bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.08.13.670171

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

Abstract

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.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wu, P.-Y., Thelen, K., Hartje, S., Muders, K., Prigge, V., Stich, B., Van Inghelandt, D.. 2025-08-16. Assessment of segregation variance estimates from derivation, simulations, and empirical data in autotetraploid species exemplified in potato. https://doi.org/10.1101/2025.08.13.670171

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Pangenome Discoveries from Clinical Long-Read Genomes in Underrepresented Populations with Neurodevelopmental Disorders

Genetic diagnoses remain elusive for many individuals with neurodevelopmental disorders (NDDs), and the gap is wider in populations poorly represented in genomic reference resources. To demonstrate the equitable advantage of long-read whole genome sequencing (LR-WGS) and pangenomes in terms of clinical utility, we analyzed 211 NDD cases and 107 unaffected relatives from 24 populations, integrating their assemblies, resulting in a total of 1204 haplotypes with the Human Pangenome Reference Consortium Release 2 (HPRC_r2) and the Arab Pangenome Reference (APR) datasets. Clinical interpretation of small variants and structural variants (SVs) revealed pathogenic or likely pathogenic findings in 38.9% of the cases (82/211), and our workflow further detected pathogenic repeat expansions in 3.8% of the cases and runs of homozygosity (ROHs) exceeding 300 Mb in 4.7% of the cases, revealing substantial autozygosity suggestive of strong inbreeding. Beyond these clinical assessments, pangenome analysis revealed 3.9 million population-level novel small variants, 39,782 SVs and an average of 2.17 Mb of novel sequence per individual absent from linear references and global pangenomes. We identified 85.9 kb of case-unique nonreference sequences in exonic segments overlapping with curated NDD gene sets. Graph-based tertiary analysis further identified rare exonic complex homozygous rearrangements in the NDD associated genes for 2.4% of the cases. Together, these findings emphasize how diverse genome assemblies extend variant discovery beyond established linear reference-based clinical interpretation.

genetics↗

Integrated exome-transcriptome analysis of a schizophrenia case with an exceptionally low polygenic risk score

Schizophrenia liability reflects both common polygenic variation and rarer variants of larger effect. We investigated a post-mortem schizophrenia case (SZ07) with a schizophrenia polygenic risk score below the first percentile among ancestry-matched cases, asking whether individual-level genomic and transcriptomic analyses could help identify potentially novel schizophrenia-relevant variants. We used exome analysis with multiple variant-prediction tools to generate a panel of variants and corresponding candidate genes potentially implicated in the pathological process in SZ07. The prioritised variants were then assessed using RNA sequencing of three cortical regions. Of these, only an IZUMO4 splice-acceptor variant showed direct RNA-level support. Beyond variant-specific analyses, we characterised broader transcriptomic abnormalities in the SZ07 brain. Pathway analysis of gene-level expression deviations implicated innate immune processes, calcium signalling, synaptic biology, and axon remodelling. Notably, compared with conventional single-sample biological pathway analysis, the analysis we used showed only a weak association with neuronal specificity, suggesting their reduced sensitivity to the main axis of variation related to cellular composition in bulk brain tissue gene expression data. Although the candidate genes showed no significant pathway-level convergence, the broader transcriptomic profile revealed coherent biological alterations largely compatible with the documented history of cerebrovascular disease in SZ07. These results demonstrate how the integration of exome and transcriptome data at the individual level enables the formulation and constraint of hypotheses regarding patient-specific genetic features and their molecular consequences, while highlighting important limitations related to tissue composition, comorbidity, and the choice of reference cohort.

genetics↗

A genetic screen to identify host factors required for commensal bacterial adherence to the gut epithelia

Bacterial adherence to gut epithelial cells likely involves host genetic components. We isolated a gut commensal bacterium, Lelliottia jeotgali, that can robustly colonize C. elegans through attachment to intestinal epithelial cells. Here, we developed a novel screen in C. elegans to identify host genes required for microbiome bacterial colonization. Using transgenic L. jeotgali strain expressing tdTomato, we conducted forward genetic screens in C. elegans to select for mutants that completely lost colonization. We identified multiple allelic mutations in five different C. elegans genes, termed knockout of Lelliottia adherence (kola) mutants, with a near complete loss of adherence. Each of kola-1-5 is uncharacterized and predicted to encode for a secreted or membrane-bound protein. We also identified gly-5, a GalNAc transferase that initiates O-linked glycosylation. Overall, we describe the first C. elegans screen to identify host factors that control microbiome bacterial colonization in the gut.

genetics↗