 2024, 15: 6048. PMID: 39025895, PMCID: PMC11258319, DOI: 10.1038/s41467-024-50308-x.Peer-Reviewed Original ResearchStatistical methods for assessing the effects of de novo variants on birth defects
Xie Y, Wu R, Li H, Dong W, Zhou G, Zhao H. Statistical methods for assessing the effects of de novo variants on birth defects. Human Genomics 2024, 18: 25. PMID: 38486307, PMCID: PMC10938830, DOI: 10.1186/s40246-024-00590-z.Peer-Reviewed Original ResearchMeSH KeywordsGenetic HeterogeneityGenomicsHigh-Throughput Nucleotide SequencingHumansSample SizeWorkflowConceptsDe novo variantsAnalyzed de novo variantsDevelopment of next-generation sequencing technologiesNext-generation sequencing technologiesSequencing technologiesImprove statistical powerGenetic heterogeneitySequenced samplesStatistical powerBirth defectsDiseased individualsLow occurrenceCongenital heart diseaseVariantsGenesDeleterious effectsSequenceGeneral workflowStatistical methods
2023
Genomic evidence of sex chromosome aneuploidy and infection-associated genotypes in the tsetse fly Glossina fuscipes, the major vector of African trypanosomiasis in Uganda
Saarman N, Son J, Zhao H, Cosme L, Kong Y, Li M, Wang S, Weiss B, Echodu R, Opiro R, Aksoy S, Caccone A. Genomic evidence of sex chromosome aneuploidy and infection-associated genotypes in the tsetse fly Glossina fuscipes, the major vector of African trypanosomiasis in Uganda. Infection Genetics And Evolution 2023, 114: 105501. PMID: 37709241, PMCID: PMC10593118, DOI: 10.1016/j.meegid.2023.105501.Peer-Reviewed Original ResearchConceptsGenome-wide association analysisNovel vector control strategiesAnimal African trypanosomiasisSex chromosome aneuploidyNatural populationsGenome assemblySex chromosomesGenomic regionsGenomic evidenceGenetic variationBp upstreamAutosomal SNPsSeq dataTrypanosome infectionTrypanosome parasitesAssociation analysisMolecular pathwaysAssembly metricsLinkage disequilibriumLecithin-cholesterol acyltransferaseAfrican trypanosomiasisMajor vectorGenomeSNPsPrimary vectorEstimation on risk of spontaneous abortions by genomic disorders from a meta‐analysis of microarray results on large case series of pregnancy losses
Peng G, Zhou Q, Chai H, Wen J, Zhao H, Taylor H, Jiang Y, Li P. Estimation on risk of spontaneous abortions by genomic disorders from a meta‐analysis of microarray results on large case series of pregnancy losses. Molecular Genetics & Genomic Medicine 2023, 11: e2181. PMID: 37013615, PMCID: PMC10422064, DOI: 10.1002/mgg3.2181.Peer-Reviewed Original ResearchConceptsGenomic disordersChromosome microarray analysisWilliams-Beuren syndromePathogenic copy number variantsPopulation genetic studiesWolf-Hirschhorn syndromeCopy number variantsDiGeorge syndromeMicroarray analysisMicroarray resultsChromosomal abnormalitiesGenetic studiesNumber variantsGenetic counseling
2022
An unbiased kinship estimation method for genetic data analysis
Jiang W, Zhang X, Li S, Song S, Zhao H. An unbiased kinship estimation method for genetic data analysis. BMC Bioinformatics 2022, 23: 525. PMID: 36474154, PMCID: PMC9727941, DOI: 10.1186/s12859-022-05082-2.Peer-Reviewed Original ResearchConceptsRigorous mathematical proofGenetic data analysisReal data analysisUnbiased estimation methodEstimation methodIndividual-level genotype dataSample correlation coefficientMathematical proofMathematical derivationMean square errorCoefficient estimationMatrix methodEstimation accuracyEstimation biasHeritability estimationRoot mean square errorData analysisSquare errorAccurate estimatesEstimationUKINVariances of genotypesSpurious associationsKinship coefficientsEstimates
2021
The impact of removing former drinkers from genome‐wide association studies of AUDIT‐C
Dao C, Zhou H, Small A, Gordon KS, Li B, Kember RL, Ye Y, Gelernter J, Xu K, Kranzler HR, Zhao H, Justice AC. The impact of removing former drinkers from genome‐wide association studies of AUDIT‐C. Addiction 2021, 116: 3044-3054. PMID: 33861876, PMCID: PMC9377185, DOI: 10.1111/add.15511.Peer-Reviewed Original Research
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