Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation

G3: Genes, Genomes, Genetics

Volumn 4, Issue 3, 2014, Pages 539-546

  Hofheinz, Nina ^a   Frisch, Matthias ^a  

^a JUSTUS LIEBIG UNIVERSITY   (Germany)

Author keywords

Disequilibrium; Genome wide; GenPred; Heteroscedastic; Linkage; Marker; Plant breeding; Populations; Prediction; Resources; Ridge regression; Shared data; Variances

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; BAYES THEOREM; CHROMOSOME; COMPUTER SIMULATION; EFFECT SIZE; GENE LINKAGE DISEQUILIBRIUM; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC VARIABILITY; HETEROSCEDASTIC RIDGE REGRESSION; MAIZE; PHENOTYPE; PLANT BREEDING; PLANT GENETICS; PREDICTION; RMLV APPROACH; RRWA APPROACH; SUGAR BEET; WHEAT;

GENOME-WIDE PREDICTION; GENPRED; HETEROSCEDASTIC MARKER VARIANCES; LINKAGE DISEQUILIBRIUM; PLANT BREEDING POPULATIONS; RIDGE REGRESSION; SHARED DATA RESOURCES;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; GENOME, PLANT; GENOTYPE; LINEAR MODELS; LINKAGE DISEQUILIBRIUM; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE; TRITICUM; ZEA MAYS;

EID: 84896982403     PISSN: None     EISSN: 21601836     Source Type: Journal    
DOI: 10.1534/g3.113.010025     Document Type: Article

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