Genetic Standard Deviation Research Articles

The effect of phenotyping, adult selection, and mating strategies on genetic gain and rate of inbreeding in black soldier fly breeding programs

BackgroundThe aim of this study was to compare genetic gain and rate of inbreeding for different mass selection breeding programs with the aim of increasing larval body weight (LBW) in black soldier flies. The breeding programs differed in: (1) sampling of individuals for phenotyping (either random over the whole population or a fixed number per full sib family), (2) selection of adult flies for breeding (based on an adult individual’s phenotype for LBW or random from larvae preselected based on LBW), and (3) mating strategy (mating in a group with unequal male contributions or controlled between two females and one male). In addition, the numbers of phenotyped and preselected larvae were varied. The sex of an individual was unknown during preselection and females had higher LBW, resulting in more females being preselected.ResultsSelecting adult flies based on their phenotype for LBW increased genetic gain by 0.06 genetic standard deviation units compared to randomly selecting from the preselected larvae. Fixing the number of phenotyped larvae per family increased the rate of inbreeding by 0.15 to 0.20% per generation. Controlled mating compared to group mating decreased the rate of inbreeding by 0.02 to 0.03% per generation. Phenotyping more than 4000 larvae resulted in a lack of preselected males due to the sexual dimorphism. Preselecting both too few and too many larvae could negatively impact genetic gain, depending on the breeding program.ConclusionsA mass selection breeding programs in which the adult fly is selected based on their larval phenotype, breeding animals mate in a group and sampling larvae for phenotyping at random over the whole population is recommended for black soldier flies, considering the positive effect on rates of genetic gain and inbreeding. The number of phenotyped and preselected larvae should be calculated based on the expected female weight deviation to ensure sufficient male and female candidates are selected.

A bio-economic model for estimating economic values of important production traits in the black soldier fly (Hermetia illucens)

Abstract Black soldier fly (BSF) larvae are exceptionally efficient at converting otherwise unusable waste products, while having a minimal impact on the environment. To optimise the production of BSF larvae, selective breeding could be used. With selective breeding, the best individuals to breed the next generation are selected, for example, with a breeding goal. This study aimed to make the first step towards defining such a breeding goal, by estimating the economic values (EV) of traits that are important for the production of BSF larvae. EV were estimated for larval mass on day 15, feed intake, feed conversion ratio, growth rate, dry mass-, protein- and fat content, development time, eggs per fly, egg hatching rate, and larval mortality. A bio-economic model was used to estimate the profit of a BSF production system that produces 1000 kg of wet larvae at harvesting. The EV of a trait was estimated as the increase in profit when the trait value was increased by 10%, whilst the other traits remained unchanged. EV were expressed per 10% increase, and changes in revenues and costs were specified. The results showed traits with the highest EV were related to composition (dry mass-, and protein-content) and growth (larval mass, development time, growth rate). Traits related to reproduction and flies (development from pupa to fly, number of eggs) had the lowest EV. Due to the lack of information on genetic parameters, the EV are expressed per +10% of the mean value of a trait. In the future, when more information is available, the EV should be expressed per genetic standard deviation. In conclusion, this is the first study that estimated EV for an insect species. The results are a first step towards developing a more sophisticated breeding program for optimizing BSF production.

Evolutionary programming approach for securing medical images using genetic algorithm and standard deviation

Evolutionary programming approach for securing medical images using genetic algorithm and standard deviation

Inbreeding Depression and Purging for Meat Performance Traits in German Sheep Breeds.

This study provides estimates on genetic parameters, inbreeding depression and purging for meat performance measures from 25 German sheep breeds. All German meat, merino sheep breeds and breeds of other breeding directions with a sufficient number of pedigree and performance data were included in this study. Phenotypic traits retrieved from the national database OviCap were evaluated: daily weight gain, meatiness score and ultrasound measurements for muscle and fat thickness. We employed animal models to estimate heritability, variance and covariance components for these meat performance traits as well as inbreeding depression and purging. The heritabilities, on average, reached estimates of 0.55, 0.34, 0.53 and 0.61 for daily weight gain, meatiness score and ultrasound measurements for muscle and fat thickness, respectively. We estimated the linear regression slopes for the individual rate of inbreeding, new and ancestral inbreeding, as well as the inbreeding coefficient and its interaction with the inbreeding coefficient of Ballou, employing animal models with non-genetic effects and the additive genetic effect of the animal. Across all breeds, inbreeding was only significant for daily weight gain, whereas for all other traits, estimates were not significant. Within sheep breeds, we found significant inbreeding depression for daily weight gain in German Mutton Merino and German Blackheaded Mutton as well as for the meatiness score in German Whiteheaded Mutton. Significant effects for purging, based on ancestral inbreeding and the interaction effect of the classical inbreeding coefficient with the inbreeding coefficient of Ballou, were not obvious either across or within any sheep breed. A 1% increase in inbreeding significantly decreased the phenotypic trait median of daily weight gain across all sheep breeds by 0.50% and 0.70% of phenotypic and genetic standard deviation, respectively. Purging effects due to ancestral inbreeding were not significant in any breed or across breeds. The results of this study may indicate that inbreeding depression may be more harmful in traits under stronger selection than in traits that exert low selection pressure. The results of this study demonstrate the different effects that result in meat performance traits due to inbreeding. With increasing rates of inbreeding and critical effective population sizes, selection intensity for breeding objectives has to be critically reviewed for each sheep breed. Inbreeding depression and purging should be evaluated in order to prevent a decrease in trait means due to inbreeding and to determine whether detrimental alleles are eliminated.

Impact of Inbreeding and Ancestral Inbreeding on Longevity Traits in German Brown Cows.

A recent study on the population structure of the German Brown population found increasing levels of classical and ancestral inbreeding coefficients. Thus, the aim of this study was to determine the effects of inbreeding depression and purging on longevity traits using classical and ancestral inbreeding coefficients according to Kalinowski (2002) (Fa_Kal, FNew), Ballou (1997) (Fa_Bal), and Baumung (2015) (Ahc). For this purpose, uncensored data of 480,440 cows born between 1990 and 2001 were available. We analyzed 17 longevity traits, including herd life, length of productive life, number of calvings, lifetime and effective lifetime production for milk, fat, and protein yield, the survival to the 2nd, 4th, 6th, 8th, and 10th lactation number, and the culling frequencies due to infertility, or udder and foot and leg problems. Inbreeding depression was significant and negative for all traits but for culling due to udder and to foot and leg problems. When expressed in percentages of genetic standard deviations, inbreeding depression per 1% increase in inbreeding was -3.61 to -10.98%, -2.42 to -2.99%, -2.21 to -4.58%, and 5.13% for lifetime production traits, lifetime traits, survival rates, and culling due to infertility, respectively. Heterosis and recombination effects due to US Brown Swiss genes were positive and counteracted inbreeding depression. The effects of FNew were not significantly different from zero, while Fa_Kal had negative effects on lifetime and lifetime production traits. Similarly, the interaction of F with Fa_Bal was significantly negative. Thus, purging effects could not be shown for longevity traits in German Brown. A possible explanation may be seen in the breed history of the German Brown, that through the introgression of US Brown Swiss bulls ancestral inbreeding increased and longevity decreased. Our results show, that reducing a further increase in inbreeding in mating plans is advisable to prevent a further decline in longevity due to inbreeding depression, as purging effects were very unlikely in this population.

Breeding objectives for Central Highland goats using participatory and bio-economic modelling approaches.

The breeding objectives of Central Highland goats rearing under a low-input production system were defined through a participatory proportional piling method and bio-economic model. Additionally, the economic values and relative economic value of the breeding objective traits were derived. A participatory proportional piling method was used to estimate the relative weights of farmers attached to a list of goat traits identified, and the relative weights were statistically evaluated using a generalized multinomial logit model analysis. A bio-economic model was used to compute the economic values of the identified traits. The most important traits for selection of does according to farmer's preference were body size, coat colour, post-weaning growth rate and weaning rate with a relative weight (odds ratio) of 1.58, 1.38, 1.37 and 1.13, respectively. Goats with dark red followed by light red coat colour were the most preferred (p < 0.001) by goat keepers compared with white-coloured goats. Farmers were more likely (p < 0.001) to allocate higher scores for does-bearing twins than for single and triplet-bearing does. Using the bio-economic model (economic value and relative economic value), post-weaning growth rate, weaning rate, and six-month weight (body size) were identified as the most important traits and if the mean of these traits is changed by one genetic standard deviation, the change in profit will range from 2.06 to 3.03 $ doe-1 year-1 . Therefore, the most important traits for the selection of Central Highland goats according to the economic-based method were post-weaning weight gain, weaning rate and body size (six-month weight). Besides, coat colour was the second preferred trait by goat keepers next to body size. Thus, this aesthetical trait should be included in the designed breeding programme besides economically important quantitative traits. The combination of the participatory proportional piling method and bio-economic model would give better insights to explore the trait preferences of farmers and enhance profitability. The economic values of traits estimated in this study can be used for the construction of selection indices for Central Highland goats.

Evaluating the potential impact of selection for the A2 milk allele on inbreeding and performance in Australian Holstein cattle

Selection decisions are generally based on estimated breeding values (EBV) for a combination of traits that are polygenic (e.g. milk production). However, in some cases, there is additional intense selection for a single allele, or SNP, for a perceived benefit, such as selection for polled or A2 milk. Using a dataset where the A2 mutation was imputed using a reference population with whole genome sequence, we tested the hypothesis that intense selection in Australian Holstein cattle for the A2 allele in the β-casein gene may have resulted in increased inbreeding. We also estimated the average difference in performance between animals homozygous for the A1 or A2 allele for a range of traits. Using high-density genotypes we compared differences in genome-wide and regional inbreeding between Holstein cows homozygous for either the A1 or A2 β-casein alleles i.e. A1/A1 or A2/A2. This study shows that between the years 2000 to 2017, the frequency of the A2/A2 genotype increased by 20% in Holstein cows (from 32% to 52%). Our results suggest that selection for homozygosity at the β-casein A2 allele has increased inbreeding both across the genome and on chromosome 6 in A2/A2 Holstein cows. Animals that were A2/A2 were twice as likely to have a run of homozygosity of at least 1Mb long across the β-casein locus compared to animals that were A1/A1. Cows that are homozygous for the A2 allele had an average protein yield EBV advantage of 0.24 genetic standard deviations (SD) compared to A1/A1 homozygous cows. In contrast, A2/A2 homozygous animals were on average 0.2 genetic SD inferior on fertility EBV. As a result, the difference in the overall economic index (that includes traits contributing to profitability) there was only a small advantage of 0.05 SD for A2/A2 cows compared to A1/A1 cows. However, strong selection for the A2 allele has likely led to a higher level of regional and overall inbreeding which in the long term could harm genetic progress for some or all economic traits. Therefore, applying approaches that mitigate rapid inbreeding while selecting for preferred alleles and quantitative traits may be desirable.

Survival analysis of productive life in Florida dairy goats using a Cox proportional hazards model.

Longevity is an economically important trait, since extending the functional life of a doe would allow us to keep the most productive females in the herd as long as possible, and this could result in the increased profitability of dairy farms. Thus, the objectives of this study were to determine the most important factors that influence the length of productive life (LPL) of female Florida goats and to estimate its genetic additive variance using a Cox proportional hazards model. The data consisted of 70,695 productive life records from 25,722 Florida females kidding between 2006 and 2020. A total of 19,495 does had completed their productive life while 6227 (24.2%) does had censored information. The pedigree contained information on 56,901 animals. The average censoring age and average failure age after first kidding for LPL were 36 and 47 months respectively. The model included, as time-independent effects, the age at first kidding and the interaction between herd, year and season of birth of the doe, and as time-dependent effects, the age at kidding, the interaction between herd, year and season of kidding, the within-herd class of milk production deviation, and the interaction between the lactation number and the stage of lactation. All fixed effects had a significant effect on LPL (p < 0.05). Does with older ages at the first kidding and an earlier age at kidding were at higher risk of being culled. A large difference among herds was observed in terms of culling risk, which highlighted the importance of adequate management practices. Also, high-producing does were less likely to be culled. The estimate of the additive genetic variance was 1.844 (in genetic standard deviation), with a heritability estimate of 0.58 ± 0.012. The results of this study are expected to contribute to the development of a genetic model for genetic evaluation of the length of the productive life of Spanish dairy goat breeds.

Scaling of estimated breeding values for stand productivity in the Australian Pinus radiata breeding program

ABSTRACT The radiata pine tree improvement program in Australia estimates breeding values for selection criteria traits (SCTs) such as diameter at breast height and height, as measured in progeny trials. The data in each trial are standardised so that estimated breeding values (EBVs) are expressed in units of additive genetic standard deviations. EBVs for harvest-age breeding objective traits (BOTs), such as stand volume mean annual increment (VMAI), are predicted from the SCT EBVs and expressed in measurement units. BOTs are not routinely measured in progeny trials because it would prolong the generation interval. However, the slope of the relationship between VMAI and BOT EBV provides an estimate of the scale of VMAI EBVs. This slope was estimated using 26 block-plot realised-gain trials with stand total produced basal area (TPBA) measurements near age ten years and 11 trials with final VMAI measurements between 15 and 25 years extrapolated to a harvest age of 25. In all trials, EBVs were positively correlated with growth, demonstrating substantial realised genetic gains. At age ten years, on average, one standard deviation of BOT EBV corresponded to a realised gain in TPBA of 3.7 m2 ha−1 and an additive coefficient of variation (CVA) of 11.2%. At the harvest age of 25 years, one standard deviation in EBV corresponded on average to realised gains in VMAI of 4.2 m3 ha−1 y−1 and a CVA of 15.0%. Methods for incorporating estimated genetic gains into growth and yield modelling and forest valuations are discussed.

A large proportion of genetic variation in cow and ewe body composition is independent of yearling composition

Context Commercial beef and sheep producers have adopted using condition score for management of cows and ewes to maximise productivity. Significant premiums are being paid for bulls and rams with higher-fat breeding values based on young animal measurements, with the aim of increased resilience in adult female progeny. Aims The aim of this study was to quantify the relationship between adult body condition score and young muscle and fat and also the genetic variation in condition that is independent of yearling traits. Methods Published genetic parameters for four large data sets from Angus and tropically adapted cattle, and composite and Merino sheep were used to partition genetic variation in adult condition that is associated with, or independent of, young composition measures. Key results One genetic standard deviation in young muscle or fat was associated with approximately just 0.1 adult body condition scores. Approximately ¾ of the genetic variation in adult body condition score (BCS) is independent of genetic variation in young weight and composition traits. Conclusions and implications Producers need to be careful with premiums paid for bulls and rams with superior yearling composition traits with the aim of changing adult BCS. The best way to achieve this is for seedstock breeders to record and report breeding values for cow and ewe BCS.

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.

Defining smallholders' breeding objectives for common Tigray highland sheep in Northern Ethiopia.

A sound breeding objective is a basis for genetic improvement in the overall economic merit of farm animals. This study aimed to define smallholders' sheep breeding objectives using a conjoint-based choice experiment and bio-economic model. Multinomial logistic regression analysis showed differences (p < 0.001) among the derived weights allocated by farmers to body size, twining rate, mothering ability, libido, tail type, colour and lambing interval which formed the high priority traits. The likelihood values of farmer's top three preferred traits for ram were body size (0.659 ± 0.009), tail type (0.325 ± 0.016) and libido (0.247 ± 0.016) while body size (0.459 ± 0.010), twining rate (0.313 ± 0.010) and mothering ability (0.261 ± 0.010) were more preferred traits for selection of breeding ewes. Conjoint-based choice experiment and bio-economic model indicated that live body weight at 6 months of age, twining rate (litter size) and mothering ability (preweaning lamb survival) were the most economically important traits of the breeding ewes. A genetic improvement by one genetic standard deviation (σa ) in these traits resulted in a profit of Ethiopian Birr 36.03-60.47/ewe/year. A fair correlation (r=0.63) was observed between farmers' traits preference (conjoint-based choice card experiment) and the estimated economic value of traits indicating a good relationship between farmers' preferences for traits and economic values of the traits. This would indicate that weighting traits in selection indexes with farmers' trait selection using a conjoint-based choice experiment would direct genetic improvement towards desired profitability. Moreover, the fair correspondence between the two methods suggests that bio-economic modelling, if designed properly considering farmers' concerns, could be used to reflect farmers' breeding objectives. Therefore, for reasonable genetic progress and sheep flock profitability, more attention should be given to litter size, preweaning lamb survival and body weight at 6 months of age as they are preferred by farmers and their economic weights. Further, research on the modalities for complementary use of the two methods to define breeding objectives under smallholder conditions is warranted.

Selection indexes and economic weights applied to runner-peanut breeding

ABSTRACT Runner peanuts are known for their high pod yields, but are late to flowering and pod maturation, and the optimal combination of these traits with pod yield is widely desired for peanut improvement. Selection indexes are useful tools for crop breeding. In this study, seven selection indexes combined with economic weights were used in a peanut population to estimate the superior and balanced genetic gains. Eleven runner genotypes were grown in three environments in the Northeast region of Brazil under a randomized block design with five replicates. The following indices were used: Smith and Hazel, Pesek and Baker, Williams, Elston, Subandi, Cruz, and Mulamba &amp; Mock, in combination with the following economic weights: weight 1 for all evaluated traits, primary and secondary traits, genetic variation coefficient, genetic standard deviation, and b coefficient, obtained via multivariate regression. Although the population is genetically uniform, statistical differences were found, indicating sufficient genetic variability to generate selection progress. The combinations involving earliness traits were not satisfactory for production gains. The index based on the Mulamba &amp; Mock rankings combined with weight 1 for all traits proved the optimal combination, as indicated by the most balanced gains. The cultivars Florunner, Cavalo, LGoPE-06, and LViPE-06 are promising germplasm for ensuring satisfactory selection gains based on production means and high heritability of the most evaluated traits.

Breeding objectives for dairy cattle under low, medium and high production systems in the tropics

A deterministic bio-economic model was developed to estimate economic weights for genetic improvement of lactation milk yield, fat yield, age at first calving, calving interval, mature weight and survival under low, medium and high production systems in the Tropics. Input parameters were obtained from dairy production systems in Kenya which has a tropical environment. The highest proportion of revenue is from the sale of milk followed by sale of heifers, cull cows and sale of male calves under all production systems. On the other hand, feed cost is the most important production cost followed by labour, marketing, reproduction and health costs, respectively. Economic values for the six traits were derived from a profit equation using revenue and production costs per cow per year. The economic values were then discounted using diffusion coefficients which account for differences between traits in the time when the improvement is expressed. Economic weights were robust to changes in input and output prices, changes in feeding strategies, and changes in milk and surplus heifer marketing strategies. Genetic standard deviations were multiplied by economic values to standardise the economic value of traits and to compare their potential for economic response. When expressed as proportion of their sum, these relative economic weights under the low, medium and high production systems for lactation milk yield were 51.36, 59.79 and 63.98%; for fat yield 4.50, 10.69 and 9.05%; for age at first calving 3.16, 2.66 and 0.55%; for calving interval 33.59, 19.88 and 20.05%; for mature weight 1.55, 1.34 and 1.19% and for survival rate 5.84, 5.64 and 5.18%, respectively. The predicted responses followed the same pattern as the relative economic weights. This shows that milk yield and calving interval were most important in all production systems but the value of response for traits differed between production systems with more emphasis on milk yield and less on calving interval in the high production systems. Moderate correlations were estimated between the breeding objective for the low, medium and high production systems. To maximise response in the overall breeding objective, different selection criteria are required for the three production systems.

Divergent selection for natural antibodies in poultry in the presence of a major gene

BackgroundNatural antibodies (NAb) are antibodies that are present in a healthy individual without requiring previous exposure to an exogenous antigen. Selection for high NAb levels might contribute to improved general disease resistance. Our aim was to analyse the genetic background of NAb based on a divergent selection experiment in poultry, and in particular the effect of a polymorphism in the TLR1A gene.MethodsThe study population consisted of a base population from a commercial pure-bred elite white leghorn layer line and seven generations of birds from a High and Low selection line. Birds were selected for total KLH-binding NAb titer (IgTotal). An enzyme-linked immunosorbent assay was performed to determine NAb titers in blood plasma for IgTotal and the antibody isotypes IgM and IgG. NAb titers were available for 10,878 birds. Genotypes for a polymorphism in TLR1A were determined for chickens in generations 5, 6 and 7. The data were analysed using mixed linear animal models.ResultsThe heritability estimate for IgM was 0.30 and higher than that for IgG and IgTotal (0.12). Maternal environmental effects explained 2 to 3% of the phenotypic variation in NAb. Selection for IgTotal resulted in a genetic difference between the High and Low line of 2.4 titer points (5.1 genetic standard deviation) in generation 7. For IgM, the selection response was asymmetrical and higher in the Low than the High line. The frequency of the TLR1A C allele was 0.45 in the base population and 0.66 and 0.04 in generation 7 of the High and Low line, respectively. The TLR1A polymorphism had large and significant effects on IgTotal and IgM. Estimated genotypic effects suggest full dominance of the TLR1A C allele. Significant TLR1A by generation interactions were detected for IgM and IgTotal.ConclusionsThe effect of a polymorphism in the TLR1A gene on IgTotal and IgM NAb was confirmed. Furthermore, we provide experimental verification of changes in allele frequencies at a major gene with dominant gene action on a quantitative trait that is subjected to mass selection. TLR1A by generation interactions indicate sensitivity to environmental factors.

Contribution of genetic variability to phenotypic differences in on-farm efficiency metrics of dairy cows based on body weight and milk solids yield

Milk solids per kilogram of body weight (BW) is growing in popularity as a measure of dairy cow lactation efficiency. Little is known on the extent of genetic variability that exist in this trait but also the direction and strength of genetic correlations with other performance traits. Such genetic correlations are important to know if producers are to consider actively selecting cows excelling in milk solids per kilogram of BW. The objective of the present study was to use a large data set of commercial Irish dairy cows to quantify the extent of genetic variability in milk solids per kilogram of BW and related traits but also their genetic and phenotypic inter-relationships. Mid-lactation BW and body condition score (BCS), along with 305-d milk solids yield (i.e., fat plus protein yield) were available on 12,413 lactations from 11,062 cows in 85 different commercial dairy herds. (Co)variance components were estimated using repeatability animal linear mixed models. The genetic correlation between milk solids and body weight was only 0.05, which when coupled with the observed large genetic variability in both traits, indicate massive potential to select for both traits in opposite directions. The genetic correlations between both milk solids and BW with BCS; however, need to be considered in any breeding strategy. The genetic standard deviation, heritability, and repeatability of milk solids per kilogram of BW was 0.08, 0.37, and 0.57, respectively. The genetic correlation between milk solids per kilogram of BW with milk solids, BW, and BCS was 0.62, -0.75, and -0.41, respectively. Therefore, based on genetic regression, each increase of 0.10 units in genetic merit for milk solids per kilogram of BW is expected to result in, on average, an increase in 16.1 kg 305-d milk solids yield, a reduction of 25.6 kg of BW and a reduction of 0.05 BCS units (scale of 1-5 where 1 is emaciated). The genetic standard deviation (heritability) for 305-d milk solids yield adjusted phenotypically to a common BW was 27.3 kg (0.22). The genetic correlation between this adjusted milk solids trait with milk solids, BW, and BCS was 0.91, -0.12, and -0.26, respectively. Once also adjusted phenotypically to a common BCS, the genetic standard deviation (heritability) for milk solids adjusted phenotypically to a common BW was 26.8 kg (0.22) where the genetic correlation with milk solids, BW and BCS was 0.91, -0.21, and -0.07, respectively. The genetic standard deviation (heritability) of BW adjusted phenotypically for differences in milk solids was 35.3 kg (0.61), which reduced to 33.2 kg when also phenotypically adjusted for differences in BCS. Results suggest considerable opportunity exists to change milk solids yield independent of BW, and vice versa. The opportunity is reduced slightly once also corrected for differences in BCS. Inter-animal BCS differences should be considered if selection on such metrics is contemplated.

Development of a routine genetic evaluation and a total merit index for sheep breeds with focus on meat production

• A routine genetic evaluation was developed for sheep with focus on meat production. • Breeding values for individual traits, a Meat and a Fitness index as well as a Total merit index are calculated weekly. • Traits are weighted in a way so that the theoretical selection response is positive for all traits. The introduction of a routine genetic evaluation for specialized meat sheep (Blackheaded meat, Suffolk, Texel, Ile de France, Dorper, and Berrichon du Cher) and land sheep with focus on meat production (Merinoland) is described. Even though performance recording has existed for several production and fitness related traits and years, breeding values have not been officially available for sheep in Austria until 2017. Meat performance traits, recorded for purebred animals of both sexes, include average daily gain, fat depth and eye muscle depth. Historic computer tomography performance recording results serve as auxiliary traits. Regarding functional traits, lambing interval, number of lambs born and number of lambs born alive are considered. The routine genetic evaluation is carried out twice a year, at the beginning of January and at the end of June. Additionally, breeding values and reliabilities are calculated at weekly intervals but only uploaded if the change in reliability exceeds 5%-points or a new individual performance recording result is available. All estimated breeding values are standardised to relative breeding values with a mean of 100 (rolling base including rams aged 5–15 years), and a genetic standard deviation of 12, higher values are favourable. Apart from breeding values for individual traits, subindices for meat and fitness traits as well as a Total merit index (TMI) are calculated. For the calculation of the Meat index, the relative weights (in %) for average daily gain (direct) : muscle depth : fat depth are 36 : 62 : 2 for Merinoland and 44 : 52 : 4 for meat sheep. For fat depth, the breeding values are transformed to an intermediate optimum in the index calculation. In the Fitness index, the relative weights (in %) for lambing interval : lambs born maternal : lambs born paternal: lambs born alive maternal : lambs born alive paternal are 35.0 : 33.3 : 8.3 : 18.3 : 5.0 (Merinoland) and 20.0 : 49.0 : 17.0 : 10.0 : 4.0 (Meat sheep). Including the trait average daily gain maternal in the meat complex, the relative weights (in %) of Meat : Fitness are 40 : 60 and 60 : 40 in Merinoland and Meat sheep, respectively. The TMI was defined in a way so that the calculated selection response is positive for all single traits in order to avoid an unfavourable development. Thus, it serves as a starting point for a breeding objective that is both, economically feasible and sustainable in the long-term. Further traits that may be considered in a future TMI are e.g. longevity and selected conformation traits.

A new selection index percent emphasis method using subindex weights and genetic evaluation accuracy

The current international standard methodology to quantify trait percent emphasis in selection indexes is based on a simple multiplication of the relative contribution of each trait's economic value (converted to absolute value) and its genetic standard deviation. This method does not reflect the actual selection emphasis applied when the index is used in practice. The economic value does not reflect selection effort when traits differ considerably in their accuracy of evaluation, and no account is taken for either favorable or antagonistic correlations among traits. A new emphasis method adjusted by both accuracy and genetic correlation is proposed. Genetically highly correlated traits are grouped into subindexes by applying a hierarchical clustering method to the genetic correlation matrix. Then each trait's subindex emphasis is calculated within its subindex group, with a weighting included for trait accuracy. Finally, each subindex emphasis is converted to a full index emphasis according to the conventional relative emphasis of its corresponding subgroup. The method can also be applied to sets of breeding values and their economic weights. When applied to a New Zealand sheep breeding selection index where trait genetic correlations are distinct across subindex groups, the new method shrank the emphasis on low-heritability traits, including survival, from 51% to 19%; and expanded that on growth traits from 30% to 49%, better reflecting the selection pressure applied in reality. When genetic correlations across traits were similar, clustering became difficult. Accounting for accuracy affected traits' within-subindex group rankings, whereas the clustering to account for correlations affected all traits within a subgroup equally. Accounting for differences in trait accuracy when describing percent emphasis within selection indexes gives a more practical indication of the likely outcome of selecting on the index. Accounting for correlations among traits when defining percent emphasis made a significant difference only in a subset of case study examples.

Supplemental Material for Hulst, De Jong, and Bijma, 2020

Table S1 contains mean (over 20 replicates) observed scale heritability estimates for disease status for different genetic standard deviations in susceptibility. FigureS2 contains a schematic overview of the steps used in the simulations.FileS3 contains the R-code used to simulate populations with genetic and permanent environmental variation in susceptibility.FileS4 contains the R-code used to simulate the endemic infectious disease.

Technical note: Automatic scaling in single-step genomic BLUP

Single-step genomic BLUP (ssGBLUP) requires compatibility between genomic and pedigree relationships for unbiased and accurate predictions. Scaling the genomic relationship matrix (G) to have the same averages as the pedigree relationship matrix (i.e., scaling by averages) is one way to ensure compatibility. This requires computing both relationship matrices, calculating averages, and changing G, whereas only the inverses of those matrices are needed in the mixed model equations. Therefore, the compatibility process can add extra computing burden. In the single-step Bayesian regression, the scaling is done by including a mean (μg) as a fixed effect in the model. The parameter μg can be interpreted as the average of the breeding values of the genotyped animals. In this study, such scaling, called automatic, was implemented in ssGBLUP via Quaas-Pollak transformation of the inverse of the relationship matrix used in ssGBLUP (H), which combines the inverses of the pedigree and genomic relationship matrices. Comparisons involved a simulated data set, and the genomic relationship matrix was computed using different allele frequencies either from the current population (i.e., realized allele frequencies), equal among all the loci, or from the base population. For all of the scenarios, we computed bias [defined as the average difference between true breeding values (TBV) and genomic estimated breeding values (GEBV)], accuracy (defined as the correlation between TBV and GEBV), and dispersion (defined as the regression coefficient of GEBV on TBV). With no scaling, the bias expressed in terms of genetic standard deviations was 0.86, 0.64, and 0.58 with realized, equal, and base population allele frequencies, respectively. With scaling by averages, which is currently used in ssGBLUP, bias was 0.07, 0.08, and 0.03, respectively. With automatic scaling, bias was 0.18 regardless of allele frequencies. Accuracies were similar among scaling methods, but about 0.1 lower in the scenario without scaling. The GEBV were more inflated without any scaling, whereas the automatic scaling performed similarly to the scaling by averages. The average dispersion for those methods was 0.94. When μg was treated as random, with the variance equal to differences between pedigree and genomic relationships, the bias was the same as with the scaling by averages. The automatic scaling is biased, especially when μg is treated as a fixed effect. The bias may be small in real data with fewer generations, when traits are undergoing weak selection, or when the number of genotyped animals is large.