Aggregate Genotype Research Articles

Relationship between genetic polymorphisms of cytokines and self-reported sleep complaints and habitual caffeine consumption

Pro-inflammatory cytokines are involved in sleep-wake regulation and are associated with caffeine consumption. This is a cross-sectional study in 1023 active French workers investigating associations between self-reported sleep complaints (>3months) and total sleep time (TST) with nine single-nucleotide-polymorphisms (SNPs) including pro-inflammatory cytokines, according to caffeine consumption. Participants were characterized as low, moderate and high (0–50, 51–300, and >300 mg/day) caffeine consumers. After adjusting the odd ratios (OR) for age, gender, and smoking, the risk of sleep complaints was higher in subjects with genetic mutations in tumor necrosis factor alpha (TNF-α, rs 1800629) (ORa [95%CI] = 1.43 [1.07–1.92] for both G/A and A/A aggregate genotypes) or interleukin-1 beta (IL-1β, rs1143627) (ORa = 1.61 [1.08–2.44] for homozygous A/A genotype), and the risk was higher when subjects carry the mutations in TNF-α plus IL-1β regardless of caffeine consumption. When stratified with caffeine consumption, the risk of sleep complaints was higher in TNF-α A allele carriers in high caffeine consumers, and in homozygous A/A genotype of IL-1β in moderate and high consumers. None of the nine SNPs influence TST, with the exception of the mutation on CYP1A2 and only when stratified with caffeine consumption. Our results also indicated more caffeine side-effects when carrying mutation on IL1β. This study showed that polymorphisms in TNF-α and/or IL-1β influenced sleep complaints but did not influence total sleep time. This suggests that management of sleep complaints, which can be addressed by clinical interventions, should consider the influence of the genetic profile of pro-inflammatory cytokines.

Emphasis on resilience in dairy cattle breeding: Possibilities and consequences.

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Consequences of Selection for Post-weaning Growth Performance Traits on Fat Partition Traits in Rabbits

Genetic and phenotypic parameters, using multi-trait animal model, for weaning weight (WW), slaughter weight (SW) and daily gain (DG) from weaning to slaughter were applied on 218 New Zealand White rabbits to improve post-weaning growth traits using selection indices. The specific objective was to improve profitability of rabbit producers through increasing body weight at slaughter and daily gain from weaning to slaughter. Expected impacts of selection for post-weaning growth traits on fat partition traits were also investigated. The fat partition traits included weights of subcutaneous fat (SF) and intermuscular fat (IF) as carcass components and heart fat (HF), kidneys fat (KF), mesenteric fat (MF) and caul fat (CF) as offal components. Growth traits showed low to moderate heritability estimates (h2,0.03 to 0.37), while fat partition traits had higher h2 estimates (0.72 to 0.93), except for SF (h2=0.27). Application of the reduced index considering WW and DG was as efficient as full index in terms of accuracy of selection (rTI=0.66); it provided higher improvement in aggregate genotype represented in SW (+170.72gm) and DG (+2.41gm/day) for each round of selection. This improvement in post-weaning traits was accompanied by unfavorable expected impact in fat traits SF, HF, MF and CF (+45.32, +12.77, +56.50 and +12.25%, respectively) and favorable expected impact on IF and KF (-32.16 and -59.47gm, respectively). The single trait selection index (I6) with SW was found to be useful for rabbit meat consumers, as it decreased the undesirable fat gain in fat depots compared to the other indices, with some reduction in accuracy. In conclusion, it is possible to improve the post-weaning growth traits through selection programs, taking into consideration the undesirable changes in fat partition traits of New Zealand White rabbits.

Assessing genetic diversity and aggregate genotype selection in a collection of cumin (Cuminum cyminum L.) accessions under drought stress: Application of BLUP and BLUE

Maintaining genetic diversity and aggregate traits selection in medicinal plants improves selection efficiency and provides the flexibility to adapt environmental stresses. The present studies aimed at evaluating genetic variation and assessing efficiency of three selection index models based on relative economic values of traits and a ranking based method in 30 cumin accessions under drought and well-watered conditions in 2015 and 2016 years. Drought stress regime applied at the flowering stage of growth cycle. The best linear unbiased estimation values (BLUE) were used for aggregate genotype selection in a desired direction in the Smith-Hazel I (SMH-I) and II (SMH-II), and Pesek-Baker (PSB) indices. The three selection indices did differ in assigning relative economic values for traits tested in cumin. Best linear unbiased predictions (BLUP) were used to predict rank summation index values (RSI). Grain yield (GY) ranged from 183.07 to 379.99 kg/ha and from 430.07 to 942.26 kg/ha in 2015 and 2016 experiments, respectively. Of the accessions tested in 2015, 20 with top rankings for GY were selected for further validation in 2016. Selections in 2015 resulted in 7.1% for plant height (PH) to 165.7% for plant weight (PW) progress in drought treatment in 2016. Among the traits, PH, thousand grain weight (TGW) and harvest index (HI) showed low changes between the two moisture regimes. Plant height with high heritability and significant correlation with GY showed low variation over seasons and watering regime treatments and is suggested for indirect selection of GY. Analysis of drought susceptibility index (DSI) showed that Kashmar with low DSI had low genotype × environment interaction for GY. Grains per plant (GPP) and biological yield traits had the highest response to selection under drought stress. The SMH-II showed higher selection efficiency in both seasons. Zarand had top rankings for RSI among accessions for aggregate traits tested under drought conditions in both years. Our results showed aggregate genotype selection is a refinement of selection which incorporates information of individual genotypes with genetic and association of traits. Overall, the efficiency of SMH II which maximized genetic gains in a desired direction was higher than SMH I and PSB indices that helps to achieve breeding goals in an uncertain number of generations in cumin.

Prediction of genetic gains for breeding objective traits and designing selection schemes for Washera and Gumuz indigenous sheep

In Ethiopia,there are 32.85 millions of sheep,more than 99 % of which are indigenous.However,the productivity of local sheep under traditional production system is low with high mortality of sheep.There are two ways of improving performance of sheep and goats,namely improving the enviroment of animals and/or improving there genetic potential.The aim of this study was to predict genetic gains of breedingobjective traits and select the best sheep selection scheme for Gumuz andWashera sheep. Body size(six month weight and yearling weight) and litter size were breeding objective traits identified by own flock animal ranking experiment and personal interview. Deterministic approach of ZPLAN computor program is used for modeling input parametres of Gumuz and Washera sheep and simulating breeding plans using gene flow method and selection index procedures. One-tier cooperative sheep breeding scheme were proposed whereby ram exchange between and within villages is the main means of genetic dissimination. Genetic gains predicted for six month weight of Gumuz and Washera sheep were 0.43 and 0.55 kg,respectively. Genetic gains predicted for yearling weight of Gumuz and Washera sheep were 0.55 and 0.60 kg,respectively. Genetic gains predicted for litter size of Gumuz and Washera sheep were 0.08 and 0.09 lambs,respectively. The lower rate of inbreeding, the higher monetary genetic gain for aggregate genotype,higher return to investmnet and higher profit/ewe/year were quality measures of breeding program considered to prefer scheme 4 for both Gumuz and Washera sheep.Hence,for both Gumuz and Washera sheep populations a sheep selection scheme designed with 15 % selection proportion and one year ram use for breeding was recommended. Special emphasis need to be given to yearling weight with higher predicted genetic response and higher percentage return to investment.

MYC Gene Mutations as Causative Pathways for Development and Treatment of Hematological Malignancies

MYC is a proto-oncogene with deregulation of >50% of human cancers. The dysregulation of MYC causetumorigenesis, cell growth and proliferation, cell growth, and apoptosis. Novel therapy approaches leadto the direct inhibition of the MYC gene by disruption of MYC/Max complex, MYC destabilization,inhibition of MYC translation and/or transcription. Cetuximab and panitumumab act on the epidermalgrowth factor receptor (EGFR). Cetuximab is an immunoglobin G1 isotype of a monoclonal antibodythat produces antibody-dependent cell-mediated. Panitumumab is an immunoglobulin G2 isotypemonoclonal antibody. This antibody acts on different site of EGFR with different degree of affinity.Genome-wide association studies e.g., TWAS determine the aggregate genotypes. Enhancer is cellspecificgene regulation that cluster for binding sites of a transcription factor with spatially coordinatedto control the expression of one or more specific target genes. In this review, we will summarize noveldrugs in targeting cancerous MYC in haematological malignancy.

Development of sustainable performance index based on production, reproduction, health and longevity for genetic improvement of Sahiwal cattle.

An attempt was made for the first time in India to develop the performance indices for quantifying the relative emphasis to be given to production, reproduction, health and longevity traits for selection and genetic improvement of Sahiwal cattle in a sustainable manner. The study was conducted using information related to various production, reproduction, health and longevity traits spread over a period of 29years in Sahiwal cattle. Using income and expenditure method, the relative economic values for 305days milk yield (305DMY), average daily milk yield (ADMY), calving to first insemination interval (CFI), days open (DO) and longevity (LNG) were estimated as 1, 1, -6.62, -6.62 and 5.96 in Sahiwal cattle. A total of three performance indices were constructed using three-trait combination, that is production, reproduction and longevity before and after excluding the days suffered by the animals. Correlation between the aggregate genotype and index (RIH ) was computed to determine the accuracy of each performance index, and based on highest RIH value, best performance index was identified for selection of Sahiwal cattle. Developed indices were further assessed for robustness by increasing the relative economic values of the traits by 25% and 50%. The performance index (305DMY, DO and LNG) was found to be the best index before as well as after excluding days suffered by the animals, but in later case emphasis to be given to different traits was found to be more balanced. The best index indicated that about 47, 42 and 11% relative emphasis to be assigned to 305days milk yield, days open and longevity for selection of Sahiwal animals for sustainable genetic improvement.

Asymptotic Genetic Response Due to Two-Stage Genomic Index Selection Derived from Correlated or Independent Indices during Both Stages

We developed equations to predict the asymptotic response due to two-stage selection, where first-stage selection was performed by using GEBVs based solely on genotypes and second-stage selection was performed with GEBVs that combined genotypes and phenotypes. The situation that we considered involved four-path selection executed as sires to breed sons, sires to breed daughters, dams to breed sons, and dams to breed daughters. We established two procedures to predict the response. The first incorporated correlated indices during the first and second-stage selections of two-stage selection. The other procedure used independent indices during two-stage selection. The response per generation in the initial generation was greater for the correlated indices than for the independent indices. However, the asymptotic response per generation was slightly greater for the independent indices than for the correlated indices. The asymptotic response per generation was lower during two-stage selection than during single-stage selection. However, the asymptotic response per year was greater for two-stage selection than for single-stage selection. In addition, that trend was more conspicuous when the economic weight was 1:3 for the first (h2 =0.3) to second (h2 =0.05) index trait compared with economic weights of 1:1 and 3:1. However, the magnitude of the response to the aggregate genotype-relative not to single-stage selection but to absolute magnitude was greater at an economic weight of 3:1 than at those of 1:1 and 1:3. The reduction in genetic variance from the initial to an asymptotic generation was greater for a scenario where young parents selected at the first-stage accounted for 30% of all parents in two-stage selection than where they accounted for 70%. The reduction in genetic variance of the aggregate genotype over generations was smaller for independent indices than correlated indices during two-stage selection. Our new formula for predicting genetic response applies to any combination of accuracies of GEBVs and intensities of selection. Therefore, the formula presented is a general equation for predicting genetic response over generations due to two-stage genomic index selection.

Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis.

Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.

Index selection as a key in the selection process for pigs

Submitted 2020-06-30 | Accepted 2020-08-09 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.74-78 Farrowing records of the Duroc, Hampshire and Pietrain breed were analysed by means of a three-trait repeatability animal model. The examined traits were number of piglets born alive (NBA), number of weaned piglets (NWE) and litter weight at weaning (LWWE). Heritability estimates were 0.10, 0.08 and 0.12 for NBA, NWE and LWWE, respectively. The ratios of the permanent environmental variance to the phenotypic variance were 0.08, 0.05 and 0.03 for NBA, NWE and LWWE, respectively. Using the estimated breeding values a desired index was constructed in order to improve each trait by one additive genetic standard deviation. NWE and NBA have a high correlation and range within breeds from 0.87 to 0.91, but LWWE had poor to moderate correlation with NBA and NWE (0.13 to 0.59). Animals ranking based on index showed better genetic merit for Duroc breed and the index scores ranged from 49.77 to 186.56. In the case of Hampshire and Pietrain breeds somewhat lower index scores (30.92 to 165.97) were observed. The estimated genetic trend for NBA was highest for the Pietrain breed (0.02), but for Duroc and Hampshire breeds the estimates were zero and negative (-0.01). For NWE the estimated genetic trends were practically zero for Duroc and Pietrain breeds and it was 0.02 for Pietrain. LWWE showed highest genetic trend for Duroc breed (0.17) but it was lower (0.09) for Pietrain and and negative (-0.24) for Hampshire. Keywords: pig, selection, breeding value, desired gain index, aggregate genotype References Brascamp E.W. (1984). Selection indices with constraints. Animal Breeding Abstracts 52, 645-654. Groeneveld E, Kovac M, Mielenz N. (2008). VCE User’s guide and reference manual. Version 6.0. Neustadt, Germany: Institute of Farm Genetics; p. 1–125. Groeneveld E. (1990). PEST Users’ manual. Institute of animal husbandry and animal behaviour Neustadt: Federal Research Centre. Hamann, H., Steinheuer, R., & Distl, O. (2004). Estimation of genetic parameters for litter size as a sow and boar trait in German herdbook Landrace and Pietrain swine. Livestock Production Science, 85(2-3), 201-207. https://doi.org/10.1016/s0301-6226(03)00135-0 Hungarian Pig Breeders Association (2017). Pig performance testing code. Budapest, 1-39. Chen, P., Baas, T. J., Mabry, J. W., Koehler, K. J., & Dekkers, J. C. M. (2003). Genetic parameters and trends for litter traits in US Yorkshire, Duroc, Hampshire, and Landrace pigs. Journal of animal science, 81(1), 46-53. https://doi.org/10.2527/2003.81146x Irgang, R., Favero, J. A., & Kennedy, B. W. (1994). Genetic parameters for litter size of different parities in Duroc, Landrace, and Large White sows. Journal of Animal Science, 72(9), 2237-2246. https://doi.org/10.2527/1994.7292237x Nagy, I. (2017). Quantitative genetic studies in multiparous species. DSc. Thesis. 1-150. Nath, M., Singh, B.P., Kataria, M.C. Singh, R.V. (2002). MIX: A software for construction of multi-trait selection index. In: Proc. of the 7th World’s Congress on Genetics Applied to Livestock Production. August 19–23, 2002, Montpellier, France. Skorupski, M. T., Garrick, D. J., & Blair, H. T. (1996). Estimates of genetic parameters for production and reproduction traits in three breeds of pigs. New Zealand Journal of Agricultural Research, 39(3), 387-395. https://doi.org/10.1080/00288233.1996.9513198 Â

Principal components for morphometric traits in Campolina horses.

Principal component analysis (PCA) was applied to evaluate the genetic variability and relationship between 15 morphometric traits in 91,483 Campolina horses, as well as to propose an index based on an aggregate genotype that promotes a particular selection objective. PCA was applied to the genetic (co)variance matrix among variables. After calculation of the principal components, the breeding values were estimated to obtain an index related to the component that explained most of the variation. The first principal component (PC1) accounted for 97.8% of the total additive genetic variance of the traits. PC1 contrasted animals in terms of body size (wither, back and croup heights, body length, and thoracic girth). PC1 traits showed higher heritabilities and positive and high genetic correlations. An index was obtained (HPC1) with the combination of the breeding values of different traits from PC1 which permitted the use of this index as an aggregate genotype to identify the best animals for selection. The second principal component (PC2) was much smaller and grouped traits related to head and neck morphometry, among others. These traits are commonly used for breed qualification, a fact explaining the small variation in this component. An evaluation of the effect of HPC1 on withers height in two-trait analysis was also made which provided positive genetic correlations of moderate to high magnitude (0.73-0.86), indicating that selection for this trait (important in Campolina horses) is accounted for in the index. The use of HCP1 could be considered as an important alternative to selection since it does not consider a single trait but rather a set of variables that capture body proportions.

Expected Genetic Gain for Selection of Preweaning Growth Traits in Egyptian Buffaloes

Data on 1946 buffalo calves born between 2000 and 2016 at Mahallet Mousa farms belonging to Animal Production Research Institute, were utilized in this study. Genetic parameters of birth weight (BW), preweaning daily gain (PWDG) and weaning weight (WW) were estimated using MTDFREML. The different selection indices for previous traits were constructed by Matlab program. Means of BW, PDWG and WW were 37.40 kg, 493.53 g and 90.37 kg, respectively. The heritability estimates for above-mentioned traits were 0.24, 0.26 and 0.29, respectively. The genetic correlations among the mentioned growth traits were ranged from 0.18 to 0.79. Corresponding phenotypic correlations among same traits were ranged from 0.13 to 0.48. The expected genetic gains for direct selection of BW, PWDG and WW were varied between 0.23 -1.18 kg, 10.80 – 23.74 g and 0.50 - 3.06 kg, respectively. Comparison among selection indices indicated that the highest expected genetic gains for PWDG (8.73 g) and WW (3.50 kg) were obtained from the index I4 included (PDWG and WW). Moreover, the accuracy of this index was closest to the highest index of accuracy. Thus, selection based on index I4 could be improve the response of aggregate genotype for preweaning growth traits of Egyptian buffalo calves.

Comparison of long-term effects of genomic selection index and genomic selection using different Bayesian methods

Aggregate genotype in selection programs commonly includes multiple traits, and response to individual traits have importance in addition to their inclusion in the aggregate genotype. The availability of the effect of all markers on genomic selection is an opportunity to select animals based on the desired marker alleles that affect different traits. Genomic selection index (GSI) method divides markers into different groups based on their effects on different traits. It combines and weighs the genomic breeding value of each marker group into an overall index such that maximize response in the aggregate genotype. The primary purpose of this study was to evaluate the long-term effect of GSI compared to genomic selection on genetic progress of a two-trait aggregate genotype and its constituent traits using different Bayesian methods. The results showed that when the correlation between traits was negative, the GSI method yielded higher gain than the genomic selection for the low heritable trait and the aggregate genotype. However, the GSI superiority was decreased as the correlation between the traits increased and a similar response to the genomic selection was obtained for the correlation of 0.5. A higher reduction in genetic variance was observed for scenarios that cause a more significant response to selection. The inbreeding rate was relatively low in all scenarios. The results of this study suggest the use of GSI rather than genomic selection, especially when traits with low heritability are present in the aggregate genotype, and have a negative correlation with other high heritability traits.

Asymptotic four-path genomic index selection response with or without accounting for the uncertainty of the predictions

Equations were developed to predict the equilibrium response to the aggregate genotype in units of genetic standard deviation by using selection index composed of GEBVs. A situation was considered in which four-path selection was executed as sires to breed sons, sires to breed daughters, dams to breed sons, and dams to breed daughters. In practice, index selection comprising standardized GEBVs is often used for the aggregate genotype in units of genetic standard deviation in accordance with the standardization of the true genetic value in the aggregate genotype. Economic weights of the breeding goal in units of genetic standard deviation are used as the coefficients of the standardized GEBVs of the practical index. However, the variance of GEBV necessary to standardize GEBV changes from generation to generation. It meant that economic weights in the breeding goal corresponding to the practical index changed during generations, although relative economic weights in the breeding goal among the component traits should be the same during generations. The practical index assumes GEBVs as estimates with no prediction errors, although GEBVs are estimates with prediction errors, which resulted in overestimation of the selection accuracy of the practical index. The asymptotic genetic gain from the practical index reacted sharply to the uncertainty in GEBVs compared with the theory-based index. Consequently, especially in practical breeding, the correct aggregate genotype in selection index theory associated with the practically used index must be examined and breeding goal should not include a component related to the uncertainty in GEBVs, because economic weights fluctuate during generations in line with the uncertainty in GEBVs.

Possibilities for a specific breeding program for organic dairy production

Organic dairy production differs from conventional dairy production in many aspects. However, breeding programs for the 2 production systems are the same in most countries. Breeding goals (BG) might be different for the 2 production systems and genotype × environment interaction may exist between organic and conventional dairy production, both of which have an effect on genetic gain in different breeding strategies. Other aspects also need to be considered, such as the application of multiple ovulation and embryo transfer (MOET), which is not allowed in organic dairy production. The general aim of this research was to assess different environment-specific breeding strategies for organic dairy production. The specific aim was to study differences in BG weights and include the effect of genotype × environment interaction, MOET, and the selection of breeding bulls from the conventional environment. Different scenarios were simulated. In the current scenario, the present-day situation for dairy production in Denmark was emulated as much as possible. The BG was based on a conventional dairy production system, MOET was applied in both environments, and conventional bulls could be selected as breeding bulls in the organic environment. Four alternative scenarios were simulated, all with a specific organic BG in the organic breeding program but differences in the usage of MOET and the selection of conventional bulls as breeding bulls. Implementation of a specific BG in organic dairy production slightly increased genetic gain in the aggregate genotype compared with the breeding program that is currently implemented in organic dairy production. Not using embryo transfer or only selecting breeding bulls from the organic environment decreased genetic gain in the aggregate genotype by as much as 24%. However, the use of embryo transfer is debatable because this is not allowed according to current regulations for organic dairy production. Assessing genetic gain on trait levels showed that a significant increase for functional traits was possible compared with the current breeding program in the organic environment without a decrease in genetic gain in the aggregate genotype. This difference on trait level was even more present when selection of conventional bulls as breeding bulls in the organic environment was not possible. This finding is very relevant when breeding for the desired cow in organic dairy production.

Asymptotic response to four-path selection due to index and single trait selection according to genomically enhanced breeding values

Formulas were developed for calculating the asymptotic response from index and single trait selection for differential selection among male and female parents and of parents to produce male versus female replacements (four-path programs). The formulas were then used to study the effects on selection response in several scenarios. Index selection based on genomically enhanced breeding value (GEBV) achieved substantial genetic improvement even for low-heritability traits when their relative economic weights are greater than those of the other moderate-heritability index traits in spite of positive or negative genetic correlation between the two index traits. Index selection overwhelmed single trait selection with regard to selection response, particularly when genetic correlation was negative between the two index traits. When the reduction in genetic (co)variance after selection was ignored, the initial genetic response overestimated the asymptotic response and the overestimation was more remarkable for a moderately heritable trait than for a lowly heritable trait. The overestimation for the moderately heritable trait was more conspicuous in single trait selection than in index selection. The underestimation of the initial response during generation 0 due to the index selection was found for the lowly heritable traits when the lowly heritable traits had negative genetic correlation with the other moderately heritable traits and when economic weights of the lowly heritable traits were substantially small as compared to the other moderately heritable traits. The accounting percentage from a selection path from dams to breed sons for the total response per generation increased approximately from 30% to 39% when the selection percentage decreased from 10% to 1%. Applying selection based on GEBV in a selection path from dams to breed sons would make a great contribution to genetic improvement. Equilibrium correlated response to an unselected trait due to index selection became negative when initial genetic correlation during generation 0 between aggregate genotype and an unselected trait was negative. Consequently, we need to construct reference population for all the traits connected with the breeding goal in order to enable the genomic evaluations of all the traits connected with the breeding goal.

Economic optimization of full-sib test group size and genotyping effort in a breeding program for Atlantic salmon

BackgroundBreeding companies may want to maximize the rate of genetic gain from their breeding program within a limited budget. In salmon breeding programs, full-sibs of selection candidates are subjected to performance tests for traits that cannot be recorded on selection candidates. While marginal gains in the aggregate genotype from phenotyping and genotyping more full-sibs per candidate decrease, costs increase linearly, which suggests that there is an optimum in the allocation of the budget among these activities. Here, we studied how allocation of the fixed budget to numbers of phenotyped and genotyped test individuals in performance tests can be optimized.MethodsGain in the aggregate genotype was a function of the numbers of full-sibs of selection candidates that were (1) phenotyped in a challenge test for sea lice resistance (2) phenotyped in a slaughter test (3) genotyped in the challenge test, and (4) genotyped in the slaughter test. Each of these activities was subject to budget constraints. Using a grid search, we optimized allocation of the budget among activities to maximize gain in the aggregate genotype. We performed sensitivity analyses on the maximum gain in the aggregate genotype and on the relative allocation of the budget among activities at the optimum.ResultsMaximum gain in the aggregate genotype was €386/ton per generation. The response surface for gain in the aggregate genotype was rather flat around the optimum, but it curved strongly near the extremes. Maximum gain was sensitive to the size of the budget and the relative emphasis on breeding goal traits, but less sensitive to the accuracy of genomic prediction and costs of phenotyping and genotyping. The relative allocation of budget among activities at the optimum was sensitive to costs of phenotyping and genotyping and the relative emphasis on breeding goal traits, but was less sensitive to the accuracy of genomic prediction and the size of the budget.ConclusionsThere is an optimum allocation of budget to the numbers of full-sibs of selection candidates that are phenotyped and genotyped in performance tests that maximizes gain in the aggregate genotype. Although potential gains from optimizing group sizes and genotyping effort may be small, they come at no extra cost.

Technical note: An approach to derive breeding goals from the preferences of decision makers.

This paper deals with the use of the Choquet integral to identify breeding objectives and construct an aggregate genotype. The Choquet integral can be interpreted as an extension of the aggregate genotype based on profit equations, substituting the vector of economic weights by a monotone function, called capacity, which allows the aggregation of traits based, for instance, on the preferences of decision makers. It allows the aggregation of traits with or without economic value, taking into account not only the importance of the breeding value of each trait but also the interaction among them. Two examples have been worked out for pig and dairy cattle breeding scenarios to illustrate its application. It is shown that the expression of stakeholders' or decision makers' preferences, as a single ranking of animals or groups of animals, could be sufficient to extract information to derive breeding objectives. It is also shown that coalitions among traits can be identified to evaluate whether a linear additive function, equivalent of the Hazel aggregate genotype where economic values are replaced by Shapley values, could be adequate to define the net merit of breeding animals.

Mendelian sampling covariability of marker effects and genetic values.

BackgroundMeasures of the expected genetic variability among full-sibs are of practical relevance, such as in the context of mating decisions. An important application field in animal and plant breeding is the selection and allocation of mates when large or small amounts of genetic variability among offspring are desired, depending on user-specific goals. Estimates of the Mendelian sampling variance can be obtained by simulating gametes from parents with known diplotypes. Knowledge of recombination rates and additive marker effects is also required. In this study, we aimed at developing an exact method that can account for both additive and dominance effects.ResultsWe derived parent-specific covariance matrices that exactly quantify the within-family (co-)variability of additive and dominance marker effects. These matrices incorporate prior knowledge of the parental diplotypes and recombination rates. When combined with additive marker effects, they allow the exact derivation of the Mendelian sampling (co-)variances of (estimated) breeding values for several traits, as well for the aggregate genotype. A comparative analysis demonstrated good average agreement between the exact values and the simulation results for a practical dataset (74,353 German Holstein cattle).ConclusionsThe newly derived method is suitable for calculating the exact amount of intra-family variation of the estimated breeding values and genetic values (comprising additive and dominance effects).

The potentials of using selection index in the assessment of breeding values of Holstein breeds in Serbia

The conducted research was aimed at constructing equations of selection index that would be used in the selection of the Holstein-Friesian breed animals in Serbia. The construction of the selection index includes the most important milk traits observed in standard lactation: milk yield (MY305), milk fat content (% MF305) and protein content (% MP305). The variance and covariance necessary for the construction of selection index are calculated using the mixed model by the method of least squares. The economic value of traits is expressed as a ratio of relative changes in costs per unit of traits included in the selection index. Livestock included in the research produced, in the first standard lactation, an average of 7681 kg of milk with 3.58% milk fat and 3.28% protein. The equation of the selection index presented in the paper is selected from the group of equations of selection index, as an equation with the highest correlation between the equation and the aggregate genotype, which amounted to 0.2156.