Theo He Meuwissen

Genomic selection (GS) has revolutionized animal breeding and accelerated genetic gains in breeding programs. Although GS has become common in cosmopolitan dairy cattle breeds, its implementation in local breeds has begun only more recently, or is still in progress. However, the introduction of GS in some cosmopolitan breeds has also been associated with increased inbreeding rates, raising concerns about the potential effects of GS on genetic diversity in smaller or local breeds. Our aim was to investigate the impact of GS on genetic diversity in 5 (small) local cattle breeds from 3 European countries: Meuse Rhine Yssel (MRY; from the Netherlands); Norwegian Red (Norsk Rødt Fe, NRF; from Norway); and Abondance (ABO; from France), Tarentaise (TAR; from France), and Vosgienne (VOS; from France). We investigated changes in population demographic structure, as well as trends and rates of kinship and inbreeding, using both pedigree- and genomic-based measures. The population size varied depending on the breed, with Vosgienne being the smallest and NRF being the largest. Single nucleotide polymorphism genotypes were available for 4,645 MRY, 193,489 NRF, 16,387 ABO, 8,578 TAR, and 4,472 VOS animals. Animals were genotyped with more than 40,000 SNPs. Overall, following the implementation of GS in these breeds, we observed a reduction of up to 4 years in generation intervals for sires, fewer calves that later became sires, and, for the French breeds, a broader sire usage. Such changes were likely due to GS enabling the preselection and screening of more young bulls. Additionally, the contributions of the top 10 sires were more evenly distributed after the introduction of GS. Although changes in inbreeding and kinship rates occurred after the introduction of GS, we found no consistent pattern across breeds: pedigree (and genomic runs of homozygosity [ROH]-based) inbreeding rates per generation increased in MRY from -0.67 before GS to 0.51 after GS (from -1.12 to 0.93) and TAR from 0.35 to 0.93 (from 0.68 to 0.86), but decreased in NRF from 0.26 to 0.05 (from 0.10 to 0.06), Abondance from 1.19 to 0.99 (from 2.39 to 0.58), and Vosgienne from 0.53 to 0.23 (from 0.88 to 0.19). Moreover, analysis of genomic ROH-based inbreeding by length class showed that after the implementation of GS, the largest changes in inbreeding level and inbreeding rates per generation occurred for shorter ROH segments. Our study suggests that changes and increases in inbreeding rates may occur after the introduction of GS, although they may not be directly due to the introduction of GS per se, but rather due to population management strategies, such as optimal contribution selection. Our findings emphasize the importance of monitoring changes in both genetic diversity and population demographic structure after implementing GS in local breeds, as well as adjusting breeding strategies when needed to ensure long-term sustainability.

Background: The goal of managing genetic diversity is to maintain both the genomic uniqueness and adaptability of a population. This entails both maintaining heterozygosity and avoiding deleterious alleles from drifting to higher frequencies. Thus, genomic management strategies should not systematically change frequencies at neutral loci, as has been observed in current genomic management. We simulated 50 replicates of a population managed with optimal contribution selection (OCS), where inbreeding was controlled using different relationship matrices, where Van Raden method I (G _VR ), runs-of-homozygosity (G _ROH ), and identity by descent relationships (IBD) only used genomic information and a linkage analysis-based matrix (G _FGLA ) leveraged both genomic and pedigree information. Results: Across 50 simulated replicates, G _FGLA achieved the highest correlation with true IBD for both inbreeding and coancestry estimates and provided the most accurate rate of inbreeding (ΔF). G _FGLA also delivered the greatest genetic gain per unit of inbreeding and was the only scheme to remain below the inbreeding target while minimizing frequency change at neutral loci. G _VR using base population frequencies produced high genetic gain and controlled drift effectively but introduced systematic allele frequency changes towards the closest extreme frequency. Using current allele frequency resulted in high levels of inbreeding compared to the genetic gain achieved. G _ROH showed strong dependence on the minimum ROH length: short ROH improved ΔF estimation but increased allele frequency changes toward intermediate values, whereas long ROH underestimated inbreeding and reduced genetic gain. Conclusions: IBD-based matrices such as G _FGLA offer the most balanced approach for sustainable population management in OCS schemes, combining accurate relationship estimates, effective inbreeding control, and neutrality at neutral loci. In contrast, G _VR and G _ROH require calibration and cause systematic allele frequency changes.

Background: Genomic relationship and inbreeding estimates are either based on genetic drift (e.g. the Genomic Relationship Matrix; GRM ), homozygosity (e.g. Runs of Homozygosity; ROH), or Identity-By-Descent (IBD). A genomic IBD-based relationship matrix, G _la , is obtained by linkage analysis which uses genomic data to distinguish paternal versus maternal inheritances of chromosomal segments to replace the 50/50 probabilities used to calculate pedigree-based relationships ( A matrix). Our aim was to develop a fast approximate algorithm, FGla, to estimate the G _la matrix in large complex pedigrees making use of dense marker genotypes, and to compare G _la to A , GRM and ROH based inbreeding ( F _ROH ) in simulated and a large scale Norwegian Red Cattle (NRF) data set. Results: Given pedigree data and ≥ 3 generations of 45 k marker genotypes, marker positions were detected that unambiguously identified maternal/paternal inheritance, and inheritances at intermediate positions were imputed by the Viterbi algorithm from the positions with known inheritance. Any remaining unknown inheritances were randomly sampled (paternal or maternal), and the sampling errors that this introduced were averaged out by the large number of marker loci used (correlation between replicated estimates: 0.9998). Also, calculations were limited to the relationship coefficients that were actually needed, assuming that relationships for a limited set of candidates were needed. The accuracy of estimated G _la coefficients increased from 0.971 to 0.998, when genotyping increased from the actually genotyped NRF cattle towards all pedigreed animals. The accuracy of the GRM was 0.936, but required only genotyping of the animals whose relationships were needed. G _la relationships were approximately unbiased in the Best Linear Unbiased Prediction (BLUP) sense. Hence, if G _la based inbreeding management predicts an increase in relationships then an identical increase in true IBD relationships is expected. G _la uses the same base population as A , namely that of the pedigree. Conclusions: An approximate computationally efficient multipoint linkage analysis algorithm was developed to estimate unbiased IBD-based relationship and inbreeding coefficients. Its unbiasedness and precise definition of the base population makes it well suited for the genomic management of inbreeding and genomic optimal contribution selection. In addition, G _la based optimal contribution selection is neutral with respect to allele frequency changes.

Genomic selection (GS) has revolutionised animal breeding and accelerated genetic gains in breeding programs. While GS has become common in major dairy cattle breeds, its implementation in local breeds has begun only more recently or is still in progress. However, the introduction of GS in some major breeds has also been associated with increased inbreeding rates, raising concerns about the potential effects of GS on the genetic diversity in smaller or local breeds. Our aim was to investigate the impact of GS on genetic diversity in five (small) local cattle breeds from three European countries. The five breeds evaluated were: MRY (from the Netherlands), Norwegian Red (from Norway), Abondance, Tarentaise, and Vosgienne (from France). We investigated changes in population demographic structure, as well as trends and rates of kinship and inbreeding, using both pedigree- and genomic-based measures. The population size varied depending on the breed, with Vosgienne being the smallest and Norwegian Red being the largest. The dataset included 4,645 MRY, 193,489 Norwegian Red, 16,427 Abondance, 8,882 Tarentaise, and 4,466 Vosgienne genotyped animals for more than 40,000 single-nucleotide polymorphisms. Overall, following the implementation of GS in these breeds, we observed a reduction in generation intervals for sires, fewer calves that later became sires, and, for the French breeds, a broader sire usage. Such changes were likely due to GS enabling the preselection and screening of more young bulls. Additionally, we observed a more balanced contribution of the top ten sires after the introduction of GS. Although changes in inbreeding and kinship rates occurred after the introduction of GS, there was no consistent pattern across breeds: rates increased in MRY and Tarentaise, but decreased in Norwegian Red, Abondance, and Vosgienne. Our study suggests that changes and increases in inbreeding rates may occur after the introduction of GS, although they may not be directly due to the introduction of GS per se , but rather due to population management strategies, such as optimal contribution selection or other breeding practices implemented at the nucleus level. Our findings emphasise the importance of monitoring changes in both genetic diversity and population demographic structure after implementing GS in local breeds, as well as adjusting breeding strategies when needed to ensure long-term sustainability. Interpretive Summary: Genomic selection (GS) has transformed cattle breeding. We investigated changes in population demographic structure and genetic diversity in five European breeds after the introduction of GS. Changes in inbreeding and kinship rates were not consistent across breeds, with both increases and decreases observed. Genetic management strategies, such as optimal contribution selection, had a greater impact on maintaining genetic diversity than the introduction of GS per se . These findings highlight the need to monitor changes in genetic diversity and population demographic structure after the implementation of GS and, when needed, to adapt management strategies to ensure long-term sustainability.

The objective of this study was to examine the effects of different methods for handling missing pedigree data on biases, stability, relative increase in accuracy, and genetic trends using national data from Norwegian Red (NRF) cattle. The dataset comprised 8,402,773 milk yield records from 3,896,116 NRF cows, a pedigree with 4,957,544 animals, and a genomic dataset from 170,293 animals with 121,741 SNPs. Missing parents were modelled using three approaches: unknown parent groups (UPG), metafounders (MF), and “Q‐Q⁺” methods. The UPG method is routinely used for genetic evaluations of NRF cattle by including 52 fixed UPG in the pedigree. In the MF method, two MF were defined: MF14 and MF52, with MF treated as random effects. The MF14 included 6 MF defined by birth year intervals for NRF breed and 8 MF defined by breed origins for other breeds. The MF52 classification included all the 52 UPG as MF considering relationships among them. The “Q‐Q⁺” approach corrects for the combined effects of UPG and “J factor” in non‐genotyped animals while avoiding such corrections in genotyped animals. The three approaches, combined with different G matrices (G_rtn matrix constructed with a 0.5 allele frequency (AF) and 10% weight (w) on A, G₀₅ constructed using AF = 0.5 and w = 0.0, and G_cal constructed with observed AF and w = 0.0), led to eight ssGBLUP models being tested. This included one UPG model (using G_rtn), four MF models (MF14 and MF52 using G_rtn or G₀₅), and three Q‐Q+ models (using G_cal, G₀₅, or G_rtn). The models were evaluated through cross‐validation by masking the phenotypes of 5000 genotyped young cows. Results showed that the Q‐Q⁺ models using the G_cal or G₀₅ matrix had significantly (p < 0.05) lower level biases and higher genetic trends than all other models. MF models with 14 or 52 groups using G₀₅ were second best for level bias and performed similarly or slightly better than Q‐Q+ models regarding inflation bias and stability. Increasing the number of MF from 14 to 52 had minimal effects on biases but significantly improved stability and genetic trend estimates. Models with G_rtn had slightly higher gain in accuracy from adding phenotypic data (2.01%) than G₀₅ (1.18%), but pedigree‐based models showed the highest improvement in accuracy due to adding phenotypic (26%) or genomic (47%) data to the partial dataset. Overall, all models with G₀₅ showed the least bias (with a small standard error) and most stable predictions, while models using G_rtn introduced biases and instability. Thus, the Q‐Q⁺ and MF models combined with G₀₅ and Q‐Q⁺ with G_cal are recommended for their improved validation results and genetic trends.

Background: The GWABLUP (Genome-Wide Association based Best Linear Unbiased Prediction) approach used GWA analysis results to differentially weigh the SNPs in genomic prediction, and was found to improve the reliabilities of genomic predictions. However, the proposed multitrait GWABLUP method assumed that the SNP weights were the same across the traits. Here we extended and validated the multitrait GWABLUP method towards using trait specific SNP weights. Results: In a 3-trait dairy data set, multitrait GWAS estimates of SNP effects and their standard errors were translated into trait specific likelihood ratios for the SNPs having trait effects, and posterior probabilities using the GWABLUP approach. This produced trait specific prior (co)variance matrices for each SNP, which were applied in a SNP-BLUP model for genomic predictions, implemented in the APEX linear model suite. In a validation population, the trait specific SNP weights resulted in more reliable predictions for all three traits. Especially, for somatic cell count, which was hardly related to the other traits, the use of the same weights across all traits was harming genomic predictions. The use of trait specific SNP weights overcame this problem. Conclusions: In multitrait GWABLUP analyses of ~ 30,000 reference population cows, trait specific SNP weights resulted in up to 13% more reliable genomic predictions than unweighted SNP-BLUP, and improved genomic predictions for all three studied traits.

Genomic selection-based breeding programs offer significant advantages over conventional phenotypic selection, particularly in accelerating genetic gains in plant breeding, as demonstrated by simulations focused on combating Fusarium head blight (FHB) in wheat. FHB resistance, a crucial trait, is challenging to breed for due to its quantitative inheritance and environmental influence, leading to slow progress using conventional breeding methods. Stochastic simulations in our study compared various breeding schemes, incorporating genomic selection (GS) and combining it with speed breeding, against conventional phenotypic selection. Two datasets were simulated, reflecting real-life genotypic data (MASBASIS) and a simulated wheat breeding program (EXAMPLE). Initially a 20-year burn-in phase using a conventional phenotypic selection method followed by a 20-year advancement phase with three GS-based breeding programs (GSF2F8, GSF8, and SpeedBreeding + GS) were evaluated alongside over a conventional phenotypic selection method. Results consistently showed significant increases in genetic gain with GS-based programs compared to phenotypic selection, irrespective of the selection strategies employed. Among the GS schemes, SpeedBreeding + GS consistently outperformed others, generating the highest genetic gains. This combination effectively minimized generation intervals within the breeding cycle, enhancing efficiency. This study underscores the advantages of genomic selection in accelerating breeding gains for wheat, particularly in combating FHB. By leveraging genomic information and innovative techniques like speed breeding, breeders can efficiently select for desired traits, significantly reducing testing time and costs associated with conventional phenotypic methods.

Background: Since the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP. Results: GWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis. Conclusions: GWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.

The proceedings of the 12th World Congress on Genetics Applied to Livestock Production provide you with 816 papers representing the leading research in livestock genetics around the globe. This boo...

Many of the important traits of livestock are complex or quantitative traits controlled by thousands of variants in the DNA sequence of individual animals and environmental factors. Identification of these causal variants would be advantageous for genomic prediction, to understand the physiology and evolution of important traits and for genome editing. However, it is difficult to identify these causal variants because their effects are small and they are in linkage disequilibrium with other DNA variants. Nevertheless, it should be possible to identify probable causal variants for complex traits just as we do for simple traits provided we compensate for the small effect size with larger sample size. In this review we consider eight types of evidence needed to identify causal variants. Large and diverse samples of animals, accurate genotypes, multiple phenotypes, annotation of genomic sites, comparisons across species, comparisons across the genome, the physiological role of candidate genes and experimental mutation of the candidate genomic site.