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A 5 yr whole-system study, beginning in June 1994, compared the productivity of high [HGM; Australian Breeding Value (ABV) of 49.1kg of fat plus protein] and low [LGM; ABV of 2.3kg of fat plus protein] genetic merit cows. Cows from both groups were fed at 3 levels of concentrate (C): 0.34 (low C), 0.84 (medium C), and 1.71 (high C) t of DM/cow per lactation. Thus, there were 6 treatments (farmlets) composed of 18 cows each. The 30 blocks of pasture on each farmlet were matched between farmlets for pasture growth before the study (and soil characteristics and aspect). Cows were culled, and pasture and feed use were managed so as not to bias any one treatment. Genetic merit, level of feeding, and their interaction were significant effects for protein content, protein/cow, and milk and protein/ha. For fat and milk yield/cow, genetic merit and level of feeding were significant, whereas there was no significant effect of genetic merit on fat content. The difference of 46.8kg of fat plus protein yield between the ABV of HGM and LGM cows and the actual difference in production between the 2 groups was not significantly different except for low C (27kg) cows. This was due to a 3-fold lower protein yield difference (6kg/cow) compared with an ABV difference for protein yield of 17.9kg/cow. The dramatic effect of treatment on protein is in line with differences in the mean protein content (2.89% for the HGM-low C cows compared with a mean of 3.02% for the remaining groups) and mean body condition score [4.3 for HGM-low C cows compared with 4.8 for the mean of the remaining groups (scale 1 to 8)], both indicators reflecting a higher negative energy balance in the HGM-low C cows. When individual cow production was plotted against ABV for production of milk or protein yield all relationships were quadratic, but the slope was relatively flat (low response to ABV) for the low C cows, steeper for the medium C cows and steepest (but not linear) for the high C cows. The relationship between ABV for fat yield and actual fat yield was linear for all levels of concentrate. The mean milk yield/ha from pasture for the 6 farmlets over the 5 yr was 11,868L, 11,417L, or 7,761L for the HGM cows fed at low C, medium C, or high C, respectively, and 10,579L, 9,800L, or 5,812L for LGM cows, fed at low C, medium C, or high C, respectively. The response to concentrates fed was very high for the HGM-medium C cows at 0.115kg fat plus protein or 1.75L milk/kg of concentrate fed, with comparable figures of 0.083kg and 1.0L, 0.86kg and 1.47L and 0.066 and 0.92 L/kg of concentrate fed for the HGM-high C, LGM-medium C, and LGM-high C, respectively. The results show a significant genetic merit by environment (level of feeding) interaction for reproduction and most production parameters when considered in terms of the individual cow and the whole farm system.

Key messageIncluding additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield.Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) “I-model” (baseline), (ii) “I + GA-model”, extending I-model with an additive genomic effect, and (iii) “I + GA + GAA-model”, extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible.

Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R2) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R2 values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R2 values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.

Key messageR/StageWise enables fully efficient, two-stage analysis of multi-environment, multi-trait datasets for genomic selection, including support for dominance heterosis and polyploidy.Plant breeders interested in genomic selection often face challenges to fully utilizing multi-trait, multi-environment datasets. R package StageWise was developed to go beyond the capabilities of most specialized software for genomic prediction, without requiring the programming skills needed for more general-purpose software for mixed models. As the name suggests, one of the core features is a fully efficient, two-stage analysis for multiple environments, in which the full variance–covariance matrix of the Stage 1 genotype means is used in Stage 2. Another feature is directional dominance, including for polyploids, to account for inbreeding depression in outbred crops. StageWise enables selection with multi-trait indices, including restricted indices with one or more traits constrained to have zero response. For a potato dataset with 943 genotypes evaluated over 6 years, including the Stage 1 errors in Stage 2 reduced the Akaike Information Criterion (AIC) by 29, 67, and 104 for maturity, yield, and fry color, respectively. The proportion of variation explained by heterosis was largest for yield but still only 0.03, likely because of limited variation for the genomic inbreeding coefficient. Due to the large additive genetic correlation (0.57) between yield and maturity, naïve selection on an index combining yield and fry color led to an undesirable response for later maturity. The restricted index coefficients to maximize genetic merit without delaying maturity were identified. The software and three vignettes are available at https://github.com/jendelman/StageWise.

Background Infectious hematopoietic necrosis (IHN) is a disease of salmonid fish that is caused by the IHN virus (IHNV). Under intensive aquaculture conditions, IHNV can cause significant mortality and economic losses. Currently, there is no proven and cost-effective method for IHNV control. Clear Springs Foods, Inc. has been applying selective breeding to improve genetic resistance to IHNV in their rainbow trout breeding program. The goals of this study were to elucidate the genetic architecture of IHNV resistance in this commercial population by performing genome-wide association studies (GWAS) with multiple regression single-step methods and to assess if genomic selection can improve the accuracy of genetic merit predictions over conventional pedigree-based best linear unbiased prediction (PBLUP) using cross-validation analysis. Results Ten moderate-effect quantitative trait loci (QTL) associated with resistance to IHNV that jointly explained up to 42% of the additive genetic variance were detected in our GWAS. Only three of the 10 QTL were detected by both single-step Bayesian multiple regression (ssBMR) and weighted single-step GBLUP (wssGBLUP) methods. The accuracy of breeding value predictions with wssGBLUP (0.33–0.39) was substantially better than with PBLUP (0.13–0.24). Conclusions Our comprehensive genome-wide scan for QTL revealed that genetic resistance to IHNV is controlled by the oligogenic inheritance of up to 10 moderate-effect QTL and many small-effect loci in this commercial rainbow trout breeding population. Taken together, our results suggest that whole genome-enabled selection models will be more effective than the conventional pedigree-based method for breeding value estimation or the marker-assisted selection approach for improving the genetic resistance of rainbow trout to IHNV in this population.

Background Genomic prediction encompasses the techniques used in agricultural technology to predict the genetic merit of individuals towards valuable phenotypic traits. It is related to Genome Interpretation in humans, which models the individual risk of developing disease traits. Genomic prediction is dominated by linear mixed models, such as the Genomic Best Linear Unbiased Prediction (GBLUP), which computes kinship matrices from SNP array data, while Genome Interpretation applications to clinical genetics rely mainly on Polygenic Risk Scores. Results In this article, we exploit the positive semidefinite characteristics of the kinship matrices that are conventionally used in GBLUP to propose a novel Genomic Multiple Kernel Learning method (GMKL), in which the multiple kinship matrices corresponding to Additive, Dominant, and Epistatic Inheritance Mechanisms are used as kernels in support vector machines, and we apply it to both worlds. We benchmark GMKL on simulated cattle phenotypes, showing that it outperforms the classical GBLUP predictors for genomic prediction. Moreover, we show that GMKL ranks the kinship kernels representing different inheritance mechanisms according to their compatibility with the observed data, allowing it to produce hypotheses on the normally unknown inheritance mechanisms generating the target phenotypes. We then apply GMKL to the prediction of two inflammatory bowel disease cohorts with more than 6500 samples in total, consistently obtaining results suggesting that epistasis might have a relevant, although underestimated role in inflammatory bowel disease (IBD). Conclusions We show that GMKL performs similarly to GBLUP, but it can formulate biological hypotheses about inheritance mechanisms, such as suggesting that epistasis influences IBD.

Fertility is one of the most economically important traits in both beef and dairy cattle production; however, only female fertility is typically subjected to selection. Male and female fertility have only a small positive genetic correlation which is likely due to the existence of a relatively small number of genetic variants within each breed that cause embryonic and developmental losses. Genomic tools have been developed that allow the identification of lethal recessive loci based upon marker haplotypes. Selection against haplotypes harbouring lethal alleles in conjunction with selection to improve female fertility will result in an improvement in male fertility. Genomic selection has resulted in a two to fourfold increase in the rate of genetic improvement of most dairy traits in US Holstein cattle, including female fertility. Considering the rapidly increasing rate of adoption of high-throughput single nucleotide polymorphism genotyping in both the US dairy and beef industries, genomic selection should be the most effective of all currently available approaches to improve male fertility. However, male fertility phenotypes are not routinely recorded in natural service mating systems and when artificial insemination is used, semen doses may be titrated to lower post-thaw progressively motile sperm numbers for high-merit and high-demand bulls. Standardization of sperm dosages across bull studs for semen distributed from young bulls would allow the capture of sire conception rate phenotypes for young bulls that could be used to generate predictions of genetic merit for male fertility in both males and females. These data would allow genomic selection to be implemented for male fertility in addition to female fertility within the US dairy industry. While the rate of use of artificial insemination is much lower within the US beef industry, the adoption of sexed semen in the dairy industry has allowed dairy herds to select cows from which heifer replacements are produced and cows that are used to produce terminal crossbred bull calves sired by beef breed bulls. Capture of sire conception rate phenotypes in dairy herds utilizing sexed semen will contribute data enabling genomic selection for male fertility in beef cattle breeds. As the commercial sector of the beef industry increasingly adopts fixed-time artificial insemination, sire conception rate phenotypes can be captured to facilitate the development of estimates of genetic merit for male fertility within US beef breeds.

Background Optimal Contribution Selection (OCS) aims at maximizing long term response to selection by balancing short term genetic gain and inbreeding rate. In this study, OCS, trait management, and group mating methods were applied to poultry data to evaluate their potential impact. Real data from a White Leghorn line with 7 generations of pedigree and estimated breeding values (EBV) for 18 traits were used to evaluate responses from a single round of selection. It was shown that the current inbreeding rate is low and cannot be substantially reduced without significant loss of genetic gain, unless implemented in parallel with genomics to enable flexibility in population structure. Results Allowing flexible mating ratios under OCS resulted in 5.3 to 23.8% more genetic gain plus lower loss of genetic diversity compared to fixed-ratio ocs. However, in the case of multiple males per female, implementation is logistically challenging and requires genotyping of all hatched chicks. Using predicted progeny trait distribution management, a 3.2 g difference in mean EBV for egg weight was obtained for two market-targeted groups, without impact on the predicted progeny mean for the multi-trait index used for selection, or on average parental coancestry, but with a small increase in progeny inbreeding. While maintaining these two egg weight groups, tactical desired gains using trait EBV was used to favourably reduce predicted progeny genetic merit for feed intake from + 0.850 to -0.005 g/day, with little impact on genetic gain for other traits, mean index values, mean parental coancestry, or mean progeny inbreeding. Using pooled semen or multi-sire mating, while accounting for variation in male reproductive success, resulted in only a 0.5% reduction in response in predicted mean progeny index and in small increases in mean parental coancestry (from 0.014 to 0.015) and mean progeny inbreeding (from 0.005 to 0.007). Conclusions Evaluating the longer-term impacts of OCS and other methods employed requires multi-generation simulations, ideally starting from the current real data as a base. However, the current approach of using a real implementation scenario is important in decision making for real-life applications. Similar benefits from the selection and mating strategies used here are expected in breeding programs for other species.

Genetic effects for many dairy traits and for total economic merit are evenly distributed across all chromosomes. A high-density scan using 38,416 single nucleotide polymorphism markers for 5,285 bulls confirmed 2 previously known major genes on Bos taurus autosomes (BTA) 6 and 14 but revealed few other large effects. Markers on BTA18 had the largest effects on calving ease, several conformation traits, longevity, and total merit. Prediction accuracy was highest using a heavy-tailed prior assuming that each marker had an effect on each trait, rather than assuming a normal distribution of effects as in a linear model, or that only some loci have nonzero effects. A prior model combining heavy tails with finite alleles produced results that were intermediate compared with the individual models. Differences between models were small (1 to 2%) for traits with no major genes and larger for heavy tails with traits having known quantitative trait loci (QTL; 6 to 8%). Analysis of bull recessive codes suggested that marker effects from genomic selection may be used to identify regions of chromosomes to search in detail for candidate genes, but individual single nucleotide polymorphisms were not tracking causative mutations with the exception of diacylglycerol O-acyltransferase 1. Additive genetic merits were constructed for each chromosome, and the distribution of BTA14-specific estimated breeding value (EBV) showed that selection primarily for milk yield has not changed the distribution of EBV for fat percentage even in the presence of a known QTL. Such chromosomal EBV also may be useful for identifying complementary mates in breeding programs. The QTL affecting dystocia, conformation, and economic merit on BTA18 appear to be related to calf size or birth weight and may be the result of longer gestation lengths. Results validate quantitative genetic assumptions that most traits are due to the contributions of a large number of genes of small additive effect, rather than support the finite locus model.

Background Our aim was to identify genomic regions via genome-wide association studies (GWAS) to improve the predictability of genetic merit in Holsteins for 10 calving and 28 body conformation traits. Animals were genotyped using the Illumina Bovine 50 K BeadChip and imputed to the Illumina BovineHD BeadChip (HD). GWAS were performed on 601,717 real and imputed single nucleotide polymorphism (SNP) genotypes using a single-SNP mixed linear model on 4841 Holstein bulls with breeding value predictions and followed by gene identification and in silico functional analyses. The association results were further validated using five scenarios with different numbers of SNPs. Results Seven hundred and eighty-two SNPs were significantly associated with calving performance at a genome-wise false discovery rate (FDR) of 5%. Most of these significant SNPs were on chromosomes 18 (71.9%), 17 (7.4%), 5 (6.8%) and 7 (2.4%) and mapped to 675 genes, among which 142 included at least one significant SNP and 532 were nearby one (100 kbp). For body conformation traits, 607 SNPs were significant at a genome-wise FDR of 5% and most of them were located on chromosomes 5 (30%), 18 (27%), 20 (13%), 6 (6%), 7 (5%), 14 (5%) and 13 (3%). SNP enrichment functional analyses for calving traits at a FDR of 1% suggested potential biological processes including musculoskeletal movement, meiotic cell cycle, oocyte maturation and skeletal muscle contraction. Furthermore, pathway analyses suggested potential pathways associated with calving performance traits including tight junction, oxytocin signaling, and MAPK signaling (P < 0.10). The prediction ability of the 1206 significant SNPs was between 78 and 83% of the prediction ability of the BovineSNP50 SNPs for calving performance traits and between 35 and 79% for body conformation traits. Conclusions Various SNPs that are significantly associated with calving performance are located within or nearby genes with potential roles in tight junction, oxytocin signaling, and MAPK signaling. Combining the significant SNPs or SNPs within or nearby gene(s) from the HD panel with the BovineSNP50 panel yielded a marginal increase in the accuracy of prediction of genomic estimated breeding values for all traits compared to the use of the BovineSNP50 panel alone.