Genetic Diversity and Population Structure of Tomato (<i>Solanum lycopersicum</i>) Germplasm Developed by Texas A&M Breeding Programs

Abstract

Genetic variation developed in plant breeding programs is fundamental to creating new combinations that result in cultivars with enhanced characteristics. Over the years, tomato (Solanum lycopersicum) breeding programs associated with the Texas A&M University system have developed morphologically diverse lines of tomatoes selected for heat tolerance, fruit quality, and disease resistance to adapt them to Texas growing conditions. Here we explored the intraspecific genetic variations of 322 cultivated tomato genotypes, including 300 breeding lines developed by three Texas A&M breeding programs, as an initial step toward implementing molecular breeding approaches. Genotyping by sequencing using low coverage whole-genome sequencing (SkimGBS) identified 10,236 high-quality single-nucleotide polymorphisms (SNPs) that were used to assess genetic diversity, population structure, and phylogenetic relationship between genotypes and breeding programs. Model-based population structure analysis, phylogenetic tree construction, and principal component analysis indicated that the genotypes were grouped into two main clusters. Genetic distance analysis revealed greater genetic diversity among the products of the three breeding programs. The germplasm developed at Texas A&M programs at Weslaco, College Station, and by Dr. Paul Leeper exhibited genetic diversity ranges of 0.175 -0.434, 0.099 -0.392, and 0.183 -0.347, respectively, suggesting that there is enough variation within and between the lines from the three programs to perform selection for cultivar development. The SNPs identified here could be used to develop molecular tools for selecting various traits of interest and to select parents for future tomato breeding.

Stability of yield and its components in grafted tomato tested across multiple environments in Texas

“…Efficient production of fresh-market tomato in Texas has long been limited by a number of challenges including biotic and abiotic stresses (especially episodes of high temperature and drought), inadequate cultivars and production practices 33 . With the goal to revitalize the industry given in part the increasing consumer demand for locally-grown vegetables, the Texas A&M AgriLife Research breeding programs have recently intensified the development of improved cultivars with potential for heat tolerance, fruit quality, and disease resistance 33 . In addition, vegetable grafting could complement these efforts in ensuring an effective management of biotic and abiotic stresses and enhancing tomato plant growth and productivity.…”

HTSQualC is a Flexible and One-Step Quality Control Software for High-throughput Sequencing Data Analysis

“…For CLI, the HTSeqQC need to installed on a local system, whereas GUI is preinstalled and ready to use online at CyVerse (https://cyverse.org/). a Combined file size of two files for paired-end sequence reads b 322 tomato genotypes were sequenced using low-coverage whole genome sequencing by Illumina HiSeq 4000 5 .…”

“…All the HTSeqQC commands used for performing the above analyses are provided in the README file. 1855 157 a Combined file size of two files for paired-end sequence reads b 322 tomato genotypes were sequenced using low-coverage whole genome sequencing by Illumina HiSeq 4000 (Kandel, et al, 2019). The number of sequences reads are average of all the datasets.…”

“…Lastly, we analyzed 322 paired-end genotyping-by-sequencing (GBS) datasets corresponding to tomato (Kandel, et al, 2019). These datasets had ~1M (×2) sequence reads per sample.…”

“…HTS largely replaced conventional low-throughput Sanger-based sequencing technologies for genome-scale studies. Multiple genome sequencing approaches (DNA-seq, RAD-seq, GBS, AgSeq) are being used to study genetic variations, discovery of novel genes, high-throughput genotyping, biomarker discovery, and precision medicine (Awika, et al, 2019;Bolger, et al, 2014;Edwards and Batley, 2010;Kandel, et al, 2019;Suwinski, et al, 2019). Similarly, transcriptome-level sequencing approaches (RNA-seq) allows determining the steadystate expression of genes, identification of novel transcripts and isoforms, alternatively splicing patterns, polymorphisms, gene co-expression networks, allele-specific expressions, and long non-coding RNAs (lincRNA) Xu, et al, 2018;Zhou, et al, 2018).…”