Including different groups of genotyped females for genomic prediction in a Nordic Jersey population

Including genotyped females in a reference population (RP) is an obvious way to increase the RP in genomic selection, especially for dairy breeds of limited population size. However, the incorporation of these females must be conducted cautiously because of the potential preferential treatment of the genotyped cows and lower reliabilities of phenotypes compared with the proven pseudo-phenotypes of bulls. Breeding organizations in Denmark, Finland, and Sweden have implemented a female-genotyping project with the possibility of genotyping entire herds using the low-density (LD) chip. In the present study, 5 scenarios for building an RP were investigated in the Nordic Jersey population: (1) bulls only, (2) bulls with females from the LD project, (3) bulls with females from the LD project plus non-LD project females genotyped before their first calving, (4) bulls with females from the LD project plus non-LD project females genotyped after their first calving, and (5) bulls with all genotyped females. The genomically enhanced breeding value (GEBV) was predicted for 8 traits in the Nordic total merit index through a genomic BLUP model using deregressed proof (DRP) as the response variable in all scenarios. In addition, (daughter) yield deviation and raw phenotypic data were studied as response variables for comparison with the DRP, using stature as a model trait. The validation population was formed using a cut-off birth year of 2005 based on the genotyped Nordic Jersey bulls with DRP. The average increment in reliability of the GEBV across the 8 traits investigated was 1.9 to 4.5 percentage points compared with using only bulls in the RP (scenario 1). The addition of all the genotyped females to the RP resulted in the highest gain in reliability (scenario 5), followed by scenario 3, scenario 2, and scenario 4. All scenarios led to inflated GEBV because the regression coefficients are less than 1. However, scenario 2 and scenario 3 led to less bias of genomic predictions than scenario 5, with regression coefficients showing less deviation from scenario 1. For the study on stature, the daughter yield deviation/daughter yield deviation performed slightly better than the DRP as the response variable in the genomic BLUP (GBLUP) model. Therefore, adding unselected females in the RP could significantly improve the reliabilities and tended to reduce the prediction bias compared with adding selectively genotyped females. Although the DRP has performed robustly so far, the use of raw data is recommended with a single-step model as an optimal solution for future genomic evaluations.

The dissemination mode of drug-resistant genes in Enterobacter cloacae

BACKGROUND

Enterobacter cloacae (E. cloacae) infection has the highest mortality rate among Enterobacter infections. This study aimed to determine the prevalence and the transmission route of the class I integron, qnr genes, and CTX-M ESBLs genes in clinical isolates and to analyse the association between the prevalence of MDR genes and the antibiotic resistance of E. cloacae.

MATERIALS AND METHODS

The antibiotic susceptibility was tested the agar dilution method. The class I integron, qnr genes, and CTX-M ESBLs genes were detected by polymerase chain reaction (PCR). The prevalence data were analysed with the Chi-square test.

RESULTS

In the 100 clinical isolates, the class I integron-positive rate was 65%, with 12% on chromosome, 15% on plasmids and 38% on both. The positive rate of qnr genes was 37% with plasmid location. The positive rates for qnrA, qnrB and qnrS were 6%, 23% and 8%, respectively. The CTX-M ESBLs-positive rate was 34%. For CTX-M-1 ESBLs, 15% were on chromosome, 6% on plasmids and 4% on both; for CTX-M-9 ESBLs, 1% was on chromosome and 7% on plasmid; for CTX-M-25 ESBLs, 3% were on chromosome and 1% on plasmid.

CONCLUSION

Antibiotic resistance genes may be horizontally and vertically disseminated among E. cloacae, which helps multidrug-resistant (MDR) strains of E. cloacae to be successful nosocomial agents.

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less-related animals is favourable for reliability of genomic prediction.

Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction

This study investigated the effect on the reliability of genomic prediction when a small number of significant variants from single marker analysis based on whole genome sequence data were added to the regular 54k single nucleotide polymorphism (SNP) array data. The extra markers were selected with the aim of augmenting the custom low-density Illumina BovineLD SNP chip (San Diego, CA) used in the Nordic countries. The single-marker analysis was done breed-wise on all 16 index traits included in the breeding goals for Nordic Holstein, Danish Jersey, and Nordic Red cattle plus the total merit index itself. Depending on the trait's economic weight, 15, 10, or 5 quantitative trait loci (QTL) were selected per trait per breed and 3 to 5 markers were selected to tag each QTL. After removing duplicate markers (same marker selected for more than one trait or breed) and filtering for high pairwise linkage disequilibrium and assaying performance on the array, a total of 1,623 QTL markers were selected for inclusion on the custom chip. Genomic prediction analyses were performed for Nordic and French Holstein and Nordic Red animals using either a genomic BLUP or a Bayesian variable selection model. When using the genomic BLUP model including the QTL markers in the analysis, reliability was increased by up to 4 percentage points for production traits in Nordic Holstein animals, up to 3 percentage points for Nordic Reds, and up to 5 percentage points for French Holstein. Smaller gains of up to 1 percentage point was observed for mastitis, but only a 0.5 percentage point increase was seen for fertility. When using a Bayesian model accuracies were generally higher with only 54k data compared with the genomic BLUP approach, but increases in reliability were relatively smaller when QTL markers were included. Results from this study indicate that the reliability of genomic prediction can be increased by including markers significant in genome-wide association studies on whole genome sequence data alongside the 54k SNP set.

Monitoring of atherosclerosis evolution by detection of inflammatory states of aortae in a rabbit model using 18F-FDG -PET/CT

AIM

In vivo dynamic evaluation of atherosclerosis could be clinically significant in the prevention of cardiovascular events. We aimed to monitor Fluorine-18 fluorodeoxyglucose (18F-FDG) uptake in different stages of atherosclerosis, and investigate the feasibility of detecting vulnerable plaques using positron emission tomography/computed tomography (PET/CT) angiography.

METHODS

Twenty-two male NZW rabbits were divided into two groups: atherosclerosis group (group A, N.=11) and atherosclerosis and statin group (group S, N.=11). The rabbits underwent two pharmacological triggerings to induce thrombus at the 18th week. In vivo PET/CT scans were performed on four time points: before cholesterol diet (baseline, N.=6), at 8th week (the middle-of-feeding, N.=4), at 18th week (the end-of-feeding, N.=22) and after triggering (post-triggering, N.=15). 18F-FDG uptake by the aorta was expressed as maximal standardized uptake value (SUVmax) and mean SUV (SUVmean). SUVs were measured on serial 7.5 mm arterial segments.

RESULTS

SUVmean and SUVmax were 0.449±0.108 and 0.550±0.132 at baseline, 0.694±0.117 and 0.754±0.129 at the middle-of-feeding, 0.788±0.121 and 0.861±0.139 in group A, and 0.651±0.194 and 0.736±0.243 in group S at the end-of feeding before triggering. SUVmean and SUVmax were 1.128±0.420 and 1.302±0.489 in thrombosis group, 0.774±0.159 and 0.859±0.191 in non-thrombosis group after triggering. Thrombus were identified in 10 of 22 rabbits (45.5%): 8 of 11 (72.3%) in group A, and 2 of 11 (18.2%) in group S (P<0.001).

CONCLUSION

The inflammatory states of atherosclerosis and vulnerable plaque can be detected by quantitative analysis of 18F-FDG uptake. PET/CT may be used for predicting thrombosis events in patients with atherosclerotic disease.

Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese-Nordic reference population

This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows genotyped with 54K chip, 510 Nordic Holstein bulls genotyped with HD chip, and 4398 Nordic Holstein bulls genotyped with 54K chip and with deregressed proofs for five milk production traits. Based on these data, the accuracy of imputation from 54K to HD marker data and the accuracy of genomic predictions in Chinese Holstein were assessed. The allele correct rate increased around 2.7 and 1.7% in imputation from the 54K to the HD marker data for Chinese Holstein bulls and cows, respectively, when the Nordic HD-genotyped bulls were included in the reference data for imputation. However, the prediction accuracy was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy of genomic predictions increased 6.5 percentage points together with a reduction of prediction bias. The HD markers did not outperform the 54K markers in genomic prediction based on the present data. The results indicate that for Chinese Holsteins, it is necessary to genotype more individuals with 54K chip to increase reference population rather than increasing marker density.

Short communication: Heterosis by environment and genotype by environment interactions for protein yield in Danish Jerseys

Crossing of lines or strains within and between breeds has been demonstrated to be beneficial for dairy cattle performance. However, even within breed, differences between strains may also give rise to heterosis. A key question is whether an interaction exists between heterosis and environment (H × E) that is independent of genotype by environment (G × E) interactions. In this study, H × E and G × E interactions were estimated in a population of approximately 300,000 Danish Jersey cows. The cows were a mixture of pure Danish Jerseys and crosses of US and Danish Jerseys. The phenotype studied was protein yield. A reaction norm model where the unknown environmental covariates are inferred simultaneously with the other parameters in the model was used to analyze the data. When H × E, but not G × E, was included in the model, heterosis was estimated to be 3.8% for the intermediate environmental level. However, when both H × E and G × E were included in the model, heterosis was estimated to be 4.1% for the intermediate environmental level. Furthermore, when only H × E was included in the model, the regression on the unknown environmental covariate was estimated to be 0.15, interpreted as meaning that an increase of average herd-year protein yield by 1 kg of protein led to an increase in heterosis of 0.15 kg above the average heterosis for a first-cross cow. When both H × E and G × E were included in the model, the regression on the unknown environmental covariate was not significantly different from zero, meaning that heterosis was similar in all environments investigated. The genetic correlation of protein yields for different environmental levels ranged from 0.72 to 0.93, which was significantly different from unity, indicating that G × E exist for protein yield.

Determination of visual prognosis in children with open globe injuries

AIMS

To determine the predictive factors of visual outcomes in children with open globe injury and to give guidance to reduce the incidence of open globe injury.

METHODS

One hundred and forty eyes of 137 consecutive open globe injury patients, who were treated at the Eye Center of Second Bethune Hospital affiliated with Jilin University between August 2005 and August 2012, were retrospectively analyzed. Data recorded included demographic characteristics, causes of injury, location and extent of injury, presenting visual acuity, detailed ocular anterior and posterior segment evaluations, details of primary and subsequent surgeries, and postoperative complications and outcomes. The follow-up data included the most recent best-corrected visual acuity, complications, and the duration of follow-up.

RESULTS

Of the 137 patients, there were 116 (84.7%) boys and 21 (15.3%) girls. Their ages ranged between 3 and 17 years old (mean=11.57±4.19 years old). Sixty (43.8%) children had a right eye injury, whereas 74 (54.0%) had a left eye injury. Only three (2.2%) children suffered bilateral eye injury. Living utensils, industrial tools, and fireworks contributed to the most common causes of open globe injury. Eighty-one (59.1%) had sharp force injuries, 23 (16.8%) had blunt injuries, and 33 (24.1%) had missile injuries.

CONCLUSIONS

Unfavorable visual outcomes were related to a younger age at presentation, poor presenting visual acuity, injuries caused by blunt or missile objects, posterior wound location, hyphema, vitreous hemorrhage, and surgical intervention of pars plana vitrectomy.

Genomic predictions based on a joint reference population for the Nordic Red cattle breeds

The main aim of this study was to compare accuracies of imputation and genomic predictions based on single and joint reference populations for Norwegian Red (NRF) and a composite breed (DFS) consisting of Danish Red, Finnish Ayrshire, and Swedish Red. The single nucleotide polymorphism (SNP) data for NRF consisted of 2 data sets: one including 25,000 markers (NRF25K) and the other including 50,000 markers (NRF50K). The NRF25K data set had 2,572 bulls, and the NRF50K data set had 1,128 bulls. Four hundred forty-two bulls were genotyped in both data sets (double-genotyped bulls). The DFS data set (DSF50K) included 50,000 markers of 13,472 individuals, of which around 4,700 were progeny-tested bulls. The NRF25K data set was imputed to 50,000 density using the software Beagle. The average error rate for the imputation of NRF25K decreased slightly from 0.023 to 0.021, and the correlation between observed and imputed genotypes changed from 0.935 to 0.936 when comparing the NRF50K reference and the NRF50K-DFS50K joint reference imputations. A genomic BLUP (GBLUP) model and a Bayesian 4-component mixture model were used to predict genomic breeding values for the NRF and DFS bulls based on the single and joint NRF and DFS reference populations. In the multiple population predictions, accuracies of genomic breeding values increased for the 3 production traits (milk, fat, and protein yields) for both NRF and DFS. Accuracies increased by 6 and 1.3 percentage points, on average, for the NRF and DFS bulls, respectively, using the GBLUP model, and by 9.3 and 1.3 percentage points, on average, using the Bayesian 4-component mixture model. However, accuracies for health or reproduction traits did not increase from the multiple population predictions. Among the 3 DFS populations, Swedish Red gained most in accuracies from the multiple population predictions, presumably because Swedish Red has a closer genetic relationship with NRF than Danish Red and Finnish Ayrshire. The Bayesian 4-component mixture model performed better than the GBLUP model for most production traits for both NRF and DFS, whereas no advantage was found for health or reproduction traits. In general, combining NRF and DFS reference populations was useful in genomic predictions for both the NRF and DFS bulls.

Exogenous bFGF promotes articular cartilage repair via up-regulation of multiple growth factors

OBJECTIVE

To investigate the roles of exogenous basic fibroblast growth factor (bFGF) on the repair of full-thickness articular cartilage defects in rabbits.

DESIGN

In the present study, a double-layered collagen membrane sandwiched with bFGF-loaded-nanoparticles between a dense layer and a loose layer was implanted into full-thickness articular cartilage defects in rabbits. By grafting the membrane in a different direction, the dense layer or the loose layer facing the surface of the subchondral bone, the effects of the released bFGF on the defects and the profiles of nine growth factors (GFs) in synovial fluid (SF) were investigated using histological methods and antibody arrays, respectively.

RESULTS

In the group with the loose layer facing the surface of the subchondral bone, fast release of bFGF was observed, and early high levels of endogenous transforming growth factor-β2 (TGF-β2), vascular endothelial growth factor (VEGF), bFGF, bone morphogenetic protein 2 (BMP-2), BMP-3, and BMP-4 in SF were detected by antibody arrays, especially on day 3. Chondrocyte-like cells were also observed in this group at an early stage. As a result, this group showed better levels of repair, as compared to the other groups in which low GF levels were detected at an early stage, and chondrocyte-like cells appeared much later.

CONCLUSIONS

Our study suggests that exogenous bFGF promotes articular cartilage repair by up-regulating the levels of multiple GFs, but administration at an early stage is required.

Antitumour effect of sesquiterpene (+)-chabranol on four human cancer cell lines by inducing apoptosis and autophagy

OBJECTIVE

To investigate the effects and mechanisms of sesquiterpene (+)-chabranol on proliferation of a panel of four human tumour cell lines (BGC-823, SGC-7901, SSMC-7721 and HepG2).

METHODS

Cell viability was assessed using a standard methyltetrazolium assay; cell-cycle analysis of BGC-823 cells was performed by flow cytometry. Transmission electron microscopy (TEM) was used to examine the ultrastructure of BGC-823 cells exposed to (+)-chabranol. Apoptosis was investigated by evaluating DNA laddering, using gel electrophoresis.

RESULTS

(+)-Chabranol had a marked time- and concentration-dependent inhibitory effect on BGC-823 cell proliferation. The effect was less marked in SGC-7901, SSMC-7721 and HepG2 cells. Exposure of BGC-823 cells to (+)-chabranol arrested the cell cycle at G(1). Evidence of apoptosis and autophagy was observed by TEM; DNA laddering in BGC-823 cells supported the presence of apoptosis.

CONCLUSIONS

This study suggested that (+)-chabranol has antitumour activity against BGC-823 cells, and may exert its action by inhibition of proliferation and induction of apoptosis and autophagy. With further development, (+)-chabranol may represent a potential novel treatment for poorly differentiated gastric cancer.

Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish Red Cattle

This study investigated the imputation accuracy of different methods, considering both the minor allele frequency and relatedness between individuals in the reference and test data sets. Two data sets from the combined population of Swedish and Finnish Red Cattle were used to test the influence of these factors on the accuracy of imputation. Data set 1 consisted of 2,931 reference bulls and 971 test bulls, and was used for validation of imputation from 3,000 markers (3K) to 54,000 markers (54K). Data set 2 contained 341 bulls in the reference set and 117 in the test set, and was used for validation of imputation from 54K to high density [777,000 markers (777K)]. Both test sets were divided into 4 groups according to their relationship to the reference population. Five imputation methods (Beagle, IMPUTE2, findhap, AlphaImpute, and FImpute) were used in this study. Imputation accuracy was measured as the allele correct rate and correlation between imputed and true genotypes. Results demonstrated that the accuracy was lower when imputing from 3K to 54K than from 54K to 777K. Using various imputation methods, the allele correct rates varied from 93.5 to 97.1% when imputing from 3K to 54K, and from 97.1 to 99.3% when imputing from 54K to 777K; IMPUTE2 and Beagle resulted in higher accuracies and were more robust under various conditions than the other 3 methods when imputing from 3K to 54K. The accuracy of imputation using FImpute was similar to those results from Beagle and IMPUTE2 when imputing from 54K to high density, and higher than the remaining 2 methods. The results also showed that a closer relationship between test set and reference set led to a higher accuracy for all the methods. In addition, the correct rate was higher when the minor allele frequency was lower, whereas the correlation coefficient was lower when the minor allele frequency was lower. The results indicate that Beagle and IMPUTE2 provide the most robust and accurate imputation accuracies, but considering computing time and memory usage, FImpute is another alternative method.

Accuracy of genomic prediction using different models and response variables in the Nordic Red cattle population

Breeding animals can be accurately evaluated using appropriate genomic prediction models, based on marker data and phenotype information. In this study, direct genomic values (DGV) were estimated for 16 traits of Nordic Total Merit (NTM) Index in Nordic Red cattle population using three models and two different response variables. The three models were as follows: a linear mixed model (GBLUP), a Bayesian variable selection model similar to BayesA (BayesA*) and a Bayesian least absolute shrinkage and selection operator model (Bayesian Lasso). The response variables were deregressed proofs (DRP) and conventional estimated breeding values (EBV). The reliability of genomic predictions was measured on bulls in the validation data set as the squared correlation between DGV and DRP divided by the reliability of DRP. Using DRP as response variable, the reliabilities of DGV among the 16 traits ranged from 0.151 to 0.569 (average 0.317) for GBLUP, from 0.152 to 0.576 (average 0.318) for BayesA* and from 0.150 to 0.570 (average 0.320) for Bayesian Lasso. Using EBV as response variable, the reliabilities ranged from 0.159 to 0.580 (average 0.322) for GBLUP, from 0.157 to 0.578 (average 0.319) for BayesA* and from 0.159 to 0.582 (average 0.325) for Bayesian Lasso. In summary, Bayesian Lasso performed slightly better than the other two models, and EBV performed slightly better than DRP as response variable, with regard to prediction reliability of DGV. However, these differences were not statistically significant. Moreover, using EBV as response variable would result in problems with the scale of the resulting DGV and potential problem due to double counting.

Selection for increased number of piglets at d 5 after farrowing has increased litter size and reduced piglet mortality

Selection for litter size at d 5 after farrowing (LS5) was introduced in 2004 to increase the number of piglets weaned and to reduce piglet mortality in Danish Landrace and Yorkshire. The objective of this study was to investigate selection responses for LS5, total number born (TNB), and mortality [MORT, defined as (TNB - LS5)/TNB] when selection for increasing LS5 was a part of the breeding goal. Data were collected from nucleus herds recorded from 2004 to 2010, including first litters of 42,807 Landrace sows and 33,225 Yorkshire sows. The data were analyzed using a 3-trait animal model of TNB, MORT, and LS5. Significant (co) variances were estimated between the 3 traits in both populations. The heritabilities of TNB, MORT, and LS5 were 0.10, 0.09, and 0.09 in Landrace and 0.12, 0.10, and 0.10 in Yorkshire. The genetic correlations were 0.28 and 0.22 between TNB and MORT, 0.74 and 0.68 between TNB and LS5, and -0.43 and -0.57 between MORT and LS5 in Landrace and Yorkshire, respectively. The results show that the genetic improvement of LS5 was a combination of increased TNB and reduced MORT. During the observation period, the genetic improvement was 1.7 piglets per litter for LS5, 1.3 piglets per litter for TNB, and 4.7% for MORT in Landrace and 2.2 piglets per litter, 1.9 piglets per litter, and 5.9% in Yorkshire. Phenotypic improvement was 1.4 piglets per litter for LS5, 0.3 piglets per litter for TNB, and 7.9% for MORT in Landrace and 2.1 piglets per litter, 1.3 piglets per litter, and 7.6% in Yorkshire. In addition, genetic gain was evaluated in 3 phenotypic groups of TNB, representing the 25% smallest litters, the 50% medium litters, and the 25% largest litters. In all 3 groups, the genetic and phenotypic gains of TNB and LS5 increased, whereas MORT reduced in both populations.