Genomic Selection For Disease Resistance Research Articles

Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes.

We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.

Improving the accuracy of genomic predictions for disease resistance traits in fish using a multiple-trait linear-threshold model

Genomic selection is effective in enhancing the selection efficiency during breeding for improved disease resistance in fish. However, given that the collection of routine disease challenge data is generally costly and that disease resistance traits are threshold traits with typically low heritability, there has been comparatively limited progress in improving the accuracy of genomic prediction. The objective of this study was to assess the advantage of a multiple-trait linear-threshold model in genomic prediction of disease resistance traits in fish. The study was based on analyses of both simulated data and real data obtained for a rainbow trout population. We simulated a linear trait (growth trait, h2 = 0.3) and a binary threshold trait (disease resistance trait, h2 = 0.1) with different genetic correlations (0.1, 0.3 and 0.5). Single- and multiple-trait models with best linear unbiased prediction (BLUP) and genomic BLUP (GBLUP) were implemented to investigate their prediction abilities. Moreover, we also assessed the impact of missing proportions of a reference population on genomic prediction for disease resistance traits. The results revealed that methods using marker information produced more accurate predictions than the pedigree-based BLUP method. Furthermore, for between-trait genetic correlations of 0.1, 0.3 and 0.5, the multiple-trait GBLUP for disease resistance traits showed 1.0%, 1.5% and 6.4% higher accuracy than single-trait GBLUP, respectively. Meanwhile, no improvement in accuracy was observed for the assessed growth trait. Moreover, with an increase in the genetic correlation between traits, we observed reductions in the consistency of estimated SNP effects using single-trait and multiple-trait GBLUP models. Our findings indicated the superiority of multiple-trait models in genomic prediction for a disease resistance trait with missing data; this superiority is more pronounced when the proportion of the reference population missing is large. Furthermore, using real data obtained for rainbow trout, we verified the advantage of the multiple-trait linear-threshold model in genomic prediction of binary survival, with the multiple-trait GBLUP model yielding a lower mean squared (absolute) error than the single-trait GBLUP model. Overall, our findings have important implications with regard to applying multiple-trait linear-threshold model in genomic selection for disease resistance in most aquaculture species.

Integrating Genetic and Genomic Analyses of Combined Health Data Across Ecotypes to Improve Disease Resistance in Indigenous African Chickens.

Poultry play an important role in the agriculture of many African countries. The majority of chickens in sub-Saharan Africa are indigenous, raised in villages under semi-scavenging conditions. Vaccinations and biosecurity measures rarely apply, and infectious diseases remain a major cause of mortality and reduced productivity. Genomic selection for disease resistance offers a potentially sustainable solution but this requires sufficient numbers of individual birds with genomic and phenotypic data, which is often a challenge to collect in the small populations of indigenous chicken ecotypes. The use of information across-ecotypes presents an attractive possibility to increase the relevant numbers and the accuracy of genomic selection. In this study, we performed a joint analysis of two distinct Ethiopian indigenous chicken ecotypes to investigate the genomic architecture of important health and productivity traits and explore the feasibility of conducting genomic selection across-ecotype. Phenotypic traits considered were antibody response to Infectious Bursal Disease (IBDV), Marek’s Disease (MDV), Fowl Cholera (PM) and Fowl Typhoid (SG), resistance to Eimeria and cestode parasitism, and productivity [body weight and body condition score (BCS)]. Combined data from the two chicken ecotypes, Horro (n = 384) and Jarso (n = 376), were jointly analyzed for genetic parameter estimation, genome-wide association studies (GWAS), genomic breeding value (GEBVs) calculation, genomic predictions, whole-genome sequencing (WGS), and pathways analyses. Estimates of across-ecotype heritability were significant and moderate in magnitude (0.22–0.47) for all traits except for SG and BCS. GWAS identified several significant genomic associations with health and productivity traits. The WGS analysis revealed putative candidate genes and mutations for IBDV (TOLLIP, ANGPTL5, BCL9, THEMIS2), MDV (GRM7), SG (MAP3K21), Eimeria (TOM1L1) and cestodes (TNFAIP1, ATG9A, NOS2) parasitism, which warrant further investigation. Reliability of GEBVs increased compared to within-ecotype calculations but accuracy of genomic prediction did not, probably because the genetic distance between the two ecotypes offset the benefit from increased sample size. However, for some traits genomic prediction was only feasible in across-ecotype analysis. Our results generally underpin the potential of genomic selection to enhance health and productivity across-ecotypes. Future studies should establish the required minimum sample size and genetic similarity between ecotypes to ensure accurate joint genomic selection.

Development of a SNP linkage map and genome-wide association study for resistance to Aeromonas hydrophila in pacu (Piaractus mesopotamicus)

BackgroundPacu (Piaractus mesopotamicus) is one of the most important Neotropical aquaculture species from South America. Disease outbreaks caused by Aeromonas hydrophila infection have been considered significant contributors to the declining levels of pacu production. The current implementation of genomic selection for disease resistance has been adopted as a powerful strategy for improvement in fish species. This study aimed to investigate the genetic architecture of resistance to A. hydrophila in pacu via Genome-Wide Association Study (GWAS), the identification of suggestive Quantitative Trait Loci (QTLs) and putative genes associated with this trait. The genetic data were obtained from 381 juvenile individuals belonging to 14 full-sibling families. An experimental challenge was performed to gain access to the levels of genetic variation for resistance against the bacteria using the following trait definitions: binary test survival (TS) and time of death (TD).ResultsThe analyses of genetic parameters estimated moderate heritability (h2) for both resistance traits: 0.20 (± 0.09) for TS and 0.35 (± 0.15) for TD. A linkage map for pacu was developed to enable the GWAS, resulting in 27 linkage groups (LGs) with 17,453 mapped Single Nucleotide Polymorphisms (SNPs). The length of the LGs varied from 79.95 (LG14) to 137.01 (LG1) cM, with a total map length of 2755.60 cM. GWAS identified 22 putative QTLs associated to A. hydrophila resistance. They were distributed into 17 LGs, and were considered suggestive genomic regions explaining > 1% of the additive genetic variance (AGV) for the trait. Several candidate genes related to immune response were located close to the suggestive QTLs, such as tbk1, trim16, Il12rb2 and lyz2.ConclusionThis study describes the development of the first medium density linkage map for pacu, which will be used as a framework to study relevant traits to the production of this species. In addition, the resistance to A. hydrophila was found to be moderately heritable but with a polygenic architecture suggesting that genomic selection, instead of marker assisted selection, might be useful for efficiently improving resistance to one of the most problematic diseases that affects the South American aquaculture.

Development of a 50K SNP Array for Japanese Flounder and Its Application in Genomic Selection for Disease Resistance

Single nucleotide polymorphism (SNP) arrays are a powerful genotyping tool used in genetic research and genomic breeding programs. Japanese flounder (Paralichthys olivaceus) is an economically-important aquaculture flatfish in many countries. However, the lack of high-efficient genotyping tools has impeded the genomic breeding programs for Japanese flounder. We developed a 50K Japanese flounder SNP array, “Yuxin No. 1,” and report its utility in genomic selection (GS) for disease resistance to bacterial pathogens. We screened more than 42.2 million SNPs from the whole-genome resequencing data of 1099 individuals and selected 48 697 SNPs that were evenly distributed across the genome to anchor the array with Affymetrix Axiom genotyping technology. Evaluation of the array performance with 168 fish showed that 74.7% of the loci were successfully genotyped with high call rates (> 98%) and that the polymorphic SNPs had good cluster separations. More than 85% of the SNPs were concordant with SNPs obtained from the whole-genome resequencing data. To validate “Yuxin No. 1” for GS, the arrayed genotyping data of 27 individuals from a candidate population and 931 individuals from a reference population were used to calculate the genomic estimated breeding values (GEBVs) for disease resistance to Edwardsiella tarda. There was a 21.2% relative increase in the accuracy of GEBV using the weighted genomic best linear unbiased prediction (wGBLUP), compared to traditional pedigree-based best linear unbiased prediction (ABLUP), suggesting good performance of the “Yuxin No. 1” SNP array for GS. In summary, we developed the “Yuxin No. 1” 50K SNP array, which provides a useful platform for high-quality genotyping that may be beneficial to the genomic selective breeding of Japanese flounder.

Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale.

Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery of gene regulatory networks, genomic selection for disease resistance and prediction of pathogen effectors. However, achieving robust performance from ML is not trivial and requires knowledge of both the methodology and the biology. I discuss common pitfalls and challenges in using ML approaches. Finally, I highlight future opportunities for ML as a tool for dissecting plant-pathogen interactions using high-throughput data, for example, through integration of diverse data sources and the analysis with higher resolution, such as from individual cells or on elaborate spatial and temporal scales.

Recent Developments in Genomic Selection for Minor Gene Quantitative Disease Resistance Plant Breeding

To speed up the development of improved crop varieties, genomics assisted plant breeding is becoming an important tool. With traditional breeding and marker assisted selection, there have been several achievements in breeding for diseases resistance. Most research for disease resistance has been focused on major disease resistance genes which are highly effective although very vulnerable to breakdown with rapid changes in pathogenic races. In contrast, breeding for minor gene quantitative resistance can produce more durable plant varieties although it is very slow and challenging breeding. As the genetic architecture of the plant disease resistance shifts from single major R genes to many minor quantitative genes, the most appropriate approach for molecular plant breeding is genomic selection (GS) than marker assisted selection or conventional breeding. With the advent of new genomic tools, GS has emerged as one of the most important approaches for predicting genotype performance to improve genetically complex quantitative traits. Consequently, GS helps to accelerate the rate of genetic gain in breeding by using whole genome sequence data to predict the breeding value of offspring. GS breeding for quantitative resistance will therefore necessitate whole genome prediction models and selection methodology as implemented for classical complex traits. With the implementation of GS for yield and other economically important traits, whole genome marker profiles are available for the entire set of breeding lines, enabling genomic selection for disease resistance with no additional direct cost. Therefore, recent developments in GS including a two stream GS + de novo GWAS models (GS+) and GS for combined highest level of quantitative resistance with R genes (QR +R gene) individuals are expected to further advance disease resistance plant breeding and briefly discussed.

Advances and Challenges in Genomic Selection for Disease Resistance

Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end-use quality. With the need to accelerate the development of improved varieties, genomics-assisted breeding is becoming an important tool in breeding programs. With marker-assisted selection, there has been success in breeding for disease resistance; however, much of this work and research has focused on identifying, mapping, and selecting for major resistance genes that tend to be highly effective but vulnerable to breakdown with rapid changes in pathogen races. In contrast, breeding for minor-gene quantitative resistance tends to produce more durable varieties but is a more challenging breeding objective. As the genetic architecture of resistance shifts from single major R genes to a diffused architecture of many minor genes, the best approach for molecular breeding will shift from marker-assisted selection to genomic selection. Genomics-assisted breeding for quantitative resistance will therefore necessitate whole-genome prediction models and selection methodology as implemented for classical complex traits such as yield. Here, we examine multiple case studies testing whole-genome prediction models and genomic selection for disease resistance. In general, whole-genome models for disease resistance can produce prediction accuracy suitable for application in breeding. These models also largely outperform multiple linear regression as would be applied in marker-assisted selection. With the implementation of genomic selection for yield and other agronomic traits, whole-genome marker profiles will be available for the entire set of breeding lines, enabling genomic selection for disease at no additional direct cost. In this context, the scope of implementing genomics selection for disease resistance, and specifically for quantitative resistance and quarantined pathogens, becomes a tractable and powerful approach in breeding programs.