Biology subjects

Iezzoni, A.

Publications and source records attributed to Iezzoni, A..

3 recordsLinked to original sources

bioRxiv2026

Landrace and bred accessions of allotetraploid sour cherry (Prunus cerasus L.) reveal variation in subgenome dosage and subgenome expression bias

Subgenome dominance is a phenomenon observed in many allopolyploids where one parental genome exhibits stronger influence over phenotype than the other parental genomes. This may present as preferential retention of one subgenome through fractionation, replacement via homoeologous exchange, or as subgenome expression bias, where one subgenome is expressed at a higher abundance compared to other subgenomes. Sour cherry (Prunus cerasus) is an allotetraploid fruit tree species resulting from an interspecific cross between extant relatives of ground cherry (P. fruticosa) and sweet cherry (P. avium). Prior comparative genomic analyses suggest that the sour cherry cultivar Montmorency contains three subgenomes. Subgenomes A and A, each present in one copy, are derived from a P. fruticosa-like ancestor, and B, present in two copies, is derived from a P. avium-like ancestor. In this study we investigated the subgenome dynamics of the three subgenomes of sour cherry in four diverse landraces and two cultivars, including Montmorency. We found evidence of 26 homoeologous exchange events and five whole-homoeolog replacements relative to Montmorency in three of the six accessions. We also detected subgenome expression bias favoring the A and A subgenomes over the B subgenome, the magnitude of which differs between accessions and changes over the course of fruit development. Lastly, we show differences in dosage variation and expression bias of four previously-described genes in Montmorency associated with fruit softening, a key trait in this crop. These findings on subgenome dominance offer valuable insights into how this phenomenon may influence traits important for sour cherry breeding.

genetics↗

bioRxiv2019

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.

genetics↗

bioRxiv2017

Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits

BackgroundSweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years.\n\nResultsHigh broad-sense heritabilities of 0.83, 0.77, and 0.75 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic mode resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance.\n\nConclusionsPredicted breeding values and genetic values a measure revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.

genetics↗