Metafounders May Reduce Bias in Composite Cattle Genomic Predictions

Metafounders are pseudo-individuals that act as proxies for animals in base populations. When metafounders are used, individuals from different breeds can be related through pedigree, improving the compatibility between genomic and pedigree relationships. The aim of this study was to investigate the use of metafounders and unknown parent groups (UPGs) for the genomic evaluation of a composite beef cattle population. Phenotypes were available for scrotal circumference at 14 months of age (SC14), post weaning gain (PWG), weaning weight (WW), and birth weight (BW). The pedigree included 680,551 animals, of which 1,899 were genotyped for or imputed to around 30,000 single-nucleotide polymorphisms (SNPs). Evaluations were performed based on pedigree (BLUP), pedigree with UPGs (BLUP_UPG), pedigree with metafounders (BLUP_MF), single-step genomic BLUP (ssGBLUP), ssGBLUP with UPGs for genomic and pedigree relationship matrices (ssGBLUP_UPG) or only for the pedigree relationship matrix (ssGBLUP_UPGA), and ssGBLUP with metafounders (ssGBLUP_MF). Each evaluation considered either four or 10 groups that were assigned based on breed of founders and intermediate crosses. To evaluate model performance, we used a validation method based on linear regression statistics to obtain accuracy, stability, dispersion, and bias of (genomic) estimated breeding value [(G)EBV]. Overall, relationships within and among metafounders were stronger in the scenario with 10 metafounders. Accuracy was greater for models with genomic information than for BLUP. Also, the stability of (G)EBVs was greater when genomic information was taken into account. Overall, pedigree-based methods showed lower inflation/deflation (regression coefficients close to 1.0) for SC14, WWM, and BWD traits. The level of inflation/deflation for genomic models was small and trait-dependent. Compared with regular ssGBLUP, ssGBLUP_MF4 displayed regression coefficient closer to one SC14, PWG, WWM, and BWD. Genomic models with metafounders seemed to be slightly more stable than models with UPGs based on higher similarity of results with different numbers of groups. Further, metafounders can help to reduce bias in genomic evaluations of composite beef cattle populations without reducing the stability of GEBVs.

13 Genetic selection using pooled semen

The use of pooled semen (PS), standard procedure at the commercial level of the swine industry, limits genetic progress, since the offspring parentage is unknown. Literature suggests that some boars have a greater number of progeny than others in the same litter when PS is used. The objective of this study was to evaluate different pedigree-relationship matrices for selection purposes when PS is used. Data on a trait with heritability of 0.4 were simulated 1,000 times for 12 scenarios: combination of sires per pool (2 or 3), number of phenotyped progeny (1 or 12), and three boar dominance levels: no dominance (equal probability of parentage), medium dominance, and complete dominance (all progeny from one boar). Ten pools were created for each scenario based on 5 sires and used for 5 dams each. Breeding values (BV) of the progeny were estimated (EBV) using three relationship matrices: known parents (A), using equal probabilities of parentage (E), and probabilities based on known boar dominance (D). Results for each relationship matrix were compared using the average of the true BV (TBV) of the 10% best animals selected based on EBV. Results are presented as percentage TBV of the selected animals compared to using A (Table). In general, D resulted in better results than E as boar dominance increased. Similar results between E and D were obtained when 12 progenies were phenotyped. When one was phenotyped, D was superior than E in the presence of boar dominance. Knowing the probability of each sire contributing to the progeny increases response to selection when pooled semen is used.

Genomic Evidences Support an Independent History of Grapevine Domestication in the Levant

The ancient grapevines of the Levant have inspired beliefs and rituals in human societies which are still practiced today in religious and traditional ceremonies around the world. Despite their importance, the original Levantine wine-grapes varieties were lost due to cultural turnovers commencing in the 7th century CE, which lead to the collapse of a flourishing winemaking industry in this region. Recently, a comprehensive survey of feral grapevines was conducted in Israel in an attempt to identify local varieties, yet the origin of these domesticated accessions is unclear. Here we study the origin of Levantine grapevines using whole-genome sequence data generated for a diversity panel of wild and cultivated accessions. Comparison between Levantine and Eurasian grapevines indicated that the Levantine varieties represent a distinct lineage from the Eurasian varieties. Demographic models further supported this observation designating that domestication in the Caucasus region predated the emergence of the Levantine samples in circa 5000 years and that authentic descendants of ancient varieties are represented among the Levantine samples. We further explore the pedigree relationship among cultivated grapevines, identify footprints of selective sweeps, and estimate the extent of genetic load in each group. We conclude that the Levantine varieties are distinct from the Eurasian varieties and that resistance to disease and abiotic stress are key traits in the development of both Eurasian and Levantine varieties.

Variance estimates are similar using pedigree or genomic relationships with or without the use of metafounders or the algorithm for proven and young animals1

With an increase in the number of animals genotyped there has been a shift from using pedigree relationship matrices (A) to genomic ones. As the use of genomic relationship matrices (G) has increased, new methods to build or approximate G have developed. We investigated whether the way variance components are estimated should reflect these changes. We estimated variance components for maternal sow traits by solving with restricted maximum likelihood, with four methods of calculating the inverse of the relationship matrix. These methods included using just the inverse of A (A−1), combining A−1 and the direct inverse of G (HDIRECT−1), including metafounders (HMETA−1), or combining A−1 with an approximated inverse of G using the algorithm for proven and young animals (HAPY−1). There was a tendency for higher additive genetic variances and lower permanent environmental variances estimated with A−1 compared with the three H−1 methods, which supports that G−1 is better than A−1 at separating genetic and permanent environmental components, due to a better definition of the actual relationships between animals. There were limited or no differences in variance estimates between HDIRECT−1, HMETA−1, and HAPY−1. Importantly, there was limited differences in variance components, repeatability or heritability estimates between methods. Heritabilities ranged between <0.01 to 0.04 for stayability after second cycle, and farrowing rate, between 0.08 and 0.15 for litter weight variation, maximum cycle number, total number born, total number still born, and prolonged interval between weaning and first insemination, and between 0.39 and 0.44 for litter birth weight and gestation length. The limited differences in heritabilities suggest that there would be very limited changes to estimated breeding values or ranking of animals across models using the different sets of variance components. It is suggested that variance estimates continue to be made using A−1, however including G−1 is possibly more appropriate if refining the model, for traits that fit a permanent environmental effect.

Application of single-step GBLUP in New Zealand Romney sheep

Context In New Zealand, Romney is the most predominant breed and is reared as a dual-purpose sheep. The number of genotypes is rapidly increasing in the sheep population, and making use of both genotypes and pedigree information is of importance for genetic evaluations. Single-step genomic best linear unbiased prediction (ssGBLUP) is a method for simultaneous prediction of genetic merits for genotyped and non-genotyped animals. The combination and the compatibility of the genomic relationship matrix (G) and the pedigree relationship matrix for genotyped animals (A22) is important for unbiased ssGBLUP. Aims The aim of the present study was to find an optimum genetic relationship matrix for ssGBLUP weaning-weight evaluation of Romney sheep in New Zealand. Methods Data consisted of adjusted weaning weights for 2422011 sheep, 50K single-nucleotide polymorphism genotypes for 13304 animals and 3028688 animals in the pedigree. Blending of G and A22 was tested with weights (k) ranging from 0.2 to 0.99 (kG + (1 – k)A22), followed by none or one of the three methods of tuning G to A22. Key results The averages of G and A22 were close to each other for overall, diagonal and off-diagonal elements. Therefore, differently tuned G performed similarly. However, elements of G showed larger variation than did the elements of A22 and, on average, genotyped animals were less related in G than in A22. Correlations between genomic estimated breeding values (GEBV) for the top 500 genotyped animals, as well as the rank correlations, were almost 1 among ssGBLUP evaluations using tuned G. The corresponding correlations with BLUP evaluations were increased by blending G with a larger proportion of A22, and were further increased by tuning G, indicating improved compatibility between G and A22. Blending and tuning G suppressed the inflation of GEBV and bias and it moved the genetic trend closer to the genetic trend obtained from BLUP. Conclusions A combination of blending and tuning G to A22, with a blending rate of 0.5 at most, is recommended for weaning weight of Romney sheep in New Zealand. Failure to do that resulted in inflated GEBV that can reduce the accuracy of selection, especially for genotyped animals. Implications There is a growing interest in the single-step GBLUP method for simultaneous genetic evaluation of genotyped and non-genotyped animals, in which genomic and pedigree relationship matrices are admixed. Using data from New Zealand Romney sheep, we have shown that adjustment of the genomic relationship matrix on the basis of the pedigree relationship matrix is necessary to avoid inflated evaluations. Improving the compatibility between genomic and pedigree relationship matrices is important for obtaining accurate and unbiased single-step GBLUP evaluations.

Effect of Heterogeneity in Recombination Rate on Variation in Realised Relationship

ABSTRACTIndividuals of specified pedigree relationship vary in the proportion of the genome they share identical by descent, i.e. in their realised or actual relationship. Basing predictions of the variance in realised relationship solely on the proportion of the map length shared implicitly assumes that both recombination rate and genetic information are uniformly distributed along the genome, ignoring the possible existence of recombination hotspots, and failing to distinguish between coding and non-coding sequences. In this paper we quantify the effects of heterogeneity in recombination rate at broad and fine scale levels on the variation in realised relationship. A chromosome with variable recombination rate usually shows more variance in realised relationship than does one having the same map length with constant recombination rate, especially if recombination rates are higher towards chromosome ends. Reductions in variance can also be found, and the overall pattern of change is quite complex. In general, local (fine-scale) variation in recombination rate, e.g. hotspots, has a small influence on the variance in realised relationship. Differences in rates across longer regions and between chromosome ends can increase or decrease the variance in realised relationship, depending on the genomic architecture.

Single-step prediction of genomic breeding value in a small dairy cattle population with strong import of foreign genes

The breeding value (EBV) of Holstein cattle milk performance from the first lactation was evaluated using a regular Animal Model or by Single-Step Prediction of the genomic breeding value (GEBV). A total of 838 bulls were genotyped using the Illumina BovineSNP50 Beadchip V2. Two overlapping sets of milk performances were evaluated: calving years 1991–2004, with 729 341 lactations and 1 394 487 animals in the pedigree and calving years 1996–2009, with 808 436 lactations and 1 487 608 animals in the pedigree. The older data set included 526 genotyped bulls, in which the daughters’ milk performance was known for 210 individuals. All of the genotyped animals were included in the newer data set. Of the young genotyped bulls from the older set, 279 had more than 50 daughters with performance records in the newer set. Genomic relationship matrices (G) were constructed from the allele frequencies of the current genotyped population or by assuming a constant value of 0.5 for all loci. Using current allele frequencies, the correlation of G with the pedigree relationship (A) was 0.74, while it was 0.77 when the constant value was used. G was blended with A with weights of 80 or 99%. The average EBV of the genotyped bulls exceeded the mean EBV of the entire population by 3 SD. Although the number of reference bulls was small, genotyping resulted in an increase of approximately 0.05 in the correlation of the GEBV of young bulls with their results after progeny testing. Only small differences in correlations were found in dependency on the methods used for the determination of G and in dependency on the weight used in blending G with A. Both EBV and GEBV in the older set showed higher correlations with the GEBV of the newer set than the EBV of the newer set.