Trait Diversity and Potential for Selection Indices Based on Variation Among Regionally Adapted Processing Tomato Germplasm

Abstract

<jats:p>For many horticultural crops, selection is based on quality as well as yield. To investigate the distribution of trait variation and identify those attributes appropriate for developing selection indices, we collected and organized information related to fruit size, shape, color, soluble solids, acid, and yield traits for 143 processing tomato (<jats:italic>Solanum lycopersicum</jats:italic> L.) lines from North America. Evaluation of the germplasm panel was conducted in a multiyear, multilocation trial. Data were stored in a flat-file format and in a trait ontology database, providing a public archive. We estimated variance components and proportion of variance resulting from genetics for each trait. Genetic variance was low to moderate (range, 0.03–0.51) for most traits, indicating high environmental influence on trait expression and/or complex genetic architecture. Phenotypic values for each line were estimated across environments as best linear unbiased predictors (BLUPs). Principal components (PC) analysis using the trait BLUPs provided a means to assess which traits explained variation in the germplasm. The first two PCs explained 28.0% and 16.2% of the variance and were heavily weighted by measures of fruit shape and size. The third PC explained 12.9% of the phenotypic variance and was determined by fruit color and yield components. Trait BLUPs and the first three PCs were also used to explore the relationship between phenotypes and the origin of the accessions. We were able to differentiate germplasm for fruit size, fruit shape, yield, soluble solids, and color based on origin, indicating regional breeding programs provide a source of trait variation. These analyses suggest that multitrait selection indices could be established that encompass quality traits in addition to yield. However, such indices will need to balance trait correlations and be consistent with market valuation.</jats:p>

Genome‐wide identification of loci affecting seed glucosinolate contents in Brassica napus L.

“…As SGC was measured in different environments with replications, an R script based on a linear model was used to obtain the best linear unbiased prediction (BLUP) of SGC for each accession (Merk et al ; Wei et al ). The resulting values of BLUP were used as phenotypes for GWAS.…”

Genome-Wide Association Study Reveals the Genetic Architecture Underlying Salt Tolerance-Related Traits in Rapeseed (Brassica napus L.)

“…The frequency distribution of each trait was calculated in R (R Core Team, Vienna, Austria). The broad-sense heritability for the panel was calculated with the R package lme4 (Merk et al, 2012), using the equation h 2 = o ,2 g /(o ,2 g + o ,2 ge / n + o ,2 e / nr ), where o ,2 g is the genetic variance, o ,2 ge is the genotype × environment interaction variance, o ,2 e is the error variance, n is the number of environments, and r is the number of replications.…”

Genome-Wide Association Mapping Reveals the Genetic Control Underlying Branch Angle in Rapeseed (Brassica napus L.)

“…For the branch angle trait, the variance components and best linear unbiased predictors (BLUP) of the multi-environment for each line were estimated using the lme4 package in R software based on a linear model (Merk et al, 2012). The final trait values for association analysis included the BLUP-value and single environment phenotypic data of each accession.…”