Biology subjects

Van Inghelandt, D.

Publications and source records attributed to Van Inghelandt, D..

6 recordsLinked to original sources

bioRxiv2026

Genome-wide association study in a maize diversity panel dissects the genetic basis of lignocellulosic recalcitrance

Lignocellulosic biomass is an abundant, renewable resource for the production of biorenewable products. Its efficient utilization is largely hindered by the inherent recalcitrance of lignocellulose to degradation. Natural genetic variation in biomass saccharification efficiency and lignocellulose components across diverse maize germplasm provides a powerful resource to dissect the genetic basis of lignocellulosic recalcitrance. Here, we performed high-throughput profiling of lignocellulosic components and saccharification efficiency in mature stalks across a diverse panel of 239 field grown maize inbred lines, and identified extensive natural variation in biomass properties. Saccharification efficiency was strongly positively associated with matrix glucose content, moderately positively associated with the arabinose-to-xylose (Ara:Xyl) molar ratio and crystalline cellulose content, whereas saccharification efficiency was strongly negatively associated with xylose and lignin content. Genome-wide association studies (GWAS) were conducted to identify single nucleotide polymorphisms (SNPs) associated with saccharification efficiency and lignocellulosic components. Numerous significant single nucleotide polymorphisms (SNPs) were identified, defining 79 quantitative trait loci (QTLs) across all 10 maize chromosomes. The presence of pleiotropic SNPs and the extensive co-localization of QTLs for multiple traits highlight the complex genetic architecture and shared genetic basis of maize biomass properties. Our study reveals substantial natural variation in maize biomass digestibility and identifies multiple novel genetic loci underlying variation in saccharification efficiency and lignocellulose components.

plant biology↗

bioRxiv2025

An Axiom SNP genotyping array for potato: development, evaluation and applications

Potato is a versatile food crop and a major component of human nutrition worldwide. Genomic-assisted breeding methods have the potential to increase the gain of selection. We report the development and validation of a high-density Axiom-based SNP array for potato (Solanum tuberosum L.). Whole-genome 10X Genomics based sequencing of 108 diverse clones representing landraces, improved cultivars, and wild relatives identified around 23.8 million sequence variants, from which 929,127 variants, alongside with 18,718 markers from a previously developed Illumina Infinium 21K array (GGP3), were tiled on the array. The array demonstrated high reproducibility, with replicate samples showing an average concordance of 99.88% in genotype calls for PotatoTools specific variants, compared to 99.93% for the Illumina Infinium 21K array variants. A panel of 1,247 diploid and tetraploid clones was genotyped with the developed array. Genotype calling, considering allele dosage, was realized using fitpoly and yielded 852,793 calls. The array informativeness was optimized by applying Euclidean distance, heterozygous strength offset metrics, call rate, minor allele frequency filtering, and diploid-based allele correction, yielding a final set of 206,616 robust and informative markers. The filtered marker set enables precise characterization of genetic variation across diverse germplasm, thereby supporting robust analyses of population structure, genome-wide association studies (GWAS), and genomic prediction in potato. Population structure analysis genotyped clones revealed clear subpopulation differentiation consistent with known ploidy groups. In addition, our discriminant analysis of principal components revealed a weak but structured diversity among clones of different market segments. GWAS analysis of 998 potato clones identified sequence variants significantly associated with polyphenol oxidase (PPO) activity, confirming the platforms efficacy for trait mapping. For the same trait, genomic prediction accuracies of 0.72-0.86 have been observed. The developed potato SNP array provides a robust platform for high-throughput genotyping, supporting genetic diversity studies, association mapping, and genomic-assisted breeding in this important crop.

plant biology↗

bioRxiv2025

Assessment of segregation variance estimates from derivation, simulations, and empirical data in autotetraploid species exemplified in potato

The optimal choice of parents and crosses and, therefore, the prediction of the segregation variance are of high relevance to maximize genetic gain in breeding programs. Several methods have been developed for the prediction of segregation variance, including correlation with genotypic diversity, progeny simulations, or algebraic derivations in case of a diploid inheritance. To the best of our knowledge, no algebraic derivation using parental genotypic information is available to predict segregation variance for autotetraploid species. The objectives of our study were to (1) derive algebraic derivation based on linkage disequilibrium (LD) between linked loci to predict the segregation variance in autotetraploid species; (2) compare the performance of segregation variance estimated based on simulated progenies and the algebraic derivation; (3) investigate by simulations how experimental parameters affect the accuracy of segregation variance prediction; and (4) compare the segregation variance estimated in empirical data of potato and the one based on the algebraic derivation. The segregation variance estimated by the developed derivations showed very high correlations with the one observed in large simulated progenies, but those were lower when phased parental haplotypes were not available or family size decreased. The correlation between segregation variance estimated by the developed derivation and the empirical data was low. This could be attributed to the small family size used in the study, which we could show to increase LD between unlinked loci. The proposed algebraic derivations promise to be a precise alternative to simulations to help breeders in optimizing their family choices and sizes considering the segregation variance.

genetics↗

bioRxiv2024

Optimal implementation of genomic selection in clone breeding programs - exemplified in potato: II. Effect of selection strategy and cross-selection method on long-term genetic gain

Different cross-selection (CS) methods incorporating genomic selection (GS) have been used in diploid species to improve long-term genetic gain and preserve diversity. However, their application to heterozygous and autotetraploid crops such as potato is lacking so far. The objectives of our study were to (i) assess how different CS methods incorporating GS with or without consideration of genetic variability affect both short- and long-term genetic gains compared to strategies using phenotypic selection (PS); (ii) evaluate the changes in genetic variability and the efficiency of converting diversity into genetic gain across different CS methods; and (iii) investigate the interaction effects between different genetic architectures and CS methods on long-term genetic gain. In our simulation results, implementing GS with optimal selection intensities had a higher short- and long-term genetic gain compared to any PS strategy. The CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) reached the highest long-term genetic gain among the assessed mean-based CS methods. Compared to MEGV-O and usefulness criteria (UC), the linear combination of UC and genome-wide diversity (called EUCD) kept the same level of genetic gain but resulted in a higher diversity and a lower number of fixed QTL. Moreover, EUCD had a relatively high efficiency in converting diversity into genetic gain. However, choosing the most appropriate weight to account for diversity in EUCD depends on the genetic architecture of the target trait and the breeders objectives. Our results provide breeders with concrete methods to improve their potato breeding programs. Key messageCross-selection method considering progeny mean performance and genetic variability can improve long-term genetic gain and keep genetic diversity in clone breeding.

genetics↗

bioRxiv2023

Genetic mapping reveals new loci and alleles for flowering time and plant height using the double round-robin population of barley

Flowering time and plant height are two critical determinants of yield potential in barley (Hordeum vulgare). Although their role as key traits, a comprehensive understanding of the genetic complexity of flowering time and plant height regulation in barley is still lacking. Through a double round-robin population originated from the crossings of 23 diverse parental inbred lines, we aimed to determine the variance components in the regulation of flowering time and plant height in barley as well as identify new genetic variants by single and multi-population quantitative trait loci (QTL) analyses and allele mining. Despite similar genotypic variance, we observed higher environmental variance components for plant height than flowering time. Furthermore, we detected one new QTL for flowering time and two new QTL for plant height. Finally, we identified a new functional allelic variant of the main regulatory gene Ppd-H1. Our results show that the genetic architecture of flowering time and plant height might be more complex than reported earlier and that a number of undetected, small effect or low frequency, genetic variants underlie the control of these two traits.

genetics↗

bioRxiv2022

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants (SNV). However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity >70% and precision >95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

genomics↗