Silphium perfoliatum L. (cup plant, silphie) and S. integrifolium Michx. (rosinweed, silflower) are in the same subfamily and tribe as sunflower (Helianthus annuus L.). Silphium perfoliatum has been grown in many countries as a forage or bioenergy crop with forage quality approaching that of alfalfa (Medicago sativa L.) and biomass yield close to maize (Zea mays L.) in some environments. Silphium integrifolium has large seeds with taste and oil quality similar to traditional oilseed sunflower. Silphium species are all long‐lived, diploid perennials. Crops from this genus could improve the yield stability, soil, and biodiversity of agricultural landscapes because, in their wild state, they are deep rooted and support a wide diversity of pollinators. In contrast with premodern domestication, de novo domestication should be intentional and scientific. We have the luxury and obligation at this moment in history to expand the domestication ideotype from food and energy production to include (i) crop‐driven ecosystem services important for sustainability, (ii) genetic diversity to enable breeding progress for centuries, (iii) natural adaptations and microbiome associations conferring resource use efficiency and stress tolerance, and (iv) improving domestication theory itself by monitoring genetic and ecophysiological changes from predomestication baselines. Achieving these goals rapidly will require the use of next‐generation sequencing for marker development and an international, interdisciplinary team committed to collaboration and strategic planning.

The classic domestication scenario for grains and fruits has been portrayed as the lucky fixation of major-effect “domestication genes.” Characterization of these genes plus recent improvements in generating novel alleles (e.g., by gene editing) have created great interest in de novo domestication of new crops from wild species. While new gene editing technologies may accelerate some genetic aspects of domestication, we caution that de novo domestication should be understood as an iterative process rather than a singular event. Changes in human social preferences and relationships and ongoing agronomic innovation, along with broad genetic changes, may be foundational. Allele frequency changes at many loci controlling quantitative traits not normally included in the domestication syndrome may be required to achieve sufficient yield, quality, defense, and broad adaptation. The environments, practices and tools developed and maintained by farmers and researchers over generations contribute to crop yield and success, yet those may not be appropriate for new crops without a history of agronomy. New crops must compete with crops that benefit from long-standing participation in human cultural evolution; adoption of new crops may require accelerating the evolution of new crops’ culinary and cultural significance, the emergence of markets and trade, and the formation and support of agricultural and scholarly institutions. We provide a practical framework that highlights and integrates these genetic, agronomic, and cultural drivers of change to conceptualize de novo domestication for communities of new crop domesticators, growers and consumers. Major gene-focused domestication may be valuable in creating allele variants that are critical to domestication but will not alone result in widespread and ongoing cultivation of new crops. Gene editing does not bypass or diminish the need for classical breeding, ethnobotanical and horticultural knowledge, local agronomy and crop protection research and extension, farmer participation, and social and cultural research and outreach. To realize the ecological and social benefits that a new era of de novo domestication could offer, we call on funding agencies, proposal reviewers and authors, and research communities to value and support these disciplines and approaches as essential to the success of the breakthroughs that are expected from gene editing techniques.

Determination of microsatellite lengths or other DNA fragment types is an important initial component of many genetic studies such as mutation detection, linkage and quantitative trait loci (QTL) mapping, genetic diversity, pedigree analysis, and detection of heterozygosity. A handful of commercial and freely available software programs exist for fragment analysis; however, most of them are platform dependent and lack high-throughput applicability. We present the R package Fragman to serve as a freely available and platform independent resource for automatic scoring of DNA fragment lengths diversity panels and biparental populations. The program analyzes DNA fragment lengths generated in Applied Biosystems® (ABI) either manually or automatically by providing panels or bins. The package contains additional tools for converting the allele calls to GenAlEx, JoinMap® and OneMap software formats mainly used for genetic diversity and generating linkage maps in plant and animal populations. Easy plotting functions and multiplexing friendly capabilities are some of the strengths of this R package. Fragment analysis using a unique set of cranberry (Vaccinium macrocarpon) genotypes based on microsatellite markers is used to highlight the capabilities of Fragman. Fragman is a valuable new tool for genetic analysis. The package produces equivalent results to other popular software for fragment analysis while possessing unique advantages and the possibility of automation for high-throughput experiments by exploiting the power of R.

The development of high-throughput genotyping has made genome-wide association (GWAS) and genomic selection (GS) applications possible for both model and non-model species. The exploitation of genome-assisted approaches could greatly benefit breeding efforts in American cranberry (Vaccinium macrocarpon) and other minor crops. Using biparental populations with different degrees of relatedness, we evaluated multiple GS methods for total yield (TY) and mean fruit weight (MFW). Specifically, we compared predictive ability (PA) differences between univariate and multivariate genomic best linear unbiased predictors (GBLUP and MGBLUP, respectively). We found that MGBLUP provided higher predictive ability (PA) than GBLUP, in scenarios with medium genetic correlation (8–17% increase with cor_g~0.6) and high genetic correlations (25–156% with cor_g~0.9), but found no increase when genetic correlation was low. In addition, we found that only a few hundred single nucleotide polymorphism (SNP) markers are needed to reach a plateau in PA for both traits in the biparental populations studied (in full linkage disequilibrium). We observed that higher resemblance among individuals in the training (TP) and validation (VP) populations provided greater PA. Although multivariate GS methods are available, genetic correlations and other factors need to be carefully considered when applying these methods for genetic improvement.