RAPD and microsatellite transferability studies in selected species of Prosopis (section Algarobia) with emphasis on Prosopis juliflora and P. pallida

The genus Prosopis (Leguminosae, Mimosoideae), comprises 44 species widely distributed in arid and semi-arid zones. Prosopis pallida (Humb. & Bonpl. ex Willd.) Kunth and P. juliflora (Sw.) DC. are the two species that are truly tropical apart from P. africana, which is native to tropical Africa (Pasiecznik et al. 2004), and they have been introduced widely beyond their native ranges. However, taxonomic confusion within the genus has hampered exploitation and better management of the species. The present study focusses primarily on evaluating the genetic relationship between Prosopis species from the section Algarobia, containing most species of economic importance, though P. tamarugo from section Strombocarpa is also included for comparison. In total, 12 Prosopis species and a putative P. pallida × P. chilensis hybrid were assessed for their genetic relationships based on RAPD markers and microsatellite transferability. The results show that P. pallida and P. juliflora are not closely related despite some morphological similarity. Evidence also agrees with previous studies which suggest that the grouping of series in section Algarobia is artificial

Enrichment of statistical power for genome-wide association studies

BACKGROUND: The inheritance of most human diseases and agriculturally important traits is controlled by many genes with small effects. Identifying these genes, while simultaneously controlling false positives, is challenging. Among available statistical methods, the mixed linear model (MLM) has been the most flexible and powerful for controlling population structure and individual unequal relatedness (kinship), the two common causes of spurious associations. The introduction of the compressed MLM (CMLM) method provided additional opportunities for optimization by adding two new model parameters: grouping algorithms and number of groups. RESULTS: This study introduces another model parameter to develop an enriched CMLM (ECMLM). The parameter involves algorithms to define kinship between groups (that is, kinship algorithms). The ECMLM calculates kinship using several different algorithms and then chooses the best combination between kinship algorithms and grouping algorithms. CONCLUSION: Simulations show that the ECMLM increases statistical power. In some cases, the magnitude of power gained by using ECMLM instead of CMLM is larger than the improvement found by using CMLM instead of MLM. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-014-0073-5) contains supplementary material, which is available to authorized users