Patterns of genetic diversity and structure of a threatened palm species (Euterpe edulis Arecaceae) from the Brazilian Atlantic Forest.

The detection of distribution patterns of genetic diversity of plant and animal species has contributed to the understanding of biodiversity and evolutionary history of the Atlantic Forest. We used microsatellite markers to access the genetic diversity and structure of 26 populations and 527 adult individuals of Euterpe edulis, a native palm which is an important food resource for fauna and is intensively exploited due to economic reasons. We found high genetic diversity and inbreeding in all populations analyzed. We report highest rates of inbreeding for this species, which could reflect the anthropic impacts of selective cutting, fragmentation, and change in foraging behavior from pollinators and less availability and mobility of large dispersers. We detected by STRUCTURE, two genetic groups, Northern and Southern, which divide the Brazilian Atlantic Forest geographically. These groups have low genetic admixtures, but we found a region of lineage hybridization in the contact zone with low recent gene flow. Distribution pattern of this species corroborates results from previous studies reporting the Last Glacial Maximum (LGM) have shaped the structuring of the species through movements of forests' expansion and contraction. The STRUCTURE analysis of each group revealed the presence of genetic subgroups with low rates of recurrent gene flow. Southern subgroups have higher rates of admixtures than the Northern subgroups, revealing greater historical connectivity of forests in this region.

Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids.

For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.