Biology subjects

Hubner, S.

Publications and source records attributed to Hubner, S..

5 recordsLinked to original sources

bioRxiv2022

The genomics of linkage drag in sunflower

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here we comprehensively analyzed the genomic and phenotypic impacts of wild introgressions into cultivated sunflower to estimate the impacts of linkage drag. First, we generated new reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge regression model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including > 3,000 new genes. While introgressions reduced genetic load at protein-coding sequences and positively affected traits associated with abiotic stress resistance, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, pre-breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

genomics↗

bioRxiv2022

GWANN: Implementing deep learning in genome wide association studies

MotivationGenome wide association studies (GWAS) are extensively used across species to identify genes that underlie important traits. Most GWAS methods apply modifications and extensions to a linear regression model in order to detect significant associations between genetic variation and a trait. Despite their popularity, these statistical models tend to suffer from high false positive rates, especially when utilized on large variant datasets or complex demographic scenarios. To overcome this, aggressive statistical corrections are applied which frequently diminish true associations. ResultsHere we consider a deep learning approach, and present an implementation of a convolutional neural network (CNN) to identify genetic variation that is associated with a trait of interest. To exploit the strength of CNNs in visual recognition, the genotype information is represented as an image, which enables the model to correctly classify genetic variants with respect to the trait, even when a population structure is present. Our proposed approach was implemented in a package called GWANN which exhibited solid performance. Overall, GWANN outperformed popular GWAS tools on both simulated and real datasets, and enabled the identification of association signals with increased sensitivity and speed. Availability and implementationThe package is available at: https://github.com/hubner-lab/GWANN

bioinformatics↗

bioRxiv2022

Novel diversity panel facilitates genomic dissection of lodging in tef (Eragrostis tef)

RationalUnderutilized species that are not widely cultivated (known as orphan crops) present opportunities to increase crop diversity and food security. Tef [Eragrostis tef (Zucc.) Trotter] is known for its high-quality grain and forage. Root-borne lodging is a major devastating problem in tef cultivation, leading to large economic losses and limiting its widespread adoption. ObjectiveThe aim of this study was to identify genomic regions that are associated with tef lodging. MethodsA tef diversity panel (TDP-300) comprised of 297 lines was assembled, genotyped, and phenotyped across 4 field environments. This unique panel, the first of its kind in tef, has the potential to facilitate tef research and breeding. ResultsGenome-wide association study identified 29 sites associated with lodging; in all cases with a minor allele conferring reduced lodging. The eleven sites of prime interest were located in or near genes, 5 of them with a putative role, of which 3 were found to be involved root development. ConclusionsThe identification of lodging-related sites in the current study may advance understanding of the mechanisms underlying tef lodging and crop improvement. The identification of genes related to root development support the importance of root traits in tef lodging, which should be targeted in future breeding.

plant biology↗

bioRxiv2021

Decoupling the molecular regulation of perenniality and flowering in bulbous barley (Hordeum bulbosum)

Global crop production is being challenged by rapid population growth, declining natural resources, and dramatic climatic turnovers. These challenges have prompted plant breeders to explore new ventures to enhance adaptation and sustainability in crops. One intriguing approach to make agriculture more sustainable is by turning annual systems into perennial which offers many economic and biodiversity-friendly benefits. Previous attempts to develop a perennial cereal crop employed a classical breeding approach and extended over a long period with limited success. Thus, elucidating the genetic basis of perenniality at the molecular level can accelerate the breeding process. Here, we investigated the genetic basis of bulb formation in the barley congener species Hordeum bulbosum by elucidating the transcripts presence/absence variation compared with other annual species in the Poaceae, and a differential expression analysis of meristem tissues. The PAV analysis recaptured the expected phylogeny and indicated that H. bulbosum is enriched with developmental and disease responsive genes that are absent among annual species. Next, the abundance of transcripts was quantified and allowed to identify differentially expressed genes that are associated with bulb formation pathways in addition to major circadian clock genes that regulate flowering. A first model for the bulb formation pathway is suggested and include developmental and starch biosynthesis genes. To the best of our knowledge this is the first transcriptome developed for H. bulbosum and the first attempt to describe the regulation of bulb initiation in cereals at the molecular level.

plant biology↗

bioRxiv2021

Image processing and genome-wide association studies in sunflower identify loci associated with seed-coat characteristics

Sunflower seeds (technically achenes) are characterized by a wide spectrum of sizes, shapes, and colors. These traits are genetically correlated with the branching plant architecture loci, which were introgressed into restorer lines to facilitate efficient hybrid production. To break this genetic correlation between branching and seed traits, high resolution mapping of the genes that regulate seed traits is necessary. Recent progress in genomics permits acquisition of comprehensive genotyping data for a large diversity panel, yet a major constraint for exploring the genetic basis of important phenotypes across large diversity panels is the ability to screen and characterize them efficiently. Here, we implement a cost-effective image analysis pipeline to phenotype seed characteristics in a large sunflower diversity panel comprised of 287 individuals that represents most of the genetic variation in cultivated sunflower. A genome-wide association analysis was performed for seed-coat size and shape traits and significant signals were identified around genes regulating phytohormone activity. In addition, significant seed-coat color QTLs were identified and candidate genes that effect pigmentation were detected including a phytomelanin regulating gene on chromosome 17. Finally, QTLs associated with the seed-coat striped pattern were identified and phytohormone regulating candidate genes were detected. The implementation of image analysis phenotyping for GWAS allowed efficient screening of a large diversity panel and identification of valuable genetic factors effecting seed characteristics at the finest resolution to date.

genetics↗