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Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

The husk—the leaf-like outer covering of maize ear—has multiple functions, including protecting the ear from diseases infection and dehydration. In previous studies, we genotyped an association panel of 508 inbred lines genotyped with a total of ~550,000 SNPs (Illumina 50 K SNP Chip and RNA-seq). Genome-Wide Association Studies (GWAS) were conducted on four husk traits: husk length (HL), husk layer number (HN), husk thickness (HT), and husk width (HW). Minimal associations were identified and none of them passed the P-value threshold after a Bonferroni multiple-test correction using a single locus test in framework of mixed linear model. In this study, we doubled the number of SNPs (~1,250,000 in total) by adding GBS and 600 K SNP Chip. GWAS, performed with the recently developed multiple loci model (BLINK), revealed six genetic loci associated with HN and HT above the Bonferroni multiple-test threshold. Five candidate genes were identified based on the linkage disequilibrium with these loci, including GRMZM2G381691 and GRMZM2G012416. These two genes were up-regulation and down-regulation in all husk related tissues, respectively. GRMZM2G381691 associated with HT encoded a CCT domain protein, which expressed higher in tropical than temperate maize. GRMZM2G012416 associated with HN encoded an Armadillo (ARM) repeat protein, which regulated GA signal pathway. These associated SNPs and candidate genes paved a path to understand the genetic architecture of husk in maize.

Southern leaf blight (SLB) and northern leaf blight (NLB) are the two major foliar diseases limiting maize production worldwide. Upon previous study with the nested association mapping (NAM) population, which consist of 5,000 recombinant inbred lines from 25 parents crossed with B73, we expanded the phenotyping environments from the United States (US) to China, and increased the marker densities from 1106 to 7386 SNPs for linkage mapping, and from 1.6 to 28.5 million markers for association mapping. We identified 49 SLB and 48 NLB resistance-related unique QTLs in linkage mapping, and multiple loci in association mapping with candidate genes involved in known plant disease-resistance pathways. Furthermore, an independent natural population with 282 diversified inbred lines were sequenced for four candidate genes selected based on their biological functions. Three of them demonstrated significant associations with disease resistance. These findings provided valuable resources for further implementations to develop varieties with superior resistance for NLB and SLB.

Maize (Zea mays, L.) cultivation has expanded greatly from tropical to temperate zones; however, its sensitivity to chilling often results in decreased germination rates, weak seedlings with reduced survival rates, and eventually lower yields. We conducted germination tests on the maize-282-diverse-panel (282 inbred lines) under normal (25 °C) and chilling (8 °C) conditions. Three raw measurements of germination were recorded under each condition: 1) germination rate, 2) days to 50% germination, and 3) germination index. Three relative traits were derived as indicators of cold-tolerance. By using the 2,271,584 single nucleotide polymorphisms (SNPs) on the panel from previous studies, and genome-wide association studies by using FarmCPU R package to identify 17 genetic loci associated with cold tolerance. Seven associated SNPs hit directly on candidate genes; four SNPs were in high linkage disequilibrium with candidate genes within 366 kb. In total, 18 candidate genes were identified, including 10 candidate genes supported by previous QTL studies and five genes supported by previous gene cloning studies in maize, rice, and Arabidopsis. Three new candidate genes revealed by two associated SNPs were supported by both QTL analyses and gene cloning studies. These candidate genes and associated SNPs provide valuable resources for future studies to develop cold-tolerant maize varieties.

Bailinggu (Pleurotus tuoliensis) is a major, commercially cultivated mushroom and widely used for nutritional, medicinal, and industrial applications. Yet, the mushroom’s genetic architecture and the molecular mechanisms underlying its formation are largely unknown. Here we performed comparative transcriptomic analysis during Bailinggu’s mycelia, primordia, and fruiting body stages to identify genes regulating fruiting body development and develop EST-SSR markers assessing the genetic value of breeding materials. The stage-specific and differentially expressed unigenes (DEGs) involved in morphogenesis, primary carbohydrate metabolism, cold stimulation and blue-light response were identified using GO and KEGG databases. These unigenes might help Bailinggu adapt to genetic and environmental factors that influence fructification. The most pronounced change in gene expression occurred during the vegetative-to-reproductive transition, suggesting that is most active and key for Bailinggu development. We then developed 26 polymorphic and informative EST-SSR markers to assess the genetic diversity in 82 strains of Bailinggu breeding materials. These EST-SSRs exhibited high transferability in closely related species P. eryngii var. ferulae and var. eryngii. Genetic population structure analysis indicated that China’s Bailinggu has low introgression with these two varieties and likely evolved independently. These findings provide new genes, SSR markers, and germplasm to enhance the breeding of commercially cultivated Bailinggu.