Combination of multipoint maximum likelihood (MML) and regression mapping algorithms to construct a high-density genetic linkage map for loblolly pine (Pinus taeda L.)

Genetic maps have been successfully applied to assist in the dissection of complex traits, provide insight on genome structure, and estimate recombination in conjunction with physical maps. Despite an extensive list of genetic maps developed for loblolly pine (Pinus taeda L.) over the past two decades, a high-density consensus map has not yet been constructed. In this study, we used two reference three-generation outbred pedigrees, base and qtl, obtained from the North Carolina State University Cooperative Tree Improvement Program, to obtain a high-density genetic consensus map. Both populations were genotyped with a parts per thousand 7,000 different markers (restriction fragment length polymorphisms, expressed sequence tag polymorphisms, simple sequence repeats, SNPs). The grouping, ordering, and spacing of the markers on each linkage group were performed with JoinMapA (R) 4.1, which implements the multipoint maximum likelihood algorithm for outbred populations. The final consensus map contains 2,466 markers, with a total length of 1,476 centimorgans (cM). The average marker density across the 12 linkage groups was 0.62 cM/marker. This high-density map provides an important resource for breeders and geneticists and will enable comparative studies across species, as well as improve the loblolly pine genome sequence assembly.