bioRxiv Science⌕ Search

bioRxiv · 10.1101/199893

A strategy to exploit surrogate sire technology in livestock breeding programs

Abstract

In this work, we performed simulations to develop and test a strategy for exploiting surrogate sire technology in animal breeding programs. Surrogate sire technology allows the creation of males that lack their own germline cells, but have transplanted spermatogonial stem cells from donor males. With this technology, a single elite male donor could give rise to huge numbers of progeny, potentially as much as all the production animals in a particular time period.\n\nOne hundred replicates of various scenarios were performed. Scenarios followed a common overall structure but differed in the strategy used to identify elite donors and how these donors were used in the product development part.\n\nThe results of this study showed that using surrogate sire technology would significantly increase the genetic merit of commercial sires, by as much as 6.5 to 9.2 years worth of genetic gain compared to a conventional breeding program. The simulations suggested that a strategy involving three stages (an initial genomic test followed by two subsequent progeny tests) was the most effective of all the strategies tested.\n\nThe use of one or a handful of elite donors to generate the production animals would be very different to current practice. While the results demonstrate the great potential of surrogate sire technology there are considerable risks but also other opportunities. Practical implementation of surrogate sire technology would need to account for these.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gottardo, P., Gorjanc, G., Battagin, M., Gaynor, C. R., Jenko, J., Freixedes, R. R., Whitelaw, B., Mileham, A., Herring, W., Hickey, J.. 2017-10-07. A strategy to exploit surrogate sire technology in livestock breeding programs. https://doi.org/10.1101/199893

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

KEEP EXPLORING

Related preprints

Mapping, stacking and characterization of a novel resistance haplotype against GpaVvrn-virulent Globodera pallida derived from wild potato relative Solanum brevicaule

Potato production is threatened by pests and pathogens. Among the most harmful are the potato cyst nematodes (PCN), including Globodera pallida. The major control measures are potato cultivars harbouring resistance genes. However, virulent nematode populations are breaking through the most broadly applied resistance genes, and therefore novel resistance genes are required to keep control of G. pallida. In this study we map a resistance locus in wild potato relative Solanum brevicaule to the bottom of chromosome 11 (42.83 - 42.85 Mb) using a k-mer based bulked segregant analysis and name the resistant haplotype GpaXIbrc. The presence of the GpaXIbrc marker in the potato genome significantly increases resistance against a GpaVvrn-virulent G. pallida population (AMPOP01). Furthermore, the GpaXIbrc haplotype was stacked with three other resistance haplotypes: GpaXItar, GpaVvrn, and GpaIXvrn. Stacking of the novel GpaXIbrc haplotype to these other resistance haplotypes resulted in a significant increase in resistance. The largest effect was seen in combinations with 'major' resistance haplotypes GpaVvrn + GpaIXvrn and GpaXItar + GpaXIbrc. To determine whether we are dealing with a novel allele, the resistance mechanism underlying the GpaXIbrc haplotype was described after performing an in vitro bioassay. The resistance mechanism was different from GpaXIspl as described in literature, which is also localized at the bottom of chromosome 11. Therefore, we conclude that we found a novel resistance allele originating from S. brevicaule.

genetics↗

Cellular context shapes the transcriptomic impact of somatic variants

Somatic SNVs are commonly prioritized by recurrence or sequence annotation, leaving the molecular consequences of private variants unresolved. We developed PrismSNV and Ligo-Seq, a single-cell full-length transcriptome sequencing platform, to quantify transcriptomic displacement associated with within-cell SNV-state toggling. Across 26 samples from three cancer types, more than 60% of candidate SNVs showed significant cell-type-specific displacement with over 30% variation in magnitude. Sequence-based deleteriousness was poorly aligned with cellular displacement, and fewer than 3% of top-displacement SNVs across six tumor types were catalogued in ClinVar. Displacement was strongest in transcriptionally plastic, less differentiated states. Analysis of 655,349 CD4+ T-cell transcriptomes further showed that SNV-gene associations changed in magnitude and sometimes direction during activation. Lack of recurrence or clinical annotation does not imply transcriptomic inertness; variant impact is shaped by cellular context rather than sequence alone, establishing the variant-cell pair as a critical unit for interpreting somatic variation.

genetics↗

Genetic and Epigenetic Landscape of Self-Identified Hispanics/Latino in All Of Us

Human genetic studies still underrepresent many populations, limiting discovery and interpretation of medically relevant variation. Extensive admixture in self-identified Hispanic/Latino populations increases the need for representative reference data and ancestry-aware interpretation of medically relevant variation. We used long-read sequencing to analyze genetic variation, ancestry, and DNA methylation in 1,490 self-identified Hispanic/Latino participants from the All of Us Research Program. We identified 10 million novel variants and more than 100 candidate phenotype associations, with fine-mapping prioritizing structural variants over nearby single-nucleotide variants in more than half. We linked 3,329 tandem repeats to DNA methylation, resolved widespread allele-specific methylation, and identified ancestry-linked regulation across paralogous genes. These results reveal previously inaccessible genetic and regulatory variation and show the value of long-read sequencing for diverse populations.

genetics↗