Design and analysis of multi-year field trials for annual crops

This chapter provides a brief discussion on the design and analysis (prediction and interpretation) of field trials, focusing on multi-environment yield trials for annual crops

The use of pedigree, molecular marker and phenotypic data to investigate population structures in 25 years of the CIMMYT global wheat breeding program

The development of high-throughput genotyping, statistical and computing technologies has enabled Genome-Wide Association Studies (GWAS) to be applied directly in populations generated from plant breeding programs. This potentially means that no extra costs are required for phenotyping, thus avoiding or minimising the problem of the “phenotypic gap” in association analyses. For example, the CIMMYT wheat breeding program has generated over 13, 000 wheat lines, for which phenotypic data from historical multienvironment trials and pedigree information are available. However populations generated from plant breeding programs are typically highly structured. Ignoring this population structure in GWAS may cause spurious associations, but accounting for population structure may also eliminate true associations. In GWAS molecular markers are commonly used to obtain population structure; however, data from a plant breeding program provides other sources of information that may be used to obtain population structure. In this paper, we investigate impacts of population structure obtained using pedigree and phenotypic data in addition to marker data in a population of 599 inbred lines that were phenotyped across 25 cycles of CIMMYT Elite Spring Wheat Yield Trials (ESWYT). The results show that while there are relationships among the different population structures identified, each structure highlighted different aspects of the data. Different marker-trait association profiles were observed when different population structures were used. The results also show that inclusion of population structure can eliminate known associations, as demonstrated by the failure to identify association with the 1BL/1RS translocation when population structure is based on molecular marker data. Therefore, since different population structures highlight different aspects of the data, the choice the most appropriate population structure will depend on the objective of the study, and comparisons of marker-trait association profiles obtained under different structures provide further information about the population under study

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

Factors controlling self-fertility in sunflower: The role of GCA/SCA effects, S alleles, and floret characteristics

Self-fertility (SF) is an essential trait that contributes to yield stability in sunflower (Helianthus annuus L.), particularly in the absence of pollinating insects. We used a full-diallel design with 12 sunflower parents, including confectionary and nonconfectionary types, to assess (i) combining ability for SF, (ii) the relationship between SF and other traits, and (iii) microsatellite markers for tracking self-incompatible S alleles. Twelve parents and their 132 Fs were evaluated for SF in two environments. Self-fertility was correlated with final seed length (r = -0.57, P < 0.01), suggesting that SF was possibly influenced by floret characteristics. A genetic analysis of SF showed that both general combining ability (GCA) and specific combining ability (SCA) were significant, although after seed length was used as a covariate, the contribution of SCA effects increased to 65% of the variance and narrow-sense heritability was almost zero. The SCA effects were in part explained by the interaction of various combinations of S alleles. These results suggest that for programs targeting high-SF confectionary sunflower it would be difficult to predict the SF of hybrids based on the performance of their parents; therefore, F hybrid evaluation would be necessary. Our study suggested that factors controlling SF included GCA/SCA effects, S alleles, and floret characteristics. It also demonstrated the value of combining molecular and quantitative genetics for elucidating the inheritance of a complex trait