Biology subjects

Lindqvist-Kreuze, H.

Publications and source records attributed to Lindqvist-Kreuze, H..

6 recordsLinked to original sources

bioRxiv2026

Broad-spectrum polerovirus resistance conferred by a potato TIR-NLR immune receptor

Potato leafroll virus (PLRV) is an economically important viral disease of potato (S. tuberosum). Genetic resistance to this phloem-limited virus is rare, and no cloned resistance (R) genes have been reported. Rladg confers resistance to PLRV in an Andean potato landrace, LOP-868 (Velasquez et al. 2007). We identified the functional Rladg gene as a homolog of the tomato TIR-NLR-encoding Bs4. Rladg interacts with the serine protease domain of the PLRV protein P1, which is essential for virus replication. This recognition is independent of the proteases enzymatic activity, and the Rladg immune receptor oligomerizes upon direct association with the protease. Like PLRV, many poleroviruses contain a serine protease. Despite their diverse amino acid sequences, these proteases are predicted to share similar structures. Rladg recognizes all ten tested polerovirus proteases, suggesting a conserved structural recognition mechanism. We propose that Rladgs broad recognition capacity could enable resistance to poleroviruses in many crop species. Rladg is the first R-gene reported to confer resistance to a phloem-limited pathogen and could provide enhanced resistance to many economically important poleroviruses.

plant biology↗

bioRxiv2026

Prediction of late blight severity in a large panel of potato genotypes using low-altitude aerial images and machine learning methods

Potato (Solanum tuberosum L.) is a staple crop crucial to global food security, yet its production is severely threatened by late blight (LB), caused by Phytophthora infestans, one of the most destructive plant diseases worldwide. Breeding programs for LB resistance have traditionally relied on labor-intensive and subjective visual assessments, which limit scalability and consistency, particularly in early-generation trials. Unmanned aerial vehicle (UAV)-based remote sensing combined with machine learning (ML) offers a promising alternative for objective, high-throughput disease phenotyping. This study evaluated the potential of UAV-derived multispectral imagery and ML techniques to estimate LB severity across large and genetically diverse potato breeding populations, comprising 2,745 clones in one trial and 492 accessions in another, conducted in Oxapampa, Pasco, Peru. We compared vegetation index-based approaches with a machine learning framework that integrates K-means clustering and Kernel Ridge Regression (KRR) and assessed their ability to capture genotypic variation and support selection decisions. NDVI consistently showed a strong correlation with visually assessed LB severity, particularly at advanced stages of disease development, enabling objective discrimination between healthy and diseased canopy tissues. However, the KRR-based approach outperformed linear NDVI-based models by capturing nonlinear relationships between spectral responses and disease progression. Estimates of LB severity derived from NDVI and KRR models, expressed as best linear unbiased estimates (BLUEs), showed strong and biologically consistent relationships with the area under the disease progress curve (AUDPC), particularly during later UAV acquisitions. Selection coincidence between UAV-derived estimates and AUDPC-based rankings was substantially higher at intermediate to advanced stages of disease progression, suggesting that UAV assessments at these stages may capture sufficient phenotypic variation to distinguish genotypes. These findings indicate that UAV-based multispectral phenotyping, especially when integrated with ML, provides a practical and scalable approach for assessing LB severity in potato breeding programs while reducing the need for time-consuming field evaluations.

plant biology↗

bioRxiv2024

Targeted genotyping-by-sequencing of potato and software for imputation

Mid-density targeted genotyping-by-sequencing (GBS) combines trait-specific markers with thousands of genomic markers at an attractive price for linkage mapping and genomic selection. A 2.5K targeted GBS assay for potato was developed using the DArTagTM technology and later expanded to 4K targets. Genomic markers were selected from the potato InfiniumTM SNP array to maximize genome coverage and polymorphism rates. The DArTag and SNP array platforms produced equivalent dendrograms in a test set of 298 tetraploid samples, and 83% of the common markers showed good quantitative agreement, with RMSE (root-mean-squared-error) less than 0.5. DArTag is suited for genomic selection candidates in the clonal evaluation trial, coupled with imputation to a higher density platform for the training population. Using the software polyBreedR, an R package for the manipulation and analysis of polyploid marker data, the RMSE for imputation by linkage analysis was 0.15 in a small half-diallel population (N=85), which was significantly lower than the RMSE of 0.42 with the Random Forest method. Regarding high-value traits, the DArTag markers for resistance to potato virus Y, golden cyst nematode, and potato wart appeared to track their targets successfully, as did multi-allelic markers for maturity and tuber shape. In summary, the potato DArTag assay is a transformative and publicly available technology for potato breeding and genetics. Core IdeasO_LIA mid-density, targeted genotyping-by-sequencing (GBS) assay was developed for potato. C_LIO_LIThe GBS assay includes markers for resistance to potato virus Y, golden cyst nematode, and potato wart. C_LIO_LIThe GBS assay includes multi-allelic markers for potato maturity and tuber shape. C_LIO_LIThe polyBreedR software has functions for manipulating and imputing polyploid marker data in Variant Call Format. C_LIO_LILinkage Analysis was more accurate than the Random Forest method when imputing from 2K to 10K markers. C_LI

genomics↗

bioRxiv2020

Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: i) do polyploid crop breeders need to invest more for additional sequencing depth? ii) how many markers are required to make selection decisions? iii) does considering non-additive genetic effects improve predictive ability (PA)? iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; [≤] 1000) are adequate for prediction, hence it is possible to get this number at the current sequencing depth from most service providers. We also show that considering dosage information and additive-effects only models had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations. Key messagePolypoid crop breeders do not need more investment for sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture.

genetics↗

bioRxiv2020

Global multi-environment resistance QTL for foliar late blight resistance in tetraploid potato with tropical adaptation

The identification of environmentally stable and globally predictable resistance to potato late blight is challenged by the crops clonal and polyploid nature and the pathogens rapid evolution. Genome-wide analysis (GWA) of multi-environment trials can add precision to breeding for complex traits. A diversity panel of tetraploid potato germplasm bread for multiple resistance and quality traits was genotyped by genotyping by sequencing (GBS) and phenotyped for late blight resistance in a trait observation network spanning three continents addressed by the International Potato Centers (CIPs) breeding program. The aims of this study were to (i) identify QTL underlying resistance in and across environments and (ii) develop prediction models to support the global deployment and use of promising resistance sources in local breeding and variety development programs. Health-indexed in vitro plants of 380 clones and varieties were distributed from CIP headquarters in Peru to China and Ethiopia and tuber seed was produced centrally in each country. Phenotypes were recorded as rAUDPC following field exposure to local isolates of Phytophthora infestans, Stringent filtering for individual read depth >60 resulted in 3,239 tetraploid SNPs. Meanwhile, 55,748 diploid SNPs were identified using diploidized data and individual read depth>17. The kinship matrix was utilized to obtain BLUP and identify best performing germplasm in each and all environments. Genotypes with high levels of resistance in all environments were identified from the B3, LBHT and B3-LTVR populations. GWA identified stable QTL for late blight resistance in chromosome 9 and environment specific QTL in chromosomes 3, 5, 6 and 10.

genomics↗

bioRxiv2017

Gene expression polymorphism underpins evasion of host immunity in an asexual lineage of the Irish potato famine pathogen

Outbreaks caused by asexual lineages of fungal and oomycete pathogens are an expanding threat to crops, wild animals and natural ecosystems (Fisher et al. 2012,Kupferschmidt 2012). However, the mechanisms underlying genome evolution and phenotypic plasticity in asexual eukaryotic microbes remain poorly understood (Seidl and Thomma 2014). Ever since the 19th century Irish famine, the oomycete Phytophthora infestans has caused recurrent outbreaks on potato and tomato crops that have been primarily caused by the successive rise and migration of pandemic asexual lineages (Cooke et al. 2012, Yoshida et al. 2013,Yoshida et al. 2014). Here, we reveal patterns of genomic and gene expression variation within a P. infestans asexual lineage by compared sibling strains belonging to the South American EC-1 clone that has dominated Andean populations since the 1990s (Forbes et al. 1997, Oyarzun et al. 1998, Delgado et al. 2013, Yoshida et al. 2013, Yoshida et al. 2014). We detected numerous examples of structural variation, nucleotide polymorphisms and gene conversion within the EC-1 clone. Remarkably, 17 genes are not expressed in one of the two EC-1 isolates despite apparent absence of sequence polymorphisms. Among these, silencing of an effector gene was associated with evasion of disease resistance conferred by a potato immune receptor. These results highlight the exceptional genetic and phenotypic plasticity that underpins host adaptation in a pandemic clonal lineage of a eukaryotic plant pathogen.

plant biology↗