title: A combined coalescence gene-dropping tool for evaluating genomic selection in complex scenarios (ms2gs).
abstract: We present ms2gs, a combined coalescence - gene dropping (i.e. backward-forward) simulator for complex traits. It therefore aims at combining the advantages of both approaches. It is primarily conceived for very short term, recent scenarios such as those that are of interest in animal and plant breeding. It is very flexible in terms of defining QTL architecture and SNP ascertainment bias, and it allows for easy modelling of alternative markers such as RADs. It can use real sequence or chip data or generate molecular polymorphisms via the coalescence. It can generate QTL conditional on extant molecular information, such as low-density genotyping. It models (simplistically) sequence, imputation or genotyping errors. It requires as input both genotypic data in plink or ms formats, and a pedigree that is used to perform the gene dropping. By default, it compares accuracy for BLUP, SNP ascertained data, sequence, and causal SNPs. It employs VanRaden's linear (GBLUP) and nonlinear method for incorporating molecular information. To illustrate the program, we present a small application in a half-sib population and a multiparental (MAGIC) cross. The program, manual and examples are available at https://github.com/mperezenciso/ms2gs.
authors.affiliation: Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Bellaterra, Spain.|Institut Català de Recerca i Estudis Avançats (ICREA), Carrer de Lluís Companys, Barcelona, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Pérez-Enciso
authors.affiliation: UMR 1388 GENPHYSE, Génétique, Physiologie et Systèmes d'Elevage, INRA, Castanet-Tolosan, France.
authors.forename: A
authors.initials: A
authors.lastname: Legarra
mesh_terms: D000818:Animals; D001947:Breeding / Q000379:methods*; D003198:Computer Simulation; D003433:Crosses, Genetic; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci
keywords: Animal breeding; genomic selection; quantitative genetics; simulation