Identification and assessment of alleles in the promoter of the Cyc-B gene that modulate levels of ß-carotene in ripe tomato fruit.

Novel diversity may be mined from databases and de novo sequencing, but functional characterization remains a limiting step to identifying new alleles. Classical breeding approaches augmented by marker-assisted selection offer a means to rapidly assess the function of new variation in coding or regulatory regions to modulate traits. We used the Cyc-B gene (B) of tomato (Solanum lycopersicum L.) for a proof of concept because of its role in the production of ß-carotene, a provitamin A carotenoid with importance to human nutrition. We measured carotenoid content in vintage and contemporary varieties and the profiles had a range of ß-carotene from 0.2 to 4.06 mg 100 g-1 fresh weight. We characterized variation in B from 84 sequences recovered from public databases and from an additional 29 high ß-carotene tomato, S. galapagense S. C. Darwin & Peralta, and S. cheesmaniae (L. Riley) Fosberg accessions. Thirteen unique haplotypes across 1600 bp of sequence 5' to the first ATG were identified with 11 occurring in high ß-carotene accessions we sequenced, and additional haplotypes were identified in public data. Phylogenetic analysis suggested that the alleles in high ß-carotene varieties were derived from wild species. Association analysis suggested two single nucleotide polymorphisms (SNPs) as the most likely causes of high ß-carotene, presumably through their influence on transcription of B that is elevated in ripening fruit. A marker-assisted backcross breeding scheme leveraging SNPs for background genome selection was used to rapidly develop germplasm resources containing different alleles of B in a uniform genetic background. Evaluation demonstrated that distinct promoter haplotypes function as different alleles that can be used to modulate the levels of ß-carotene in tomato.

Quantifying intraclass correlations for count and time-to-event data.

The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.