Biology subjects

Gruneberg, W.

Publications and source records attributed to Gruneberg, W..

3 recordsLinked to original sources

bioRxiv2019

Development of diagnostic SNP markers for quality assurance and control in sweetpotato breeding programs

Quality assurance and control (QA/QC) is an essential element of a breeding programs optimization efforts towards increased genetic gains. Due to auto-hexaploid genome complexity, a low-cost marker platform for routine QA/QC in sweetpotato breeding programs is still unavailable. We used 662 parents of the International Potato Center (CIP)s global breeding program spanning Peru, Uganda, Mozambique and Ghana, to develop a low-density highly informative single nucleotide polymorphism (SNP) marker set to be deployed for routine QA/QC. Segregation of the selected 30 SNPs (two SNPs per base chromosome) in a recombined breeding population was evaluated using 282 progeny from some of the parents above. The progeny were replicated from in-vitro, screenhouse and field, and the selected SNP-set was confirmed to identify relatively similar mislabeling error rates as a high density SNP-set of 10,159 markers. Six additional trait-specific markers were added to the selected SNP set from previous quantitative trait loci mapping. The 36-SNP set will be deployed for QA/QC in breeding pipelines and in fingerprinting of advanced clones or released varieties to monitor genetic gains in famers fields. The study also enabled evaluation of CIPs global breeding population structure and the effect of some of the most devastating biotic stresses like sweetpotato virus disease on genetic variation management. These results will inform future deployment of genomic selection in sweetpotato.\n\nKey MessageA 36-SNP diagnostic marker set has been developed for quality assurance and control to support global sweetpotato breeding optimization efforts. Breeding population structure is shaped by sweetpotato virus disease prevalence.

genetics↗

bioRxiv2019

When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato

Experimental error, especially through genotype misclassification and pedigree errors, negatively affects breeding decisions by creating noise that compounds the genetic signals for selection. Unlike genotype-by-environment interactions, for which different methods have been proposed to address, the effect of noise due to pedigree errors and misclassification has not received much attention in most crops. We used two case studies in sweetpotato, based on data from the International Potato Centers breeding program to estimate the level of phenotype misclassification and pedigree error and to demonstrate the consequences of such errors when combining phenotypes with the respective genotypes. In the first case study, 27.7% phenotype misclassification was observed when moving genotypes from a diversity panel through in-vitro, screenhouse and field trialing. Additionally, 22.7% pedigree error was observed from misclassification between and within families. The second case study involving multi-environment testing of a full-sib population and quantitative trait loci (QTL) mapping showed reduced genetic correlations among pairs of environments in mega-environments with higher phenotype misclassification errors when compared to the mega-environments with lower phenotype misclassification errors. Additionally, no QTL could be identified in the low genetic correlation mega-environments. Simulation analysis indicated that phenotype misclassification was more detrimental to QTL detection when compared to missingness in data. The current information is important to inform current and future breeding activities involving genomic-assisted breeding decisions in sweetpotato, and to facilitate putting in place improved workflows that minimize phenotype misclassification and pedigree errors.

genetics↗

bioRxiv2017

Assembly Of Whole-Chromosome Pseudomolecules For Polyploid Plant Genomes Using Outcrossed Mapping Populations

The assembly of whole-chromosome pseudomolecules for plant genomes remains challenging due to polyploidy and high repeat content. We developed an approach for constructing complete pseudomolecules for polyploid species using genotyping-by-sequencing data from outcrossing mapping populations coupled with high coverage whole genome sequence data of a reference genome. Our approach combines de novo assembly with linkage mapping to arrange scaffolds into pseudomolecules. We show that the method is able to reconstruct simulated chromosomes for both diploid and tetraploid genomes. Comparisons to three existing genetic mapping tools show that our method outperforms the other methods in accuracy on both grouping and ordering, and is robust to the presence of substantial amounts of missing data and genotyping errors. We applied our method to three real datasets including a diploid Ipomoea trifida and two tetraploid potato mapping populations. The linkage maps show significant concordance with the reference chromosomes. We resolved seven assembly errors for the published Ipomoea trifida genome assembly as well as anchored an unplaced scaffold in the published potato genome.

bioinformatics↗