2021
Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example
Hatoum AS, Wendt FR, Galimberti M, Polimanti R, Neale B, Kranzler HR, Gelernter J, Edenberg HJ, Agrawal A. Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example. Drug And Alcohol Dependence 2021, 229: 109115. PMID: 34710714, PMCID: PMC9358969, DOI: 10.1016/j.drugalcdep.2021.109115.Peer-Reviewed Original ResearchConceptsGenome-wide significant variantsCandidate gene predictionGenetic predictionRandom SNPsPolygenic traitRandom phenotypeCandidate SNPsSimulated phenotypesPsychiatric geneticsGenetic machineSignificant variantsBinary phenotypesCandidate variantsSNPsAncestryPhenotypeAllele frequenciesVariantsMachine learning modelsGenetic testsLearning model
2013
Exploring the genetic architecture of alcohol dependence in African-Americans via analysis of a genomewide set of common variants
Yang C, Li C, Kranzler HR, Farrer LA, Zhao H, Gelernter J. Exploring the genetic architecture of alcohol dependence in African-Americans via analysis of a genomewide set of common variants. Human Genetics 2013, 133: 617-624. PMID: 24297757, PMCID: PMC3988209, DOI: 10.1007/s00439-013-1399-8.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsPhenotypic varianceGenetic architectureSubset of SNPsTop single nucleotide polymorphismsKb of genesCommon variantsAD risk genesCommon single nucleotide polymorphismsGenome partitioningGenomewide association studiesPolygenic traitChromosome 4Illumina OmniAssociation studiesRisk genesGenetic variantsGenomewide setComplex psychiatric disorderGenesFunctional partitioningMultiple variantsGenetic factorsDevelopment of ADVariants