title: Ridge, Lasso and Bayesian additive-dominance genomic models.
abstract: BACKGROUND
  A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes).
  
  
  RESULTS
  G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close.
  
  
  CONCLUSIONS
  Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (-2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.
authors: Department of Statistics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. camila.azevedo@ufv.br.
authors: Camila Ferreira
authors: CF
authors: Azevedo
authors: Department of Statistics, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. marcos.deon@gmail.com.|Embrapa Forestry, Colombo, Paraná, Brazil. marcos.deon@gmail.com.
authors: Marcos Deon Vilela
authors: MD
authors: de Resende
authors: Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. fabyanofonseca@ufv.br.
authors: Fabyano Fonseca
authors: FF
authors: E Silva
authors: Department of General Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. jmsviana@ufv.br.
authors: José Marcelo Soriano
authors: JM
authors: Viana
authors: Department of General Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. magnosavio@yahoo.com.br.
authors: Magno Sávio Ferreira
authors: MS
authors: Valente
authors: RAPiD Genomics, Florida Innovation Hub, Gainesville, Florida, USA. mresende@rapid-genomics.com.
authors: Márcio Fernando Ribeiro
authors: MF
authors: Resende
authors: Agronomy Department, University of Florida, Gainesville, Florida, USA. pmdelvalle@gmail.com.
authors: Patricio
authors: P
authors: Muñoz
mesh_terms: D000465:Algorithms; D005799:Genes, Dominant; D055106:Genome-Wide Association Study / Q000379:methods; D008957:Models, Genetic; D015233:Models, Statistical; D040641:Quantitative Trait Loci; D015203:Reproducibility of Results; D012641:Selection, Genetic