Modelling missing pedigree with metafounders and validating single-step genomic predictions in a small dairy cattle population with a great influence of foreign genetics.

Genetic improvement in small countries rely heavily on foreign genetics. In an importing country such as Uruguay, consideration of unknown parent groups (UPG) for foreign sires is essential. However, the use of UPG in genomic model evaluations may lead to bias in genomic estimated breeding values. The objective of this study was to study different models including UPG or Metafounders (MF) in the Uruguayan Holstein evaluation and to analyze bias, dispersion, and accuracy of (G)EBV predictions in BLUP and ssGBLUP. Gamma matrix (Γ) was estimated either by using base allele population frequencies obtained by bounded linear regression (MFbounded), or by using 2 values to design Γ, i.e., a single value for the diagonal and a different value for the off-diagonal (MFrobust). Both Γ estimators performed well in terms of GEBV predictions, but MFbounded was the best option. There is, however, some bias whose origin was not completely understood. UPG or MF seem to model correctly genetic progress for unknown parents except for the very first groups (earlier time period). As for validation bulls, bias was observed across all models, whereas for validation cows it was only observed with UPG in BLUP. Overdispersion was found in all models, but it was mostly detected in validation bulls. Ratio of accuracies indicated that ssGBLUP gave better predictions than BLUP.

Microbiability of milk composition and genetic control of microbiota effects in sheep.

Recently, high-dimensional omics data are becoming available in larger quantities, and models have been developed that integrate them with genomics to understand in finer detail the relationship between genotype and phenotype, and thus improve the performance of genetic evaluations. Our objectives are to quantify the effect of the inclusion of microbiome data in the genetic evaluation for dairy traits in sheep, through the estimation of the heritability, microbiability, and how the microbiome effect on dairy traits decomposes into genetic and nongenetic parts. In this study we analyzed milk and rumen samples of 795 Lacaune dairy ewes. We included, as phenotype, dairy traits and milk fatty acids and proteins composition; as omics measurements, 16S rRNA rumen bacterial abundances; and as genotyping, 54K SNP chip for all ewes. Two nested genomic models were used: a first model to predict the individual contributions of the genetic and microbial abundances to phenotypes, and a second model to predict the additive genetic effect of the microbial community. In addition, microbiome-wide association studies for all dairy traits were applied using the 2,059 rumen bacterial abundances, and the genetic correlations between microbiome principal components and dairy traits were estimated. Results showed that in general the inclusion of both genetic and microbiome effect did not improve the fit of the model compared with the model with the genetic effect only. In addition, for all dairy traits the total heritability was equal to the direct heritability after fitting microbiota effects, due to a microbiability being almost zero for most dairy traits and heritability of the microbial community was very close to zero. Microbiome-wide association studies did not show operational taxonomic units with major effect for any of the dairy traits evaluated, and the genetic correlations between the first 5 principal components and dairy traits were low to moderate. So far, we can conclude that, using a substantial data set of 795 Lacaune dairy ewes, rumen bacterial abundances do not provide improved genetic evaluation for dairy traits in sheep.

Partitioning of the genetic trends of French dairy sheep in Mendelian samplings and long-term contributions.

The genetic trend of milk yield for 4 French dairy sheep breeds (Lacaune, Basco-Béarnaise, Manech Tête Noire, and Manech Tête Rousse) was partitioned in Mendelian sampling trends by categories of animals defined by sex and by selection pathways. Five categories were defined, as follows: (1) artificial insemination (AI) males (after progeny testing), (2) males discarded after progeny testing, (3) natural mating males, (4) dams of males, and (5) dams of females. Dams of males and AI males were the most important sources of genetic progress, as observed in the decomposition in Mendelian sampling trends. The yearly contributions were more erratic for AI males than for dams of males, as AI males are averaged across a smaller number of individuals. Natural mating males and discarded males did not contribute to the trend in terms of Mendelian sampling, as their estimated Mendelian sampling term is either null (natural mating males) or negative (discarded males). Overall, in terms of Mendelian sampling, females contributed more than males to the total genetic gain, and we interpret that this is because females constitute a larger pool of genetic diversity. In addition, we computed long-term contributions from each individual to the following pseudo-generations (one pseudo-generation spanning 4 years). With this information, we studied the selection decisions (selected or not selected) for females, and the contributions to the following generations. Mendelian sampling was more important than parent average to determine the selection of individuals and their long-term contributions. Long-term contributions were greater for AI males (with larger progeny sizes than females) and in Basco-Béarnaise than in Lacaune (with the latter being a larger population).

Effect of subdivision of the Lacaune dairy sheep breed on the accuracy of genomic prediction.

Genomic selection was deployed in Lacaune dairy breed in 2015. Lacaune population split in 1972 into 2 breeding companies with associated flocks, and there have been very few exchanges of animals between the subpopulations, leading to divergence of the 2 subpopulations. In spite of that, there is a joint genomic prediction. The objective of this study is to understand how this structuring affects prediction accuracy. We analyzed all the data available from Lacaune breeding program for milk yield: around 6 million phenotypes, 2 million animals in the pedigree and more than 29,000 genotyped animals, including 3,434 and 2,868 AI rams for each company. To consider missing pedigree, we set up genetic groups using the theory of metafounders. First, we studied the pedigree and genomic structures of the 2 subpopulations calculating Fst, evolution of average pedigree relationships across time and principal components analysis of genomic relationships. In a second part, we compared the reliability between different scenarios: an evaluation with a single reference population (Alone), an evaluation with a joint reference population (Together) and an evaluation of one subpopulation based on the reference population of the other group (Indirect). The low Fst value (0.02) reveals that the 2 subpopulations are still genetically close. Nevertheless, a low and constant average relationship between the animals of the 2 subpopulations confirms the absence of recent connections between them. We can see with principal component analysis results that even if they are close, they diverge over time. Finally, we observe small gains in accuracy of Together versus Alone, in spite of whereas doubling the reference population size in Together. These gains vary across years and subpopulations: less than 0.08 (0.46 to 0.54; ratio of accuracy for the partial and whole evaluations-corresponding to the greatest change in this ratio for breeding company 1, observed for the cohort 2016) for one subpopulation and between 0.03 (0.55 to 0.58) and 0.17 (0.48 to 0.65) for the other. To conclude, the 2 subpopulations remain close enough genetically so that their combined evaluation is advantageous, even if only slightly.

Transmission ratio distortion regions in the context of genomic evaluation and their effects on reproductive traits in cattle.

Transmission ratio distortion (TRD), which is a deviation from Mendelian expectations, has been associated with basic mechanisms of life such as sperm and ova fertility and viability at developmental stages of the reproductive cycle. In this study different models including TRD regions were tested for different reproductive traits [days from first service to conception (FSTC), number of services, first service nonreturn rate (NRR), and stillbirth (SB)]. Thus, in addition to a basic model with systematic and random effects, including genetic effects modeled through a genomic relationship matrix, we developed 2 additional models, including a second genomic relationship matrix based on TRD regions, and TRD regions as a random effect assuming heterogeneous variances. The analyses were performed with 10,623 cows and 1,520 bulls genotyped for 47,910 SNPs, 590 TRD regions, and several records ranging from 9,587 (FSTC) to 19,667 (SB). The results of this study showed the ability of TRD regions to capture some additional genetic variance for some traits; however, this did not translate into higher accuracy for genomic prediction. This could be explained by the nature of TRD itself, which may arise in different stages of the reproductive cycle. Nevertheless, important effects of TRD regions were found on SB (31 regions) and NRR (18 regions) when comparing at-risk versus control matings, especially for regions with allelic TRD pattern. Particularly for NRR, the probability of observing nonpregnant cow increases by up to 27% for specific TRD regions, and the probability of observing stillbirth increased by up to 254%. These results support the relevance of several TRD regions on some reproductive traits, especially those with allelic patterns that have not received as much attention as recessive TRD patterns.

Nonparallel genome changes within subpopulations over time contributed to genetic diversity within the US Holstein population.

Maintaining genetic variation in a population is important for long-term genetic gain. The existence of subpopulations within a breed helps maintain genetic variation and diversity. The 20,990 genotyped animals, representing the breeding animals in the year 2014, were identified as the sires of animals born after 2010 with at least 25 progenies, and females measured for type traits within the last 2 yr of data. K-means clustering with 5 clusters (C1, C2, C3, C4, and C5) was applied to the genomic relationship matrix based on 58,990 SNP markers to stratify the selected candidates into subpopulations. The general higher inbreeding resulting from within-cluster mating than across-cluster mating suggests the successful stratification into genetically different groups. The largest cluster (C4) contained animals that were less related to each animal within and across clusters. The average fixation index was 0.03, indicating that the populations were differentiated, and allele differences across the subpopulations were not due to drift alone. Starting with the selected candidates within each cluster, a family unit was identified by tracing back through the pedigree, identifying the genotyped ancestors, and assigning them to a pseudogeneration. Each of the 5 families (F1, F2, F3, F4, and F5) was traced back for 10 generations, allowing for changes in frequency of individual SNPs over time to be observed, which we call allele frequencies change. Alternative procedures were used to identify SNPs changing in a parallel or nonparallel way across families. For example, markers that have changed the most in the whole population, markers that have changed differently across families, and genes previously identified as those that have changed in allele frequency. The genomic trajectory taken by each family involves selective sweeps, polygenic changes, hitchhiking, and epistasis. The replicate frequency spectrum was used to measure the similarity of change across families and showed that populations have changed differently. The proportion of markers that reversed direction in allele frequency change varied from 0.00 to 0.02 if the rate of change was greater than 0.02 per generation, or from 0.14 to 0.24 if the rate of change was greater than 0.005 per generation within each family. Cluster-specific SNP effects for stature were estimated using only females and applied to obtain indirect genomic predictions for males. Reranking occurs depending on SNP effects used. Additive genetic correlations between clusters show possible differences in populations. Further research is required to determine how this knowledge can be applied to maintain diversity and optimize selection decisions in the future.

Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes.

Gene expression is supposed to be an intermediate between DNA and the phenotype, and it can be measured. Thus, for a trait, we may have intermediate measures, which are in fact a series of genetically controlled traits. Similarly, several traits may be measured or predicted using infrared spectra, accelerometers, and similar high-throughput measures that we will call "omics." Although these measurements have errors, many of them are heritable, and they may be more accurate or easier to record than the trait of interest. It is therefore important to develop methods to use intermediate measurements in selection. Here, we present methods and perspectives for selection based on massively recorded intermediate traits (omics). Recent developments allow a hierarchical integrated framework for prediction, in which a trait is partially controlled by omics. In addition, the omics measures are themselves partly controlled by genetics ("mediated breeding values") and partly by environment or residual factors. Thus, a part of the genetic determinism of a trait is mediated by omics, whereas the remaining part is not mediated, which results in "residual breeding values." In such a framework, genetic evaluations consist of 2 nested genomic BLUP-based models. In the first, the effect of omics on the trait (which can be seen as an improved estimate of the phenotype) and the residual breeding values are estimated. The second model extracts the mediated breeding values from the improved estimate of the phenotype, considering that omics themselves are heritable. The whole procedure is called GOBLUP (genomics omics BLUP) and it allows measures in only some individuals; that is, it is a "single-step"-like method. In this model, heritability is split into "mediated" and "not mediated" parts. This decomposition allows us to predict how accurate the omics measure of the trait would be compared with the direct measure. The ideal omics measure is heritable and explains a large part of the phenotypic variation of the trait. Ideally, this could be the case for some traits with low heritability. However, even if the omics measure explains only a small part of the phenotypic variation, when omics measurement themselves are heritable, the use of such a model would lead to more accurate selection. Expressions for upper bounds of reliability given omics measurements are also presented. More studies are needed to confirm the usefulness of omics or high-throughput prediction. Usefulness of the technology likely needs to be checked on a case-by-case basis.

High genetic correlation for milk yield across Manech and Latxa dairy sheep from France and Spain.

Spanish Latxa and French Manech are dairy sheep breeds that split into Blond (Latxa Cara Rubia, LCR; Manech Tête Rousse, MTR) and Black (Latxa Cara Negra of Navarre, LCN; Manech Tête Noire, MTN) strains. Exchange of genetic material (artificial insemination doses) is becoming more and more frequent across these breeds, within color, to boost both genomic precision using a larger reference population and genetic progress using a larger selection base. This exchange leads to some rams having descendance across both countries. However, additional gains can only be achieved if the selected traits are genetically similar across countries. The objective of this work was to estimate the genetic correlation across breeds for milk yield. We combine across-country, within-color records, pedigree, and marker information. The number of animals with records oscillates from 65,000 (LCN) to 544,000 (MTR), whereas the number of connecting artificial insemination rams (with more than 10 daughters in the other country) is 381 MTR rams in LCR and 58 MTN rams in LCN. Blond strains had a stronger and more extended-in-time connection. The number of genotyped rams goes from 328 (LCN) to 4,901 (MTR). The relatedness of populations was assessed by principal component analysis and Fst coefficients. The genetic correlation was estimated using 2 (one per color) 2-trait models (each country a trait), including all available data (records, pedigree and genotypes), by maximum profile likelihood while fixing other variance components to within-population estimates. Results showed a closer genetic relationship of Blond strains than of Black strains (Fst: 0.01 vs. 0.05, respectively). Genetic correlation estimates for milk yield were 0.70 in both cases. Based on Fst distances, we expected a lower correlation for Black strains than for Blond ones if dominance or epistasis are important. Thus, we attribute the value of this correlation not being close to 1 mostly to genotype-by-environment interaction, including on-farm management and trait modeling. Regardless, the correlation of 0.7 across populations is encouraging for future joint work of Latxa and Manech breeders, including joint genetic evaluations.

Multibreed genomic evaluation for production traits of dairy cattle in the United States using single-step genomic best linear unbiased predictor.

Official multibreed genomic evaluations for dairy cattle in the United States are based on multibreed BLUP evaluation followed by single-breed estimation of SNP effects. Single-step genomic BLUP (ssGBLUP) allows the straight computation of genomic (G)EBV in a multibreed context. This work aimed to develop ssGBLUP multibreed genomic predictions for US dairy cattle using the algorithm for proven and young (APY) to compute the inverse of the genomic relationship matrix. Only purebred Ayrshire (AY), Brown Swiss (BS), Guernsey (GU), Holstein (HO), and Jersey (JE) animals were considered. A 3-trait model with milk (MY), fat (FY), and protein (PY) yields was applied using about 45 million phenotypes recorded from January 2000 to June 2020. The whole data set included about 29.5 million animals, of which almost 4 million were genotyped. All the effects in the model were breed specific, and breed was also considered as fixed unknown parent groups. Evaluations were done for (1) each single breed separately (single); (2) HO and JE together (HO_JE); (3) AY, BS, and GU together (AY_BS_GU); (4) all the 5 breeds together (5_BREEDS). Initially, 15k core animals were used in APY for AY_BS_GU and 5_BREEDS, but larger core sets with more animals from the least represented breeds were also tested. The HO_JE evaluation had a fixed set of 30k core animals, with an equal representation of the 2 breeds, whereas HO and JE single-breed analysis involved 15k core animals. Validation for cows was based on correlations between adjusted phenotypes and (G)EBV, whereas for bulls on the regression of daughter yield deviations on (G)EBV. Because breed was correctly considered in the model, BLUP results for single and multibreed analyses were the same. Under ssGBLUP, predictability and reliability for AY, BS, and GU were on average 7% and 2% lower in 5_BREEDS compared with single-breed evaluations, respectively. However, validation parameters for these 3 breeds became better than in the single-breed evaluations when 45k animals were included in the core set for 5_BREEDS. Evaluations for Holsteins were more stable across scenarios because of the greatest number of genotyped animals and amount of data. Combining AY, BS, and GU into one evaluation resulted in predictions similar to the ones from single breed, especially when using about 30k core animals in APY. The results showed that single-step large-scale multibreed evaluations are computationally feasible, but fine tuning is needed to avoid a reduction in reliability when numerically dominant breeds are combined. Having evaluations for AY, BS, and GU separated from HO and JE may reduce inflation of GEBV for the first 3 breeds.

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions.

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ∼1,900,000 animals, and ∼5,900 genotyped elite artificial insemination rams. For the ∼8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare "partial" and "whole" predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the "official" (routine) evaluation, which is a mixture of both single and multiple trait, and "deletion" of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.

Islands of runs of homozygosity indicate selection signatures in Ovis aries 6 (OAR6) of French dairy sheep.

Runs of homozygosity (ROH) are contiguous homozygous segments of the genome where the haplotypes inherited from each parent are identical. The occurrence of ROH is not randomly distributed across the genome, and ROH islands across many animals may be the result of selective pressure. The objective of this study was to demonstrate that the presence of ROH islands may be indicative of selection signatures in French dairy sheep breeds and subpopulations. The data set available included animals (artificial insemination males) from various breeds and subpopulations: Basco-Béarnaise breed (321 individuals), Manech Tête Noire breed (329 individuals), Manech Tête Rousse breed (1,906 individuals), Lacaune Confederation subpopulation (3,030 individuals), and Lacaune Ovitest subpopulation (3,114 individuals). Animals were genotyped with the Illumina OvineSNP50 BeadChip. After applying filtering criteria, the genomic data included 38,287 autosomal SNP distributed across 26 chromosomes and 8,700 individuals. One island of ROH was detected on OAR6 in the same genomic position across animals (between 30 and 40 Mb). Global Wright's differentiation coefficients for 2 SNP within this ROH island were high (0.67-0.68). The linkage disequilibrium between both SNP was also elevated (0.98). The divergence in allele frequencies in those SNP grouped Basco-Béarnaise, Manech Tête Noire, and Manech Tête Rousse breeds in one cluster and Lacaune Confederation and Lacaune Ovitest subpopulations in another cluster. The closest candidate genes are NCAPG and LCORL, which have been reported to be under positive selection and suggested to control weight and height in sheep. The preliminary identification of ROH suggests the presence of selection. However, for the identification of potential candidate genes, ROH detection should be combined with other approaches to improve mapping accuracy.

Genomic and pedigree estimation of inbreeding depression for semen traits in the Basco-Béarnaise dairy sheep breed.

Inbreeding depression is associated with a decrease in performance and fitness of the animals. The goal of this study was to evaluate pedigree-based and genomic methods to estimate the level of inbreeding and inbreeding depression for 3 semen traits (volume, concentration, and motility score) in the Basco-Béarnaise sheep breed. Data comprised 16,196 (or 15,071) phenotypic records from 620 rams (of which 533 rams had genotypes of 36,464 SNPs). The pedigree included 8,266 animals, composed of the 620 rams and their ancestors. The number of equivalent complete generations for the 620 rams was 7.04. Inbreeding coefficients were estimated using genomic and pedigree-based information. Genomic inbreeding coefficients were estimated from individual SNP and using segments of homozygous SNP (runs of homozygosity, ROH). Short ROH are of old origin, whereas long ROH are due to recent inbreeding. Considering that the equivalent number of generations in Basco-Béarnaise was 6, inbreeding coefficients for ROH with a length >4 Mb refer to all (recent + old) inbreeding, those with a length >17 Mb correspond to recent inbreeding, and the difference between them indicates old inbreeding. Pedigree-based inbreeding coefficients were also estimated classically, or accounting for nonzero relationships for unknown parents, or including metafounder relationships (estimated using markers) to account for missing pedigree information. Finally, inbreeding coefficients combining genotyped and nongenotyped animal information were computed from matrix H of the single-step approach, also including metafounders. Inbreeding depression was estimated differently depending on the approach used to compute inbreeding coefficients. These 8 estimators of inbreeding coefficients were included as covariates in different animal models. No inbreeding depression was detected for sperm volume or sperm concentration. Inbreeding depression was significant for the motility of spermatozoa. The effect of old and recent inbreeding on motility was null and negative, respectively, demonstrating the existence of purging by selection of deleterious recessive alleles affecting motility. A 10% increase in inbreeding would result in a reduction in mean motility ranging between 0.09 and 0.22 points in the score (from 0 to 5). Motility is unfavorably affected by increasing recent inbreeding but the impact is very small. Runs of homozygosity and metafounders allow us to accurately estimate inbreeding depression and detect recent inbreeding.

Genome-wide association study for feed efficiency in collective cage-raised rabbits under full and restricted feeding.

Feed efficiency (FE) is one of the most economically and environmentally relevant traits in the animal production sector. The objective of this study was to gain knowledge about the genetic control of FE in rabbits. To this end, GWASs were conducted for individual growth under two feeding regimes (full feeding and restricted) and FE traits collected from cage groups, using 114 604 autosome SNPs segregating in 438 rabbits. Two different models were implemented: (1) an animal model with a linear regression on each SNP allele for growth trait; and (2) a two-trait animal model, jointly fitting the performance trait and each SNP allele content, for FE traits. This last modeling strategy is a new tool applied to GWAS and allows information to be considered from non-genotyped individuals whose contribution is relevant in the group average traits. A total of 189 SNPs in 17 chromosomal regions were declared to be significantly associated with any of the five analyzed traits at a chromosome-wide level. In 12 of these regions, 20 candidate genes were proposed to explain the variation of the analyzed traits, including genes such as FTO, NDUFAF6 and CEBPA previously associated with growth and FE traits in monogastric species. Candidate genes associated with behavioral patterns were also identified. Overall, our results can be considered as the foundation for future functional research to unravel the actual causal mutations regulating growth and FE in rabbits.

Exploring the inclusion of genomic information and metafounders in Latxa dairy sheep genetic evaluations.

The availability of genomic marker panels has made possible more precise estimates of breeding values. Sheep breeding programs are implementing genomic selection. In Latxa dairy sheep breed, a previous study using pre-corrected data and a small number of genotyped animals did not show a clear advantage of genomic selection. The objective of the present study was to ascertain the possible benefits of GS for the Latxa breed based on more data than before and using better tools, in particular single-step genomic BLUP using metafounders to model missing pedigree. Goodness of prediction of pedigree and genomic evaluations was analyzed by cross-validation comparing predictions of young rams using whole and partial (truncated) data sets. The results showed that with the current available data, genetic and genomic evaluations have the same accuracy. Contrary to the previous study, predictions were nearly unbiased, which shows the advantage of using single-step genomic BLUP. However, genomic information did not yield more precise evaluations. This could be explained by the small number of sibs in the young rams.

Inbreeding, effective population size, and coancestry in the Latxa dairy sheep breed.

Traditionally, breeding programs have estimated and managed inbreeding based on pedigree information. The availability of genomic marker panels has made possible new alternatives to achieve more precise estimates, for example in case of missing pedigree. The objective of the present study was to assess and compare, different estimation methods (pedigree-based methodologies, single SNP-based approach (homozygosity) and runs of homozygosity-based method) to analyze the evolution of genetic diversity measured as inbreeding or as coancestry of 3 selected populations of Latxa dairy sheep (Latxa Cara Rubia and Latxa Cara Negra from Euskadi and Navarre). Genomic data came from 972 artificial insemination rams genotyped with the Illumina OvineSNP50 BeadChip (Illumina Inc., San Diego, CA) whose genealogy consisted of 4,484 animals. Inbreeding estimates based on molecular data were more similar between them than compared with those based on pedigree information. However, the SNP-based approach estimations of effective population size differed more, reflecting the sensitivity of effective population size to small changes in the evolution of inbreeding. The 2 Latxa Cara Negra populations showed increases of inbreeding rates with time and effective population sizes between 64 and 103 animals, depending on breed and methodology used. The Latxa Cara Rubia population did not show an increase in inbreeding rate, mainly due to semen importation from the related French population of Manech Tête Rousse. The effective size estimates based on coancestry increase show a higher variability and they are more sensitive to the source of information and the data structure considered. Realized effective population size based on individual increase in inbreeding were in agreement with the previous estimates. Coancestry evolution analysis based on DNA information showed an increase on coancestry during the last 10 yr in all breeds, as a consequence of the selection process. Moreover, the increase on coancestry between Latxa Cara Rubia and Manech Tête Rousse was more noticeable between than within each of those breeds.

Short communication: Methods to compute genomic inbreeding for ungenotyped individuals.

The genomic measure of inbreeding is closer to the actual inbreeding than the pedigree-based measure. However, it cannot be computed for ungenotyped animals. An estimate of genomic inbreeding comes from the diagonal of matrix H used in single-step methods. This matrix projects genomic relationships to all ungenotyped members of the pedigree. The diagonal element of H-1 gives an estimate of the genomic inbreeding coefficient. However, so far no computational methods are available to compute the diagonal of H. Here we propose 3 exact methods to compute this diagonal. The first uses an already-existing algorithm to compute, for each ungenotyped individual, products of the form Hx to obtain the corresponding diagonal element of H. The second method computes, for each ungenotyped individual, a term that can be written as a quadratic form involving pedigree and genomic relationships. For both methods, the computational burden is linear in the number of ungenotyped animals. The last method reorders the computations of the second method so that they become linear in the number of genotyped animals, which is usually much smaller. We tested the methods in 3 small data sets (with ~2,000 genotyped animals and 30,000-500,000 animals in pedigree) and in a large simulated population (with 1,220,000 animals in pedigree and 36,000 genotyped animals). Tests resulted in satisfactory computing times (<10 min in the largest example using 10 parallel threads). Computing times were much shorter for the third method, as expected. Using these methods, estimates of genomic inbreeding in ungenotyped animals can be obtained on a regular basis.

Behavior of the Linear Regression method to estimate bias and accuracies with correct and incorrect genetic evaluation models.

Bias in genetic evaluations has been a constant concern in animal genetics. The interest in this topic has increased in the last years, since many studies have detected overestimation (bias) in estimated breeding values (EBV). Detecting the existence of bias, and the realized accuracy of predictions, is therefore of importance, yet this is difficult when studying small data sets or breeds. In this study, we tested by simulation the recently presented method Linear Regression (LR) for estimation of bias, slope, and accuracy of pedigree EBV. The LR method computes statistics by comparing EBV from a data set containing old, partial information with EBV from a data set containing all information (old and new, a whole data set) for the same individuals. The method proposes an estimator for bias (Δpˆ), an estimator of slope (bpˆ), and 3 estimators related to accuracies: the ratio between accuracies [Formula: see text] the reliability of the partial data set (accp2ˆ), and the ratio of reliabilities (ρp,w2ˆ). We simulated a dairy scheme for low (0.10) and moderate (0.30) heritabilities. In both cases, we checked the behavior of the estimators for 3 scenarios: (1) when the evaluation model is the same as the model used to simulate the data; (2) when the evaluation model uses an incorrect heritability; and (3) when the data includes an environmental trend. For scenarios in which the evaluation model was correct, the LR method was capable of correctly estimating bias, slope, and accuracies, with better performance for higher heritability [i.e., corr(bp,bpˆ) was 0.45 for h2 = 0.10 and 0.59 for h2 = 0.30]. In cases of the use of incorrect heritabilities in the evaluation model, the bias was correctly estimated in direction but not in magnitude. In the same way, the magnitudes of bias and of slope were underestimated in scenarios with environmental trends in data, except for cases in which contemporary groups were random and greatly shrunken. In general, accuracies were well estimated in all scenarios. The LR method is capable of checking bias and accuracy in all cases, if the evaluation model is reasonably correct or robust, and its estimations are more precise with more information (e.g., high heritability). If the model uses an incorrect heritability or a hidden trend exists in the data, it is still possible to estimate the direction and existence of bias and slope but not always their magnitudes.

Alternative SNP weighting for single-step genomic best linear unbiased predictor evaluation of stature in US Holsteins in the presence of selected sequence variants.

Causal variants inferred from sequence data analysis are expected to increase accuracy of genomic selection. In this work we evaluated the gain in reliability of genomic predictions, for stature in US Holsteins, when adding selected sequence variants to a pre-existent SNP chip. Two prediction methods were tested: de-regressed proofs assuming heterogeneous (genomic BLUP; GBLUP) residual variances and by single-step GBLUP (ssGBLUP) using actual phenotypes. Phenotypic data included 3,999,631 records for stature on 3,027,304 Holstein cows. Genotypes on 54,087 SNP markers (54k) were available for 26,877 bulls. Additionally, 16,648 selected sequence variants were combined with the 54k markers, for a total of 70,735 (70k) markers. In all methods, SNP in the genomic relationship matrix (G) were unweighted or weighted iteratively, with weights derived either by SNP effects squared or by a nonlinear method that resembles BayesA (nonlinear A). Reliability of genomic predictions were obtained by cross validation. With unweighted G derived from 54k markers, the reliabilities (× 100) were 72.4 for GBLUP and 75.3 for ssGBLUP. With unweighted G derived from 70k markers, the reliabilities were 73.4 and 76.0, respectively. Weighting by nonlinear A changed reliabilities to 73.3, and 75.9, respectively. Addition of selected sequence variants had a small effect on reliabilities. Weighting by quadratic functions reduced reliabilities. Weighting by nonlinear A increased reliabilities for GBLUP but had only a small effect in ssGBLUP. Reliabilities for direct genomic values extracted from ssGBLUP using unweighted G with 54k were higher than reliabilities by any GBLUP. Thus, ssGBLUP seems to capture more information than GBLUP and there is less room for extra reliability. Improvements in GBLUP may be because the weights in G change the covariance structure, which can explain a proportion of the variance that is accounted for when a heterogeneous residual variance is assumed by considering a different number of daughters per bull.

Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits.

The partition of the total genetic variance into its additive and non-additive components can differ from trait to trait, and between purebred and crossbred populations. A quantification of these genetic variance components will determine the extent to which it would be of interest to account for dominance in genomic evaluations or to establish mate allocation strategies along different populations and traits. This study aims at assessing the contribution of the additive and dominance genomic variances to the phenotype expression of several purebred Piétrain and crossbred (Piétrain × Large White) pig performances. A total of 636 purebred and 720 crossbred male piglets were phenotyped for 22 traits that can be classified into six groups of traits: growth rate and feed efficiency, carcass composition, meat quality, behaviour, boar taint and puberty. Additive and dominance variances estimated in univariate genotypic models, including additive and dominance genotypic effects, and a genomic inbreeding covariate allowed to retrieve the additive and dominance single nucleotide polymorphism variances for purebred and crossbred performances. These estimated variances were used, together with the allelic frequencies of the parental populations, to obtain additive and dominance variances in terms of genetic breeding values and dominance deviations. Estimates of the Piétrain and Large White allelic contributions to the crossbred variance were of about the same magnitude in all the traits. Estimates of additive genetic variances were similar regardless of the inclusion of dominance. Some traits showed relevant amount of dominance genetic variance with respect to phenotypic variance in both populations (i.e. growth rate 8%, feed conversion ratio 9% to 12%, backfat thickness 14% to 12%, purebreds-crossbreds). Other traits showed higher amount in crossbreds (i.e. ham cut 8% to 13%, loin 7% to 16%, pH semimembranosus 13% to 18%, pH longissimus dorsi 9% to 14%, androstenone 5% to 13% and estradiol 6% to 11%, purebreds-crossbreds). It was not encountered a clear common pattern of dominance expression between groups of analysed traits and between populations. These estimates give initial hints regarding which traits could benefit from accounting for dominance for example to improve genomic estimated breeding value accuracy in genetic evaluations or to boost the total genetic value of progeny by means of assortative mating.

Inbreeding and effective population size in French dairy sheep: Comparison between genomic and pedigree estimates.

Before availability of dense SNP data, genetic diversity was characterized and managed with pedigree-based information. Besides this classical approach, 2 methodologies have been proposed in recent years to characterize and manage diversity from dense SNP data: the SNP-by-SNP approach and the alternative based on runs of homozygosity (ROH). The establishment of criteria to identify ROH is a current constraint in the literature dealing with ROH. The objective of this study was, using a medium-density SNP chip, to quantify by 3 methods (pedigree, SNP-by-SNP, and ROH) the genetic diversity on 5 selected French dairy sheep subpopulations and breeds and to assess the effect of the definition of ROH on these estimates. The data set available included individuals from the breeds Basco-Béarnaise, Manech Tête Noire, Manech Tête Rousse, and 2 subpopulations of Lacaune: Lacaune Confederation and Lacaune Ovitest. Animals were genotyped with the Illumina OvineSNP50 BeadChip (Illumina Inc., San Diego, CA). After filtering, the genomic data included 38,287 autosomal SNP and 8,700 individuals, which comprised 72,803 animals in the pedigree. The results indicated that no significant differences were observed in effective population size estimates obtained from pedigree or genomic (SNP-by-SNP or ROH) information. In general, estimates of effective population size were above 200 in Lacaune Confederation and Lacaune Ovitest subpopulations and below 200 in Basco-Béarnaise, Manech Tête Noire, and Manech Tête Rousse breeds. The minimum length that constituted a ROH, the minimum number of SNP that constituted a ROH, as well as the minimum density and the maximum distance allowed between 2 homozygous SNP are ROH-defining factors with important implications in the estimation of the rate of inbreeding. The ROH-based rates of inbreeding in concordance with those obtained from pedigree information require a specific set of values. This particular set of values is different from that identified to obtain ROH-based rates of inbreeding similar to those obtained on a SNP-by-SNP basis. Factors to define ROH do not change the results much unless extreme values are considered, although further research on ROH-based inbreeding is still required.