Browsing by Author "Bradford, Heather L."
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- Comparison of reproductive performance of AI- and natural service-sired beef females under commercial managementMarrella, Mackenzie A.; White, Robin R.; Dias, Nicholas W.; Timlin, Claire; Pancini, Stefania; Currin, John F.; Clark, Sherrie G.; Stewart, Jamie L.; Mercadante, Vitor R. G.; Bradford, Heather L. (Oxford University Press, 2021-07)The objective of this study was to assess differences in reproductive performance of natural service and artificial insemination (AI) sired beef females based on pregnancy outcomes, age at first calving, and calving interval. Data were sourced from 8,938 cows sired by AI bulls and 3,320 cows sired by natural service bulls between 2010 and 2017. All cows were in a commercial Angus herd with 17 management units located throughout Virginia and represented spring and fall calving seasons. All calves were born to dams managed with estrus synchronization. Pregnancy was analyzed with generalized linear mixed models and other reproductive measures with linear mixed models in R. Six models were evaluated with the dependent variables of pregnancy status at the first diagnosis, pregnancy status at the second diagnosis, pregnancy type (AI or natural service) at the first diagnosis, pregnancy type at the second diagnosis, calving interval, and age at first calving. Independent variables differed by model but included sire type of the female (AI or natural service), prebreeding measures of age, weight, and body condition score, postpartum interval, sex of the calf nursing the cow, and management group. No differences were observed between AI- and natural service-sired females based on pregnancy status at first and second pregnancy diagnosis (P > 0.05). Sire type was only found to be significant for age at first calving (P < 0.05) with AI-sired females being 26.6 ± 1.6 d older at their first calving, which was expected because AI-sired females were born early in the calving season making them older at breeding. Surprisingly, age and body condition score were not significant predictors of pregnancy (P > 0.05). Body weight at breeding was not significant for pregnancy (P > 0.05) but was significant for age at first calving (P < 0.05). These data suggested that lighter heifers calved earlier which contradicts our original hypothesis. Overall, commercial Angus females sired by AI or natural service bulls had similar reproductive performance. Factors that were commonly associated with reproductive success were not significant in this commercial Angus herd managed with estrus synchronization. Given the size of these data, the importance of body condition, age, and weight should be reassessed in modern genetics and management practices.
- Designing and modeling high-throughput phenotyping data in quantitative geneticsYu, Haipeng (Virginia Tech, 2020-04-09)Quantitative genetics aims to bridge the genome to phenome gap. The advent of high-throughput genotyping technologies has accelerated the progress of genome to phenome mapping, but a challenge remains in phenotyping. Various high-throughput phenotyping (HTP) platforms have been developed recently to obtain economically important phenotypes in an automated fashion with less human labor and reduced costs. However, the effective way of designing HTP has not been investigated thoroughly. In addition, high-dimensional HTP data bring up a big challenge for statistical analysis by increasing computational demands. A new strategy for modeling high-dimensional HTP data and elucidating the interrelationships among these phenotypes are needed. Previous studies used pedigree-based connectetdness statistics to study the design of phenotyping. The availability of genetic markers provides a new opportunity to evaluate connectedness based on genomic data, which can serve as a means to design HTP. This dissertation first discusses the utility of connectedness spanning in three studies. In the first study, I introduced genomic connectedness and compared it with traditional pedigree-based connectedness. The relationship between genomic connectedness and prediction accuracy based on cross-validation was investigated in the second study. The third study introduced a user-friendly connectedness R package, which provides a suite of functions to evaluate the extent of connectedness. In the last study, I proposed a new statistical approach to model high-dimensional HTP data by leveraging the combination of confirmatory factor analysis and Bayesian network. Collectively, the results from the first three studies suggested the potential usefulness of applying genomic connectedness to design HTP. The statistical approach I introduced in the last study provides a new avenue to model high-dimensional HTP data holistically to further help us understand the interrelationships among phenotypes derived from HTP.
- Direct and correlated responses to selection for autumn lambing in sheepAsadi-Fozi, Masood; Bradford, Heather L.; Notter, David R. (2020-10-02)Background Seasonal reproduction limits productivity, flexibility, and profitability in commercial sheep production. Hormonal and (or) photoperiodic manipulation can be used to control estrous cycles in sheep and reduce limitations that are imposed by the seasonal anestrous but are often impractical or incompatible with the extensive management systems preferred for ruminant livestock. Thus, the current study investigated the use of selection to improve realized fertility (i.e., the proportion of ewes that lambed) following an out-of-season spring joining period (May and June) in a crossbred sheep population. Results Over 17 years, estimated breeding values (EBV) for fertility in selected (S) ewes increased by 0.175 (0.01 per year). The mean EBV for fertility of S ewes was greater than that of control ewes by year 10 (P = 0.02), and the fertility of adult (≥ 3 years old) ewes reached 0.88 ± 0.05 by year 17. Lambing began approximately 140 days after the introduction of rams, and 64% of the S ewes that lambed did so in the first 17 days of the potential lambing season, which indicated that most of the S ewes were cycling at the time of ram introduction and were not induced to cycle by the introduction of breeding males (i.e., the so-called “ram effect”). Animals in the S line had modest increases in body weight and scrotal circumference. A modest negative trend in the additive maternal effect on birth weight was observed but was reversed by additional selection on EBV for maternal birth weight. The heritability of litter size in autumn lambing was low (0.04) and could potentially limit the response to selection for this trait. Conclusions Selection improved realized ewe fertility in out-of-season mating, with absolute increases of approximately 1% per year in the percentage of joined ewes that lambed in the autumn. Genetic antagonisms with other performance traits were generally small. A modest antagonism with maternal breeding values for birth weight was observed but it could be accommodated by selection on EBV for maternal birth weight. Our results support results of previous studies that indicate that these selected ewes had one of the shortest seasonal anestrous periods reported for temperate sheep breeds and that spring-lambing lactating ewes from the selection line were capable of relatively rapid rebreeding in the spring.
- Evaluating the relationship between fecal egg count, FAMACHA score, and weight in dewormed and non-dewormed Katahdin rams during a parasite challengeGalyon, Hailey R.; Zajac, Anne M.; Wright, D. Lee; Greiner, Scott P.; Bradford, Heather L. (Oxford University Press, 2020-10-01)The objective of this study was to evaluate and to estimate the relationship between fecal egg counts (FECs) and FAMACHA score and the body weight of growing Katahdin rams during a parasite challenge. One of the largest factors negatively influencing reproduction and economics in the sheep industry is gastrointestinal nematode (GIN) parasites. Due to anthelmintic resistance of these parasites, animals are selected for parasite resistance using FEC and FAMACHA scores. Data were used from the Virginia Tech Southwest Agricultural Research and Extension Center Ram Test in Glade Spring, VA, from the year 2012 to 2018 in which animals were tested in 14-d intervals for 70 d. Mixed models for repeated weight measurements were made from backward stepwise selection to evaluate the relationships between weight and GIN FEC. A total of 576 animals within 23 contemporary groups derived from test year and pasture group were analyzed. Ram, contemporary group, and consignor were considered random effects, and fixed effects were birth type, test day, age, age squared, starting weight, FEC, and FAMACHA score. Pairwise contrasts were used in the statistical analysis of parameters and their interactions. Weight and age were found to have a quadratic relationship. Increased FEC was associated with weight loss at a rate of 0.00030 kg/FEC (P < 0.0001). Animals dewormed at any point during the trial weighed less than those that were not and increased with test day to a maximum difference of 4.66 kg (P < 0.001). FAMACHA score was found to be significant (P < 0.05), but a direct relationship with weight was not conclusive. Overall, rams with severe enough parasite load to require deworming had lesser weights, which could impact the profitability of sheep production and reinforced the need to select animals that had greater innate parasite resistance.
- Modeling missing pedigree in single-step genomic BLUPBradford, Heather L.; Masuda, Yutaka; VanRaden, Paul M.; Legarra, Andres; Misztal, Ignacy (2019-03)The objective was to compare methods of modeling missing pedigree in single-step genomic BLUP (ssGBLUP). Options for modeling missing pedigree included ignoring the missing pedigree, unknown parent groups (UPG) based on A (the numerator relationship matrix) or H (the unified pedigree and genomic relationship matrix), and metafounders. The assumptions for the distribution of estimated breeding values changed with the different models. We simulated data with heritabilities of 0.3 and 0.1 for dairy cattle populations that had more missing pedigrees for animals of lesser genetic merit. Predictions for the youngest generation and UPG solutions were compared with the true values for validation. For both traits, ssGBLUP with metafounders provided accurate and unbiased predictions for young animals while also appropriately accounting for genetic trend. Accuracy was least and bias was greatest for ssGBLUP with UPG for H for the trait with heritability of 0.3 and with UPG for A for the trait with heritability of 0.1. For the trait with heritability of 0.1 and UPG for H, the UPG accuracy (SD) was -0.49 (0.12), suggesting poor estimates of genetic trend despite having little bias for validations on young, genotyped animals. Problems with UPG estimates were likely caused by the lesser amount of information available for the lower heritability trait. Hence, UPG need to be defined differently based on the trait and amount of information. More research is needed to investigate accounting for UPG in A(22) to better account for missing pedigrees for genotyped animals.
- Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasetsBradford, Heather L.; Masuda, Yutaka; Cole, John B.; Misztal, Ignacy; VanRaden, Paul M. (2019-03)The objective of this study was to model differences in pedigree accuracy caused by selective genotyping. As genotypes are used to correct pedigree errors, some pedigree relationships are more accurate than others. These accuracy differences can be modeled with uncertain parentage models that distribute the paternal (maternal) contribution across multiple sires (dams). In our case, the parents were the parent on record and an unknown parent group to account for pedigree relationships that were not confirmed through genotypes. Pedigree accuracy was addressed through simulation and through North American Holstein data. Data were simulated to be representative of the dairy industry with heterogeneous pedigree depth, pedigree accuracy, and genotyping. Holstein data were obtained from the official evaluation for milk, fat, and protein. Two models were compared: the traditional approach, assuming accurate pedigrees, and uncertain parentage, assuming variable pedigree accuracy. The uncertain parentage model was used to add pedigree relationships for alternative parents when pedigree relationships were not certain. The uncertain parentage model included 2 possible sires (dams) when the sire (dam) could not be confirmed with genotypes. The 2 sires (dams) were the sire (dam) on record with probability 0.90 (0.95) and the unknown parent group for the birth year of the sire (dam) with probability 0.10 (0.05). An additional set of assumptions was tested in simulation to mimic an extensive dairy production system by using a sire probability of 0.75, a darn probability of 0.85, and the remainder attributed to the unknown parent groups. In the simulation, small bias differences occurred between models based on pedigree accuracy and genotype status. Rank correlations were strong between traditional and uncertain parentage models in simulation ( >= 0.99) and in Holstein ( >= 0.99). For Holsteins, the estimated breeding value differences between models were small for most animals. Thus, traditional models can continue to be used for dairy genomic prediction despite using genotypes to improve pedigree accuracy. Those genotypes can also be used to discover maternal parentage, specifically maternal grandsires and great grandsires when the dam is not known. More research is needed to understand how to use discovered maternal pedigrees in genetic prediction.