Genomic Selection Breeding Research Articles

Deterministic simulations to determine the impacts of economic and non-economic breeding objectives on sustainable intensification of developing smallholder dairy farms

Dairy cattle farming in developing countries is mainly by resource poor smallholder farmers. Intensification of production within smallholder production systems is being driven by increasing demand for animal products. Current intensification is driven by importation of high-input high-output genotypes. This puts in question the sustainability of these resource constrained systems under harsh environmental conditions. Sustainability of production systems can be defined as long-term resilience and productivity. We use deterministic simulations to compare the impacts of three criteria to define breeding objectives i.e., economic, desired gains and non-market value, on annual genetic and monetary gains in production (market) and functional (non-market) traits for a small-sized nucleus genomic selection breeding program under developing country conditions. Market traits considered were milk yield (MY) and mature body weight (MBW). Non-market traits were calving interval (CI) and production lifetime (PLT). Fat yield (FY) was also considered a non-market trait as it had no market value. With the economic objective, traits were weighted on economic values. Weights for desired gains and non-market (NM) values were derived iteratively. Economic objectives placed highest emphasis on MY with very little gains in non-market traits but had the highest returns on investment. For desired gains objective, maximal achievable gains in PLT, CI and FY were lower than the indicated desired gains. The non-market value objective achieved the best compromise between gains in MY and other traits. We conclude that non-market value objectives can be applied to direct selection towards more robust genotypes but with loss in monetary gain. Breeding robust genotypes can contribute to sustainable intensification in developing small-holder dairy systems. However, for such objectives to be satisfactory, a sound criterion is required to determine declines in genetic gains for market traits that are acceptable by producers.

A new chicken 55K SNP genotyping array

BackgroundChina has the richest local chicken breeding resources in the world and is the world’s second largest producer of meat-type chickens. Development of a moderate-density SNP array for genetic analysis of chickens and breeding of meat-type chickens taking utility of those resources is urgently needed for conventional farms, breeding industry, and research areas.ResultsEight representative local breeds or commercial broiler lines with 3 pools of 48 individuals within each breed/line were sequenced and supplied the major SNPs resource. There were 7.09 million - 9.41 million SNPs detected in each breed/line. After filtering using multiple criteria such as preferred incorporation of trait-related SNPs and uniformity of distribution across the genome, 52.18 K SNPs were selected in the final array. It consists of: (i) 19.22 K SNPs from the genomes of yellow-feathered, cyan-shank partridge and white-feathered chickens; (ii) 5.98 K SNPs related to economic traits from the Illumina 60 K SNP Bead Chip, which were found as significant associated SNPs with 15 traits in a Beijing-You crossed Cobb F2 resource population by genome-wide association study analysis; (iii) 7.63 K SNPs from 861 candidate genes of economic traits; (iv) the 0.94 K SNPs related to residual feed intake; and (v) 18.41 K from chicken SNPdb. The polymorphisms of 9 extra local breeds and 3 commercial lines were examined with this array, and 40 K - 47 K SNPs were polymorphic (with minor allele frequency > 0.05) in those breeds. The MDS result showed that those breeds can be clearly distinguished by this newly developed genotyping array.ConclusionsWe successfully developed a 55K genotyping array by using SNPs segregated from typical local breeds and commercial lines. Compared to the existing Affy 600 K and Illumina 60 K arrays, there were 21,41 K new SNPs included on our Affy 55K array. The results of the 55K genotyping data can therefore be imputed to high-density SNPs genotyping data. The array offers a wide range of potential applications such as genomic selection breeding, GWAS of interested traits, and investigation of diversity of different chicken breeds.

A financial analysis of using improved planting stock of white spruce and lodgepole pine in Alberta, Canada: genomic selection versus traditional breeding

Genomics-assisted tree breeding (GATB) is an emerging biotechnology method that has the potential to produce improved planting stock in selected traits, such as greater volume or higher wood quality, more quickly and effectively compared with traditional field-based breeding (TB) methods. In this study, we conducted an ex-ante stand-level financial benefit-cost analysis that is linked to a provincial tree growth and yield projection model to investigate the potential financial benefits of using improved white spruce (Picea glauca (Moench) Voss) and lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) planting stock identified via alternative tree improvement strategies (i.e. GATB vs. TB methods) for forest companies in the province of Alberta, Canada. With consideration of major tree breeding and establishment costs, expected sawlog price premiums at harvest, a shorter breeding cycle time, and additional genetic gain in volume achieved from using the GATB method relative to the TB method in the financial analysis; this study found that the use of GATB-selected third-generation planting stock was difficult to justify economically alone compared with TB-selected seedlings under the current sawlog market value and the current planting rate with improved stock in the province. However, results of our sensitivity analysis also revealed that the GATB method is: (1) more financially supported at lower discount rates; (2) strongly supported through decreased seedling costs with increased areas planted with improved stock; and (3) strongly supported through an increase in log price premiums at harvest resulting from potentially better wood quality and higher volume production relative to TB-selected stock. The findings of this study highlight several important market and biophysical factors that forest managers should take into account when adopting biotechnology in forestry.

Genomic, Transcriptomic, and Epigenomic Features Differentiate Genes That Are Relevant for Muscular Polyunsaturated Fatty Acids in the Common Carp.

Polyunsaturated fatty acids (PUFAs) are a set of important nutrients that mainly include arachidonic acid (ARA4), docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and α-linolenic acid (ALA). Recently, fish-derived PUFAs have been associated with cardiovascular health, fetal development, and improvement of brain functions. Studies have shown that fish muscular tissues are rich in PUFAs, which are influenced by various factors, including genetic variations, regulatory profiles, and methylation status of desaturase genes during fatty acid desaturation and elongation processes. However, the genetic mechanism and the pathways involved in fatty acid metabolism in fishes remain unclear. The overall aim of this study was to assess differences in gene expression responses among fishes with different fatty acid levels. To achieve this goal, we conducted genome-wide association analysis (GWAS) using a 250K SNP array in a population of 203 samples of common carp (Cyprinus carpio) and identified nine SNPs and 15 genes associated with muscular PUFA content. Then, RNA-Seq and whole genome bisulfite sequencing (WGBS) of different groups with high and low EPA, DHA, ARA4, and ALA contents in muscle, liver and brain tissues were conducted, resulting in 6,750 differentially expressed genes and 5,631 genes with differentially methylated promoters. Gene ontology and KEGG pathway enrichment analyses of RNA-Seq and WGBS results identified enriched pathways for fatty acid metabolism, which included the adipocytokine signaling pathway, ARA4 and linoleic acid metabolism pathway, and insulin signaling pathway. Integrated analysis indicated significant correlations between gene expression and methylation status among groups with high and low PUFA contents in muscular tissues. Taken together, these multi-level results uncovered candidate genes and pathways that are associated with fatty acid metabolism and paved the way for further genomic selection and carp breeding for PUFA traits.

An association study using imputed whole-genome sequence data identifies novel significant loci for growth-related traits in a Duroc×Erhualian F2 population.

The average daily gain (ADG) and body weight (BW) are very important traits for breeding programs and for the meat production industry, which have attracted many researchers to delineate the genetic architecture behind these traits. In the present study, single- and multi-trait genome-wide association studies (GWAS) were performed between imputed whole-genome sequence data and the traits of the ADG and BW at different stages in a large-scale White Duroc×Erhualian F2 population. A bioinformatics annotation analysis was used to assist in the identification of candidate genes that are associated with these traits. Five and seven genome-wide significant quantitative trait loci (QTLs) were identified by single- and multi-trait GWAS, respectively. Furthermore, more than 40 genome-wide suggestive loci were detected. On the basis of the whole-genome sequence association study and the bioinformatics analysis, NDUFAF6, TNS1 and HMGA1 stood out as the strongest candidate genes. The presented single- and multi-trait GWAS analysis using imputed whole-genome sequence data identified several novel QTLs for pig growth-related traits. Integrating the GWAS with bioinformatics analysis can facilitate the more accurate identification of candidate genes. Higher imputation accuracy, time-saving algorithms, improved models and comprehensive databases will accelerate the identification of causal genes or mutations, which will contribute to genomic selection and pig breeding in the future.

Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection.

Cassava (Manihot esculenta Crantz) is a major source of dietary carbohydrates for >700 million people globally. However, its long breeding cycle has slowed the rate of genetic gain for target traits. This study aimed to asses genetic variation, the level of inbreeding, and trait correlations in genomic selection breeding cycles. We used phenotypic and genotypic data from the National Crops Resources Research Institute (NaCRRI) foundation population (Cycle 0, C0) and the progeny (Cycle 1, C1) derived from crosses of 100 selected C0 clones as progenitors, both to evaluate and optimize genomic selection. The highest broad-sense heritability (H2 = 0.95) and narrow-sense heritability (h2 = 0.81) were recorded for cassava mosaic disease severity and the lowest for root weight per plot (H2 = 0.06 and h2 = 0.00). We observed the highest genetic correlation (rg= 0.80) between cassava brown streak disease root incidence measured at seedling and clonal stages of evaluation, suggesting the usefulness of seedling data in predicting clonal performance for cassava brown streak root necrosis. Similarly, high genetic correlations were observed between cassava brown streak disease severity (rg= 0.83) scored at 3 and 6 mo after planting (MAP) and cassava mosaic disease, scored at 3 and 6 MAP (rg= 0.95), indicating that data obtained on these two diseases at 6 MAP would suffice. Population differentiation between C0 and C1 was not well defined, implying that the 100 selected progenitors of C1 captured the diversity in the C0. Overall, genetic gain for most traits were observed from C0 to C1.

Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model

Milk is regarded as an important nutrient for humans, and Chinese Holstein cows provide high-quality milk for billions of Chinese people. Therefore, detecting quantitative trait nucleotides (QTN) or candidate genes for milk production traits in Chinese Holstein is important. In this study, we performed genome-wide association studies (GWAS) in a Chinese Holstein population of 6,675 cows and 71,633 SNP using deregressed proofs (DRP) as phenotypes to replicate our previous study in a population of 1,815 cows and 39,163 SNP using estimated breeding values (EBV) as phenotypes. The associations between 3 milk production traits-milk yield (MY), fat percentage (FP), and protein percentage (PP)-and the SNP were determined by using an efficient rotated linear mixed model, which benefits from linear transformations of genomic estimated values and Eigen decomposition of the genomic relationship matrix algorithm. In total, we detected 94 SNP that were significantly associated with one or more milk production traits, including 7 SNP for MY, 76 for FP, and 36 for PP; 87% of these SNP were distributed across Bos taurus autosomes 14 and 20. In total, 83 SNP were found to be located within the reported quantitative trait loci (QTL) regions, and one novel segment (between 1.41 and 1.49 Mb) on chromosome 14 was significantly associated with FP, which could be an important candidate QTL region. In addition, the detected intervals were narrowed down from the reported regions harboring causal variants. The top significant SNP for the 3 traits was ARS-BFGL-NGS-4939, which is located within the DGAT1 gene. Five detected genes (CYHR1, FOXH1, OPLAH, PLEC, VPS28) have effects on all 3 traits. Our study provides a suite of QTN, candidate genes, and a novel QTL associated with milk production traits, and thus forms a solid basis for genomic selection and molecular breeding for milk production traits in Chinese Holstein.

ADAM-Plant: A Software for Stochastic Simulations of Plant Breeding From Molecular to Phenotypic Level and From Simple Selection to Complex Speed Breeding Programs.

Making decisions on plant breeding programs require plant breeders to be able to test different breeding strategies by taking into account all the crucial factors affecting crop genetic improvement. Due to the complexity of the decisions, computer simulation serves as an important tool for researchers and plant breeders. This paper describes ADAM-plant, which is a computer software that models breeding schemes for self-pollinated and cross-pollinated crop plants using stochastic simulation. The program simulates a population of plants and traces the genetic changes in the population under different breeding scenarios. It takes into account different population structures, genomic models, selection (strategies and units) and crossing strategies. It also covers important features e.g., allowing users to perform genomic selection (GS) and speed breeding, simulate genotype-by-environment interactions using multiple trait approach, simulate parallel breeding cycles and consider plot sizes. In addition, the software can be used to simulate datasets produced from very complex breeding program in order to test new statistical methodology to analyze such data. As an example, three wheat-breeding strategies were simulated in the current study: (1) phenotypic selection, (2) GS, and (3) speed breeding with genomic information. The results indicate that the genetic gain can be doubled by GS compared to phenotypic selection and genetic gain can be further increased considerably by speed breeding. In conclusion, ADAM-plant is an important tool for comparing strategies for plant breeding and for estimating the effects of allocation of different resources to the breeding program. In the current study, it was used to compare different methodologies for utilizing genomic information in cereal breeding programs for selection of best-fit breeding strategy as per available resources.

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.

Combined QTL and Genome Scan Analyses With the Help of 2b-RAD Identify Growth-Associated Genetic Markers in a New Fast-Growing Carp Strain.

Common carp is one of the oldest and most popular cultured freshwater fish species both globally and in China. In a previous study, we used a carp strain with a long breeding tradition in China, named Huanghe, to create a new fast-growing strain by selection for fast growth for 6 years. The growth performance at 8 months of age has been improved by 20.84%. To achieve this, we combined the best linear unbiased prediction with marker-assisted selection techniques. Recent progress in genome-wide association studies and genomic selection in livestock breeding inspired common carp breeders to consider genome-based breeding approaches. In this study, we developed a 2b-RAD sequence assay as a means of investigating the quantitative trait loci in common carp. A total of 4,953,017,786 clean reads were generated for 250 specimens (average reads/specimen = 19,812,071) with BsaXI Restriction Enzyme. From these, 56,663 SNPs were identified, covering 50 chromosomes and 3,377 scaffolds. Principal component analysis indicated that selection and control groups are relatively clearly distinct. Top 1% of Fst values was selected as the threshold signature of artificial selection. Among the 244 identified loci, genes associated with sex-related factors and nutritional metabolism (especially fat metabolism) were annotated. Eighteen QTL were associated with growth parameters. Body length at 3 months of age and body weight (both at 3 and 8 months) were controlled by polygenic effects, but body size (length, depth, width) at 8 months of age was controlled mainly by several loci with major effects. Importantly, a single shared QTL (IGF2 gene) partially controlled the body length, depth, and width. By merging the above results, we concluded that mainly the genes related to neural pathways, sex and fatty acid metabolism contributed to the improved growth performance of the new Huanghe carp strain. These findings are one of the first investigations into the potential use of genomic selection in the breeding of common carp. Moreover, our results show that combining the Fst, QTL mapping and CRISPR–Cas9 methods can be an effective way to identify important novel candidate molecular markers in economic breeding programs.

Benefits of testing in both bio-secure and production environments in genomic selection breeding programs for commercial broiler chicken

BackgroundA breeding program for commercial broiler chicken that is carried out under strict biosecure conditions can show reduced genetic gain due to genotype by environment interactions (G × E) between bio-secure (B) and commercial production (C) environments. Accuracy of phenotype-based best linear unbiased prediction of breeding values of selection candidates using sib-testing in C is low. Genomic prediction based on dense genetic markers may improve accuracy of selection. Stochastic simulation was used to explore the benefits of genomic selection in breeding schemes for broiler chicken that include birds in both B and C for assessment of phenotype.ResultsWhen genetic correlations (r_{g}) between traits measured in B and C were equal to 0.5 and 0.7, breeding schemes with 15, 30 and 45% of birds assessed in C resulted in higher genetic gain for performance in C compared to those without birds in C. The optimal proportion of birds phenotyped in C for genetic gain was 30%. When the proportion of birds in C was optimal and genotyping effort was limited, allocating 30% of the genotyping effort to birds in C was also the optimal genotyping strategy for genetic gain. When r_{g} was equal to 0.9, genetic gain for performance in C was not improved with birds in C compared to schemes without birds in C. Increasing the heritability of traits assessed in C increased genetic gain significantly. Rates of inbreeding decreased when the proportion of birds in C increased because of a lower selection intensity among birds retained in B and a reduction in the probability of co-selecting close relatives.ConclusionsIf G × E interactions (r_{g} of 0.5 and 0.7) are strong, a genomic selection scheme in which 30% of the birds hatched are phenotyped in C has larger genetic gain for performance in C compared to phenotyping all birds in B. Rates of inbreeding decreased as the proportion of birds moved to C increased from 15 to 45%.

Mapping Grain Iron and Zinc Content Quantitative Trait Loci in an Iniadi-Derived Immortal Population of Pearl Millet.

Pearl millet is a climate-resilient nutritious crop requiring low inputs and is capable of giving economic returns in marginal agro-ecologies. In this study, we report large-effect iron (Fe) and zinc (Zn) content quantitative trait loci (QTLs) using diversity array technology (DArT) and simple sequence repeats (SSRs) markers to generate a genetic linkage map using 317 recombinant inbred line (RIL) population derived from the (ICMS 8511-S1-17-2-1-1-B-P03 × AIMP 92901-S1-183-2-2-B-08) cross. The base map [seven linkage groups (LGs)] of 196 loci was 964.2 cM in length (Haldane). AIMP 92901-S1-183-2-2-B-08 is an Iniadi line with high grain Fe and Zn, tracing its origin to the Togolese Republic, West Africa. The content of grain Fe in the RIL population ranged between 20 and 131 ppm (parts per million), and that of Zn from 18 to 110 ppm. QTL analysis revealed a large number of QTLs for high grain iron (Fe) and zinc (Zn) content. A total of 19 QTLs for Fe and Zn were detected, of which 11 were for Fe and eight were for Zn. The portion of the observed phenotypic variance explained by different QTLs for grain Fe and Zn content varied from 9.0 to 31.9% (cumulative 74%) and from 9.4 to 30.4% (cumulative 65%), respectively. Three large-effect QTLs for both minerals were co-mapped in this population, one on LG1 and two on LG7. The favorable QTL alleles of both mineral micronutrients were contributed by the male parent (AIMP 92901-deriv-08). Three putative epistasis interactions were observed for Fe content, while a single digenic interaction was found for Zn content. The reported QTLs may be useful in marker-assisted selection (MAS) programs, in genomic selection (GS) breeding pipelines for seed and restorer parents, and in population improvement programs for pearl millet.

Mapping QTLs Controlling Flowering Time and Important Agronomic Traits in Pearl Millet.

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a staple crop for the people of arid and semi-arid regions of the world. It is fast gaining importance as a climate resilient nutricereal. Exploiting the bold seeded, semi-dwarf, and early flowering genotypes in pearl millet is a key breeding strategy to enhance yield, adaptability, and for adequate food in resource-poor zones. Genetic variation for agronomic traits of pearl millet inbreds can be used to dissect complex traits through quantitative trait locus (QTL) mapping. This study was undertaken to map a set of agronomically important traits like flowering time (FT), plant height (PH), panicle length (PL), and grain weight (self and open-pollinated seeds) in the recombinant inbred line (RIL) population of ICMB 841-P3 × 863B-P2 cross. Excluding grain weight (open pollinated), heritabilities for FT, PH, PL, grain weight (selfed) were in high to medium range. A total of six QTLs for FT were detected on five chromosomes, 13 QTLs for PH on six chromosomes, 11 QTLs for PL on five chromosomes, and 14 QTLs for 1,000-grain weight (TGW) spanning five chromosomes. One major QTL on LG3 was common for FT and PH. Three major QTLs for PL, one each on LG1, LG2, and LG6B were detected. The large effect QTL for TGW (self) on LG6B had a phenotypic variance (R2) of 62.1%. The R2 for FT, TGW (self), and PL ranged from 22.3 to 59.4%. A total of 21 digenic interactions were discovered for FT (R2 = 18–40%) and PL (R2 = 13–19%). The epistatic effects did not reveal any significant QTL × QTL × environment (QQE) interactions. The mapped QTLs for flowering time and other agronomic traits in present experiment can be used for marker-assisted selection (MAS) and genomic selection (GS) breeding programs.

Progress in the molecular and genetic modification breeding of beef cattle in China.

The studies of beef cattle breeding in China have been greatly improved with the rapid development of the international beef cattle industrialization. The beef cattle breeding technologies have rapidly transformed from traditional breeding to molecular marker-assisted breeding, genomic selection and genetic modification breeding. Hundreds of candidate genes and molecular markers associated with growth, meat quality, reproduction performance and diseases resistance have been identified, and some of them have already been used in cattle breeding. Genes and molecular markers associated with growth and development are focused on the growth hormone, muscle regulatory factors, myostatin and insulin-like growth factors. Meat quality is mediated by fatty acid transport and deposition related signals, calpains and calpain system, muscle regulatory factors and muscle growth regulation pathways. Reproduction performance is regulated by GnRH-FSH-LH, growth differentiation factor 9, prolactin receptor and forkhead box protein O1. Disease resistance is modulated by the major histocompatibility complex gene family, toll-like receptors, mannose-binding lectin and interferon gene signals. In this review, we summarize the most recent progress in beef cattle breeding in marker-assisted selection, genome-wide selection and genetic modification breeding, aiming to provide a reference for further genetic breeding research of beef cattle in China.

Strategies for implementing genomic selection for feed efficiency in dairy cattle breeding schemes.

Alternative genomic selection and traditional BLUP breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for milk yield and residual feed intake, as a measure of feed efficiency. When including feed efficiency in genomic BLUP schemes, it was possible to achieve high selection accuracies for genomic selection, and all genomic BLUP schemes gave better genetic gain for feed efficiency than BLUP using a pedigree relationship matrix. However, introducing a second trait in the breeding goal caused a reduction in the genetic gain for milk yield. When using contracted test herds with genotyped and feed efficiency recorded cows as a reference population, adding an additional 4,000 new heifers per year to the reference population gave accuracies that were comparable to a male reference population that used progeny testing with 250 daughters per sire. When the test herd consisted of 500 or 1,000 cows, lower genetic gain was found than using progeny test records to update the reference population. It was concluded that to improve difficult to record traits, the use of contracted test herds that had additional recording (e.g., measurements required to calculate feed efficiency) is a viable option, possibly through international collaborations.

QTL analysis and genomic selection using RADseq derived markers in Sitka spruce: the potential utility of within family data

Sitka spruce (Picea sitchensis (Bong.) Carr) is the most common commercial plantation species in Britain and a breeding programme based on traditional lines has been in operation since the early 1960s. Rotation lengths of 40-years have led breeders to adopt a process of indirect selection at younger ages based on traits well correlated with final selection, but still the generation interval is unlikely to reduce much below twenty years. Recent successful developments with genomic selection in animal breeding have led tree breeders to consider the application of this technology. In this study a RAD sequence assay was developed as a means of investigating the potential of molecular breeding in a non-model species. DNA was extracted from nearly 500 clonally replicated trees growing in a single full-sibling family at one site in Britain. The technique proved successful in identifying 132 QTLs for 5-year bud-burst and 2 QTLs for 6-year height. In addition, the accuracy of predicting phenotypes by genomic selection was strikingly high at 0.62 and 0.59 respectively. Sensitivity analysis with 200 offspring found only a slight fall in correlation values (0.54 and 0.38) although when the training population reduced to 50 offspring predictive values fell further (0.33 and 0.25). This proved an encouraging first investigation into the potential use of genomic selection in the breeding of Sitka spruce. The authors investigate how problems associated with effective population size and linkage disequilibrium can be avoided and suggest a practical way of incorporating genomic selection into a dynamic breeding programme.

Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries

In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2- and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.

Applications of Genomic Selection in Breeding Wheat for Rust Resistance.

There are a lot of methods developed to predict untested phenotypes in schemes commonly used in genomic selection (GS) breeding. The use of GS for predicting disease resistance has its own particularities: (a) most populations shows additivity in quantitative adult plant resistance (APR); (b) resistance needs effective combinations of major and minor genes; and (c) phenotype is commonly expressed in ordinal categorical traits, whereas most parametric applications assume that the response variable is continuous and normally distributed. Machine learning methods (MLM) can take advantage of examples (data) that capture characteristics of interest from an unknown underlying probability distribution (i.e., data-driven). We introduce some state-of-the-art MLM capable to predict rust resistance in wheat. We also present two parametric R packages for the reader to be able to compare.

Improving production efficiency in the presence of genotype by environment interactions in pig genomic selection breeding programmes.

We simulated a genomic selection pig breeding schemes containing nucleus and production herds to improve feed efficiency of production pigs that were cross-breed. Elite nucleus herds had access to high-quality feed, and production herds were fed low-quality feed. Feed efficiency in the nucleus herds had a heritability of 0.3 and 0.25 in the production herds. It was assumed the genetic relationships between feed efficiency in the nucleus and production were low (rg =0.2), medium (rg =0.5) and high (rg =0.8). In our alternative breeding schemes, different proportion of production animals were recorded for feed efficiency and genotyped with high-density panel of genetic markers. Genomic breeding value of the selection candidates for feed efficiency was estimated based on three different approaches. In one approach, genomic breeding value was estimated including nucleus animals in the reference population. In the second approach, the reference population was containing a mixture of nucleus and production animals. In the third approach, the reference population was only consisting of production herds. Using a mixture reference population, we generated 40-115% more genetic gain in the production environment as compared to only using nucleus reference population that were fed high-quality feed sources when the production animals were offspring of the nucleus animals. When the production animals were grand offspring of the nucleus animals, 43-104% more genetic gain was generated. Similarly, a higher genetic gain generated in the production environment when mixed reference population was used as compared to only using production animals. This was up to 19 and 14% when the production animals were offspring and grand offspring of nucleus animals, respectively. Therefore, in genomic selection pig breeding programmes, feed efficiency traits could be improved by properly designing the reference population.

Improving virus production through quasispecies genomic selection and molecular breeding.

Virus production still is a challenging issue in antigen manufacture, particularly with slow-growing viruses. Deep-sequencing of genomic regions indicative of efficient replication may be used to identify high-fitness minority individuals suppressed by the ensemble of mutants in a virus quasispecies. Molecular breeding of quasispecies containing colonizer individuals, under regimes allowing more than one replicative cycle, is a strategy to select the fittest competitors among the colonizers. A slow-growing cell culture-adapted hepatitis A virus strain was employed as a model for this strategy. Using genomic selection in two regions predictive of efficient translation, the internal ribosome entry site and the VP1-coding region, high-fitness minority colonizer individuals were identified in a population adapted to conditions of artificially-induced cellular transcription shut-off. Molecular breeding of this population with a second one, also adapted to transcription shut-off and showing an overall colonizer phenotype, allowed the selection of a fast-growing population of great biotechnological potential.