Biology subjects

Oome, S.

Publications and source records attributed to Oome, S..

3 recordsLinked to original sources

bioRxiv2026

Bundling Alleles Within Haplotype Blocks Improves QTL Detection Under Allelic Heterogeneity

Allelic heterogeneity is a term from genetics which means that alleles that differ in primary sequence can have a similar phenotypic outcome. In other words, they are functional equivalents, and they naturally appear through convergent evolution under selection. Current GWAS has trouble detecting these instances, as allelic heterogeneity leads to signal dilution in these analyses, often leading to a LOD score that stays below detection thresholds. In this paper, we show a method that can overcome this problem by bundling haplotypes into Artificial Combined Markers. The created marker matrix can then easily be used in existing GWAS software.

genetics↗

bioRxiv2026

Potato yield can be predicted by using drone-captured and environmental measurements early in the growing season

Accurate pre-harvest prediction of crop yield informs variety selection, optimizes management, and accelerates breeding. As potato is the worlds leading non-grain staple, here we evaluate a diverse panel of varieties in a three-year field trial across five European locations. Canopy development and environmental parameters are monitored throughout the growing season using drone-based imaging, in-field sensors and gene expression measurements, while tuber yield and quality traits are quantified at harvest. We show that these data enable the identification of climate-resilient, high-yielding genotypes and support the development of machine learning models that explain over 80% of yield variation in independent test sets. Strikingly, measurements collected within the first two months after planting achieve predictive performance comparable to models trained on full-season data. Model interrogation further shows that over 70% of yield variation can already be predicted based on a simple five-parameter linear equation. Our framework thus demonstrates the potential of integrative field phenotyping and data-driven modeling to improve variety selection across heterogeneous environments.

plant biology↗

bioRxiv2022

SMRT-AgRenSeq-d in potato (Solanum tuberosum) identifies candidates for the nematode resistance Gpa5

Potato is the third most important food crop in the world. Diverse pathogens threaten sustainable crop production but can be controlled, in many cases, through the deployment of disease resistance genes belonging to the family of nucleotide-binding, leucine-rich-repeat (NLR) genes. To identify functional NLRs in established varieties, we have successfully established SMRT-AgRenSeq in tetraploid potatoes and have further enhanced the methodology by including dRenSeq in an approach that we term SMRT-AgRenSeq-d. The inclusion of dRenSeq enables the filtering of candidates after the association analysis by establishing a presence/absence matrix across resistant and susceptible potatoes that is translated into an F1 score. Using a SMRT-RenSeq based sequence representation of the NLRome from the cultivar Innovator, SMRT-AgRenSeq-d analyses reliably identified the late blight resistance benchmark genes R1, R2-like, R3a and R3b in a panel of 117 varieties with variable phenotype penetrations. All benchmark genes were identified with an F1 score of 1 which indicates absolute linkage in the panel. When applied to the elusive nematode disease resistance gene Gpa5 that controls the Potato Cyst Nematode (PCN) species Globodera pallida (pathotypes Pa2/3), SMRT-AgRenSeq-d identified nine strong candidates. These map to the previously established position on potato chromosome 5 and are potential homologs of the late blight resistance gene R1. Assuming that NLRs are involved in controlling many types of resistances, SMRT-AgRenSeq-d can readily be applied to diverse crops and pathogen systems. In potato, SMRT-AgRenSeq-d lends itself, for example, to further study the elusive PCN resistances H1 or H3 for which phenotypic data exist.

plant biology↗