Featured Publications
Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences
Ganel L, Chen L, Christ R, Vangipurapu J, Young E, Das I, Kanchi K, Larson D, Regier A, Abel H, Kang CJ, Scott A, Havulinna A, Chiang CWK, Service S, Freimer N, Palotie A, Ripatti S, Kuusisto J, Boehnke M, Laakso M, Locke A, Stitziel NO, Hall IM. Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences. Human Genomics 2021, 15: 34. PMID: 34099068, PMCID: PMC8185936, DOI: 10.1186/s40246-021-00335-2.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedApoptosis Regulatory ProteinsCell LineageDNA Copy Number VariationsDNA, MitochondrialExome SequencingFemaleGenetic Predisposition to DiseaseGenome, MitochondrialGenome-Wide Association StudyGTP-Binding ProteinsHumansMaleMembrane ProteinsMendelian Randomization AnalysisMiddle AgedPhenotypePolymorphism, Single NucleotideProto-Oncogene Proteins c-mybSequence Analysis, DNAConceptsCell type compositionGenome copy numberBlood-derived DNAMitochondrial genome copy numberCombination of genomesCopy numberBulk DNA sequencingDNA sequencingPolygenic risk scoresNumber of mitochondriaExome sequencing dataRelated traitsSequencing dataMetabolic traitsTraitsCommon variantsLociRare variantsSequencingDNAFinnish individualsMendelian randomization frameworkUK BiobankMetS traitsGenomeGenomic Analysis in the Age of Human Genome Sequencing
Lappalainen T, Scott AJ, Brandt M, Hall IM. Genomic Analysis in the Age of Human Genome Sequencing. Cell 2019, 177: 70-84. PMID: 30901550, PMCID: PMC6532068, DOI: 10.1016/j.cell.2019.02.032.Peer-Reviewed Original ResearchMeSH KeywordsBiological Specimen BanksChromosome MappingGenetic Predisposition to DiseaseGenetic TestingGenetic VariationGenome, HumanGenome-Wide Association StudyGenomicsHigh-Throughput Nucleotide SequencingHuman Genome ProjectHumansPolymorphism, Single NucleotideSequence Analysis, DNAWhole Genome SequencingConceptsFunctional genomics approachAllele frequency spectrumHuman genome sequencingGene mapping studiesGenome sequencing technologiesRare human diseasesWhole-genome sequencingGenomic approachesGenetic variant discoveryGenome variationHuman genomeGenome analysisGenomic analysisSequencing technologiesGenome sequencingVariant discoveryHuman diseasesHuman geneticsGenomeFunctional interpretationMapping studiesFunctional effectsSequencingGermline variantsGeneticsThe impact of structural variation on human gene expression
Chiang C, Scott AJ, Davis JR, Tsang EK, Li X, Kim Y, Hadzic T, Damani FN, Ganel L, Montgomery S, Battle A, Conrad D, Hall I. The impact of structural variation on human gene expression. Nature Genetics 2017, 49: 692-699. PMID: 28369037, PMCID: PMC5406250, DOI: 10.1038/ng.3834.Peer-Reviewed Original ResearchSVScore: an impact prediction tool for structural variation
Ganel L, Abel HJ, , Hall IM. SVScore: an impact prediction tool for structural variation. Bioinformatics 2017, 33: 1083-1085. PMID: 28031184, PMCID: PMC5408916, DOI: 10.1093/bioinformatics/btw789.Peer-Reviewed Original ResearchMeSH KeywordsGene FrequencyGenomic Structural VariationGenomicsHumansPolymorphism, Single NucleotideSequence DeletionSoftwareSpeedSeq: ultra-fast personal genome analysis and interpretation
Chiang C, Layer RM, Faust GG, Lindberg MR, Rose DB, Garrison EP, Marth GT, Quinlan AR, Hall IM. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nature Methods 2015, 12: 966-968. PMID: 26258291, PMCID: PMC4589466, DOI: 10.1038/nmeth.3505.Peer-Reviewed Original ResearchRecurrent DNA copy number variation in the laboratory mouse
Egan CM, Sridhar S, Wigler M, Hall IM. Recurrent DNA copy number variation in the laboratory mouse. Nature Genetics 2007, 39: 1384-1389. PMID: 17965714, DOI: 10.1038/ng.2007.19.Peer-Reviewed Original ResearchConceptsCopy number variationsCopy numberNumber variationsGenome-wide analysisDNA copy number variationsRecent common ancestryGenerations of inbreedingRecurrent copy number variationsHigh-resolution microarraysCommon ancestryNatural variationGenetic differencesDifferent speciesDifferent lociGenerational timeLaboratory miceNonrandom processGenomeRecurrent mutationsLociAdditional strainsInbreedingLineagesGenesDiscrete segments
2022
High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios
Byrska-Bishop M, Evani U, Zhao X, Basile A, Abel H, Regier A, Corvelo A, Clarke W, Musunuri R, Nagulapalli K, Fairley S, Runnels A, Winterkorn L, Lowy E, Consortium H, Eichler E, Korbel J, Lee C, Marschall T, Devine S, Harvey W, Zhou W, Mills R, Rausch T, Kumar S, Alkan C, Hormozdiari F, Chong Z, Chen Y, Yang X, Lin J, Gerstein M, Kai Y, Zhu Q, Yilmaz F, Xiao C, Flicek P, Germer S, Brand H, Hall I, Talkowski M, Narzisi G, Zody M. High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios. Cell 2022, 185: 3426-3440.e19. PMID: 36055201, PMCID: PMC9439720, DOI: 10.1016/j.cell.2022.08.004.Peer-Reviewed Original ResearchFemaleGenome, HumanHigh-Throughput Nucleotide SequencingHumansINDEL MutationMalePolymorphism, Single NucleotideWhole Genome Sequencing