Peanut Germplasm Evaluation for Agronomic Traits and Disease Resistance under a Two-Season Cropping System in Taiwan

Abstract

Cultivated peanut (Arachis hypogaea L.) is an important crop worldwide, and peanut germplasm is an important genetic resource for peanut breeding. The two-season cropping system is common in tropical and subtropical regions, which are the main peanut production areas. The weather in the two cropping seasons is usually distinct and makes germplasm evaluation challenging. In this study, random stratified sampling based on market type was applied to build a core collection. Comparisons between the original entire collection and core collection were conducted. Two seasons field trials were performed with additional three seasons rust resistance evaluation trials. Principal component analysis and genotype-by-trait biplots were utilized as selection tools. Which-won-where/what and stability plot relationships were determined to provide breeders with an easy and efficient method for selection. Rust resistance simple sequence repeat and single nucleotide polymorphism markers were used to screen the germplasm. Some resistant accessions showed susceptible phenotypes, indicating that under Taiwan’s environment, the favored rust physiological races are different from those of other areas. Some potential rust resistance lines were discovered and validated, which can survive under variable weather conditions in a two-season cropping system. A set of markers was developed for utilization for rust resistance screening in Taiwan.

Double‐digest restriction‐associated DNA sequencing‐based genotyping and its applications in sesame germplasm management

“…Many approaches for selecting a CC using SNP datasets were proposed, and the most commonly used were CoreHunter3 (de Beukelaer et al., 2018), PowerCore (Kim et al., 2007), and GenoCore (Jeong et al., 2017). Different data types are also in practice to define the core samples, such as genealogical data in wheat ( Triticum aestivum L.) (Stehno et al., 2006), agronomic data in groundnut ( Arachis hypogaea L.) (Kuo et al., 2021), and molecular data in rice ( Oryza sativa L.) (J.‐C. Wang et al., 2007).…”

“…Many approaches for selecting a CC using SNP datasets were proposed, and the most commonly used were CoreHunter3 (de Beukelaer et al, 2018), PowerCore (Kim et al, 2007), and GenoCore (Jeong et al, 2017). Different data types are also in practice to define the core samples, such as genealogical data in wheat (Triticum aestivum L.) (Stehno et al, 2006), agronomic data in groundnut (Arachis hypogaea L.) (Kuo et al, 2021), and molecular data in rice (Oryza sativa L.) (J.-C. Wang et al, 2007). In this study, besides using these CC tools, a coreset was designated with 384 accessions by combining multiple approaches such as heterozygosity, genetic distance (k-mer [subsequence of length 'k' in a sequence read] mash and distance calculation methods Euclidean, Manhattan, Pearson, etc.…”