Biology subjects

Sarup, P.

Publications and source records attributed to Sarup, P..

4 recordsLinked to original sources

bioRxiv2026

Temporal changes in allele frequency facilitate detection of adaptive variants in winter wheat (Triticum aestivum L.) breeding programs

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

genetics↗

bioRxiv2022

Metabolomic-genomic prediction drastically improves prediction accuracy of breeding values in crop breeding

Metabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. Objective was to investigate heritabilities and accuracies of genetic evaluation of malting quality (MQ) traits by integrating both genomic and metabolomic information. In total, 2,430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 Nuclear Magnetic Resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO). Plot heritabilities ranged from 0.060 to 0.279 for GBLUP, and increased to range from 0.123 to 0.283 for MGBLUP. LOLO scheme yielded higher accuracies than LOYO scheme, and MGBLUP yielded higher accuracies than GBLUP regardless of cross-validation scheme. Author SummaryMetabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. We carried out a study on 2,430 plots of 562 malting spring barley lines and 24,018 Nuclear Magnetic Resonance intensities measured on each wort sample, to investigate the prediction of breeding values of five malting quality traits by integrating genomic and metabolomic information using a metabolomics-genomic model. Results showed an increase in prediction accuracy for this model compared to a genomic model.

genomics↗

bioRxiv2020

Genomic prediction for malting quality traits in practical barley breeding programs

Genomic prediction can be advantageous in barley breeding for traits such as yield and malting quality to increase selection accuracy and minimize expensive phenotyping. In this paper, we investigate the possibilities of genomic selection for malting quality traits using a limited training population. The size of the training population is an important factor in determining the prediction accuracy of a trait. We investigated the potential for genomic prediction of malting quality within breeding cycles with leave one out (LOO) cross-validation, and across breeding cycles with leave set out (LSO) cross-validation. In addition, we investigated the effect of training population size on prediction accuracy by random two, four, and ten-fold cross-validation. The material used in this study was a population of 1329 spring barley lines from four breeding cycles. We found medium to high narrow sense heritabilities of the malting traits (0.31 to 0.65). Accuracies of predicting breeding values from LOO tests ranged from 0.6 to 0.9 making it worth the effort to use genomic prediction within breeding cycles. Accuracies from LSO tests ranged from 0.39 to 0.70 showing that genomic prediction across the breeding cycles were possible as well. Accuracy of prediction increased when the size of the training population increased. Therefore, prediction accuracy might be increased both within and across breeding cycle by increasing size of the training population

genetics↗

bioRxiv2020

Prediction of complex phenotypes using the Drosophila metabolome

Understanding the genotype - phenotype map and how variation at different levels of biological organization are associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes; such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Secondly, we identify genes associated with metabolome variation. Thirdly, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analysed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance e.g. in human medicine and animal and plant breeding.

genetics↗