Dealing with high dimensionality for the identification of common and rare variants as main effects and for gene-environment interaction

Genetic Epidemiology

Volumn 35, Issue SUPPL. 1, 2011, Pages

  Bickeböller, Heike ^a   Houwing Duistermaat, Jeanine J ^b   Wang, Xuefeng ^c   Yan, Xiting ^d  

^a UNIVERSITY MEDICAL CENTER GÖTTINGEN   (Germany)

^b LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

^c CASE WESTERN RESERVE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d YALE UNIVERSITY   (United States)

Author keywords

Common variants; Exome sequencing; Gene environment interaction; Generalized linear mixed models; High dimensionality; Penalized regression; Rare variants

Indexed keywords

VASCULOTROPIN;

ARTICLE; BAYES THEOREM; CONTROLLED STUDY; EFFECT SIZE; EXOME; FAMILY; GENE ENVIRONMENT INTERACTION; GENE FREQUENCY; GENE IDENTIFICATION; GENE INTERACTION; GENE REPLICATION; GENE SEQUENCE; GENETIC ASSOCIATION; GENETIC EPIDEMIOLOGY; GENETIC VARIABILITY; GENOTYPE; HUMAN; KERNEL METHOD; POPULATION STRUCTURE; RANDOM FOREST; REDUCTION; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION; SINGLE NUCLEOTIDE POLYMORPHISM; SPECIES DIFFERENCE;

BAYES THEOREM; EXOME; GENE-ENVIRONMENT INTERACTION; HUMANS; MODELS, GENETIC; MOLECULAR EPIDEMIOLOGY; REGRESSION ANALYSIS; SEQUENCE ANALYSIS;

EID: 82455192525     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.20647     Document Type: Article

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