Enhancing grapevine breeding efficiency through genomic prediction and selection index

Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20-25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype, and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on 2 existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and interspecific resistant varieties to powdery and downy mildews. Thirty traits were evaluated for each program, using 2 genomic prediction methods: Genomic Best Linear Unbiased Predictor and Least Absolute Shrinkage Selection Operator. The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.

Designing an early selection morphological traits index for reproductive efficiency in Pura Raza Española mares

The low rate of reproductive efficiency in horses may be linked to the equine industry's practice of maintaining breeding stock that stands out for its athletic or morphological performance but exhibits poor reproductive efficiency. In this study, the age at first foaling, age at last foaling, interval between first and second foaling, average interval between foaling, foaling number and reproductive efficiency, and their relationship with the morphological records in 19,758 Pura Raza Española (PRE) mares were analyzed. After a partial least squares analysis height at withers, lateral hock angle, dorsal-sternal diameter, perimeter of anterior cannon bone, angle of shoulder, thoracic perimeter, hip-stifle distance, and angle of croup were the most important traits related with reproductive traits in PRE mares. A multivariate animal model with both morphological and reproductive traits was applied, including age, geographical area, coat color, and average stud size in the decade of the mare's first foaling. The results indicate that reproductive selection is feasible, and is enhanced by the use of morphological traits, given the moderate to high magnitudes of heritability values in both types of traits, which oscillated between 0.1 (interval between first and second foaling) and 0.95 (height at withers). The resulting genetic parameters were used to develop a series of selection indexes based on morphological or morphological and reproductive combination traits to improve reproductive efficiency traits in PRE mares and thus compute the expected genetic response (EGR) for the different strategies. The increase in EGR, when comparing EGR using only reproductive traits as selection criteria vs. using a combined index of both morphological and reproductive traits, oscillated between 4.0% for the age at first foaling to 46.8% for the interval between first and second foaling. In addition, two precocity indexes showed positive EGR when the age at last foaling and the interval between first and second foaling, both with and without morphological traits, were used as selection criteria. Therefore, this analysis reveals that the preselection of reproductive traits based on morphological traits is possible in PRE mares. Ultimately, this knowledge will help breeders achieve genetic progress in reproductive traits, leading to healthier and more successful breeding outcomes in horses.

Muscle growth affects the metabolome of the pectoralis major muscle in red-winged tinamou (Rhynchotus rufescens)

The aim of the present study was to identify and quantify the metabolites (metabolome analysis) of the pectoralis major muscle in male red-winged tinamou (Rhynchotus rufescens) selected for growth traits. A selection index was developed for females [body weight (BW), chest circumference (CC), and thigh circumference (TC)] and males [BW, CC, TC, semen volume, and sperm concentration] in order to divide the animals into 2 experimental groups: selection group with a higher index (TinamouS) and commercial group with a lower index (TinamouC). Twenty male offspring of the 2 groups (TinamouS, n = 10; TinamouC, n = 10) were confined for 350 d. The birds were slaughtered and pectoralis major muscle samples were collected, subjected to polar and apolar metabolites extractions and analyzed by proton nuclear magnetic resonance (1H NMR) spectroscopy. Analysis of the polar metabolomic profile identified 65 metabolites; 29 of them were differentially expressed between the experimental groups (P < 0.05). The TinamouS groups exhibited significantly higher concentrations (P < 0.05) of 25 metabolites, including anserine, aspartate, betaine, carnosine, creatine, glutamate, threonine, 3-methylhistidine, NAD+, pyruvate, and taurine. Significantly higher concentrations of cysteine, beta-alanine, lactose, and choline were observed in the TinamouC group (P < 0.05). The metabolites identified in the muscle provided information about the main metabolic pathways (higher impact value and P < 0.05), for example, phenylalanine, tyrosine and tryptophan biosynthesis; alanine, aspartate and glutamate metabolism; D-glutamine and D-glutamate metabolism; β-alanine metabolism; glycine, serine and threonine metabolism; taurine and hypotaurine metabolism; histidine metabolism; phenylalanine metabolism. The NMR spectra of apolar fraction showed 8 classes of chemical compounds. The metabolome analysis shows that the selection index resulted in the upregulation of polyunsaturated fatty acids, unsaturated fatty acids, phosphocholines, phosphoethanolamines, triacylglycerols, and glycerophospholipids. The present study suggests that, despite few generations, the selection based on muscle growth traits promoted changes in metabolite concentrations in red-winged tinamou.

Incorporation of feeding behaviour traits to increase the genetic gain of feed efficiency in Pietrain pigs

Improved feed efficiency is an essential goal for the sustainability of pig production in economic and environmental terms. Traits such as feed conversion rate (FCR), residual feed intake (RFI), residual body weight gain (RG) and feeding behaviour, such as duration (TPV) and feeding rate per visit (FR) can now be measured by automatic feeding systems. The aim of this study was to evaluate the benefits of incorporating feeding behaviour traits into a selection index to improve feed efficiency in a nucleus of purebred Pietrain pigs. Data on body weight, feed intake and duration were recorded at each visit in 1608 animals. The information contained in 843,605 visits was grouped by animal ID to obtain a set of feed efficiency and feeding behaviour traits. These traits were obtained in three periods (first, second and total period). Bayesian models were built to estimate the posterior marginal distribution of the variance components. The heritabilities were between 0.44 and 0.59 for feeding behaviour traits and between 0.31 and 0.49 for feed efficiency traits. The FCR and RFI showed a considerable genetic correlation with daily feed intake (~0.65). FCR showed a genetic correlation with feeding behaviour traits, such as feed intake per visit (FPV) (0.44) and FR (0.33). Furthermore, the fast-eating pigs were less efficient. This was due to the positive genetic correlation found between the FR and the FCR (0.33) and the RFI (0.23), and the negative correlation found with the RG (-0.28). On the other hand, the inclusion of the feeding behaviour traits into a selection index slightly increased the selection response for FCR (4%) and RFI (1.8%). However, there was an increase of up to 19% in the selection response for RG and an improvement in accuracy from 0.59 to 0.70. Therefore, we concluded that it would be interesting to include feeding behaviour traits in a selection index to improve the selection response and accuracy of feed efficiency traits.

Decision tree scoring system to guide selection for consumer preference in sweetpotato breeding trials

BACKGROUND

Previously, a lexicon and protocol for quantitative descriptive analysis (QDA) was established for the Uganda sweetpotato breeding program. The implication of QDA scores for priority sensory attributes on consumer preference should be determined to interpret results efficiently and make decisions effectively. This study aimed to develop a gender-responsive decision tree to obtain an overall sweetpotato eating quality score to facilitate demand-led targeted breeding selection. It focused on Kamuli and Hoima districts (Uganda) and uses pre-lease advanced clones ('NKB3', 'NKB105', 'NKB135', 'D11', and 'D20'), released varieties ('NASPOT 8', 'NAROSPOT 1'), and landraces ('Muwulu-Aduduma', 'Umbrella').

RESULTS

Including boiled sweetpotato sensory characteristics, namely mealy, sweet taste, sweetpotato smell, firm and not fibrous in breeding design would benefit end-users, especially women given their role in varietal selection, food preparation and marketing. 'D20', 'NASPOT 8' and 'NAROSPOT 1' were most liked in both districts. 'NKB3' and 'D11' were the least liked in Hoima, while 'Muwulu-Aduduma' was the least liked in Kamuli. There was a positive correlation between color and overall liking (R² = 0.8) and consumers liked the color (average rating ≥ 6 on 9-point hedonic scale) of all genotypes. Threshold values (average rating on 11-point scales) for consumer acceptability were identified (sweet taste=6, sweetpotato aroma and flavor=6, firmness=3, and mealiness=4). A regression decision tree tool was created to calculate an eating quality selection index when screening lines in breeding programs using the values.

CONCLUSION

Decision trees that include consumer needs and gender considerations would facilitate demand-led breeding and make varietal selection in sweetpotato breeding programs more effective. This article is protected by copyright. All rights reserved.

Selection of Short-Day Strawberry Genotypes through Multivariate Analysis

Strawberries are produced in tropical regions using imported cultivars adapted to temperate and subtropical climates. These cultivars, under tropical conditions, produce below their genetic potential. Through multivariate analyses, the objective was to evaluate and select short-day strawberry genotypes based on intraspecific crosses, product characteristics, and fruit quality. The genotypes were obtained from the cross between 'Camino Real' (female parent) and the first-generation genotypes RVCA16, RVCS44, RVFS06, RVFS07, and RVDA11 (male parent), obtained in previous selections. The experimental design consisted of augmented blocks with standard controls, consisting of first-generation genotypes and commercial cultivars. The fruits were harvested and evaluated for productivity and post-harvest characteristics: total fruit mass (MTF), total number of fruits (TFN), average fruit mass (AFM), commercial fruit mass (CFM), fruit commercial number (CFN), average commercial mass of fruits (ACFM), total soluble solids (TSS), firmness (F), brightness (L), hue angle (°Hue), and chroma (C). The selection index of Mulamba and Mock (1978) was used with an intensity of 3% to obtain superior genotypes and submitted to multivariate analysis for comparative purposes. Of the 1500 genotypes evaluated, it was possible to select 44 genotypes with characteristics superior to the 13 controls. The RVDA11CR59 genotype showed better values for the attributes of interest, but the RVCS44CR population, from the cross between 'Camino Real' × RVCS44 ('Camarosa' × 'Sweet Charlie'), obtained the highest number (16) of individuals among those selected. Significant traits had high heritability but were not necessarily reflected in high selection gain. Coefficients of genetic variation were high, indicating sufficient genetic variability to select genotypes for these traits. When multivariate analyses were used, it was possible to group the selected genotypes into the same cluster according to the similarity and balance in the responses to the evaluated variables, demonstrating that these analyses help other parameters choose superior genotypes. The multivariate analysis allowed the selection of more balanced genotypes for production and post-harvest traits for tropical climates.

Association of Phenotypic Markers of Heat Tolerance with Australian Genomic Estimated Breeding Values and Dairy Cattle Selection Indices

Dairy cattle predicted by genomic breeding values to be heat tolerant are known to have less milk production decline and lower core body temperature increases in response to elevated temperatures. In a study conducted at the University of Melbourne's Dookie Robotic Dairy Farm during summer, we identified the 20 most heat-susceptible and heat-tolerant cows in a herd of 150 Holstein Friesian lactating cows based on their phenotypic responses (changes in respiration rate, surface body temperature, panting score, and milk production). Hair samples were collected from the tip of the cows' tails following standard genotyping protocols. The results indicated variation in feed saved and HT genomic estimated breeding values (GEBVs) (p ≤ 0.05) across age, indicating a potential for their selection. As expected, the thermotolerant group had higher GEBVs for HT and feed saved but lower values for milk production. In general, younger cows had superior GEBVs for the Balanced Performance Index (BPI) and Australian Selection Index (ASI), whilst older cows were superior in fertility, feed saved (FS), and HT. This study demonstrated highly significant (p ≤ 0.001) negative correlations (-0.28 to -0.74) between HT and GEBVs for current Australian dairy cattle selection indices (BPI, ASI, HWI) and significant (p ≤ 0.05) positive correlations between HT and GEBVs for traits like FS (0.45) and fertility (0.25). Genomic selection for HT will help improve cow efficiency and sustainability of dairy production under hot summer conditions. However, a more extensive study involving more lactating cows across multiple farms is recommended to confirm the associations between the phenotypic predictors of HT and GEBVs.

Effect of Genetic and Environmental Factors on Twin Pregnancy in Primiparous Dairy Cows

Twin pregnancies are highly undesirable in dairy cattle; they compromise the health and wellbeing of a cow and dramatically impair the farm economy. Recently, a genomic prediction for twin pregnancies has been developed. The objective of this study was to assess environmental and management risk factors affecting the incidence of twin pregnancies in high-producing dairy cows in their first lactation, with a special emphasis placed on the genomic prediction values of twin pregnancy. Our study population of primiparous cows proved valuable in identifying factors other than genomic predictive values that influence twin pregnancy rates. The odds ratio for twin pregnancies was 0.85 (p < 0.0001) for each unit of a prediction value increase, 3.5 (p = 0.023) for cows becoming pregnant during the negative photoperiod, and 0.33 (p = 0.016) for cows producing ≥42 kg of milk at AI, compared with the remaining cows who produced <42 kg of milk. As a general conclusion, the practical implication of our findings is that genomic prediction values can identify the risk of twin pregnancy at a herd level. Given the cumulative effect of genomic selection, selecting animals with a reduced genetic risk of twin pregnancies can contribute to reducing the incidence of twin pregnancies in dairy herds.

Identification of Superior Barley Genotypes Using Selection Index of Ideal Genotype (SIIG)

The main objective of the study was to evaluate and select the superior barley genotypes based on grain yield and some pheno-morphological traits using a new proposed selection index (SIIG). For this purpose, one-hundred-eight pure and four local cultivars (Norouz, Auxin, Nobahar, and WB-97-11) were evaluated as reference genotypes in four warm regions of Iran, including Ahvaz, Darab, Zabol, and Gonbad, during the 2020-2021 cropping seasons. The results of REML analysis showed that the heritability of all traits (except plant height) was higher in Gonbad than in other environments, while the lowest values were estimated in Ahvaz and Zabol environments. In addition, among the measured traits, the thousand kernel weight and grain filling period showed the highest and lowest values of heritability (0.83 and 0.01, respectively). The results showed that the seed yield of genotypes 1, 108, 3, 86, 5, 87, 19, 16, 15, 56, and 18 was higher than the four reference genotypes, and, on the other hand, the SIIG index of these genotypes was greater than or equal to 0.60. Based on the SIIG discriminator index, 4, 8, 31, and 28 genotypes with values greater than or equal to 0.60 were identified as superior for Darab, Ahvaz, Zabol, and Gonbad environments, respectively. As a conclusion, our results revealed that the SIIG index has ideal potential to identify genotypes with high yield and desirable traits. Therefore, the use of this index can be beneficial in screening better genotypes in the early stages of any breeding program for any crop.

Characterizing quality traits of boiled yam: texture and taste for enhanced breeding efficiency and impact

BACKGROUND

Boiled yam key quality attributes typical for West African consumers are that it is crumbly, easy to break and has a sweet taste. New yam varieties are being developed but high- or medium-throughput tools to assess the required quality traits and their range of acceptance are limited. This study assessed the acceptance thresholds of these quality attributes and established predictive models for screening yam varieties that meet the required consumer preferences.

RESULTS

Overall liking was associated with sweet taste, crumbliness and easiness to break (r-values 0.502, 0.291 and -0.087, respectively). These parameters and selected biophysical parameters highly discriminated the boiled yam varieties. Crumbly texture and easiness to break were well predicted by penetration force and dry matter, whereas sweet taste were well predicted by dry matter and sugar intensity. A high crumbliness and sweet taste are preferred (sensory scores above 6.19 and 6.22 for crumbly and sweet taste, respectively, on a 10 cm unstructured line scale), while a too high easiness to break is disliked (sensory scores ranging from 4.72 to 7.62). Desirable biophysical targets were between 5.1 and 7.1 N for penetration force, dry matter around 39% and sugar intensity below 3.62 g 100 g^-1 . Some improved varieties fulfilled the acceptable thresholds, and screening was improved through deviation from the optimum.

CONCLUSION

Acceptance thresholds and deviation from optimum for boiled yam assessed through the instrumental measurements are promising tools for yam breeders. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Genetic Comparisons of Body Weight, Average Daily Gain, and Breast Circumference between Slow-Growing Thai Native Chickens (Pradu Hang dum) Raised On-Site Farm and On-Station

To ensure that any new technology developed within an experimental station is appropriate to the community’s needs and compatible with the existing systems, on-site farm research is an important component in examining the effectiveness of agricultural research. The present study examined the growth performance and genetics of Thai native chickens under conditions typically experienced by farmers on smallholder farms (on-site farms) compared with at an experimental unit (on-station). There were 1694 Thai native chickens (Pradu Hang dum) used in this experiment, and they were divided into 613 chickens for the on-station and 1081 chickens for the on-site farm experiments. The individual chicken data included the birth weight (BW0) and body weight at 4, 8, 12, and 16 weeks of age (BW4, BW8, BW12, and BW16, respectively), ADG from 0−4, 4−8, 8−12, 12−16 weeks of age (ADG0−4, ADG4−8, ADG8−12, ADG12−16, respectively), and breast circumference at 8, 12, and 16 weeks of age (BrC8, BrC12, BrC16, respectively). A multiple traits animal model and a selection index were used to estimate the variance components, genetic parameters, and breeding values of growth traits. The results showed that the body weight, average daily gain, and breast circumference at 8, 12, and 16 weeks of age of Thai native chickens raised on-station were higher than those raised on-site at the farm among mixed-sex and sex-segregated chickens, while the birth weight and body weight at four weeks of age (BW0 and BW4) and ADG from 0−4 weeks of age (ADG0−4) were not significantly different (p > 0.05). The heritability estimates of body weight, average daily gain, and breast circumference in the chickens raised at the on-site farm and on-station were moderate to high, with on-station values slightly higher than on-site farm values. The heritability estimates of body weight were 0.236 to 0.499 for the on-site farm, and 0.291 to 0.499 for on-station. For average daily gain, the heritability estimates were 0.274 to 0.283 for the on-site farm and 0.298 to 0.313 for on-station; meanwhile, and for breast circumference, the heritability estimates were 0.204 to 0.268 for the on-site farm and 0.278 to 0.296 for on-station. Both phenotypic and genetic correlations among and between growth traits were positive and ranged from low to high values. The top 20% of the estimated breeding values and selection indices in the on-site farm and on-station experiments showed that the body weight at eight weeks of age (BW8), ADG from 4−8 weeks of age (ADG4−8), and breast circumference at eight weeks of age (BrC8) should be used as selection criteria for Thai native chicken breeding programs. In conclusion, the genetic parameters and breeding values in on-station experiments showed that the breeding program by selection index for improving growth performance is valid. Therefore, to implement such a breeding program in an on-site farm, an intensive or semi-intensive farm system should be considered to minimize the effect of genotype-environment interaction.

Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p < 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future.

Drug Combinations to Prevent Antimicrobial Resistance: Various Correlations and Laws, and Their Verifications, Thus Proposing Some Principles and a Preliminary Scheme

Antimicrobial resistance (AMR) has been a serious threat to human health, and combination therapy is proved to be an economic and effective strategy for fighting the resistance. However, the abuse of drug combinations conversely accelerates the spread of AMR. In our previous work, we concluded that the mutant selection indexes (SIs) of one agent against a specific bacterial strain are closely related to the proportions of two agents in a drug combination. To discover probable correlations, predictors and laws for further proposing feasible principles and schemes guiding the AMR-preventing practice, here, three aspects were further explored. First, the power function (y = axb, a > 0) correlation between the SI (y) of one agent and the ratio (x) of two agents in a drug combination was further established based on the mathematical and statistical analyses for those experimental data, and two rules a1 × MIC1 = a2 × MIC2 and b1 + b2 = −1 were discovered from both equations of y = a1xb1 and y = a2xb2 respectively for two agents in drug combinations. Simultaneously, it was found that one agent with larger MPC alone for drug combinations showed greater potency for narrowing itself MSW and preventing the resistance. Second, a new concept, mutation-preventing selection index (MPSI) was proposed and used for evaluating the mutation-preventing potency difference of two agents in drug combination; a positive correlation between the MPSI and the mutant prevention concentration (MPC) or minimal inhibitory concentration (MIC) was subsequently established. Inspired by this, the significantly positive correlation, contrary to previous reports, between the MIC and the corresponding MPC of antimicrobial agents against pathogenic bacteria was established using 181 data pairs reported. These results together for the above three aspects indicate that the MPCs in alone and combination are very important indexes for drug combinations to predict the mutation-preventing effects and the trajectories of collateral sensitivity, and while the MPC of an agent can be roughly calculated from its corresponding MIC. Subsequently, the former conclusion was further verified and improved via antibiotic exposure to 43 groups designed as different drug concentrations and various proportions. The results further proposed that the C/MPC for the agent with larger proportion in drug combinations can be considered as a predictor and is the key to judge whether the resistance and the collateral sensitivity occur to two agents. Based on these above correlations, laws, and their verification experiments, some principles were proposed, and a diagram of the mutation-preventing effects and the resistant trajectories for drug combinations with different concentrations and ratios of two agents was presented. Simultaneously, the reciprocal of MPC alone (1/MPC), proposed as the stress factors of two agents in drug combinations, together with their SI in combination, is the key to predict the mutation-preventing potency and control the trajectories of collateral sensitivity. Finally, a preliminary scheme for antimicrobial combinations preventing AMR was further proposed for subsequent improvement research and clinic popularization, based on the above analyses and discussion. Moreover, some similar conclusions were speculated for triple or multiple drug combinations.

Genomic Prediction for Abortion in Lactating Holstein Dairy Cows

Abortion in dairy cattle causes great economic losses due to reduced animal health, increase in culling rates, reduction in calf production, and milk yield, among others. Although the etiology of abortions can be of various origins, previous research has shown a genetic component. The objectives of this study were to (1) describe the development of the genomic prediction for cow abortions in lactating Holstein dairy cattle based on producer-recorded data and ssGBLUP methodology and (2) evaluate the efficacy of genomic predictions for cow abortions in commercial herds of US Holstein cows using data from herds that do not contribute phenotypic information to the evaluation. We hypothesized that cows with greater genomic predictions for cow abortions (Z_Abort STA) would have a reduced incidence of abortion. Phenotypic data on abortions, pedigree, and genotypes were collected directly from commercial dairy producers upon obtaining their permission. Abortion was defined as the loss of a confirmed pregnancy after 42 and prior to 260 days of gestation, treated as a binary outcome (0, 1), and analyzed using a threshold model. Data from a different subset of animals were used to test the efficacy of the prediction. The additive genetic variance for the cow abortion trait (Z_Abort) was 0.1235 and heritability was 0.0773. For all animals with genotypes (n = 1,662,251), mean reliability was 42%, and genomic predicted transmitting abilities (gPTAs) ranged from −8.8 to 12.4. Z_Abort had a positive correlation with cow and calf health traits and reproductive traits, and a negative correlation with production traits. Z_Abort effectively identified cows with a greater or lesser risk of abortion (16.6% vs. 11.0% for the worst and best genomics groups, respectively; p < 0.0001). The inclusion of cow abortion genomic predictions in a multi-trait selection index would allow dairy producers and consultants to reduce the incidence of abortion and to select high-producing, healthier, and more profitable cows.

Selection of parental lines for plant breeding via genomic prediction

A set of superior parental lines is imperative for the development of high-performing inbred lines in any biparental crossing program for crops. The main objectives of this study are to (a) develop a genomic prediction approach to identify superior parental lines for multi-trait selection, and (b) generate a software package for users to execute the proposed approach before conducting field experiments. According to different breeding goals of the target traits, a novel selection index integrating information from genomic-estimated breeding values (GEBVs) of candidate accessions was proposed to evaluate the composite performance of simulated progeny populations. Two rice (Oryza sativa L.) genome datasets were analyzed to illustrate the potential applications of the proposed approach. One dataset applied to the parental selection for producing inbred lines with satisfactory performance in primary and secondary traits simultaneously. The other one applied to demonstrate the application of producing inbred lines with high adaptability to different environments. Overall, the results showed that incorporating GEBV and genomic diversity into a selection strategy based on the proposed selection index could assist in selecting superior parents to meet the desired breeding goals and increasing long-term genetic gain. An R package, called IPLGP, was generated to facilitate the widespread application of the approach.

Are subjectively scored linear type traits suitable predictors of the genetic merit for feed intake in grazing Holstein-Friesian dairy cows?

Measuring dry matter intake (DMI) in grazing dairy cows using currently available techniques is invasive, time consuming, and expensive. An alternative to directly measuring DMI for use in genetic evaluations is to identify a set of readily available animal features that can be used in a multitrait genetic evaluation for DMI. The objectives of the present study were thus to estimate the genetic correlations between readily available body-related linear type traits and DMI in grazing lactating Holstein-Friesian cows, but importantly also estimate the partial genetic correlations between these linear traits and DMI, after adjusting for differences in genetic merit for body weight. Also of interest was whether the predictive ability derived from the estimated genetic correlations materialized upon validation. After edits, a total of 8,055 test-day records of DMI, body weight, and milk yield from 1,331 Holstein-Friesian cows were available, as were chest width, body depth, and stature from 47,141 first lactation Holstein-Friesian cows. In addition to considering the routinely recorded linear type traits individually, novel composite traits were defined as the product of the linear type traits as an approximation of rumen volume. All linear type traits were moderately heritable, with heritability estimates ranging from 0.27 (standard error = 0.14) to 0.49 (standard error = 0.15); furthermore, all linear type traits were genetically correlated (0.29 to 0.63, standard error 0.14 to 0.12) with DMI. The genetic correlations between the individual linear type traits and DMI, when adjusted for genetic differences in body weight, varied from -0.51 (stature) to 0.48 (chest width). These genetic correlations between DMI and linear type traits suggest linear type traits may be useful predictors of DMI, even when body weight information is available. Nonetheless, estimated genetic merit of DMI derived from a multitrait genetic evaluation of linear type traits did not correlate strongly with actual DMI in a set of validation animals; the benefit was even less if body weight data were also available.

Accelerating the Development of Heat Tolerant Tomato Hybrids through a Multi-Traits Evaluation of Parental Lines Combining Phenotypic and Genotypic Analysis

The constitution of heat tolerant F₁ hybrids is a challenge to ensure high yield and good fruit quality in the global climate. In the present work, we evaluated 15 genotypes for yield-related traits highly affected by high temperatures (HT). This phenotypic analysis allowed to identify four parental genotypes showing promising yield performances under HT conditions. Two of these genotypes also exhibited good fruit quality traits. A molecular marker analysis was carried out for six resistance genes to pathogens mostly affecting tomatoes. This analysis evidenced the presence of a maximum of three resistant alleles in parental genotypes. Exploring single nucleotide polymorphisms (SNPs) revealed by two high-throughput genotyping platforms allowed identifying additional 12 genes potentially involved in resistance to biotic stress, to be further investigated. Following these considerations, 13 F₁ hybrids were constituted combining the parental genotypes and then evaluated for multiple traits under HT conditions. By estimating a hybrid index based on yield performances, desirable quality and resistance gene, we identified seven hybrids showing the best performances. The promising results obtained in the present work should be confirmed by evaluating the best hybrids selected for additional years and environments before proposing them as novel commercial hybrids that could maintain high performances under HT conditions.

Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks

Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

Pea Breeding for Intercropping With Cereals: Variation for Competitive Ability and Associated Traits, and Assessment of Phenotypic and Genomic Selection Strategies

Mixed stand (MS) cropping of pea with small-grain cereals can produce more productive and environment-friendly grain crops relative to pure stand (PS) crops but may require selection to alleviate the pea competitive disadvantage. This study aimed to assess the pea variation for competitive ability and its associated traits and the efficiency of four phenotypic or genomic selection strategies. A set of 138 semi-leafless, semi-dwarf pea lines belonging to six recombinant inbred line populations and six parent lines were genotyped using genotyping-by-sequencing and grown in PS and in MS simultaneously with one barley and one bread wheat cultivar in two autumn-sown trials in Northern Italy. Cereal companions were selected in a preliminary study that highlighted the paucity of cultivars with sufficient earliness for association. Pea was severely outcompeted in both years albeit with variation for pea proportion ranging from nearly complete suppression (<3%) to values approaching a balanced mixture. Greater pea proportion in MS was associated with greater total yield of the mixture (r ≥ 0.46). The genetic correlation for pea yield across MS and PS conditions slightly exceeded 0.40 in both years. Later onset of flowering and taller plant height at flowering onset displayed a definite correlation with pea yield in MS (r ≥ 0.46) but not in PS, whereas tolerance to ascochyta blight exhibited the opposite pattern. Comparisons of phenotypic selection strategies within or across populations based on predicted or actual yield gains for independent years indicated an efficiency of 52-64% for indirect selection based on pea yield in PS relative to pea yield selection in MS. The efficiency of an indirect selection index including onset of flowering, plant height, and grain yield in PS was comparable to that of pea yield selection in MS. A genome-wide association study based on 5,909 SNP markers revealed the substantial diversity of genomic areas associated with pea yield in MS and PS. Genomic selection for pea yield in MS displayed an efficiency close to that of phenotypic selection for pea yield in MS, and nearly two-fold greater efficiency when also taking into account its shorter selection cycle and smaller evaluation cost.

Phenotypic Characterization of Quinoa (Chenopodium quinoa Willd.) for the Selection of Promising Materials for Breeding Programs

Quinoa is an ancestral crop in the Andean region, characterized by its adaptability to different agroclimatic conditions, great nutritional value, and broad genetic variability. A preliminary approach for understanding the genetics of quinoa materials entails a morphologic characterization, which can provide the basis for the selection of materials that satisfy the needs of farmers and consumers. Therefore, this study aimed to evaluate the phenotypic characteristics of thirty genetic C. quinoa accessions for the selection of outstanding accessions in terms of yield and grain quality. A randomized complete block design was used, with nine replications for each accession under greenhouse conditions. Nine quantitative and twelve qualitative descriptors were evaluated with descriptive analysis, Spearman correlation variance, and multivariate and cluster analysis. The results showed that the accessions with heights greater than the average (>176.72 cm) and long panicles (>57.94 cm) presented lower yields and smaller seed sizes, thus decreasing the grain quality. The multivariate and cluster analyses established groups of accessions with good yields (>62.02 g of seeds per plant) and stable morphological characteristics. The proposed selection index, based on yield components and morphological descriptors, indicated four accessions as potential parents for quinoa breeding programs in Colombia.

Commercial beef farms excelling in terminal and maternal genetic merit generate more gross profit

Validation of beef total merit breeding indexes for improving performance and profitability has previously been undertaken at the individual animal level; however, no herd-level validation of beef genetic merit and profit has been previously investigated. The objective of the present study was to quantify the relationship between herd profitability and both herd-average terminal and maternal genetic merit across 1,311 commercial Irish beef herds. Herd-level physical and financial performance data were available from a financial benchmarking tool used by Irish farmers and their extension advisors. Animal genetic merit data originated from the Irish Cattle Breeding Federation who undertake the national beef and dairy genetic evaluations. Herd-average genetic merit variables included the terminal index of young animals, the maternal index of dams, and the terminal index of service sires. The herds represented three production systems: 1) cow-calf to beef, 2) cow-calf to weanling/yearling, and 3) weanling/yearling to beef. Associations between herd financial performance metrics and herd average genetic merit variables were quantified using a series of linear mixed models with year, production system, herd size, stocking rate, concentrate input, and the two-way interactions between production system and herd size, stocking rate, and concentrate input included as nuisance factors. Herd nested within the county of Ireland (n = 26) was included as a repeated effect. Herds with young cattle excelling in terminal index enjoyed greater gross and net profit per hectare (ha), per livestock unit (LU), and per kg net live-weight output. The change in gross profit per LU per unit change in the terminal index of young animals was €1.41 (SE = 0.23), while the respective regression coefficient for net profit per LU was €1.37 (SE = 0.30); the standard deviation of the terminal index is €37. Herd-average dam maternal index and sire terminal index were both independently positively associated with gross profit per ha and gross profit per LU. Each one unit increase in dam maternal index (standard deviation of €38) was associated with a €1.40 (SE = 0.48) and €0.76 (SE = 0.29) greater gross profit per ha and per LU, respectively. Results from the present study at the herd-level concur with previous validation studies at the individual animal level thus instilling further confidence among stakeholders as to the expected improvement in herd profitability with improving genetic merit.

Effects of Incorporating Dry Matter Intake and Residual Feed Intake into a Selection Index for Dairy Cattle Using Deterministic Modeling

The inclusion of feed efficiency in the breeding goal for dairy cattle has been discussed for many years. The effects of incorporating feed efficiency into a selection index were assessed by indirect selection (dry matter intake) and direct selection (residual feed intake) using deterministic modeling. Both traits were investigated in three ways: (1) restricting the trait genetic gain to zero, (2) applying negative selection pressure, and (3) applying positive selection pressure. Changes in response to selection from economic and genetic gain perspectives were used to evaluate the impact of including feed efficiency with direct or indirect selection in an index. Improving feed efficiency through direct selection on residual feed intake was the best scenario analyzed, with the highest overall economic response including favorable responses to selection for production and feed efficiency. Over time, the response to selection is cumulative, with the potential for animals to reduce consumption by 0.16 kg to 2.7 kg of dry matter per day while maintaining production. As the selection pressure increased on residual feed intake, the response to selection for production, health, and fertility traits and body condition score became increasingly less favorable. This work provides insight into the potential long-term effects of selecting for feed efficiency as residual feed intake.

Optimal breeding-value prediction using a sparse selection index

Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a sparse selection index (SSI) that integrates selection index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-Best Linear Unbiased Predictor (G-BLUP) (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in 10 different environments) that the SSI can achieve significant (anywhere between 5 and 10%) gains in prediction accuracy relative to the G-BLUP.

Genomic Prediction for Twin Pregnancies

Simple Summary

Twinning in dairy cattle is caused by many different factors, both genetic (i.e., inherited) and non-genetic (i.e., animal management). In dairy operations, twinning is an undesirable trait associated with other reproductive and metabolic diseases, higher operational costs, and higher rates of culling on farm. The animal welfare and economic impacts have resulted in the development of a genomic prediction for twinning (i.e., TWIN) by Zoetis such that producers can make informed breeding decisions for breeding Holstein females that are less likely to become pregnant with twins in a given lactation. This prediction is included in a holistic breeding tool (i.e., selection index) for producers so that they can improve multiple health, fertility, and production traits in parallel with reducing twinning when making breeding decisions for future generations. The objectives of the present study were (1) to describe how the twinning prediction was developed (and included in a selection index), (2) show that the prediction works effectively using real life farm data, and (3) propose how this genetic tool can be used in collaboration with management practices to proactively reduce twin pregnancies on farm. The results of this study provide evidence that twinning can be proactively managed on dairy farms using genetically powered tools.

Abstract

Twinning is a multifactorial trait influenced by both genetic and environmental factors that can negatively impact animal welfare and economic sustainability on commercial dairy operations. To date, using genetic selection as a tool for reducing twinning rates on commercial dairies has been proposed, but not yet implemented. In response to this market need, Zoetis (Kalamazoo, MI, USA) has developed a genomic prediction for twin pregnancies, and included it in a comprehensive multitrait selection index. The objectives of this study were to (1) describe a genetic evaluation for twinning in Holstein cattle, (2) demonstrate the efficacy of the predictions, (3) propose strategies to reduce twin pregnancies using this information. Data were retrieved from commercial dairies and provided directly by producers upon obtaining their permission. The twin pregnancies trait (TWIN) was defined as a pregnancy resulting in birth or abortion of twin calves, classified as a binary (0,1) event, and analysed using a threshold animal model. Predictions for a subset of cows were compared to their on-farm twin records. The heritability for twin pregnancies was 0.088, and genomic predicted transmitting abilities ((g)PTAs) ranged from −7.45–20.79. Genetic correlations between TWIN and other traits were low, meaning that improvement for TWIN will not negatively impact improvement for other traits. TWIN was effectively demonstrated to identify cows most and least likely to experience a twin pregnancy in a given lactation, regardless of reproductive protocol used. Effective inclusion of the prediction in a multitrait selection index offers producers a comprehensive tool to inform selection and management decisions. When combined with sound management practices, this presents a compelling opportunity for dairy producers to proactively reduce the incidence of twin pregnancies on commercial dairy operations.

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.

An index framework founded on the future profit potential of female beef cattle to aid the identification of candidates for culling

Meticulous culling decisions, coupled with careful breeding decisions, are fundamental to shifting a population distribution in the favorable direction and improving profit per cow. Nevertheless, there is a paucity of easy-to-use dynamic tools to aid in culling decisions in beef cattle. The motivation for the present study was to develop a monetary-based culling tool, here referred to as the Beef Female's Profit Potential (BFPP), to identify females for culling. The BFPP reflects the expected lifetime profitability of an individual female in a herd for the expected remainder of her lifetime; this profit included that of the beef female herself as well as her progeny. The BFPP index framework was composed of 4 subindexes reflecting the value of an animal: (1) as a nulliparae (this was voided if the cow had already calved), (2) for the remainder of her current parity, (3) summed across each of her expected remaining parities, and (4) when she is retained within the herd and not voluntarily culled. Each subindex was comprised of different components reflecting both genetic and non-genetic effects associated with each female. Transition matrices predicting the expected longevity of each female and their expected month of calving were also utilized in calculating the expected remaining lifetime profitability of each female. The BFPP index was validated on 21,102 beef cows as well as their harvested progeny from 875 herds by stratifying the cows, within herd, into 4 strata based on their BFPP. The mean of the within-herd correlation between the BFPP and the Irish national replacement (i.e., breeding) index was, on average, 0.45 indicating the shortcomings of the breeding index as a culling tool. Cows within the top BFPP stratum had a genetic expectation of accruing almost an additional €36 profit per calving, relative to cows within the worst stratum; when validated on the cow's own calving interval and survival performance as well as their progeny's carcass performance, the actual phenotypic value was estimated to be an additional €32 profit per calving. A proportion of this additional profit was due to the harvested progeny of the high BFPP cows having, on average, heavier, more conformed carcasses with less fat cover relative to their poor BFPP contemporaries. This BFPP framework is a useful and easy-to-use tool to aid in producer decision making on the choice of females to voluntarily cull but also on which replacement heifers to graduate into the mature herd.

Selection Criteria for Improving Fertility in Spanish Goat Breeds: Estimation of Genetic Parameters and Designing Selection Indices for Optimal Genetic Responses

The aim of this study was to estimate genetic parameters for several female fertility criteria and to choose the most suitable selection index in Spanish Florida and Payoya goat breeds. In this study, we analyzed as fertility traits, the age at first kidding (AgFiKid), and the interval between the first and second kiddings (Int12Kid), between the second, third, and remaining kiddings (Int3toKid), and between all kiddings (IntAllKid) in 51,123 and 22,049 Florida and Payoya females, respectively. Genetic parameters were estimated by fitting animal models using restricted maximum likelihood (REML) methodology. We proposed six selection indices to compare the genetic responses for all traits included, based on a new selection index theory. The heritability and repeatability estimates of the traits were low, as expected. The genetic correlations among fertility traits covered a wide range of values from 0.07 (AgFiKid-Int12Kid) to 0.71 (Int3toKid-IntAllKid) in Florida and from -0.02 (AgFiKid-Int12Kid) to 0.82 (Int3toKid-IntAllKid) in Payoya. Overall, the results of this study indicate that IntAllKid gives the highest genetic responses in both breeds but is expressed late in a female's life. However, AgFiKid and Int12Kid could be recommended as early selection criteria for female fertility in both breeds.

Genetics and breeding of a black-bone and blue eggshell chicken line. 1. Body weight, skin color, and their combined selection

An experimental population of chickens was developed from the cross between 2 indigenous Chinese breeds, Dongxiang blue eggshell and Jiangshan black-bone. This breeding was aimed at eventually combining dark heavy black-bone body and blue eggshell, into a single dual-purpose breed. BW was recorded and skin L∗, a∗, and b∗ color parameters were measured by a Chroma Meter at several ages (56, 105, 150, 200, 250, and 300 d). At 250 d, 3 independent observers classified skin darkness using a 3-level visual scale (1 = light, 2 = intermediate, 3 = dark). The 7-level average visual skin darkness, calculated for each chicken, was highly correlated (−0.658 and −0.612 in females and males, respectively) with skin L∗ (lightness), indicating that the accurately measured L∗ is reliable and useful reverse expression of visual skin darkness of black-bone chickens. Mean BW and skin L∗ of both sexes increased with age, to 2,063 and 1,522 g in males and females, respectively, at 300 d, and to 63 and 55 L∗ units in males and females, respectively, at 250 d. The population's full-pedigree allowed estimating heritability and genetic correlations between traits. The heritability estimates of BW were similar in both sexes, increasing from around 0.25 at 56 d, to 0.53 to 0.60 at 150 d, and 0.57 to 0.62 at 300 d. Over the 5 ages, heritability estimates of skin L∗ were moderate to high, ranging from 0.45 to 0.58 in females, and from 0.31 to 0.65 in males, and the genetic correlations between BW and L∗ ranged mostly from 0.20 to 0.45. These low-to-moderate correlations between high BW and high L∗ (low darkness) are unfavorable; hence they were combined into an index, standardized BW minus standardized L∗, allowing future selection for high BW with low L∗. With high heritability of this index, 0.487 (females at 300 d) and 0.410 to 0.555 (males at 150 d or older), simultaneous improvements in BW and skin darkness appear to be feasible. The methodology used in this study can be useful in chicken populations experimentally bred for combination of high BW and other body characteristics.

Efficiency of the Polycross and Controlled Hybridization Methods in Sweetpotato Breeding in Uganda

Sweetpotato is an important crop in many parts of the world especially in developing countries. It is used for both human consumption as well as livestock feed. It is an important source of carbohydrates, vitamin C, fibre, iron, potassium, protein and β-carotene. Its production is, however, constrained by several biotic and abiotic factors, including pests and diseases, low soil fertility, drought, cold and salinity. Breeding is one of the ways to overcome some of these constraints and in sweetpotato the polycross or controlled cross methods can be used. To determine which of the two methods was more efficient, genotypes generated by both methods were evaluated over two seasons at Namulonge and Kachwekano. The type of cross (polycross or controlled) was significantly (P ≤ 0.05) different for storage root yield, response to sweetpotato virus disease, Alternaria blight, and harvest index (HI). The controlled cross families had a significantly higher mean HI of 43.2% than the polycross families with a mean HI of 31.8%. Therefore, controlled crosses could be deployed to systematically increase the HI in sweetpotato breeding populations. Significant (P ≤ 0.05) differences were observed among families for all traits. This stresses that the parents used in a cross are very important in generating genotypes with desired attributes. It was apparent that both the polycross and controlled crosses are good methods for generating new sweetpotato genotypes in a sweetpotato breeding program. Where aggregate performance was considered (selection index) the controlled crosses method produced more (75% of the top 20 desirable genotypes) than the polycross method across the two sites. However, the best three genotypes over the two sites were from the polycross family of Ejumula. Therefore, sweetpotato controlled crosses could be very useful for population improvement using recurrent selection while polycrosses could be suitable for variety development. Both hybridization methods require cautious selection of parents to match the breeding objectives.

Validation of genomic predictions for a lifetime merit selection index for the US dairy industry

Selection indices are a critical component of many breeding programs. A common purpose of a selection index is to predict an animal's genetic potential for total economic merit. The objective of this study was to evaluate retrospectively whether a specific selection index comprising genomically-enhanced predicted transmitting abilities had the ability to predict observed lifetime profit in US Holstein animals. The selection index evaluated was dairy wellness profit (DWP$). In total, 2,185 animals were included in this study. Index values were used to rank and assign animals to quartiles (genetic groups: worst 25%, 26-50%, 51-75%, and best 25%). Generalized linear mixed effects models were applied to estimate the associations between index quartile and defined economic outcomes. Similar analyses were conducted to estimate associations between index quartile and observed phenotype to characterize the extent to which profitability outcomes were driven by economically relevant production and health traits. Differences in lifetime profit and annuity value between the best and worst genetic groups for DWP$ were $811 (±297) and $232 (±88), respectively. Significant differences were also observed between top and bottom quartiles for milk production (8,077 kg), fat production (336 kg), protein production (264 kg), live calves (0.5), time spent in the lactating herd (6.6 mo), and cow mortality (8.4%). Additionally, differences in disease incidence were significant between the best and worst DWP$ quartiles for metritis (5.2%), mastitis (14.9%), and lameness (15.9%). The observed results of this study demonstrated the ability of DWP$ predictions to predict lifetime profitability of Holstein animals and its potential utility as a tool to guide selection and breeding programs. Improving DWP$ through genetic selection, when combined with good management practices, provides an opportunity for dairy producers to improve overall herd profitability.

Feed and production efficiency of young crossbred beef cattle stratified on a terminal total merit index

Few studies have attempted to quantify the association between a terminal total merit index with phenotypic feed and production efficiency in beef cattle, particularly when feed efficiency is itself explicitly absent as a goal trait in the index. The objective of the present study was to quantify the differences in phenotypic performance for feed intake, feed efficiency, and carcass traits of crossbred bulls, steers, and heifers differing in a terminal total merit index. A validation population of 614 bulls, steers, and heifers that were evaluated for feed intake and efficiency in the same feedlot and subsequently slaughtered at the end of their test period was constructed. The Irish national genetic evaluations for a terminal index of calving performance, docility, feed intake, and carcass traits were undertaken with the phenotypic records of animals present in the validation population masked. The validation population animals were subsequently stratified into four groups, within sex, according to their terminal index value. Mixed models were used to quantify the association between terminal genetic merit and phenotypic performance; whether the associations differed by sex were also investigated. The regression coefficient of phenotypic feed intake, carcass weight, carcass conformation, or carcass fat on its respective estimated breeding values was 0.86 kg dry matter 0.91 kg, 1.01 units, and 1.29 units, respectively, which are close to the expectation of one. On average, cattle in the very high terminal index stratum had a 0.63 kg DM/d lower feed intake, a 25.05 kg heavier carcass, a 1.82 unit better carcass conformation (scale 1 to 15), and a 1.24 unit less carcass fat score (scale 1 to 15), relative to cattle in the very low terminal index stratum. Cattle of superior total genetic merit were also more feed efficient (i.e., had a lower energy conversion ratio, lower residual feed intake, and greater residual gain), had a greater proportion of their live-weight as carcass weight (i.e., better dressing percentage) and were slaughtered at a younger age relative to their inferior total genetic merit counterparts. This study provides validation of an all-encompassing total merit index and demonstrates the benefits of selection on a total merit index for feed and production efficiency, which should impart confidence among stakeholders in the contribution of genetic selection to simultaneous improvements in individual animal performance and efficiency.

The value of incorporating carcass trait phenotypes in terminal sire selection indexes to improve carcass weight and quality of heavy lambs

Genetic selection for carcass traits is paramount to maximize the profitability and long-term sustainability of any meat-producing livestock species. The main objectives of this research were to evaluate the efficiency of indicator traits for the genetic improvement of lamb carcass traits and to determine the value of including carcass traits into terminal sire selection indexes for the Canadian sheep industry. The carcass traits included hot carcass weight (HCW), fat depth at the GR site (FATGR) and average carcass conformation score (AVGCONF), and were measured on heavy lambs (slaughter age less than 365 days and HCW greater than 16.3 kg) in commercial abattoirs. Growth traits were found to be moderately efficient indicator traits for the genetic improvement of HCW but selection on ultrasound traits was necessary to substantially improve the carcass quality traits (FATGR and AVGCONF). Economic selection indexes were designed by adding various combinations of carcass traits into the Canadian Sheep Genetic Evaluation System terminal indexes. Records measured on individuals and progeny were assumed to be the sources of information for live animal and carcass traits, respectively. The changes in index accuracy, efficiency and expected correlated response were used to assess the value of their inclusion. HCW was found to have a large economic value, and its inclusion into terminal selection indexes was expected to substantially increase their accuracy (0.08-0.12 points) and efficiency (20%-30%). However, further including FATGR (measured 110 mm from the carcass midline over the 12th rib) and AVGCONF had little impact on the accuracy (≤0.03) and efficiency (1%-7%) of the proposed indexes. Thus, the inclusion of carcass traits into the existing terminal selection indexes could be beneficial for the genetic improvement of HCW, but further research is needed to determine optimal methods of increasing carcass fatness and muscularity.

Sire selection and genetic improvement of dairy cattle assuming pure market competition

Breeding companies and farmers rely on selection indices to identify sires they expect to improve production system profitability. Such indices combine estimates of genetic merit for individual traits with corresponding economic values that are fixed based on circumstances at a particular time. Perfect market competition has also been proposed as an economic basis to value alternative sires. The objective of this study was to propose an economic model and develop it for the evaluation of dairy sires. The pure competition model (PUC) was used to evaluate the relationship between a profitability index based on the PUC approach versus the traditional selection index approach for 330 dairy sires comprising Holstein-Friesians, Jerseys, and Ayrshires. The correlation between these 2 selection indices was only 0.56, indicating that the conventional selection index did not correlate well with an index based on the PUC model. In particular, the higher ranking bulls were overvalued using the conventional selection index. Our study concluded that the use of fixed economic values is problematic for the delivery of consistent rankings in selection indices. In contrast, sire rankings based on PUC are more reliable because the sires are evaluated on the basis of efficiency gains rather than production while accounting for market prices and marginal values of dairy outputs over time.

Observed progeny performance validates the benefit of mating genetically elite beef sires to dairy females

While several studies in cattle have confirmed the improved performance achievable from selection on total merit indexes, these studies have solely been confined to specific-purpose beef or dairy total merit indexes. Validation studies of total merit indexes used to select beef sires for use on dairy females are lacking. The objective here was to fill this void by quantifying the performance of beef × dairy progeny where the sire excels in either a total merit index encompassing calving performance and beef performance traits (dairy-beef index; DBI) or excels in a subindex based solely on calving performance (CLV); for comparative purposes, these beef × dairy progeny were also compared with dairy × dairy progeny. A total of 123,785 calving records from 101,773 dairy cows calving in 3,065 dairy herds were used; of these, 48,875 progeny also had carcass information. The beef sires were stratified into 5 equally sized groups based separately on their DBI or CLV. Linear and threshold mixed models were used to compare calving and carcass performance of all 3 sire genotypes. Of the 415 sires that ranked in the highest of the 5 strata on the CLV subindex, only 52% of them ranked in the highest stratum for the DBI. The percentage of primiparae requiring any assistance at calving was 2 to 3 percentage units greater for the higher DBI sires relative to both the higher CLV beef sires and the dairy sires (not ranked on anything); no difference existed in multiparae. The extent of calving difficulty in primiparae was, however, less in higher DBI beef sires relative to both the higher CLV beef sires and the dairy sires, although the differences were biologically small. Perinatal mortality was greatest in the beef sires relative to the dairy sires, but no difference existed between the high CLV or high DBI beef sires. No difference in progeny gestation length was evident between the high DBI or high CLV beef sires, although both were >2 d longer than progeny from dairy sires. The higher DBI sires produced progeny with heavier, more conformed carcasses relative to the progeny from both high CLV beef sires and dairy sires. No differences existed between the progeny of the beef sires ranked highly on the CLV versus those ranked highly on the DBI for the probability of achieving the specification for carcass weight (between 270 and 380 kg) or fat score; the higher DBI animals, however, had a 4 to 10% greater probability of achieving the minimum carcass conformation required. In all instances, the beef sires had a greater probability of achieving all specifications relative to the progeny from the dairy sires with the difference for conformation being particularly large. Results indicate that more balanced progeny can be generated using a DBI, helping meet the requirements of both dairy and beef producers. Ignoring market failure across sectors, using higher DBI sires could increase dairy herd profit by 3 to 5% over and above the status quo approach to selection in dairy (i.e., CLV subindex).

Establishing a Model for Evaluating Chicken Coccidiosis Resistance Based on Principal Component Analysis

Simple Summary

Avian coccidiosis, an infectious disease caused by seven species of Eimeria that can infect a bird’s digestive tract and significantly retard its growth, is a serious economic disease for chickens. Many studies have demonstrated that host resistance to coccidiosis related to genetic variations can be improved by selective breeding. The parameters for evaluation of resistance to coccidiosis could be objective indicators, such as body weight gain and cecal lesion score, or biochemical indices, such as immune factors or cytokines in the plasma or serum. The aim of the study is to establish an optimal comprehensive evaluation model including a resistance index that can be detected in live chickens (slaughter traits cannot be selected in breeding) based on principal component analysis. The value of individual chickens calculated with the optimal evaluation model is associated with the cecum lesion score; the larger the value, the stronger the resistance to coccidiosis. This illustrated that the optimal model is effective in coccidiosis resistance selection.

Abstract

To establish a coccidiosis resistance evaluation model for chicken selection, the different parameters were compared between infected and control Jinghai yellow chickens. Validation parameters were selected for principal component analysis (PCA), and an optimal comprehensive evaluation model was selected based on the significance of a correlation coefficient between coccidiosis resistance parameters and principal component functions. The following six different parameters were identified: body weight gain 3–5 days post infection and catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), malondialdehyde (MDA) and γ-interferon (IFN-γ) concentrations on the eight day post inoculation. Six principal components and one accumulated contribution of up to 80% of the evaluation models were established by PCA. The results showed that the first model was significantly or highly significantly related to nine resistance parameters (p < 0.01 or p < 0.05), especially to cecal lesions (p < 0.01). The remaining models were related to only 2–3 parameters (p < 0.01 or p < 0.05) and not to cecal lesions (p > 0.05). The values calculated by the optimal model (first model) were significantly negatively correlated with cecal lesion performance; the larger the value, the more resistant to coccidiosis. The model fi1 = −0.636 zxi1 + 0.311 zxi2 + 0.801 zxi3 − 0.046 zxi4 − 0.076 zxi5 + 0.588 zxi6 might be the best comprehensive selection index model for chicken coccidiosis resistance selection.

Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle

Feed represents a substantial proportion of production costs in the dairy industry and is a useful target for improving overall system efficiency and sustainability. The objective of this study was to develop methodology to estimate the economic value for a feed efficiency trait and the associated methane production relevant to Canada. The approach quantifies the level of economic savings achieved by selecting animals that convert consumed feed into product while minimizing the feed energy used for inefficient metabolism, maintenance and digestion. We define a selection criterion trait called Feed Performance (FP) as a 1 kg increase in more efficiently used feed in a first parity lactating cow. The impact of a change in this trait on the total lifetime value of more efficiently used feed via correlated selection responses in other life stages is then quantified. The resulting improved conversion of feed was also applied to determine the resulting reduction in output of emissions (and their relative value based on a national emissions value) under an assumption of constant methane yield, where methane yield is defined as kg methane/kg dry matter intake (DMI). Overall, increasing the FP estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow's first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane with the economic values of CAD $0.82 and CAD $0.07, respectively. Therefore, the estimated total economic value for FP is CAD $0.89/unit. The proposed model is robust and could also be applied to determine the economic value for feed efficiency traits within a selection index in other production systems and countries.

iPASTIC: An online toolkit to estimate plant abiotic stress indices

PREMISE

In crop breeding programs, breeders use yield performance in both optimal and stressful environments as a key indicator for screening the most tolerant genotypes. During the past four decades, several yield-based indices have been suggested for evaluating stress tolerance in crops. Despite the well-established use of these indices in agronomy and plant breeding, a user-friendly software that would provide access to these methods is still lacking.

METHODS AND RESULTS

The Plant Abiotic Stress Index Calculator (iPASTIC) is an online program based on JavaScript and R that calculates common stress tolerance and susceptibility indices for various crop traits including the tolerance index (TOL), relative stress index (RSI), mean productivity (MP), harmonic mean (HM), yield stability index (YSI), geometric mean productivity (GMP), stress susceptibility index (SSI), stress tolerance index (STI), and yield index (YI). Along with these indices, this easily accessible tool can also calculate their ranking patterns, estimate the relative frequency for each index, and create heat maps based on Pearson's and Spearman's rank-order correlation analyses. In addition, it can also render three-dimensional plots based on both yield performances and each index to separate entry genotypes into Fernandez's groups (A, B, C, and D), and perform principal component analysis. The accuracy of the results calculated from our software was tested using two different data sets obtained from previous experiments testing the salinity and drought stress in wheat genotypes, respectively.

CONCLUSIONS

iPASTIC can be widely used in agronomy and plant breeding programs as a user-friendly interface for agronomists and breeders dealing with large volumes of data. The software is available at https://mohsenyousefian.com/ipastic/.

Response to a selection index including environmental costs and risk preferences of producers

Genetic improvement of animals plays an important role in improving the economic and environmental sustainability of livestock production systems. This paper proposes a method to incorporate mitigation of environmental impacts and risk preferences of producers into a breeding objective via economic values (EVs). The paper assesses the effects of using these alternative EVs of breeding goal traits on discounted economic response to selection and on environmental impacts at commercial farm level. The application focuses on a Brazilian pig production system. Separate dam- and sire-line breeding programs that supply parents in a 3-tier production system for producing crossbreds (fattening pigs) at commercial level were assumed. Using EVs that are derived from utility functions by incorporating risk aversion increases the cumulative discounted economic response to selection in sire-line selection (6%) while reducing response in dam-line selection (12%) compared with the use of traditional EVs. The use of EVs that include environmental costs increases the cumulative discounted social response to selection in both dam-line (5%) and sire-line (10%) selections. Emission of greenhouse gases, and excretion of nitrogen and phosphorus can be reduced more with genetic improvements of production traits than reproduction traits for the typical Brazilian farrow-to-finish pig farm. Reductions in environmental impacts do not, however, depend on the use of the different EVs (i.e., with and without taking into account environmental costs and risk). Both environmental costs and risk preferences of producers need to be considered in sire-line selection, and only environmental costs in dam-line selection to improve, at the same time, the economic and environmental sustainability of the Brazilian pig production system.

Appropriate selection indices for functional traits in dairy cattle breeding schemes

The objective of this study was to establish different single or multiple trait selection indices to calculate genetic and economic gains by combining some production, reproduction and udder health traits in a population similar to the overall practical situation in Iran, with and without imposing restrictions on genetic change for some traits. The SelAction software was used to perform the analyses based on selection index theory through a deterministic model. Results indicated that among established indices, the index that showed the highest genetic gain for milk yield did not maximize the total genetic and economic gains. Rather, the index that included all production, reproduction and udder health traits yielded the highest genetic and economic gains. When we placed restriction on the selection indices, the economic gain decreased and the amount of reduction depended on the heritability and the correlation of restricted trait(s) with other traits. Generally, regarding the economic genetic gain per generation, the indices based on records of 200 offspring were 4.819% more efficient than those that used information of 100 offspring.

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat

To evaluate the potential of genomic selection (GS), a selection experiment with GS and phenotypic selection (PS) was performed in an allogamous crop, common buckwheat (Fagopyrum esculentum Moench). To indirectly select for seed yield per unit area, which cannot be measured on a single-plant basis, a selection index was constructed from seven agro-morphological traits measurable on a single plant basis. Over 3 years, we performed two GS and one PS cycles per year for improvement in the selection index. In GS, a prediction model was updated every year on the basis of genotypes of 14,598-50,000 markers and phenotypes. Plants grown from seeds derived from a series of generations of GS and PS populations were evaluated for the traits in the selection index and other yield-related traits. GS resulted in a 20.9% increase and PS in a 15.0% increase in the selection index in comparison with the initial population. Although the level of linkage disequilibrium in the breeding population was low, the target trait was improved with GS. Traits with higher weights in the selection index were improved more than those with lower weights, especially when prediction accuracy was high. No trait changed in an unintended direction in either GS or PS. The accuracy of genomic prediction models built in the first cycle decreased in the later cycles because the genetic bottleneck through the selection cycles changed linkage disequilibrium patterns in the breeding population. The present study emphasizes the importance of updating models in GS and demonstrates the potential of GS in mass selection of allogamous crop species, and provided a pilot example of successful application of GS to plant breeding.

Symposium review: Novel strategies to genetically improve mastitis resistance in dairy cattle

Mastitis is a disease of major economic importance to the dairy cattle sector because of the high incidence of clinical mastitis and prevalence of subclinical mastitis and, consequently, the costs associated with treatment, production losses, and reduced animal welfare. Disease-recording systems compiling data from a large number of farms are still not widely implemented around the world; thus, selection for mastitis resistance is often based on genetically correlated indicator traits such as somatic cell count (SCC), udder depth, and fore udder attachment. However, in the past years, several countries have initiated collection systems of clinical mastitis, based on producers recording data in most cases. The large data sets generated have enabled researchers to assess incidence of this disease and to investigate the genetic background of clinical mastitis itself, as well as its relationships with other traits of interest to the dairy industry. The genetic correlations between clinical mastitis and its previous proxies were estimated more accurately and confirmed the strong relationship of clinical mastitis with SCC and udder depth. New traits deriving from SCC were also studied, with the most relevant findings being associated with mean somatic cell score (SCS) in early lactation, standard deviation of SCS, and excessive test-day SCC pattern. Genetic correlations between clinical mastitis and other economically important traits indicated that selection for mastitis resistance would also improve resistance against other diseases and enhance both fertility and longevity. However, milk yield remains negatively correlated with clinical mastitis, emphasizing the importance of including health traits in the breeding objectives to achieve genetic progress for all important traits. These studies enabled the establishment of new genetic and genomic evaluation models, which are more efficient for selection to mastitis resistance. Further studies that are potential keys for future improvement of mastitis resistance are deep investigation of the bacteriology of mastitis, identification of novel indicator traits and tools for selection, and development of a larger female reference population to improve reliability of genomic evaluations. These cutting-edge studies will result in a better understanding of the genetic background of mastitis resistance and enable a more accurate phenotyping and genetic selection to improve mastitis resistance, and consequently, animal welfare and industry profitability.

Selection of Drought Tolerant Maize Hybrids Using Path Coefficient Analysis and Selection Index

BACKGROUND

In drought-prone environments, direct selection for yield is not adequate because of the variable environment and genotype x environment interaction. Therefore, the use of secondary traits in addition to yield has been suggested. The relative usefulness of secondary traits as indirect selection criteria for maize grain yield is determined by the magnitudes of their genetic variance, heritability and genetic correlation with the grain yield.

MATERIALS AND METHODS

Forty eight testcross hybrids derived from lines with different genetic background and geographical origins plus 7 checks were evaluated in both well-watered and water-stressed conditions over two years for grain yield and secondary traits to determine the most appropriate secondary traits and select drought tolerant hybrids.

RESULTS

Study found that broad-sense heritability of grain yield and Ear Per Plant (EPP) increased under drought stress. Ear aspect (EASP) and ear height (EHT) had larger correlation coefficients and direct effect on grain yield but in opposite direction, negative and positive respectively. Traits like, EPP, Tassel Size (TS) and Plant Recovery (PR) contributed to increase yield via EASP by a large negative indirect effect. Under drought stress, EHT had positive and high direct effect and negative indirect effect via plant height on grain yield indicating that the ratio between ear and plant heights (R-EPH) was associated to grain yield.

CONCLUSION

Path coefficient analysis showed that traits EPP, TS, PR, EASP, R-EPH were important secondary traits in the present experiment. These traits were used in a selection index to classify hybrids according to their performance under drought. The selection procedure included also a Relative Decrease in Yield (RDY) index. Some secondary traits reported as significant selection criteria for selection under drought stress were not finally established in the present study. This is because the relationship between grain and secondary traits can be affected by various factors including germplasm, environment and applied statistical analysis. Therefore, different traits and selection procedure should be applied in the selection process of drought tolerant genotypes for diverse genetic materials and growing conditions.

Feed resource selection of Criollo goats artificially infected with Haemonchus contortus: nutritional wisdom and prophylactic self-medication

Previous cafeteria studies suggested that a moderate natural gastrointestinal nematode (GIN) infection did not modify the resource selection of adult Criollo goats towards tannin-rich plants compared with worm-free goats. A higher infection with Haemonchus contortus could trigger a change in the resource selection behaviour towards tannin-rich foliage. Alternatively, goats might select plant species solely to meet their nutritional requirements. A cafeteria study investigated the effect of a high artificial infection with H. contortus on the feed resource selection of goats. Adult Criollo goats (37.5±4.8 kg BW) with browsing experience were distributed in two groups: the infected group (IG) with six animals artificially infected with H. contortus (6000 L3/animal); and the non-infected group (NIG) with six animals maintained worm-free. The experiment included two 5-day periods with additional 5-day adaptation period. In the first period, animals were offered foliage of five plant species with a decreasing gradient of condensed tannins (CT) (Mimosa bahamensis, Gymnopodium floribundum, Havardia albicans, Acacia pennatula, Lysiloma latisiliqum), and three plant species with negligible CT content (Leucaena leucocephala, Piscidia piscipula and Brosimum alicastrum). In the second period the foliage of B. alicastrum was withdrawn. A grain-based concentrate feed was offered daily at 1% BW in DM basis. Dry matter and nutrient intake was determined. Foliage selection of each experimental group was determined using the Chesson selection index. The H. contortus egg count per gram of faeces (EPG) was determined for infected goats twice daily. Chesson index showed a similar pattern of foliage selection on periods 1 and 2. Mean EPG of goats in IG was 2028±259 EPG during period 1 and 1 293±198 EPG during period 2 (P>0.05). During period 1, the selection pattern was highest for B. alicastrum (tannin-free), followed by a tannin-rich plant (M. bahamensis). These two plants remained as highly selected during period 2. The Chesson index showed that both experimental groups (IG and NIG) selected the same plant species in both periods. Thus, a high H. contortus infection did not affect selection of goats fed with CT-rich plants. Apparently, goats balanced their nutrient intake with the plants selected, showing evidence of nutritional wisdom. This balance may have helped to prevent excess protein in the diet and also to maintain a low GIN infection, both considered as examples of prophylactic self-medication.

Genetic correlations among milk yield, morphology, performance test traits and somatic cells in dual-purpose Rendena breed

Selection in native local breeds needs great carefulness due to the small population size and the risk of inbreeding. Furthermore, most breeds are dual-purpose, and milk and beef attitudes are antagonistic. For preservation purposes functional traits need to be considered. Focusing on the small local Rendena cattle, this study aimed to analyse the genetic correlations among milk, beef and udder health traits and the response to selection predicted under different scenarios. The study considered milk, fat and protein yields (MY), factor scores for udder volume (UV), conformation (UC) and muscularity obtained from type traits scored on primiparous cows, and performance test traits (PT) measured on young bulls at test station: average daily gain, in vivo SEUROP fleshiness, in vivo dressing percentage. Somatic cell score (SCS) was considered as a functional trait, with a possibility of restricting its genetic gain to zero. The study considered 281 497 MY test-day data collected on 16 974 cows, and data from linear type evaluation on 11 992 primiparous cows for factor scores. The PT data were recorded on 1428 young bulls, and SCS obtained from cell counts at milk recording. Bi-trait restricted maximum likelihood animal model analyses were performed to assess genetic parameters. Heritability varied from 0.157 (fat) to 0.442 (dressing percentage). Udder volume and MY resulted positively genetically correlated (average correlation 0.427), whereas the low-negative genetic correlation between MY and UC (-0.141) suggested a negative impact of milk gain on udder form. Beef traits of factor muscularity and PT showed medium-high favourable genetic correlations (from 0.357 to 0.984), excluding a null correlation between daily gain and muscularity. The genetic correlation MY v. muscularity was unfavourable (-0.328 on average), whereas null correlations were found in MY v. PT, apart from fat v. dressing percentage (-0.151). Somatic cell score showed low unfavourable correlations with protein (0.111) and UV (0.092), and favourable correlations with UC (-0.193). Response to selection in different scenarios indicated a good balanced gain for milk and beef when standardized economic weights of 0.66 and 0.34 are given to the two attitudes, and SCS genetic gain is restricted. Current genetic trends (MY and PT increasing, but muscularity lessening) reflect a stronger selection for milk, suggesting a slight progressive change towards a milk conformation. Aiming to preserve the dual-purpose characteristics of a breed, proper breeding policies taking into account the genetic relationships among traits and including functional traits should be applied in local dual-purpose populations.

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.

Improving the reliability of female fertility breeding values using type and milk yield traits that predict energy status in Australian Holstein cattle

The objectives of this study were (1) to propose changing the selection criteria trait for evaluating fertility in Australia from calving interval to conception rate at d 42 after the beginning of the mating season and (2) to use type traits as early fertility predictors, to increase the reliability of estimated breeding values for fertility. The breeding goal in Australia is conception within 6 wk of the start of the mating season. Currently, the Australian model to predict fertility breeding values (expressed as a linear transformation of calving interval) is a multitrait model that includes calving interval (CVI), lactation length (LL), calving to first service (CFS), first nonreturn rate (FNRR), and conception rate. However, CVI has a lower genetic correlation with the breeding goal (conception within 6 wk of the start of the mating season) than conception rate. Milk yield, type, and fertility data from 164,318 cow sired by 4,766 bulls were used. Principal component analysis and genetic correlation estimates between type and fertility traits were used to select type traits that could subsequently be used in a multitrait analysis. Angularity, foot angle, and pin set were chosen as type traits to include in an index with the traits that are included in the multitrait fertility model: CVI, LL, CFS, FNRR, and conception rate at d 42 (CR42). An index with these 8 traits is expected to achieve an average bull first proof reliability of 0.60 on the breeding objective (conception within 6 wk of the start of the mating season) compared with reliabilities of 0.39 and 0.45 for CR42 only or the current 5-trait Australian model. Subsequently, we used the first eigenvector of a principal component analysis with udder texture, bone quality, angularity, and body condition score to calculate an energy status indicator trait. The inclusion of the energy status indicator trait composite in a multitrait index with CVI, LL, CFS, FNRR, and CR42 achieved a 12-point increase in fertility breeding value reliability (i.e., increased by 30%; up to 0.72 points of reliability), whereas a lower increase in reliability (4 points, i.e., increased by 10%) was obtained by including angularity, foot angle, and pin set in the index. In situations when a limited number of daughters have been phenotyped for CR42, including type data for sires increased reliabilities compared with when type data were omitted. However, sires with more than 80 daughters with CR42 records achieved reliability estimates close to 80% on average, and there did not appear to be a benefit from having daughters with type records. The cost of phenotyping to obtain such reliabilities (assuming a cost of AU$14 per cow with type data and AU$5 per cow with pregnancy diagnosed) is lower if more pregnancy data are collected in preference to type data. That is, efforts to increase the reliability of fertility EBV are most cost effective when directed at obtaining a larger number of pregnancy tests.

A Genomic Selection Index Applied to Simulated and Real Data

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.

Genetic selection for increased mean and reduced variance of twinning rate in Belclare ewes

It is sometimes possible to breed for more uniform individuals by selecting animals with a greater tendency to be less variable, that is, those with a smaller environmental variance. This approach has been applied to reproduction traits in various animal species. We have evaluated fecundity in the Irish Belclare sheep breed by analyses of flocks with differing average litter size (number of lambs per ewe per year, NLB) and have estimated the genetic variance in environmental variance of lambing traits using double hierarchical generalized linear models (DHGLM). The data set comprised of 9470 litter size records from 4407 ewes collected in 56 flocks. The percentage of pedigreed lambing ewes with singles, twins and triplets was 30, 54 and 14%, respectively, in 2013 and has been relatively constant for the last 15 years. The variance of NLB increases with the mean in this data; the correlation of mean and standard deviation across sires is 0.50. The breeding goal is to increase the mean NLB without unduly increasing the incidence of triplets and higher litter sizes. The heritability estimates for lambing traits were NLB, 0.09; triplet occurrence (TRI) 0.07; and twin occurrence (TWN), 0.02. The highest and lowest twinning flocks differed by 23% (75% versus 52%) in the proportion of ewes lambing twins. Fitting bivariate sire models to NLB and the residual from the NLB model using a double hierarchical generalized linear model (DHGLM) model found a strong genetic correlation (0.88 ± 0.07) between the sire effect for the magnitude of the residual (VE ) and sire effects for NLB, confirming the general observation that increased average litter size is associated with increased variability in litter size. We propose a threshold model that may help breeders with low litter size increase the percentage of twin bearers without unduly increasing the percentage of ewes bearing triplets in Belclare sheep.

GplusE: beyond genomic selection

GplusE is a strategy for genomic selection in which the accuracy of assessment in the reference population for a primary trait such as yield is increased by the incorporation of data from high‐ throughput field phenotyping platforms. This increase in precision comes from both exploiting genetic relationships between traits and reducing the effect of environmental influences upon them. We describe a collaborative project among researchers and breeders to develop a large reference population of elite UK wheat lines. This will be used to test the method, to study the design of the reference population, and to test genotyping strategies and imputation methods. Finally, it will provide data to pump‐prime the application of genomic selection to UK winter wheat breeding.

Correlation of chemical compositions of cassava varieties to their resistance to Prostephanus truncatus Horn (Coleoptera: Bostrichidae)

The preference of cassava as a major host by Prostephanus truncatus Horn is a major constraint to ample production of cassava, Manihot esculenta Crantz and storage. This study analyzed the nutritional and secondary metabolite compositions in 15 cassava varieties, evaluated levels of damage and reproduction by P. truncatus, and assessed their resistance to attack. One hundred grams of dried cassava chips in 250-ml Kilner jars were infested with 10 adult larger grain borerof 0-10 days old and held for 3 months. The nutritional and secondary metabolites compositions of the dry cassava chips were determined using the method of Association of Analytical Chemists . Chip perforation rates in the cassava varieties ranged from 17.7 to 71.6%. The weight of cassava powder varied by about threefold. The final number of larger grain borer in the cassava varieties varied by about sixfold with 63 in 01/0040 and 379 in 01/1368. Hydrocyanic acid content content varied by over 10-fold and correlated negatively with number of larger grain borer. Flavonoid content varied by ∼10%. Tannins and saponin content of the cassava negatively correlated with number of adult P. truncatus. The cassava varieties 95/0166, 92/0326, 01/0040, 05/0024, and 34 91934 had selection index <0.8 and were classified as resistant to larger grain borer damage, while others with selection index >0.8 were classified as susceptible. The resistance to high damage in the resistant varieties was conferred by secondary metabolites such as tannins, saponins, alkaloids, and hydrocyanic acid content. The genetic variation in cassava varieties could be explored to breed resistant cassava varieties for use in larger grain borer-endemic areas.