RESUMEN
The Cucurbita genus is home to a number of economically and culturally important species. We present the analysis of genotype data generated through genotyping-by-sequencing of the USDA germplasm collections of Cucurbita pepo, C. moschata, and C. maxima. These collections include a mixture of wild, landrace, and cultivated specimens from all over the world. Roughly 1,500 - 32,000 high-quality single nucleotide polymorphisms (SNPs) were called in each of the collections, which ranged in size from 314 to 829 accessions. Genomic analyses were conducted to characterize the diversity in each of the species. Analysis revealed extensive structure corresponding to a combination of geographical origin and morphotype/market class. Genome-wide associate studies (GWAS) were conducted using both historical and contemporary data. Signals were observed for several traits, but the strongest was for the bush (Bu) gene in C. pepo. Analysis of genomic heritability, together with population structure and GWAS results, was used to demonstrate a close alignment of seed size in C. pepo, maturity in C. moschata, and plant habit in C. maxima with genetic subgroups. These data represent a large, valuable collection of sequenced Cucurbita that can be used to direct the maintenance of genetic diversity, for developing breeding resources, and to help prioritize whole-genome re-sequencing.
RESUMEN
The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice (Oryza sativa L.) breeding programs has been limited. In recent years, efforts have begun to work toward implementing GS into the Louisiana State University (LSU) Agricultural Center rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost-effective genotyping platform capable of providing informative marker data with an adequate turnaround time. The objective of this study was to develop a marker set for routine GS and demonstrate its effectiveness in southern U.S. rice germplasm. The utility of the resulting marker set, the LSU500, for GS applications was demonstrated using four years of breeding data across 7,607 experimental lines and four elite biparental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within biparental populations demonstrates similar performance of GS compared with phenotypic selection in predicting future performance. The prediction accuracies obtained with the LSU500 marker set demonstrates the utility of this marker set for cost-effective GS applications in southern U.S. rice breeding programs. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.
A SNP marker set was developed for genomic selection in southern U.S. rice breeding programs. Predictive ability across target germplasm was shown with 3 yr of data (4,078 lines). Within-population predictive ability was shown across four biparental populations. Genomic and phenotypic selection ability to predict future performance was compared.
Asunto(s)
Oryza , Genómica/métodos , Humanos , Oryza/genética , Fenotipo , Fitomejoramiento/métodos , Selección GenéticaRESUMEN
Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.