Modeling suggests gene editing combined with vaccination could eliminate a persistent disease in livestock.

Recent breakthroughs in gene-editing technologies that can render individual animals fully resistant to infections may offer unprecedented opportunities for controlling future epidemics in farm animals. Yet, their potential for reducing disease spread is poorly understood as the necessary theoretical framework for estimating epidemiological effects arising from gene-editing applications is currently lacking. Here, we develop semistochastic modeling approaches to investigate how the adoption of gene editing may affect infectious disease prevalence in farmed animal populations and the prospects and time scale for disease elimination. We apply our models to the porcine reproductive and respiratory syndrome (PRRS), one of the most persistent global livestock diseases to date. Whereas extensive control efforts have shown limited success, recent production of gene-edited pigs that are fully resistant to the PRRS virus have raised expectations for eliminating this deadly disease. Our models predict that disease elimination on a national scale would be difficult to achieve if gene editing was used as the only disease control. However, from a purely epidemiological perspective, disease elimination may be achievable within 3 to 6 y, if gene editing were complemented with widespread and sufficiently effective vaccination. Besides strategic distribution of genetically resistant animals, several other key determinants underpinning the epidemiological impact of gene editing were identified.

High-resolution genetic linkage map and height-related QTLs in an oil palm (Elaeis guineensis) family planted across multiple sites.

A refined SNP array containing 92,459 probes was developed and applied for chromosome scanning, construction of a high-density genetic linkage map and QTL analysis in a selfed Nigerian oil palm family (T128). Genotyping of the T128 mapping family generated 76,447 good quality SNPs for detailed scanning of aberration and homozygosity in the individual pseudo-chromosomes. Of them, 25,364 polymorphic SNPs were used for linkage analysis resulting in an 84.4% mapping rate. A total of 21,413 SNPs were mapped into 16 linkage groups (LGs), covering a total map length of 1364.5 cM. This genetic map is 16X denser than the previous version used to establish pseudo-chromosomes of the oil palm reference genome published in 2013. The QTLs associated with height, height increment and rachis length were identified in LGs TT05, 06, 08, 15 and 16. The present QTLs as well as those published previously were tagged to the reference genome to determine their chromosomal locations. Almost all the QTLs identified in this study were either close to or co-located with those reported in other populations. Determining the QTL position on chromosomes was also helpful in mining for the underlying candidate genes. In total, 55 putative genes and transcription factors involved in the biosynthesis, conjugation and signalling of the major phytohormones, especially for gibberellins and cell wall morphogenesis were found to be present in the identified genomic QTL regions, and their potential roles in plant dwarfism are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-023-01360-2.

Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds.

BACKGROUND: Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds. METHODS: Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10-6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model. RESULTS: We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN. CONCLUSIONS: Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.

The effects of training population design on genomic prediction accuracy in wheat.

Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder's equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125-0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.

Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis.

Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle the genetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships. Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadays common practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritability estimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and high heritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. The application of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidation of the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures for the morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausible candidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, an ethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified.

Genomic and phenotypic characterization of finger millet indicates a complex diversification history.

Advances in sequencing technologies mean that insights into crop diversification can now be explored in crops beyond major staples. We use a genome assembly of finger millet, an allotetraploid orphan crop, to analyze DArTseq single nucleotide polymorphisms (SNPs) at the whole and sub-genome level. A set of 8778 SNPs and 13 agronomic traits was used to characterize a diverse panel of 423 landraces from Africa and Asia. Through principal component analysis (PCA) and discriminant analysis of principal components, four distinct groups of accessions were identified that coincided with the primary geographic regions of finger millet cultivation. Notably, East Africa, presumed to be the crop's origin, exhibited the lowest genetic diversity. The PCA of phenotypic data also revealed geographic differentiation, albeit with differing relationships among geographic areas than indicated with genomic data. Further exploration of the sub-genomes A and B using neighbor-joining trees revealed distinct features that provide supporting evidence for the complex evolutionary history of finger millet. Although genome-wide association study found only a limited number of significant marker-trait associations, a clustering approach based on the distribution of marker effects obtained from a ridge regression genomic model was employed to investigate trait complexity. This analysis uncovered two distinct clusters. Overall, the findings suggest that finger millet has undergone complex and context-specific diversification, indicative of a lengthy domestication history. These analyses provide insights for the future development of finger millet.

Chromosome-scale Elaeis guineensis and E. oleifera assemblies: comparative genomics of oil palm and other Arecaceae.

Elaeis guineensis and E. oleifera are the two species of oil palm. E. guineensis is the most widely cultivated commercial species, and introgression of desirable traits from E. oleifera is ongoing. We report an improved E. guineensis genome assembly with substantially increased continuity and completeness, as well as the first chromosome-scale E. oleifera genome assembly. Each assembly was obtained by integration of long-read sequencing, proximity ligation sequencing, optical mapping, and genetic mapping. High interspecific genome conservation is observed between the two species. The study provides the most extensive gene annotation to date, including 46,697 E. guineensis and 38,658 E. oleifera gene predictions. Analyses of repetitive element families further resolve the DNA repeat architecture of both genomes. Comparative genomic analyses identified experimentally validated small structural variants between the oil palm species and resolved the mechanism of chromosomal fusions responsible for the evolutionary descending dysploidy from 18 to 16 chromosomes.

Phenomic and genomic prediction of yield on multiple locations in winter wheat.

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.

Haplotype diversity: the link between statistical and biological association.

In the rapidly growing field of association mapping in plants, the use of (marker) haplotypes rather than single markers can be an effective way of improving detection power. Here, we highlight the information that can be obtained from deducing the historical relationships between haplotypes. The ordering of haplotype classes according to deduced historical relationships should further enhance association detection power, but can also be used to predict the genotypic and phenotypic values of unobserved germplasm.

SNPWave: a flexible multiplexed SNP genotyping technology.

Scalable multiplexed amplification technologies are needed for cost-effective large-scale genotyping of genetic markers such as single nucleotide polymorphisms (SNPs). We present SNPWave, a novel SNP genotyping technology to detect various subsets of sequences in a flexible fashion in a fixed detection format. SNPWave is based on highly multiplexed ligation, followed by amplification of up to 20 ligated probes in a single PCR. Depending on the multiplexing level of the ligation reaction, the latter employs selective amplification using the amplified fragment length polymorphism (AFLP) technology. Detection of SNPWave reaction products is based on size separation on a sequencing instrument with multiple fluorescence labels and short run times. The SNPWave technique is illustrated by a 100-plex genotyping assay for Arabidopsis, a 40-plex assay for tomato and a 10-plex assay for Caenorhabditis elegans, detected on the MegaBACE 1000 capillary sequencer.

Detection of bovine meat and bone meal in animal feed at a level of 0.1%.

For the control of the transmission of bovine spongiform encephalopathy in cattle via feedstuff, a real-time polymerase chain reaction assay was developed with ruminant-specific Bov-B SINE primers, SYBR Green fluorescence detection, and melting curve analysis. In formulated cattle and chicken feed samples spiked with pure bovine and sheep meat and bone meal heated at 133 degrees C for 20 min, a contamination level of 0.1% was detected.

Real-time PCR detection of ruminant DNA.

To control the spread of bovine spongiform encephalopathy, several DNA methods have been described for the detection of the species origin of meat and bone meal. Most of these methods are based on the amplification of a mitochondrial DNA segment. We have developed a semiquantitative method based on real-time PCR for detection of ruminant DNA, targeting an 88-bp segment of the ruminant short interspersed nuclear element Bov-A2. This method is specific for ruminants and is able to detect as little as 10 fg of bovine DNA. Autoclaving decreased the amount of detectable DNA, but positive signals were observed in feeding stuff containing 10% bovine material if this had not been rendered in accordance with the regulations, i.e., heated at 134 degrees C for 3 instead of 20 min.