2024
HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data
Cheng Y, Cai B, Li H, Zhang X, D’Souza G, Shrestha S, Edmonds A, Meyers J, Fischl M, Kassaye S, Anastos K, Cohen M, Aouizerat B, Xu K, Zhao H. HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data. Genome Biology 2024, 25: 273. PMID: 39407252, PMCID: PMC11476968, DOI: 10.1186/s13059-024-03411-7.Peer-Reviewed Original ResearchConceptsMethylation quantitative trait lociQuantitative trait lociTrait lociMethylation dataFunctional annotation of genetic variantsAnnotation of genetic variantsGenetic variantsBisulfite sequencing dataEffects of genetic variantsBiologically relevant cell typesDNA methylation levelsCell typesFunctional annotationSequence dataComplex traitsMethylation datasetsRelevant cell typesMeQTLsMethylation levelsMethylation regulatorsReal data analysesLociVariantsMethylationDNAFine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes
Chen Z, Guo X, Tao R, Huyghe J, Law P, Fernandez-Rozadilla C, Ping J, Jia G, Long J, Li C, Shen Q, Xie Y, Timofeeva M, Thomas M, Schmit S, Díez-Obrero V, Devall M, Moratalla-Navarro F, Fernandez-Tajes J, Palles C, Sherwood K, Briggs S, Svinti V, Donnelly K, Farrington S, Blackmur J, Vaughan-Shaw P, Shu X, Lu Y, Broderick P, Studd J, Harrison T, Conti D, Schumacher F, Melas M, Rennert G, Obón-Santacana M, Martín-Sánchez V, Oh J, Kim J, Jee S, Jung K, Kweon S, Shin M, Shin A, Ahn Y, Kim D, Oze I, Wen W, Matsuo K, Matsuda K, Tanikawa C, Ren Z, Gao Y, Jia W, Hopper J, Jenkins M, Win A, Pai R, Figueiredo J, Haile R, Gallinger S, Woods M, Newcomb P, Duggan D, Cheadle J, Kaplan R, Kerr R, Kerr D, Kirac I, Böhm J, Mecklin J, Jousilahti P, Knekt P, Aaltonen L, Rissanen H, Pukkala E, Eriksson J, Cajuso T, Hänninen U, Kondelin J, Palin K, Tanskanen T, Renkonen-Sinisalo L, Männistö S, Albanes D, Weinstein S, Ruiz-Narvaez E, Palmer J, Buchanan D, Platz E, Visvanathan K, Ulrich C, Siegel E, Brezina S, Gsur A, Campbell P, Chang-Claude J, Hoffmeister M, Brenner H, Slattery M, Potter J, Tsilidis K, Schulze M, Gunter M, Murphy N, Castells A, Castellví-Bel S, Moreira L, Arndt V, Shcherbina A, Bishop D, Giles G, Southey M, Idos G, McDonnell K, Abu-Ful Z, Greenson J, Shulman K, Lejbkowicz F, Offit K, Su Y, Steinfelder R, Keku T, van Guelpen B, Hudson T, Hampel H, Pearlman R, Berndt S, Hayes R, Martinez M, Thomas S, Pharoah P, Larsson S, Yen Y, Lenz H, White E, Li L, Doheny K, Pugh E, Shelford T, Chan A, Cruz-Correa M, Lindblom A, Hunter D, Joshi A, Schafmayer C, Scacheri P, Kundaje A, Schoen R, Hampe J, Stadler Z, Vodicka P, Vodickova L, Vymetalkova V, Edlund C, Gauderman W, Shibata D, Toland A, Markowitz S, Kim A, Chanock S, van Duijnhoven F, Feskens E, Sakoda L, Gago-Dominguez M, Wolk A, Pardini B, FitzGerald L, Lee S, Ogino S, Bien S, Kooperberg C, Li C, Lin Y, Prentice R, Qu C, Bézieau S, Yamaji T, Sawada N, Iwasaki M, Le Marchand L, Wu A, Qu C, McNeil C, Coetzee G, Hayward C, Deary I, Harris S, Theodoratou E, Reid S, Walker M, Ooi L, Lau K, Zhao H, Hsu L, Cai Q, Dunlop M, Gruber S, Houlston R, Moreno V, Casey G, Peters U, Tomlinson I, Zheng W. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes. Nature Communications 2024, 15: 3557. PMID: 38670944, PMCID: PMC11053150, DOI: 10.1038/s41467-024-47399-x.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesCredible causal variantsColorectal cancer susceptibility genesSusceptibility genesAssociation signalsAnalysis of single-cell RNA-seq dataAnalysis of whole-exome sequencing dataGenome-wide association study dataColorectal cancer risk lociSingle-cell RNA-seq dataTarget genesWhole-exome sequencing dataFunctional genomic investigationsFine-mapping analysisRNA-seq dataExome sequencing dataTissue-specific transcriptomesColorectal cancerCancer susceptibility genesCausal variantsFine-mappingRisk lociMethylome dataSequence dataGenomic investigations
2001
Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test
Zhang S, Zhang K, Li J, Sun F, Zhao H. Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test. Genetic Epidemiology 2001, 21: s370-s375. PMID: 11793701, DOI: 10.1002/gepi.2001.21.s1.s370.Peer-Reviewed Original ResearchConceptsQuantitative pedigree disequilibrium testPedigree disequilibrium testQuantitative traitsTraits of interestGenetic Analysis Workshop 12Disequilibrium testGeneral pedigreesSequence dataCandidate genesGenetic markersGenetic linkageQualitative traitsLinkage disequilibriumTraitsLarge pedigreePresence of linkagePedigreeStatistical methodsFamilyNuclear familiesTests of associationGenesUnrelated nuclear familiesLinkageDisequilibrium
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