Biology subjects

Renner, J.

Publications and source records attributed to Renner, J..

4 recordsLinked to original sources

bioRxiv2025

Interregional human assembloids recapitulate fetal brain morphologies and enhance neuronal complexity

Neuronal morphology governs how neurons connect, integrate, and process information, offering critical insights into the functional architecture of the brain. Characterizing the three-dimensional (3D) morphology of individual neurons is key not only for mapping circuit connectivity but also for understanding the cellular diversity that emerges during development. Neural organoids are valuable models of human brain development and disease, yet their morphological complexity remains poorly characterized despite advances in single-cell transcriptomics. Here, we use 3D confocal imaging and manual reconstruction of 735 neurons to analyze forebrain (dorsal and ventral) and thalamic (dorsal and ventral) organoids, as well as forebrain, thalamic, and corticothalamic assembloids. We find that organoids and assembloids exhibit distinct morphologies resembling fetal brain neurons, including immature pyramidal-like, double-bouquet, and bushy-like neurons. Interregional assembloids show greater neuronal morphological complexity than individual organoids, with more extensive dendritic branching, longer projections, and diverse soma shapes. Corticothalamic assembloids further display features of emerging connectivity. We observe dendritic spines with excitatory and inhibitory profiles and varicosities, indicative of maturing synaptic architecture. Together, our work makes an initial effort in describing the diversity of neuronal morphology in human neural organoids and assembloids. It further establishes structural phenotyping as a critical dimension for validating human neural models and underscores their value for modeling morphofunctional disorders.

neuroscience↗

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↗

bioRxiv2022

Optimal implementation of genomic selection in clone breeding programs - exemplified in potato: I. Effect of selection strategy, implementation stage, and selection intensity on short-term genetic gain

Genomic selection (GS) is used in many animal and plant breeding programs to enhance genetic gain for complex traits. However, its optimal integration in clone breeding programs that up to now relied on phenotypic selection (PS) requires further research. The objectives of this study were to (i) investigate under a fixed budget how the weight of GS relative to PS, the stage of implementing GS, the correlation between an auxiliary trait assessed in early generations and the target trait, the variance components, and the prediction accuracy affect the genetic gain of the target trait of GS compared to PS, (ii) determine the optimal allocation of resources maximizing the genetic gain of the target trait in each selection strategy and for varying cost scenarios, and (iii) make recommendations to breeders how to implement GS in clone and especially potato breeding programs. In our simulation results, any selection strategy involving GS had a higher short-term genetic gain for the target trait than Standard-PS. In addition, we show that implementing GS in consecutive selection stages can largely enhance short-term genetic gain and recommend the breeders to implement GS at single hills and A clone stages. Furthermore, we observed for selection strategies involving GS that the optimal allocation of resources maximizing the genetic gain of the target trait differed considerably from those typically used in potato breeding programs. Therefore, our study provides new insight for breeders regarding how to optimally implement GS in a commercial potato breeding program to improve the short-term genetic gain for their target trait.

genetics↗

bioRxiv2019

Benchmarking Database Systems for Genomic Selection Implementation

MotivationWith high-throughput genotyping systems now available, it has become feasible to fully integration genotyping information into breeding programs [22]. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize them in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs one would need an efficient genotype data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems.\n\nResultsWe found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix.\n\nAvailabilityhttp://gobiinx1.bti.cornell.edu:6083/projects/GBM/repos/benchmarking/browse\n\nContactyn259@cornell.edu

genomics↗