bluuebunny/crossref_metadata_2025_split huggingface dataset:
PSI-10 Genetic parameters for soybean allergy response in divergently-selected pigs
Authors: Chen Yuwen , Schinckel Allan P , Stewart Terry , Brito Luiz , de Oliveira Hinayah Rojas , Alvarenga Amanda 
Published: November 2020

Abstract
Genetically selecting for improved animal resilience has become of great interest in modern livestock breeding. In this context, piglets experience a transitory allergic response to soybean proteins that has a negative impact on their growth and welfare. The main objective of this study was to determine whether soybean allergy response is a heritable trait, using divergently selected pig lines. The base generation pigs were generated from nine sire lines (primarily Yorkshire and Landrace) and two dam sources (Yorkshire × Chester White F1 and Landrace × Yorkshire F1). Soybean allergic response was measured, following a skin test protocol, on pigs that received a diet containing 28% soybean meal for 21 consecutive days post-weaning. The skin test was performed with an intradermal injection of soybean protein. Wheel and flare scores (ranging from 0 to 3) were assigned approximately 60 minutes post-injection. Based on the skin test total scores (i.e., the sum of the wheal and flair scores, ranging from 0 to 6), animals were selected to create two (a high and a low) soybean protein reacting lines, and bred within lines for nine generations. Phenotypes used in this study were measured on a total of 5,505 animals born from generations one to nine, in both high and low reacting lines. Significant systematic effects (P &lt; 0.05) included in the statistical model were birth year, birth season, sex, and weaning weight (as a covariate). Genetic parameters for the skin test were estimated using a pedigree-based relationship matrix containing 9,201 animals, and a threshold animal mixed model. The genetic variance estimated for soybean allergy was 0.091, indicating that there is genetic variability in this trait. The heritability estimated for soybean allergy response was 0.139, which indicates that food allergy response is a heritable trait and can be improved through selective breeding.
PSIII-3 Effect of Day of Induction on Sow and Piglet Performance
Authors: McGuire Miranda R , Dunckelberger Jenelle , Eggert John , Schinckel Allan P , Knol Egbert 
Published: November 2020

Abstract
Induction can be a useful herd management tool. However, inducing too early can negatively impact critical biological processes for the sow and her piglets. Although inducing at 115 d of gestation is widely accepted for commercial sows, the natural gestation length of purebred lines can exceed 115 days. Therefore, the objective of this study was to estimate the effect of induction at 115 vs. 117 d of gestation on the performance of purebred sows and their piglets. Purebred Landrace females from a commercial multiplier were used for this study. Sows were mated to one of the following genetic lines: A, B, or C. Of the 601 sows placed on test, 264 and 135 sows farrowed as a result of induction at 115 or 117 days of gestation, respectively. From these 399 sows, piglets sired by line C (n=1,715) were used for subsequent analyses. Traits of the sow analyzed included: percent stillborn, sow mortality (from 0 to 24 days post-farrowing), and removal rate (between farrowing and weaning). Traits of the piglet analyzed included: weaning weight, lactation ADG, and pre-weaning mortality. All traits were analyzed using a generalized linear model, except for mortality and removal rate, which were analyzed using a LOGIT model. Piglets from the 117 group had significantly greater weaning weight (P=0.02), significantly greater ADG (P=0.0009), and numerically lower pre-weaning mortality (P=0.16) than piglets from the 115 group. Sows induced at 117 days of gestation had numerically lower percent stillborn, lower mortality, and lower removal rate than sows induced at 115 days of gestation, although not significant (P=0.13, P=0.48, and P=0.50, respectively). In conclusion, there was a numerically or significantly favorable effect of inducing sows at 117 vs. 115 d of gestation on both sow performance and early-life piglet performance.
PSIII-9 Performance by weaned Katahdin lambs offered diets with and without Actifor® Pro
Authors: Edwards Kandace , Shanks Bruce C , Caldwell James D , Adams Kevin 
Published: November 2020

Abstract
Using all-natural phytogenics could be an alternative to using growth promotors within animal agriculture; particularly, since consumers are becoming more concerned with the use of these types of products. Our objective was to determine if using Actifor® Pro (PRO) could improve weaned lamb performance. On June 17th, 2019, a total of 60 (18 ± 0.92 kg BW) Katahdin ram lambs were weaned and stratified by BW and allocated randomly to 1 of 10 groups representing 2 treatments: 1) C (no PRO added; n = 5) and 2) PRO (5 g/hd/d; n = 5). During the 84-d study, lambs were housed in 2.4 × 4.9-meter pens and offered a 16% CP commercial grower diet. All lambs had ad libitum access to feed and water. Prior to the start of the study, all lambs were administered a CD&T vaccine plus drenched with an oral dewormer. Lambs were weighed at the start, d 28, d 56, and at the end of the study. Data were analyzed using Proc Mixed of SAS with pen(treatment) as the error term. Start, d 28, d 56, final BW, d 56 gain, d 56 ADG, final gain, and final ADG did not differ (P ≥ 0.31) between treatments. However, d 28 gain and d 28 ADG tended (P = 0.07) to be greater from PRO compared with C. Intake and G:F did not differ (P ≥ 0.19) between treatments throughout the study. Therefore, feeding Actifor® Pro may improve gain within the first 28 days after weaning, but those difference may not persist throughout the growing phase. Furthermore, this product may need to be investigated in a creep feed diet.
PSIII-11 The effect of GALR2 genotype and differing implant strategies on blood metabolite concentrations in finishing steers
Authors: Anderson Ellie S , Hall Jerica R , Hirchert Mara R , Swanson Kendall C , Maddock Carlin Kasey R , Dahlen Carl R , Ward Alison K 
Published: November 2020

Abstract
The neuropeptide receptor galanin receptor 2 (GALR2) is involved in appetite regulation and is therefore a potential target for marker-assisted management of finishing cattle. The objective of this study was to determine the interaction between implant strategy and GALR2 genotype on the metabolic status of feedlot steers. Angus crossbred steers (n = 93) were selected for the study based on GALR2 c.-199G &gt;T genotype (n = 19 GG, 36 TT and 38 TG steers). Steers were blocked by initial body weight into two groups (248 ± 50 and 293± 43 kg) and finished for 166 or 202 days. Steers were assigned one of two implant strategies in a 2 (implant) × 3 (genotype) factorial design. The implant strategies were: one implant of Revalor-S (Merk, New Jersey) on day 77 of finishing (1X) or two implants with the first on day 0 and the second on day 77 (2X). All steers were fed a standard feedlot ration throughout the study. Blood samples were collected every 28 days. Serum samples were analyzed for blood urea nitrogen (BUN) and glucose concentration. Data were analyzed using the mixed procedure of SAS with repeated measures and significance was set at P &lt; 0.05. Day × Implant interaction had a significant effect on BUN (P = 0.0035), and day × genotype had a significant effect on serum glucose concentration (P = 0.0049). There was no effect of implant × genotype interaction for BUN or glucose (P &gt; 0.05). On days 84 and 140 glucose concentration was greater in GG steers than GT steers (P &lt; 0.05) and tended to be greater than TT steers (P ≤ 0.0667). We conclude that there is no interaction between implant strategy and GALR2 genotype on metabolism in finishing steers.
7 Mixing aggression and floor type during gestation have implications for sow reproductive performance
Authors: Lagoda Martyna E , Boyle Laura A , Marchewka Joanna , Calderón Díaz Julia A 
Published: November 2020

Abstract
Aggressive behavior resulting from mixing to establish a dominance hierarchy is a welfare concern for group-housed sows. The associated stress could negatively impact reproductive performance (RP). Objectives of this study were to investigate associations between 1) mixing aggression intensity and RP within and between parities one (P1) and two (P2), and 2) aggression intensity, floor type and RP. A total of 164 gilts were followed through two parities. Gilts were mixed into stable groups of eight unfamiliar individuals four days after insemination, and housed on fully-slatted floors, either uncovered (CON; n = 84) or covered (RUB; n = 80) by a rubber slat-mat until farrowing (for both parities). Skin lesion scores (SLS) (proxy for mixing aggression intensity), were recorded 24–72 hrs post-mixing (0 = no lesions to 5 = severe lesions) on five body regions on left and right sides, and on the tail, in both parities. Total SLS was calculated for each sow. RP data were retrospectively acquired from farm records for both parities. Data were analyzed using GLMM in PROC GLIMMIX of SAS (v9.4) where 1) each parity analyzed separately, and 2) SLS in P1 used to predict RP in P2. Gilts with higher SLS in P2 had higher piglet mortality (P = 0.021) and longer cycle length (P = 0.003) in P2. Gilts with higher SLS in P1 had more non-productive days (P &lt; 0.001) in P2. CON gilts had higher SLS than RUB gilts (P = 0.017) in P1, but not in P2 (P &gt; 0.05). CON gilts also had more piglets born dead (P = 0.004) and tended to crush more piglets (P = 0.097) in P2. Our results confirm that mixing aggression impacts RP in the current parity negatively. They also suggest that mixing aggression experienced by gilts may have long-term negative carry-over effects on performance in subsequent parities. Use of rubber flooring appears to mitigate detrimental effects of mixing aggression, perhaps through improved sow comfort.
47 Distortion of Mendelian segregation across Angus cattle genome reveal novel lethal haplotype affecting reproduction
Authors: Lahoucine Samir Id , Casellas Joaquim , Lu Duc , Sargolzaei Mehdi , Miller Stephen P , Canovas Angela 
Published: November 2020

Abstract
The rapid development of DNA technologies, together with the adoption and daily routine use of commercial SNP arrays in livestock industries, provided a valuable resource of large and powerful data that can be explored for innovative applications for animal breeding purposes. Thus, the current availability of trios (i.e., sire-dam-offspring) of genotyped families enables the implementation of the transmission ratio distortion (TRD) approach to discover lethal alleles. Lethal alleles, which are crucial genetic determinants for reproduction, do not follow Mendelian principles but deviate from inheritance expectations, displaying signals of TRD. In this research, TRD was characterized using allelic (specific- and unspecific-parent TRD) and genotypic parameterizations (additive- and dominance-TRD) using both SNP- and haplotype-based methods. The analyses were performed using 258,140 Angus animals with 92,942 autosomal SNP genotypes, including 7,486 sires, 72,688 dams and 205,966 offspring. Across the whole genome, 852 regions displaying TRD were identified with different statistical significance. Among these findings, 19 haplotypes with recessive patterns (potential lethality for homozygote individuals) and 52 genomic regions with allelic patterns exhibiting complete or quasi-complete absence for homozygous individuals in addition to under-representation (potentially reduced viability) of heterozygous offspring were found. The average number of under-represented offspring (i.e., expected but not observed) across 52 allelic TRD regions ranged from 5,000 to 41,008. In contrast, the number of non-observed homozygous offspring for the 19 regions with recessive pattern ranges from 10 to 564. In addition, 64 and 20 genomic regions with TRD showed significant effects on the trait heifer pregnancy P &lt; 0.05 and P &lt; 0.01, respectively, reducing the progeny rate up to 15%. These novel findings in Angus present new candidate genomic regions putatively carrying lethal and semi-lethal alleles providing opportunities to reduce the rates of embryonic losses or death of offspring which could improve fertility and fitness in beef cattle populations.
22 Accuracy of indirect predictions for large datasets based on prediction error covariance of SNP effects from single-step GBLUP
Authors: Garcia Andre , Aguilar Ignacio , Legarra Andres , Miller Stephen P , Tsuruta Shogo , Misztal Ignacy , Lourenco Daniela 
Published: November 2020

Abstract
With an ever-increasing number of genotyped animals, there is a question of whether to include all genotypes into single-step GBLUP (ssGBLUP) evaluations or to include only genotyped animals with phenotypes and use indirect predictions (IP) for the remaining young genotyped animals. Under ssGBLUP, SNP effects can be backsolved from GEBV, and IP can be calculated as the sum of SNP effects weighted by the gene content. To publish IP, a measure of accuracy that reflects the standard error of prediction, and that is comparable to GEBV accuracy, is needed. Our first objective was to test formulas to compute accuracy of IP by backsolving prediction error covariance (PEC) of GEBV into PEC of SNP effects. The second objective was to investigate the number of genotyped animals needed to obtain robust IP accuracy. Data were provided by the American Angus Association, with 38,000 post-weaning gain phenotypes and 60,000 genotyped animals. Correlations between GEBV and IP were ≥0.99. When all genotyped animals were used for PEC computations, accuracy correlations were also ≥0.99. Additionally, GEBV and IP accuracies were compatible, with both direct inversion of the genomic relationship matrix (G) or using the algorithm for proven and young (APY) to obtain G inverse. As the number of genotyped animals in PEC computations decreased to 15,000, accuracy correlations were still high (≥0.96), but IP accuracies were biased downwards. Indirect prediction accuracy can be successfully obtained from ssGBLUP without running an extra SNP-BLUP evaluation to compute SNP PEC. It is possible to reduce the number of genotyped animals in PEC computations, but accuracies may be slightly underestimated. When the amount of genomic and phenotypic data is large, the polygenic part of GEBV becomes small and IP can be very accurate. Further research is needed to approximate SNP PEC with a large number of genotyped animals.
21 President Oral Presentation Pick: Using Deep Neural Networks to determine birth weight data quality for genetic evaluations in beef cattle
Authors: Ribeiro Andre , Golden Bruce L , Spangler Matthew L 
Published: November 2020

Abstract
The objective of this work was to evaluate the use of deep neural networks (DNN) for classifying contemporary groups based on the method used to generate birth weight (BWT) phenotypes. Contemporary groups (CG; n = 120,000) ranging between 10 and 500 animals were simulated assuming 12 data collection and CG formation scenarios that could impact CG phenotypic variance, including weights recorded with a digital scale (REAL), hoof tape (TAPE), and those that were fabricated (FAB). The performance of 6 activation functions (AF; ReLu, sigmoid, exponential, ReLu6, Softmax, Softplus) were evaluated. Four hidden layers were used with 7 different scenarios relative to the number of neurons. The training procedure was implemented in Python 3 with TensorFlow 1.14 and the ADAM optimization. Simulated CG were divided into training (80%) and testing (20%). The correlation between the observed and predicted CG types, averaged across 10 replicates, was used to assess accuracy and the correlations of predictions between replicates were used to measure the consistency of the model. In general, accuracy across AF and numbers of neurons were similar, with mean correlations ranging between 0.91 and 0.99. The AF ReLu, Sigmoid, Exponential and ReLu6 had the greatest consistency between the replicates, with an average correlation greater than 0.90. Independent of the number of neurons used, the sigmoid function produced the highest accuracy (0.99) and consistency (0.96). The DNN was retrained using 10-fold the number of CG and mimicking the CG size distribution observed in real data obtained from the American Hereford Association (n = 46,177 CG). In the real data, the lowest phenotypic variance was for FAB CG (2.98 kg2), REAL CG had the largest (18.33 kg2) and TAPE CG was intermediate (8.64 kg2). Results suggest that a well-trained DNN can be effectively used to classify data based on quality metrics prior to inclusion in routine genetic evaluation.
31 Changes in genomic predictions when new data is included
Authors: Hidalgo Jorge , Lourenco Daniela , Tsuruta Shogo , Miller Stephen P , Garcia Andre , Masuda Yutaka , Misztal Ignacy 
Published: November 2020

Abstract
Persistence of genomic evaluations depends on the amount of data and population parameters. When the data are large enough to estimate nearly all chromosome segments (about 10,000 in Angus), the persistence can be high. Subsequently, the regular evaluations can be run less often, and indirect predictions based on backsolved SNP effects may be accurate over an extended period as full predictions. The objective of this study was to investigate changes in GEBV across monthly genomic evaluations in a large genotyped population. A dataset from American Angus that contained 8,186,503 records for birth weight, 8,881,124 for weaning weight, and 4,386,184 for post-weaning gain was used. A total of 10,129,980 animals born up to December of 2017 had pedigree information, of which 484,074 were genotyped. A truncated dataset kept animals that were born up to December of 2016 (Dec2016). To mimic a scenario where evaluations are run every 30 days, the 2017 data was added month by month and new GEBV were computed using ssGBLUP. Genomic predictions from the Dec2016 evaluation were contrasted to the monthly evaluations for all genotyped animals, genotyped animals with or without phenotypes, genotyped animals with or without new phenotypes after Dec2016 and for young genotyped animals (born in 2016, with no phenotypes or progeny). The average number of animals added by month was 26,225, with a larger concentration from January to April. The correlations between GEBV from Dec2016 and all the 12 consecutive evaluations were greater or equal than 0.99 in all cases. The average (maximum) changes for young animals from Dec2016 to January and December of 2017 ranged from 0.05 (0.25) to 0.10 (0.53) additive standard deviations. The same changes for animals with records past Dec2016 were from 0.05 (0.11) to 0.88 (1.59). Genomic evaluations have high persistence over a year; however, outliers show larger changes.
10 Can single-step genome-wide association analysis (ssGWAA) account for population structure?
Authors: Mancin Enrico 
Published: November 2020

Abstract
Several methods are available for genome-wide association analysis, including the classical GWAA (cGWAA) based on fixed, single-SNP regression; efficient mixed-model association expedited (EMMAX) that fits single-SNP regressions together with a relationship matrix to account for population structure; and single-step GWAA (ssGWAA) where all data, including non-genotyped animals, are used. The objectives of this study were to: 1) investigate the ability of ssGWAA to account for population structure and correctly identify quantitative trait nucleotides (QTN); and 2) compare ssGWAA with cGWAA and EMMAX. Three simulated datasets were used, which mimic fish, beef cattle, and dairy cattle populations. The fish population was composed of 2,040 fish, out of which 1,040 were genotyped and had phenotypes for a trait with heritability of 0.25. The beef cattle population had 6,010 animals in the pedigree, but only 1,500 with phenotypes (h2 = 0.35) and genotypes. Lastly, the dairy cattle population had 40,800 pedigreed animals, of which 20,000 females had phenotypes (h2 = 0.32) and 2,400 males were genotyped. All phenotypes, pedigree, and genotypes were used in ssGWAA, whereas only genotypes and phenotypes were used in cGWAA and EMMAX for the fish and beef cattle analyses. For the dairy cattle analysis using the last two methods, deregressed proofs had to be used instead of phenotypes. The ability to correctly identify QTN and the number of statistically significant SNP (P &lt; 0.05/number of SNP) was assessed among methods. In all populations, cGWAA was able to identify some of the strongest QTN but showed a large number of false positives. EMMAX and ssGWAA did not show false associations and correctly identified the top QTN, with more signals observed in ssGWAA. The ssGWAA accounts for population structure and is a proper association method, especially for livestock populations where sparse genotyping is a reality and phenotypes may not be recorded in genotyped animals.
28 Genomic prediction for marbling score in Hanwoo cattle using sequence data
Authors: Jang Sungbong , Garcia Andre , Lee Seunghwan , Tsuruta Shogo , Lourenco Daniela 
Published: November 2020

Abstract
As sequence information becomes available for some livestock species, there is a question on the amount of non-redundant information that may be embedded in the extra millions of SNPs or in causative variants within sequence. The objective of this study was to assess the gain in accuracy by using imputed whole-genome and selected SNP from sequence data in a beef cattle population. The dataset consisted of marbling score phenotypes for 545K Hanwoo cattle and pedigree information for 1.3M, of which 1,160 were genotyped for 50K SNPs. Imputation was done first to 777K and then to whole-genome sequence (WGS), which comprised 11,146,536 SNPs. Additionally, differentially expressed genes (DEG) and their 321,614 harboring SNPs were identified based on RNA-seq analysis of animals with high and low marbling score. An extra scenario combined 50K with DEG SNPs. Genomic predictions were obtained using GBLUP and single-step GBLUP (ssGBLUP) with and without weights, and BayesR. The last method could not be used for the WGS data because of the large number of SNPs. Predictive ability was calculated as the correlation between phenotypes adjusted for fixed effects and GEBV for 169 young animals. For all the methods, WGS and DEG had a slightly negative impact on predictive ability. Both GBLUP and BayesR had similar performances when using 50K, DEG, and 50K+ DEG, with predictive abilities equal to 0.19, 0.16, and 0.18, respectively. Predictive ability for ssGBLUP was 0.27, 0.26, and 0.27, in the aforementioned order. Using WGS, predictive ability for GBLUP was 0.17 and for ssGBLUP was 0.26. Weighting SNP differently did not improve predictions. As ssGBLUP uses all data available, not only genotyped animals with phenotypes as the other methods, it is more robust for genomic predictions. No gain in accuracy was observed, possibly because the selected sequence variants were not causative.