Additive Genetic Relationships Research Articles

Dynamical BLUP modeling of reaction norm evolution, accommodating changing environments, overlapping generations, and multivariate data

AbstractFor theoretical studies, reaction norm evolution in a changing environment can be modeled by means of the multivariate breeder's equation, with the reaction norm parameters treated as traits in their own right. This is, however, not a feasible approach for the use of field data, where the intercept and slope values are not available. An alternative approach is to use infinite‐dimensional characters and smooth covariance function estimates found by, e.g., random regression. This is difficult because of the need to find, for example, polynomial basis functions that fit the data reasonably well over time, and because reaction norms in multivariate cases are correlated, such that they cannot be modeled independently. Here, I present an alternative approach based on a multivariate linear mixed model of any order, with dynamical incidence and residual covariance matrices that reflect the changing environment. From such a mixed model follows a dynamical BLUP model for the estimation of the individual reaction norm parameter values at any given parent generation, and for updating of the mean reaction norm parameter values from generation to generation by means of Robertson's secondary theorem of natural selection. This will, for example, make it possible to disentangle the microevolutionary and plasticity components in climate change responses. The BLUP model incorporates the additive genetic relationship matrix in the usual way, and overlapping generations can easily be accommodated. Additive genetic and environmental model parameters are assumed to be known and constant, but it is discussed how they can be estimated by means of a prediction error method. The identifiability by the use of field or laboratory data containing environmental, phenotypic, fitness, and additive genetic relationship data is an important feature of the proposed model.

Single-step genomic evaluation for growth traits in a Mexican Braunvieh cattle population.

The objective was to compare (pedigree-based) BLUP, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. The random contemporary group (CG) effect explained about 50, 45, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study

As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic relationship matrix (G matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the A matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the G matrix based on VanRaden's methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study.

Strategies for accommodating gene-edited sires and their descendants in genetic evaluations.

Gene editing has the potential to expedite the rate of genetic gain for complex traits. However, changing nucleotides (i.e., QTN) in the genome can affect the additive genetic relationship among individuals and, consequently, impact genetic evaluations. Therefore, the objectives of this study were to estimate the impact of including gene-edited individuals in the genetic evaluation and investigate modeling strategies to mitigate potential errors. For that, a beef cattle population was simulated for nine generations (N = 13,100). Gene-edited sires (1, 25, or 50) were introduced in generation 8. The number of edited QTN was 1, 3, or 13. Genetic evaluations were performed using pedigree, genomic data, or a combination of both. Relationships were weighted based on the effect of the edited QTN. Comparisons were made using the accuracy, average absolute bias, and dispersion of the estimated breeding values (EBV). In general, the EBV of the first generation of progeny of gene-edited sires were associated with greater average absolute bias and overdispersion than the EBV of the progeny of non-gene-edited sires (P ≤ 0.001). Weighting the relationship matrices increased (P ≤ 0.001) the accuracy of EBV when the gene-edited sires were introduced by 3% and decreased (P ≤ 0.001) the average absolute bias and dispersion for the progeny of gene-edited sires. For the second generation of descendants of gene-edited sires, the absolute bias increased as the number of edited alleles increased; however, the rate of increase in absolute bias was 0.007 for each allele edited when the relationship matrices were weighted compared with 0.10 when the relationship matrices were not weighted. Overall, when gene-edited sires are included in genetic evaluations, error is introduced in the EBV, such that the EBV of progeny of gene-edited sires are underestimated. Hence, the progeny of gene-edited sires would be less likely to be selected to be parents of the next generation than what was expected based on their true genetic merit. Therefore, modeling strategies such as weighting the relationship matrices are essential to avoid incorrect selection decisions if animals that have been edited for QTN underlying complex traits are introduced into genetic evaluations.

Autosomal and Z-linked genetic evaluation for body weight in Mazandaran native chicken using different models for dosage compensation on the Z chromosome.

The objectives of this study were to assess the autosomal and sex-linked genetic inheritance of growth traits and identify the effective dosage compensation on the Z chromosome in Mazandaran native chickens. The data included body weights at hatching (BW0), 8 weeks (BW8) and 12 weeks (BW12) of age, related to the first 21 generations of selection, were collected from Mazandaran native chicken breeding centre. The fixed effects included sex of birds in two classes, hatch in five classes and generation in 21 classes. The inverse of the sex-linked additive genetic relationship matrix was constructed using nadiv package in R considering different models for dosage compensation on the Z chromosome. The setup inversed matrix was then supplied externally to WOMBAT using the GIN option. Twelve univariate animal models separating participation of autosomal additive genetic, sex-linked additive genetic and maternal effects (both genetic and permanent environment effects) with considering the five different dosage compensation methods for models with sex-linked effects were analysed by WOMBAT software. BW0 was not affected by sex-linked additive genetic effects. For BW8 and BW12 the model which included autosomal, sex-linked direct additive and maternal effects with no global dosage compensation for the Z chromosome was the most appropriate model. Autosomal heritability estimates were 0.05 ± 0.02, 0.10± 0.01 and 0.11 ± 0.01, for BW0, BW8 and BW12, respectively. For BW8 and BW12, sex-linked heritability estimates were 0.07 and 0.27, respectively. Spearman rank correlation coefficient between autosomal and sex-linked breeding values were 0.45 and 0.12 for BW8 and BW12, respectively. Spearman rank correlation coefficient between autosomal and sex-linked breeding values were 0.45 and 0.12 for BW8 and BW12, respectively. The autosomal direct additive genetic correlations between all traits were positive. The estimate of direct sex-linked additive genetic correlation between BW8 and BW12 was high (0.88). Also, maternal genetic correlations were 0.53, 0.54 and 0.91 between BW0-BW8, BW0-BW12 and BW8-BW12, respectively. Given the importance of Z-linked genes for BW8 and BW12, it is recommended that Z-linked effects be separated from autosomal effects in order to increase the accuracy of genetic evaluation of birds for these traits.

A Note on the Conditioning of the H-1 Matrix Used in Single-Step GBLUP.

The single-step genomic BLUP (ssGBLUP) is used worldwide for the simultaneous genetic evaluation of genotyped and non-genotyped animals. It is easily extendible to all BLUP models by replacing the pedigree-based additive genetic relationship matrix (A) with an augmented pedigree-genomic relationship matrix (H). Theoretically, H does not introduce any artificially inflated variance. However, inflated genetic variances have been observed due to the incomparability between the genomic relationship matrix (G) and A used in H. Usually, G is blended and tuned with A22 (the block of A for genotyped animals) to improve its numerical condition and compatibility. If deflation/inflation is still needed, a common approach is weighting G-1-A22-1 in the form of τG-1-ωA22-1, added to A-1 to form H-1. In some situations, this can violate the conditional properties upon which H is built. Different ways of weighting the H-1 components (A-1, G-1, A22-1, and H-1 itself) were studied to avoid/minimise the violations of the conditional properties of H. Data were simulated on ten populations and twenty generations. Responses to weighting different components of H-1 were measured in terms of the regression of phenotypes on the estimated breeding values (the lower the slope, the higher the inflation) and the correlation between phenotypes and the estimated breeding values (predictive ability). Increasing the weight on H-1 increased the inflation. The responses to weighting G-1 were similar to those for H-1. Increasing the weight on A-1 (together with A22-1) was not influential and slightly increased the inflation. Predictive ability is a direct function of the slope of the regression line and followed similar trends. Responses to weighting G-1-A22-1 depend on the inflation/deflation of evaluations from A-1 to H-1 and the compatibility of the two matrices with the heritability used in the model. One possibility is a combination of weighting G-1-A22-1 and weighting H-1. Given recent advances in ssGBLUP, conditioning H-1 might become an interim solution from the past and then not be needed in the future.

Conserved QTL and chromosomal inversion affect resistance to columnaris disease in 2 rainbow trout (Oncorhyncus mykiss) populations.

We present a comparative genetic analysis of the quantitative trait loci underlying resistance to warm water columnaris disease in 2 farmed rainbow trout (Oncorhynchus mykiss) populations. We provide evidence for the conservation of a major quantitative trait loci on Omy03, and the putative role played by a chromosomal rearrangement on Omy05. A total of 3,962 individuals from the 2 populations experienced a natural Flavobacterium columnare outbreak. Data for 25,823 genome-wide SNPs were generated for both cases (fatalities) and controls (survivors). FST and pairwise additive genetic relationships suggest that, despite being currently kept as separate broodstocks, the 2 populations are closely related. Association analyses identified a major quantitative trait loci on chromosome Omy03 and a second smaller quantitative trait loci on Omy05. Quantitative trait loci on Omy03 consistently explained 3–11% of genetic variation in both populations, whereas quantitative trait loci on Omy05 showed different degree of association across populations and sexes. The quantitative trait loci on Omy05 was found within a naturally occurring, 54.84 cM long inversion which is easy to tag due to a strong linkage disequilibrium between the 375 tagging SNPs. The ancestral haplotype on Omy05 was associated with decreased mortality. Genetic correlation between mortality in the 2 populations was estimated at 0.64, implying that the genetic basis of resistance is partly similar in the 2 populations. Our quantitative trait loci validation identifies markers that can be potentially used to complement breeding value evaluations to increase resistance against columnaris disease, and help to mitigate effects of climate change on aquaculture.

Metadata analysis indicates biased estimation of genetic parameters and gains using conventional pedigree information instead of genomic-based approaches in tree breeding

Forest tree improvement helps provide adapted planting stock to ensure growth productivity, fibre quality and carbon sequestration through reforestation and afforestation activities. However, there is increasing doubt that conventional pedigree provides the most accurate estimates for selection and prediction of performance of improved planting stock. When the additive genetic relationships among relatives is estimated using pedigree information, it is not possible to take account of Mendelian sampling due to the random segregation of parental alleles. The use of DNA markers distributed genome-wide (multi-locus genotypes) makes it possible to estimate the realized additive genomic relationships, which takes account of the Mendelian sampling and possible pedigree errors. We reviewed a series of papers on conifer and broadleaf tree species in which both pedigree-based and marker-based estimates of genetic parameters have been reported. Using metadata analyses, we show that for heritability and genetic gains, the estimates obtained using only the pedigree information are generally biased upward compared to those obtained using DNA markers distributed genome-wide, and that genotype-by-environment (GxE) interaction can be underestimated for low to moderate heritability traits. As high-throughput genotyping becomes economically affordable, we recommend expanding the use of genomic selection to obtain more accurate estimates of genetic parameters and gains.

Structure and genetic variability of the Mexican Sardo Negro breed

ABSTRACT: This study analyzed the Sardo Negro breed pedigree (41,521 animals registered from 1958 to 2019) to determine its structure, evolution, and genetic variability (GV). The population genetic parameters evaluated were effective number of founders (fe) and ancestors (fa), pedigree integrity, additive genetic relationship (AGR); number of complete generations (NCG), maximum generations traced (NMGT), and equivalent complete generations (NECG); effective population size (Ne), inbreeding coefficient (F), and generation interval (GI). The average GI was 7.45 years. A total of 7,804 founders and 4,856 ancestors were identified for a fe of 185 and a fa of 97. The average and maximum values of NCG, NECG, and NMGT were 1.6 and 5.0, 2.5 and 6.5, 4.3 and 12, with Ne estimates of 15.9, 25.9, and 69.0, respectively. The increase in F, linked to Ne, ranged from 0.72% to 3.1% per generation. The average values for F and AGR were 3.6% and 1.0%, respectively. The proportion of inbred individuals was 32.0%, with F values ranging from 0.01 to 62.2% and an average of 11.3%. The rate of inbred population was 1.3% per year. The annual rate of AGR was 0.04%. For the continuity and projection of the breed, the evolution of F as a function of Ne and the possible implications of the selection schemes must be considered. The genetic variability sustained over time results from the Ne.

Use of insemination data for joint evaluation of male and female fertility in predominantly seasonal-calving dairy herds

Conception in dairy cattle is influenced by the fertility of the cow and the bull and their interaction. Despite genetic selection for female fertility in many countries, selection for male fertility is largely not practiced. The primary objective of this study was to quantify variation in male and female fertility using insemination data from predominantly seasonal-calving herds. Nonreturn rate (NRR) was derived by coding each insemination as successful (1) or failed (0) based on a minimum of at least 25 d. The NRR was treated as a trait of the bull with semen (male fertility) and the cow that is mated (female fertility). The data (805,463 cows that mated to 5,776 bulls) were used to estimate parameters using either models that only included bulls with mating data or models that fitted the genetic and permanent environmental (PE) effects of bulls and cows simultaneously. We also evaluated whether fitting genetic and PE effects of bulls as one term is better for ranking bulls based on NRR compared with a model that ignored genetic effect. The age of cows that were mated, age of the bulls with semen data, season of mating, breed of cow that mated, inbreeding of cows and bulls, and days from calving to mating date were found to have a significant effect on NRR. Only about 3% of the total variance was explained by the random effects in the model, despite fitting the genetic and PE effects of the bull and cow. The 2 components of fertility (male and fertility) were not correlated. The heritability of male fertility was low (0.001 to 0.008), and that of female fertility was also low (~0.016). The highest heritability estimate for male fertility was obtained from the model that fitted the additive genetic relationship matrix and PE component of the bull as one term. When this model was used to calculate bull solutions, the difference between bulls with at least 100 inseminations was up to 19.2% units (-9.6 to 9.6%). Bull solutions from this model were compared with bull solutions that were predicted fitting bull effects ignoring pedigree. Bull solutions that were obtained considering pedigree had (1) the highest accuracy of prediction when early insemination was used to predict yet-to-be observed insemination data of bulls, and (2) improved model stability (i.e., a higher correlation between bull solutions from 2 randomly split herds) compared with the model which fitted bull with no pedigree. For practical purposes, the model that fitted genetic and PE effect as one term can provide more accurate semen fertility values for bulls than the model without genetic effect. To conclude, insemination data from predominantly seasonal-calving herds can be used to quantify variability between bulls for male fertility, which makes their ranking on NRR feasible. Potentially this information can be used for monitoring bulls and can supplement efforts to improve herd fertility by avoiding or minimizing the use of semen from subfertile bulls.

Alternative Ways of Computing the Numerator Relationship Matrix.

Pedigree relationships between every pair of individuals forms the elements of the additive genetic relationship matrix (A). Calculation of A−1 does not require forming and inverting A, and it is faster and easier than the calculation of A. Although A−1 is used in best linear unbiased prediction of genetic merit, A is used in population studies and post-evaluation procedures, such as breeding programs and controlling the rate of inbreeding. Three pedigrees with 20,000 animals (20K) and different (1, 2, 4) litter sizes, and a pedigree with 180,000 animals (180K) and litter size 2 were simulated. Aiming to reduce the computation time for calculating A, new methods [Array-Tabular method, (T−1)−1 instead of T in Thompson's method, iterative updating of D in Thompson's method, and iteration by generation] were developed and compared with some existing methods. The methods were coded in the R programming language to demonstrate the algorithms, aiming for minimizing the computational time. Among 20K, computational time decreased with increasing litter size for most of the methods. Methods deriving A from A−1 were relatively slow. The other methods were either using only pedigree information or both the pedigree and inbreeding coefficients. Calculating inbreeding coefficients was extremely fast (<0.2 s for 180K). Parallel computing (15 cores) was adopted for methods that were based on solving A−1 for columns of A, as those methods allowed implicit parallelism. Optimizing the code for one of the earliest methods enabled A to be built in 13 s (faster than the 31 s for calculating A−1) for 20K and 17 min 3 s for 180K. Memory is a bottleneck for large pedigrees but attempts to reduce the memory usage increased the computational time. To reduce disk space usage, memory usage, and computational time, relationship coefficients of old animals in the pedigree can be archived and relationship coefficients for parents of the next generation can be saved in an external file for successive updates to the pedigree and the A matrix.

Genetic Relationships Among Physiological Processes, Phenology, and Grain Yield Offer an Insight Into the Development of New Cultivars in Soybean (Glycine max L. Merr).

Soybean grain yield has steadily increased during the last century because of enhanced cultivars and better agronomic practices. Increases in the total biomass, shorter cultivars, late maturity, and extended seed-filling period are frequently reported as main contributors for better soybean performance. However, there are still processes associated with crop physiology to be improved. From the theoretical standpoint, yield is the product of efficiency of light interception (Ei), radiation use efficiency (RUE), and harvest index (HI). The relative contribution of these three parameters on the final grain yield (GY), their interrelation with other phenological–physiological traits, and their environmental stability have not been well established for soybean. In this study, we determined the additive–genetic relationship among 14 physiological and phenological traits including photosynthesis (A) and intrinsic water use efficiency (iWUE) in a panel of 383 soybean recombinant inbred lines (RILs) through direct (path analyses) and indirect learning methods [least absolute shrinkage and selection operator (LASSO) algorithm]. We evaluated the stability of Ei, RUE, and HI through the slope from the Finley and Wilkinson joint regression and the genetic correlation between traits evaluated in different environments. Results indicate that both supervised and unsupervised methods effectively establish the main relationships underlying changes in Ei, RUE, HI, and GY. Variations in the average growth rate of canopy coverage for the first 40 days after planting (AGR40) explain most of the changes in Ei. RUE is primarily influenced by phenological traits of reproductive length (RL) and seed-filling (SFL) as well as iWUE, light extinction coefficient (K), and A. HI showed a strong relationship with A, AGR40, SFL, and RL. According to the path analysis, an increase in one standard unit of HI promotes changes in 0.5 standard units of GY, while changes in the same standard unit of RUE and Ei produce increases on GY of 0.20 and 0.19 standard units, respectively. RUE, Ei, and HI exhibited better environmental stability than GY, although changes associated with year and location showed a moderate effect in Ei and RUE, respectively. This study brings insight into a group of traits involving A, iWUE, and RL to be prioritized during the breeding process for high-yielding cultivars.

Genetic relationships among founders of a silver carp (Hypophthalmichthys molitrix) genetic improvement program in Bangladesh

Silver carp (Hypophthalmichthys molitrix) is an important aquaculture species in Bangladesh and globally. Multiple introductions have been made of this exotic species to Bangladesh since 1969. Accordingly, the genetic composition of the species in the country is complex and imperfectly understood. In 2015–16, WorldFish sourced silver carp individuals from 21 Bangladeshi hatcheries as ‘candidate founders’ of a family-based genetic improvement program. In total, 544 candidate founders were sampled, of which 220 from 17 hatcheries were ultimately spawned as the ‘actual founders’ of the WorldFish Silver Carp Genetic Improvement Program (WSCGIP) population. The extent of relatedness among candidate founders was unknown when they were sourced from hatcheries. Candidate founders were genotyped using the DArTseq platform – with a total of 15,102 single nucleotide polymorphisms (SNPs) and 13,504 silicoDArT markers obtained – and genetic affinities among hatcheries examined. Based on unsupervised k-means clustering and hatchery-identified origins, each hatchery was assigned to one of six genetic groups to enable the adoption of genetic group models in pedigree-based analyses. Within genetic groups, sibship was assigned using COLONY software, and a pedigree constructed and validated against genomic relationships generated from 2007 SNPs retained after quality control. The mean pedigree-derived additive genetic relationship between actual founders was small (0.0093), indicating that relationships between actual founders are unlikely to have a meaningful impact on future parent selection, mating decisions or rates of inbreeding.

PEDIGREE ANALYSIS AND POPULATION STRUCTURE OF BRAHMAN CATTLE FROM COSTA RICA

The objective of this study was to evaluate the population of Brahman cattle from Costa Rica. Pedigree analysis of 98,749 animals was used to estimate the following parameters: pedigree integrity, as a proportion of known ancestors; the number of complete generations, maximum generations traced, and equivalent complete generations; inbreeding coefficient (F); generation interval (GI) across four selection paths; additive genetic relationship (AGR); effective number of founders (fe) and ancestors (fa); and effective population size (Ne). The analysis was carried out using the ENDOG software. The maximum proportions of unknown parents, grandparents, and great-grandparents were 14.1%, 38.9%, and 61.2%, respectively. Average F for the whole population was 0.86, while 27.4% of the population was inbred, with an average F of 3.1%. The inbred population increased through the time, while F decreased. The average AGR was 0.64, with maximum values of 4.1 and positive trends. The fe and fa were 617.8 and 172.0, respectively. The Ne indicated increases of F, with values lower than 1%. The GI fluctuated from 6.4 to 7.4, with an average of 6.85 years. These results summarize previous breeding practices and represent useful information for designing breeding programs by anticipating the relationship between the selection response and increases of inbreeding.

Ggroups: an R package for pedigree and genetic groups data

BackgroundR is a multi-platform statistical software and an object oriented programming language. The package archive network for R provides CRAN repository that features over 15,000 free open source packages, at the time of writing this article (https://cran.r-project.org/web/packages, accessed in October 2019). The package ggroups is introduced in this article. The purpose of this package is providing functions for checking and processing the pedigree, calculation of the additive genetic relationship matrix and its inverse, which are used to study the population structure and predicting the genetic merit of animals. Calculation of the dominance relationship matrix and its inverse are also covered. A concept in animal breeding is genetic groups, which is about the inequality of the average genetic merits for groups of unknown parents. The package provides functions for the calculation of the matrix of genetic group contributions (Q). Calculating Q is computationally demanding, and depending on the size of the pedigree and the number of genetic groups, it might not be feasible using personal computers. Therefore, a computationally optimised function and its parallel processing alternative are provided in the package.ResultsUsing sample data, outputs from different functions of the package were presented to illustrate a real experience of working with the package.ConclusionsThe presented R package is a free and open source tool mainly for quantitative geneticists and ecologists, who deal with pedigree data. It provides numerous functions for handling pedigree data, and calculating various pedigree-based matrices. Some of the functions are computationally optimised for large-scale data.

Segment-based coancestry, additive relationship and genetic variance within and between the Norwegian and the Swedish Fjord horse populations

ABSTRACTThe Fjord horse originates from Norway but forms a global population due to several small populations in foreign countries. There exists no information about the additive relationship and the genetic variance between these subpopulations. By collecting blood samples from Norwegian and Swedish Fjord horses, a sample of 311 Norwegian and 102 Swedish horses gave 485,918 SNPs available for analysis. Their inbreeding coefficients were calculated and compared to the pairwise coancestry and the shared genomic segments. The effective population size was almost similar with the two methods in the Norwegian Fjord horse population (63 and 71), but very different in the Swedish population (269 and 1136) and unprecise due to a much smaller number of observations. The study showed that coancestry from shared genomic segments can be used to estimate additive genetic relationship and genetic variation within and between the global populations of the Fjord horse.

Demographic structure and population dynamics of Maremmana cattle local breed after 35 years of traditional selection

Thirty-five years of pedigree data has been studied to investigate the role of the Herd Book in maintaining the genetic diversity and control the inbreeding while applying a selection scheme in local cattle breeds.More than 64,000 genealogical records of Maremmana beef cattle born from 1951 to 2019 were used to investigate demographic and genealogical structure of Maremmana breed in a time frame 1980–2015.Along these 35 years, the number of herds in the Herd Book increased 2.5-fold, and the number of pure animals raised from 4,849 to 10,984. The Effective Population Size (Ne) based on inbreeding coefficient (F) rate was 123.5 on average, with large periods under 100 (since 1990 to 2007) or even under 50 (since 2000 to 2005). With the support of the Herd Book, Maremmana recovered from a very critical Ne only in the last timeframe explored attaining a Ne of 380 in 2009, however still critical. The average generation interval for the whole population was 6.31 years, characterized by a larger interval in dams respect to sire (7.16 vs 5.36 years respectively).In 2015, the pedigree completeness index (PCI) over the 6 generation was 94.6% as valuable effect of the raised awareness about data recordings promoted by the Herd Book.In spite of the low population size, the smooth selection pressure (1 out of 10 in sire and 1 out of 2 in dams lines respectively) contributed to keep the inbreeding rate under control, (inbreeding raised from 0% of 1980 to 2.78% in 2015 with an average rate of 0.0725% per year) still improving the genetic make-up of the breed. However, in the last years both the average additive genetic relationships (AGR) and the inbreeding accelerate alarmingly (3.65% in 2017), calling for urgent safeguard measures.Our data support the Herd Book as an effective infrastructure to manage, improve and conserve local genetic resources: contributing to maintain pure animals, stimulate the record of reproductive information, keep inbreeding under observation, increase the effective population size and support the overall economic sustainability of the herds, ultimate in situ conservation sites. A step further into the genomics, likely guided by the Herd Book data, will boost the sustainable conservation of Maremmana genetic diversity, enabling the application of genomic breeding plan and models based on the optimal contribution approach.

A note on genetic parameters and accuracy of estimated breeding values in honey bees

BackgroundIn honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen.ResultsHere, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history.ConclusionsOur results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

A Unified Model for Inclusive Inheritance in Livestock Species.

For years, animal selection in livestock species has been performed by selecting animals based on genetic inheritance. However, evolutionary studies have reported that nongenetic information that drives natural selection can also be inherited across generations (epigenetic, microbiota, environmental inheritance). In response to this finding, the concept of inclusive heritability, which combines all sources of information inherited across generations, was developed. To better predict the transmissible potential of each animal by taking into account these diverse sources of inheritance and improve selection in livestock species, we propose the “transmissibility model.” Similarly to the animal model, this model uses pedigree and phenotypic information to estimate variance components and predict the transmissible potential of an individual, but differs by estimating the path coefficients of inherited information from parent to offspring instead of using a set value of 0.5 for both the sire and the dam (additive genetic relationship matrix). We demonstrated the structural identifiability of the transmissibility model, and performed a practical identifiability and power study of the model. We also performed simulations to compare the performances of the animal and transmissibility models for estimating the covariances between relatives and predicting the transmissible potential under different combinations of sources of inheritance. The transmissibility model provided similar results to the animal model when inheritance was of genetic origin only, but outperformed the animal model for estimating the covariances between relatives and predicting the transmissible potential when the proportion of inheritance of nongenetic origin was high or when the sire and dam path coefficients were very different.

Conservation of a domestic metapopulation structured into related and partly admixed strains.

Preservation of genetic diversity is one of the most pressing challenges in the planetary boundaries concept. Within this context, we focused on genetic diversity in a native, unselected and highly admixed domesticated metapopulation. A set of 1,828 individuals from 60 different cattle breeds was analysed using a medium density SNP chip. Among these breeds, 14 Buša strains formed a metapopulation represented by 350 individuals, while the remaining 46 breeds represented the global cattle population. Genetic analyses showed that the scarcely selected and less differentiated Buša metapopulation contributed a substantial proportion (52.6%) of the neutral allelic diversity to this global taurine population. Consequently, there is an urgent need for synchronized maintenance of this highly fragmented domestic metapopulation, which is distributed over several countries without sophisticated infrastructure and highly endangered by continuous replacement crossing as part of the global genetic homogenization process. This study collected and evaluated samples, data and genomewide information and developed genome-assisted cross-border conservation concepts. To detect and maintain genetic integrity of the metapopulation strains, we designed and applied a composite test that combines six metrics based on additive genetic relationships, a nearest neighbour graph and the distribution of semiprivate alleles. Each metric provides distinct information components about past admixture events and offers an objective and powerful tool for the detection of admixed outliers. The here developed conservation methods and presented experiences could easily be adapted to comparable conservation programmes of domesticated or other metapopulations bred and kept in captivity or under some other sort of human control.