Ppe.RPT/SSC‑1: from QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach

Genomic regions associated with ripening time (RPT) and soluble solids concentration (SSC) were mapped using a pedigreed population including multiple F1 and F2 families from the Clemson University peach breeding program (CUPBP). RPT and SSC QTLs were consistently identifed in two seasons (2011 and 2012) and the average datasets (average of two seasons). A target region spanning 10,981,971–11,298,736 bp on chromosome 4 of peach reference genome used for haplotype analysis revealed four haplotypes with signifcant diferences in trait values among diferent diplotype combinations. Favorable alleles at the target region for both RPT and SSC were determined and a DNA test for predicting RPT and SSC was developed. Two Kompetitive Allele Specifc PCR (KASP) assays were validated on 84 peach cultivars and 163 seedlings from the CUPBP, with only one assay (Ppe.RPT/SSC‑1) needed to predict between early and late-season ripening cultivars and low and high SSC. These results advance our understanding of the genetic basis of RPT and SSC and facilitate selection of new peach cultivars with the desired RPT and SSCThis work was funded by USDA’s National Institute of Food and Agriculture-Specialty Crop Research Initiative Projects, “RosBREED: Enabling marker-assisted breeding in Rosaceae” (2009-51181-05858) and “RosBREED: Combining disease resistance and horticultural quality in new rosaceous cultivars” (2014-51181-22378).info:eu-repo/semantics/publishedVersio

Mixed layer depth climatology over the northeast US continental shelf (1993-2018)

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cai, C., Kwon, Y.-O., Chen, Z., & Fratantoni, P. Mixed layer depth climatology over the northeast US continental shelf (1993-2018). Continental Shelf Research, 231, (2021): 104611 https://doi.org/10.1016/j.csr.2021.104611.The Northeast U.S. (NEUS) continental shelf has experienced rapid warming in recent decades. Over the NEUS continental shelf, the circulation and annual cycle of heating and cooling lead to local variability of water properties. The mixed layer depth (MLD) is a key factor that determines the amount of upper ocean warming. A detailed description of the MLD, particularly its seasonal cycle and spatial patterns, has not been developed for the NEUS continental shelf. We compute the MLD using an observational dataset from the Northeast Fisheries Science Center hydrographic monitoring program. The MLD exhibits clear seasonal cycles across five eco-regions on the NEUS continental shelf, with maxima in January–March and minima in July or August. The seasonal cycle is largest in the western Gulf of Maine (71.9 ± 24.4 m), and smallest in the southern Mid-Atlantic Bight (34.0 ± 7.3 m). Spatial variations are seasonally dependent, with greatest homogeneity in summer. Interannual variability dominates long-term linear trends in most regions and seasons. To evaluate the sensitivity of our results, we compare the MLDs calculated using a 0.03 kg/m3 density threshold with those using a 0.2 °C temperature threshold. Temperature-based MLDs are generally consistent with density-based MLDs, although a small number of temperature-based MLDs are biased deep compared to density-based MLDs particularly in spring and fall. Finally, we compare observational MLDs to the MLDs from a high-resolution ocean reanalysis GLORYS12V1. While the mean values of GLORYS12V1 MLDs compare well with the observed MLDs, their interannual variability are not highly correlated, particularly in summer. These results can be a starting point for future studies on the drivers of temporal and spatial MLD variability on the NEUS continental shelf.The authors gratefully acknowledge the support from the NOAA Modeling, Analysis, Predictions, and Projections (MAPP) Program (NA17OAR4310111) and Climate Variability and Predictability Program (NA20OAR4310482). Cassia Cai acknowledges the Woods Hole Oceanographic Institution Summer Student Fellowship program for participation, the Northwestern University (NU) Earth and Planetary Science Independent Study for supporting the writing of this manuscript, and the NU Climate Change Research Group for providing some of the technical tools to conduct analysis

Mapping and characterization QTLs for phenological traits in seven pedigree-connected peach families

Background: Environmental adaptation and expanding harvest seasons are primary goals of most peach [Prunus persica (L.) Batsch] breeding programs. Breeding perennial crops is a challenging task due to their long breeding cycles and large tree size. Pedigree-based analysis using pedigreed families followed by haplotype construction creates a platform for QTL and marker identification, validation, and the use of marker-assisted selection in breeding programs. Results: Phenotypic data of seven F1 low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. Three QTLs were discovered for bloom date (BD) and mapped on linkage group 1 (LG1) (172–182 cM), LG4 (48–54 cM), and LG7 (62–70 cM), explaining 17–54%, 11–55%, and 11–18% of the phenotypic variance, respectively. The QTL for ripening date (RD) and fruit development period (FDP) on LG4 was co-localized at the central part of LG4 (40–46 cM) and explained between 40 and 75% of the phenotypic variance. Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles and the presence of multiple functional alleles with different effects for a single locus for RD and FDP. Conclusions: A multiple pedigree-linked families approach validated major QTLs for the three key phenological traits which were reported in previous studies across diverse materials, geographical distributions, and QTL mapping methods. Haplotype characterization of these genomic regions differentiates this study from the previous QTL studies. Our results will provide the peach breeder with the haplotypes for three BD QTLs and one RD/FDP QTL to create predictive DNA-based molecular marker tests to select parents and/or seedlings that have desired QTL alleles and cull unwanted genotypes in early seedling stages.</p

Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach

Background: Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (Prunus persica (L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for the industry to improve fruit quality and increase consumption. Pedigree-based analysis across multiple peach pedigrees can identify the genomic basis of complex traits for direct implementation in marker-assisted selection. This strategy provides breeders with better-informed decisions and improves selection efficiency and, subsequently, saves resources and time. Results: Phenotypic data of seven F1 low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40-46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For soluble solids concentration (SSC), one QTL was mapped on LG5 at 60-72 cM and explained from 17 to 39% of the phenotypic variance. A major QTL for titratable acidity (TA) co-localized with the major locus for low-acid fruit (D-locus). It was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles. Conclusions: A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL at the distal end of LG5 and validated other QTLs which were reported in previous studies. Haplotype characterization of the mapped QTLs distinguishes this work from the previous QTL studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired QTL alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding.</p

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.</p