Gregorio Alvarado

A Genomic Selection Index Applied to Simulated and Real Data.

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.

The global adaptation of bread wheat at high latitudes

AbstractSpring sown bread wheat is grown at high latitudes in Europe, Asia and North America. However, it is not clear what the associations are among environments, particularly in Asia and North America, and whether or not cultivars developed in one region may adapt in another. A yield trial comprised of cultivars developed in northern Kazakhstan, western Siberia, the Canadian Prairies, northern USA, northeastern China and broadly adapted genotypes bred by CIMMYT in Mexico was planted in all the above mentioned environments in 2002–2004. In general, cultivars performed best within the regions they were developed. However, cultivars developed in northern Kazakhstan/western Siberia were the most broadly adapted at high latitudes; they were not significantly different for grain yield from the locally developed cultivars in both China and Canada. Stronger photoperiod response, greater plant height and larger seed weight appeared to be key adaptive features of these materials. At lower latitudes, the Kazakh/Siberian cultivars were significantly lower yielding than all other materials. When low latitude Mexican sites were removed from the analysis, the Chinese locations tended to associate, whereas most Canadian and Kazak/Siberian locations were negatively associated with those from China. SSR analysis of the cultivars from each region split the materials into two general groups, one based on North American cultivars and one comprised of Kazakh/Siberian and Chinese cultivars. Lines developed in Mexico were spread across these two groupings. Evidence suggests that considerable scope exists to improve bread wheat adaptation at high latitudes globally through intercrossing materials originating from Asia and North America.

Rindsel: an R package for phenotypic and molecular selection indices used in plant breeding.

Selection indices are estimates of the net genetic merit of the individual candidates for selection and are calculated based on phenotyping and molecular marker information collected on plants under selection in a breeding program. They reflect the breeding value of the plants and help breeders to choose the best ones for next generation. Rindsel is an R package that calculates phenotypic and molecular selection indices.

RIndSel: Selection Indices with R

RIndSel is a graphical unit interface that uses selection index theory to select individual candidates as parents for the next selection cycle. The index can be a linear combination of phenotypic values, genomic estimated breeding values, or a linear combination of phenotypic values and marker scores. Based on the restriction imposed on the expected genetic gain per trait, the index can be unrestricted, null restricted, or predetermined proportional gain indices. RIndSel is compatible with any of the following versions of Windows: XP, 7, 8, and 10. Furthermore, it can be installed on 32-bit and 64-bit computers. In the context of fixed and mixed models, RIndSel estimates the phenotypic and genetic covariance using two main experimental designs: randomized complete block design and lattice or alpha lattice design. In the following, we explain how RIndSel can be used to determine individual candidates as parents for the next cycle of improvement.

SASHAYDIALL: A SAS Program for Hayman’s Diallel Analysis

Different methods of diallel crossing are commonly used in plant breeding. The diallel cross analysis method proposed by Hayman is particularly useful because it provides information, among others, on additive and dominance effects of genes, average degree of dominance, proportion of dominance, direction of dominance, distribution of genes, maternal and reciprocal effects, number of groups of genes that control a trait and exhibit dominance, ratio of dominant to recessive alleles in all the parents, and broad-sense and narrow-sense heritability. In this paper, we fully describe a SAS-based software SASHAYDIALL for performing a complete diallel cross analysis based on Hayman’s model with or without reciprocals. We demonstrate the use of SASHAYDIALL with two data sets; one is a published diallel cross data set with reciprocals in cabbage (Brassica oleracea L.), and the second is a data set from a multilocation diallel cross trial in maize (Zea mays L.) without reciprocals. With SASHAYDIALL, diallel experiments conducted in single sites can be analyzed to estimate various genetic parameters, and this analysis is extended over locations or environments to assess genetic effect × environment interaction. SASHAYDIALL is user-friendly software that provides detailed genetic information from diallel crosses involving any number of parents and locations.