Heritability of longevity in Large White and Landrace sows using continuous time and grouped data models

Background

Using conventional measurements of lifetime, it is not possible to differentiate between productive and non-productive days during a sow''s lifetime and this can lead to estimated breeding values favoring less productive animals. By rescaling the time axis from continuous to several discrete classes, grouped survival data (discrete survival time) models can be used instead.

Methods

The productive life length of 12319 Large White and 9833 Landrace sows was analyzed with continuous scale and grouped data models. Random effect of herd*year, fixed effects of interaction between parity and relative number of piglets, age at first farrowing and annual herd size change were included in the analysis. The genetic component was estimated from sire, sire-maternal grandsire, sire-dam, sire-maternal grandsire and animal models, and the heritabilities computed for each model type in both breeds.

Results

If age at first farrowing was under 43 weeks or above 60 weeks, the risk of culling sows increased. An interaction between parity and relative litter size was observed, expressed by limited culling during first parity and severe risk increase of culling sows having small litters later in life. In the Landrace breed, heritabilities ranged between 0.05 and 0.08 (s.e. 0.014-0.020) for the continuous and between 0.07 and 0.11 (s.e. 0.016-0.023) for the grouped data models, and in the Large White breed, they ranged between 0.08 and 0.14 (s.e. 0.012-0.026) for the continuous and between 0.08 and 0.13 (s.e. 0.012-0.025) for the grouped data models.

Conclusions

Heritabilities for length of productive life were similar with continuous time and grouped data models in both breeds. Based on these results and because grouped data models better reflect the economical needs in meat animals, we conclude that grouped data models are more appropriate in pig.     

Connectedness among herds of beef cattle bred under natural service

Background

A procedure to measure connectedness among herds was applied to a beef cattle population bred by natural service. It consists of two steps: (a) computing coefficients of determination (CDs) of comparisons among herds; and (b) building sets of connected herds.

Methods

The CDs of comparisons among herds were calculated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. Once the CD matrix was estimated, a clustering method that can handle a large number of comparisons was applied to build compact clusters of connected herds of the Bruna dels Pirineus beef cattle. Since in this breed, natural service is predominant and there are almost no links with reference sires, to estimate CDs, an animal model was used taking into consideration all pedigree information and, especially, the connections with dams. A sensitivity analysis was performed to contrast single-trait sire and animal model evaluations with different heritabilities, multiple-trait animal model evaluations with different degrees of genetic correlations and models with maternal effects.

Results

Using a sire model, the percentage of connected herds was very low even for highly heritable traits whereas with an animal model, most of the herds of the breed were well connected and high CD values were obtained among them, especially for highly heritable traits (the mean of average CD per herd was 0.535 for a simulated heritability of 0.40). For the lowly heritable traits, the average CD increased from 0.310 in the single-trait evaluation to 0.319 and 0.354 in the multi-trait evaluation with moderate and high genetic correlations, respectively. In models with maternal effects, the average CD per herd for the direct effects was similar to that from single-trait evaluations. For the maternal effects, the average CD per herd increased if the maternal effects had a high genetic correlation with the direct effects, but the percentage of connected herds for maternal effects was very low, less than 12%.

Conclusions

The degree of connectedness in a bovine population bred by natural service mating, such as Bruna del Pirineus beef cattle, measured as the CD of comparisons among herds, is high. It is possible to define a pool of animals for which estimated breeding values can be compared after an across-herds genetic evaluation, especially for highly heritable traits.     

The placental growth factor (PGF) – a positional and functional candidate gene influencing calving ease and stillbirth in German dairy cattle

The bovine placental growth factor‐encoding gene (PGF) was analysed as a positional and functional candidate gene for the maternal effect on stillbirth and calving ease in first parity. Prominent levels of PGF expression have been reported for the whole human placenta and umbilical vein endothelial cells. Modulation of angiogenesis, vessel remodelling and vascular permeability during implantation and placentation suggest an influence on trophoblast function during pregnancy. Changes of expression or protein function may therefore be crucial to pregnancy and parturition. By comparative sequencing of bulls with extreme approximate daughter yield deviations for calving traits, we identified 37 SNPs and two insertions/deletions within the PGF gene. Seventeen of the identified polymorphisms were genotyped in 368 selected bulls and tested for association with approximate daughter yield deviations for calving traits. In a single marker analysis, all SNPs were significantly associated with maternal stillbirth and calving ease first parity. The allele substitutions of the significant SNPs explain 8% to 14% and 8% to 15% of the additive genetic variance for maternal stillbirth and maternal calving ease first parity, respectively. There is no evidence that any of the polymorphisms identified within this study could be the causal mutation underlying the QTL, which is likely to be a regulatory mutation. In summary, we report polymorphisms in the bovine PGF gene significantly associated with the maternal effect on stillbirth and calving ease in animals under selection. These results should be confirmed and extended in further studies to identify the causal mutation underlying the QTL analysed.     

Consequences of paternally inherited effects on the genetic evaluation of maternal effects

Background

Mixed models are commonly used for the estimation of variance components and genetic evaluation of livestock populations. Some evaluation models include two types of additive genetic effects, direct and maternal. Estimates of variance components obtained with models that account for maternal effects have been the subject of a long-standing controversy about strong negative estimates of the covariance between direct and maternal effects. Genomic imprinting is known to be in some cases statistically confounded with maternal effects. In this study, we analysed the consequences of ignoring paternally inherited effects on the partitioning of genetic variance.

Results

We showed that the existence of paternal parent-of-origin effects can bias the estimation of variance components when maternal effects are included in the evaluation model. Specifically, we demonstrated that adding a constraint on the genetic parameters of a maternal model resulted in correlations between relatives that were the same as those obtained with a model that fits only paternally inherited effects for most pairs of individuals, as in livestock pedigrees. The main consequence is an upward bias in the estimates of the direct and maternal additive genetic variances and a downward bias in the direct-maternal genetic covariance. This was confirmed by a simulation study that investigated five scenarios, with the trait affected by (1) only additive genetic effects, (2) only paternally inherited effects, (3) additive genetic and paternally inherited effects, (4) direct and maternal additive genetic effects and (5) direct and maternal additive genetic plus paternally inherited effects. For each scenario, the existence of a paternally inherited effect not accounted for by the estimation model resulted in a partitioning of the genetic variance according to the predicted pattern. In addition, a model comparison test confirmed that direct and maternal additive models and paternally inherited models provided an equivalent fit.

Conclusions

Ignoring paternally inherited effects in the maternal models for genetic evaluation can lead to a specific pattern of bias in variance component estimates, which may account for the unexpectedly strong negative direct-maternal genetic correlations that are typically reported in the literature.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0141-5) contains supplementary material, which is available to authorized users.     

Modeling relationships between calving traits: a comparison between standard and recursive mixed models

Background

The use of structural equation models for the analysis of recursive and simultaneous relationships between phenotypes has become more popular recently. The aim of this paper is to illustrate how these models can be applied in animal breeding to achieve parameterizations of different levels of complexity and, more specifically, to model phenotypic recursion between three calving traits: gestation length (GL), calving difficulty (CD) and stillbirth (SB). All recursive models considered here postulate heterogeneous recursive relationships between GL and liabilities to CD and SB, and between liability to CD and liability to SB, depending on categories of GL phenotype.

Methods

Four models were compared in terms of goodness of fit and predictive ability: 1) standard mixed model (SMM), a model with unstructured (co)variance matrices; 2) recursive mixed model 1 (RMM1), assuming that residual correlations are due to the recursive relationships between phenotypes; 3) RMM2, assuming that correlations between residuals and contemporary groups are due to recursive relationships between phenotypes; and 4) RMM3, postulating that the correlations between genetic effects, contemporary groups and residuals are due to recursive relationships between phenotypes.

Results

For all the RMM considered, the estimates of the structural coefficients were similar. Results revealed a nonlinear relationship between GL and the liabilities both to CD and to SB, and a linear relationship between the liabilities to CD and SB.Differences in terms of goodness of fit and predictive ability of the models considered were negligible, suggesting that RMM3 is plausible.

Conclusions

The applications examined in this study suggest the plausibility of a nonlinear recursive effect from GL onto CD and SB. Also, the fact that the most restrictive model RMM3, which assumes that the only cause of correlation is phenotypic recursion, performs as well as the others indicates that the phenotypic recursion may be an important cause of the observed patterns of genetic and environmental correlations.     

Genomic prediction of disease occurrence using producer-recorded health data: a comparison of methods

Background

Genetic selection has been successful in achieving increased production in dairy cattle; however, corresponding declines in fitness traits have been documented. Selection for fitness traits is more difficult, since they have low heritabilities and are influenced by various non-genetic factors. The objective of this paper was to investigate the predictive ability of two-stage and single-step genomic selection methods applied to health data collected from on-farm computer systems in the U.S.

Methods

Implementation of single-trait and two-trait sire models was investigated using BayesA and single-step methods for mastitis and somatic cell score. Variance components were estimated. The complete dataset was divided into training and validation sets to perform model comparison. Estimated sire breeding values were used to estimate the number of daughters expected to develop mastitis. Predictive ability of each model was assessed by the sum of χ² values that compared predicted and observed numbers of daughters with mastitis and the proportion of wrong predictions.

Results

According to the model applied, estimated heritabilities of liability to mastitis ranged from 0.05 (SD=0.02) to 0.11 (SD=0.03) and estimated heritabilities of somatic cell score ranged from 0.08 (SD=0.01) to 0.18 (SD=0.03). Posterior mean of genetic correlation between mastitis and somatic cell score was equal to 0.63 (SD=0.17). The single-step method had the best predictive ability. Conversely, the smallest number of wrong predictions was obtained with the univariate BayesA model. The best model fit was found for single-step and pedigree-based models. Bivariate single-step analysis had a better predictive ability than bivariate BayesA; however, the latter led to the smallest number of wrong predictions.

Conclusions

Genomic data improved our ability to predict animal breeding values. Performance of genomic selection methods depends on a multitude of factors. Heritability of traits and reliability of genotyped individuals has a large impact on the performance of genomic evaluation methods. Given the current characteristics of producer-recorded health data, single-step methods have several advantages compared to two-step methods.     

Genomic best linear unbiased prediction method including imprinting effects for genomic evaluation

Background

Genomic best linear unbiased prediction (GBLUP) is a statistical method used to predict breeding values using single nucleotide polymorphisms for selection in animal and plant breeding. Genetic effects are often modeled as additively acting marker allele effects. However, the actual mode of biological action can differ from this assumption. Many livestock traits exhibit genomic imprinting, which may substantially contribute to the total genetic variation of quantitative traits. Here, we present two statistical models of GBLUP including imprinting effects (GBLUP-I) on the basis of genotypic values (GBLUP-I1) and gametic values (GBLUP-I2). The performance of these models for the estimation of variance components and prediction of genetic values across a range of genetic variations was evaluated in simulations.

Results

Estimates of total genetic variances and residual variances with GBLUP-I1 and GBLUP-I2 were close to the true values and the regression coefficients of total genetic values on their estimates were close to 1. Accuracies of estimated total genetic values in both GBLUP-I methods increased with increasing degree of imprinting and broad-sense heritability. When the imprinting variances were equal to 1.4% to 6.0% of the phenotypic variances, the accuracies of estimated total genetic values with GBLUP-I1 exceeded those with GBLUP by 1.4% to 7.8%. In comparison with GBLUP-I1, the superiority of GBLUP-I2 over GBLUP depended strongly on degree of imprinting and difference in genetic values between paternal and maternal alleles. When paternal and maternal alleles were predicted (phasing accuracy was equal to 0.979), accuracies of the estimated total genetic values in GBLUP-I1 and GBLUP-I2 were 1.7% and 1.2% lower than when paternal and maternal alleles were known.

Conclusions

This simulation study shows that GBLUP-I1 and GBLUP-I2 can accurately estimate total genetic variance and perform well for the prediction of total genetic values. GBLUP-I1 is preferred for genomic evaluation, while GBLUP-I2 is preferred when the imprinting effects are large, and the genetic effects differ substantially between sexes.     

Cumulative t-link threshold models for the genetic analysis of calving ease scores

In this study, a hierarchical threshold mixed model based on a cumulative t-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte Carlo (MCMC) algorithm was carried out on simulated data from normally and t₄ (i.e. a t-distribution with four degrees of freedom) distributed populations using the deviance information criterion (DIC) and a pseudo Bayes factor (PBF) measure to validate recently proposed model choice criteria. The simulation study indicated that although inference on the degrees of freedom parameter is possible, MCMC mixing was problematic. Nevertheless, the DIC and PBF were validated to be satisfactory measures of model fit to data. A sire and maternal grandsire cumulative t-link model was applied to a calving ease dataset from 8847 Italian Piemontese first parity dams. The cumulative t-link model was shown to lead to posterior means of direct and maternal heritabilities (0.40 ± 0.06, 0.11 ± 0.04) and a direct maternal genetic correlation (-0.58 ± 0.15) that were not different from the corresponding posterior means of the heritabilities (0.42 ± 0.07, 0.14 ± 0.04) and the genetic correlation (-0.55 ± 0.14) inferred under the conventional cumulative probit link threshold model. Furthermore, the correlation (> 0.99) between posterior means of sire progeny merit from the two models suggested no meaningful rerankings. Nevertheless, the cumulative t-link model was decisively chosen as the better fitting model for this calving ease data using DIC and PBF.     

Genome-wide association study for calving performance using high-density genotypes in dairy and beef cattle

Background

Calving difficulty and perinatal mortality are prevalent in modern-day cattle production systems. It is well-established that there is a genetic component to both traits, yet little is known about their underlying genomic architecture, particularly in beef breeds. Therefore, we performed a genome-wide association study using high-density genotypes to elucidate the genomic architecture of these traits and to identify regions of the bovine genome associated with them.

Results

Genomic regions associated with calving difficulty (direct and maternal) and perinatal mortality were detected using two statistical approaches: (1) single-SNP (single nucleotide polymorphism) regression and (2) a Bayesian approach. Data included high-density genotypes on 770 Holstein-Friesian, 927 Charolais and 963 Limousin bulls. Several novel or previously identified genomic regions were detected but associations differed by breed. For example, two genomic associations, one each on chromosomes 18 and 2 explained 2.49 % and 3.13 % of the genetic variance in direct calving difficulty in the Holstein-Friesian and Charolais populations, respectively. Imputed Holstein-Friesian sequence data was used to refine the genomic regions responsible for significant associations. Several candidate genes on chromosome 18 were identified and four highly significant missense variants were detected within three of these genes (SIGLEC12, CTU1, and ZNF615). Nevertheless, only CTU1 contained a missense variant with a putative impact on direct calving difficulty based on SIFT (0.06) and Polyphen (0.95) scores. Using imputed sequence data, we refined a genomic region on chromosome 4 associated with maternal calving difficulty in the Holstein-Friesian population and found the strongest association with an intronic variant in the PCLO gene. A meta-analysis was performed across the three breeds for each calving performance trait to identify common variants associated with these traits in the three breeds. Our results suggest that a portion of the genetic variation in calving performance is common to all three breeds.

Conclusion

The genomic architecture of calving performance is complex and mainly influenced by many polymorphisms of small effect. We identified several associations of moderate effect size but the majority were breed-specific, indicating that breed-specific alleles exist for calving performance or that the linkage phase between genotyped allele and causal mutation varies between breeds.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0126-4) contains supplementary material, which is available to authorized users.     

Cognitive Function in Adult Offspring of Women with Gestational Diabetes–The Role of Glucose and Other Factors

Objective

We aimed to evaluate cognitive function in adult offspring of women with diet-treated gestational diabetes and to study potential associations with maternal glucose values.

Materials and Methods

In 2003–2005 cognitive function was assessed in a cohort of 18–27 year old offspring of women with diet-treated gestational diabetes mellitus (n = 153) and offspring from the background population (n = 118). The main outcome measure was global cognitive score derived from Raven’s Progressive Matrices and three verbal subtests from the Weschler Adult Intelligence Scale. Maternal fasting- and 2-hour blood glucose values from the diagnostic oral glucose tolerance test were used as exposure variables.

Results

Offspring of women with gestational diabetes mellitus had a lower global cognitive score, than offspring from the background population (93.1 vs. 100.0, P<0.001). However, when adjusted for maternal age at delivery, parity, smoking during pregnancy, pre-pregnancy overweight, family social class, parental educational level, gender, birth weight, gestational age, perinatal complications and offspring age at follow-up, the difference was no longer statistically significant. Offspring global cognitive score decreased significantly with increasing maternal fasting glucose (β = −4.5, 95% CI −8.0 to −0.9, P = 0.01) and 2-hour glucose (β = −1.5, −2.9 to −0.2, P = 0.03) in univariate general linear models, but not when adjusted for family social class and parental educational level.

Conclusions

Lower cognitive test scores in adult offspring of women with diet-treated gestational diabetes were explained by well known predictors of cognitive function, but not by maternal hyperglycaemia during pregnancy. We find it reassuring that mild intrauterine hyperglycaemia does not seem to have adverse effect on offspring cognitive function.     

Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects

Background

The distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal, which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers, one option is to fit models with residuals having a heavy-tailed distribution. Here, a Student''s-t model was considered for the distribution of the residuals with the degrees of freedom treated as unknown. Bayesian inference was used to investigate a bivariate Student''s-t (BSt) model using Markov chain Monte Carlo methods in a simulation study and analysing field data for gestation length and birth weight permitted to study the practical implications of fitting heavy-tailed distributions for residuals in linear mixed models.

Methods

In the simulation study, bivariate residuals were generated using Student''s-t distribution with 4 or 12 degrees of freedom, or a normal distribution. Sire models with bivariate Student''s-t or normal residuals were fitted to each simulated dataset using a hierarchical Bayesian approach. For the field data, consisting of gestation length and birth weight records on 7,883 Italian Piemontese cattle, a sire-maternal grandsire model including fixed effects of sex-age of dam and uncorrelated random herd-year-season effects were fitted using a hierarchical Bayesian approach. Residuals were defined to follow bivariate normal or Student''s-t distributions with unknown degrees of freedom.

Results

Posterior mean estimates of degrees of freedom parameters seemed to be accurate and unbiased in the simulation study. Estimates of sire and herd variances were similar, if not identical, across fitted models. In the field data, there was strong support based on predictive log-likelihood values for the Student''s-t error model. Most of the posterior density for degrees of freedom was below 4. Posterior means of direct and maternal heritabilities for birth weight were smaller in the Student''s-t model than those in the normal model. Re-rankings of sires were observed between heavy-tailed and normal models.

Conclusions

Reliable estimates of degrees of freedom were obtained in all simulated heavy-tailed and normal datasets. The predictive log-likelihood was able to distinguish the correct model among the models fitted to heavy-tailed datasets. There was no disadvantage of fitting a heavy-tailed model when the true model was normal. Predictive log-likelihood values indicated that heavy-tailed models with low degrees of freedom values fitted gestation length and birth weight data better than a model with normally distributed residuals.Heavy-tailed and normal models resulted in different estimates of direct and maternal heritabilities, and different sire rankings. Heavy-tailed models may be more appropriate for reliable estimation of genetic parameters from field data.     

A common genetic variant at 15q25 modifies the associations of maternal smoking during pregnancy with fetal growth: the generation R study

Objective

Maternal smoking during pregnancy is associated with fetal growth retardation. We examined whether a common genetic variant at chromosome 15q25 (rs1051730), which is known to be involved in nicotine metabolism, modifies the associations of maternal smoking with fetal growth characteristics.

Methods

This study was performed in 3,563 European mothers participating in a population-based prospective cohort study from early pregnancy onwards. Smoking was assessed by postal questionnaires and fetal growth characteristics were measured by ultrasound examinations in each trimester of pregnancy.

Results

Among mothers who did not smoke during pregnancy (82.9%), maternal rs1051730 was not consistently associated with any fetal growth characteristic. Among mothers who continued smoking during pregnancy (17.1%), maternal rs1051730 was not associated with head circumference. The T-allele of maternal rs1051730 was associated with a smaller second and third trimester fetal femur length [differences −0.23 mm (95%CI −0.45 to −0.00) and −0.41 mm (95%CI −0.69 to −0.13), respectively] and a smaller birth length [difference −2.61 mm (95%CI −5.32 to 0.11)]. The maternal T-allele of rs1051730 was associated with a lower third trimester estimated fetal weight [difference −33 grams (95%CI −55 to −10)], and tended to be associated with birth weight [difference −38 grams (95%CI −89 to 13)]. This association persisted after adjustment for smoking quantity.

Conclusions

Our results suggest that maternal rs1051730 genotype modifies the associations of maternal smoking during pregnancy with impaired fetal growth in length and weight. These results should be considered as hypothesis generating and indicate the need for large-scale genome wide association studies focusing on gene – fetal smoke exposure interactions.     

Variance components for susceptibility to Mycobacterium bovis infection in dairy and beef cattle

Background

Infection of livestock with bovine tuberculosis (bTB; Mycobacterium bovis) is of major economical concern in many countries; approximately 15 000 to 20 000 cattle are infected per year in Ireland. The objective of this study was to quantify the genetic variation for bTB susceptibility in Irish dairy and beef cattle.

Methods

A total of 105 914 cow, 56 904 heifer and 21 872 steer single intra-dermal comparative tuberculin test records (i.e., binary trait) collected from the years 2001 to 2010 from dairy and beef herds were included in the analysis. Only animal level data pertaining to periods of herd bTB infection were retained. Variance components for bTB were estimated using animal linear and threshold mixed models and co-variances were estimated using sire linear mixed models.

Results

Using a linear model, the heritability for susceptibility to bTB in the entire dataset was 0.11 and ranged from 0.08 (heifers in dairy herds) to 0.19 (heifers in beef herds) among the sub-populations investigated. Differences in susceptibility to bTB between breeds were clearly evident. Estimates of genetic correlations for bTB susceptibility between animal types (i.e., cows, heifers, steers) were all positive (0.10 to 0.64), yet different from one. Furthermore, genetic correlations for bTB susceptibility between environments that differed in herd prevalence of bTB ranged from 0.06 to 0.86 and were all different from one.

Conclusions

Genetic trends for bTB susceptibility observed in this study suggest a slight increase in genetic susceptibility to bTB in recent years. Since bTB is of economic importance and because all animals are routinely tested at least once annually in Ireland and some other countries, the presence of genetic variation for bTB susceptibility suggests that bTB susceptibility should be included in a national breeding program to halt possible deterioration in genetic susceptibility to bTB infection.     

Adaptive Capacity of the Habitat Modifying Sea Urchin Centrostephanus rodgersii to Ocean Warming and Ocean Acidification: Performance of Early Embryos

Background

Predicting effects of rapid climate change on populations depends on measuring the effects of climate stressors on performance, and potential for adaptation. Adaptation to stressful climatic conditions requires heritable genetic variance for stress tolerance present in populations.

Methodology/Principal Findings

We quantified genetic variation in tolerance of early development of the ecologically important sea urchin Centrostephanus rodgersii to near-future (2100) ocean conditions projected for the southeast Australian global change hot spot. Multiple dam-sire crosses were used to quantify the interactive effects of warming (+2–4°C) and acidification (−0.3−0.5 pH units) across twenty-seven family lines. Acidification, but not temperature, decreased the percentage of cleavage stage embryos. In contrast, temperature, but not acidification decreased the percentage of gastrulation. Cleavage success in response to both stressors was strongly affected by sire identity. Sire and dam identity significantly affected gastrulation and both interacted with temperature to determine developmental success. Positive genetic correlations for gastrulation indicated that genotypes that did well at lower pH also did well in higher temperatures.

Conclusions/Significance

Significant genotype (sire) by environment interactions for both stressors at gastrulation indicated the presence of heritable variation in thermal tolerance and the ability of embryos to respond to changing environments. The significant influence of dam may be due to maternal provisioning (maternal genotype or environment) and/or offspring genotype. It appears that early development in this ecologically important sea urchin is not constrained in adapting to the multiple stressors of ocean warming and acidification. The presence of tolerant genotypes indicates the potential to adapt to concurrent warming and acidification, contributing to the resilience of C. rodgersii in a changing ocean.     

The Burden of OASIS Increases along with Socioeconomic Position – Register-Based Analysis of 980,733 Births in Finland

Background

Obstetric anal sphincter injury (OASIS) has been identified as a major preventable risk factor for anal incontinence.

Objective

Aim was to measure national variation in incidence of OASIS by socioeconomic status (SES).

Methods

A retrospective population based case-control study using the data derived from the Finnish Medical Birth Register for the years 1991–2010. A total population of singleton vaginal births was reviewed. We calculated unadjusted incidences of OASIS stratified by SES and vaginal parity, and adjusted risks for OASIS in each social class, after controlling for parity, birthweight, mode of delivery, maternal age and maternal smoking. SES was recorded into five categories based on mother’s occupation at time of birth; upper white-collar workers such as physicians, lower white-collar workers such as nurses, blue-collar workers such as cleaners, others such as students, and cases with missing information.

Results

Seven per thousand (6,404 of 980,733) singleton births were affected by OASIS. In nulliparae the incidence of OASIS was 18% higher (adjusted OR 1.18 95% CI 1.04−1.34) for upper white-collar workers and 12% higher (adjusted OR 1.12 95% CI 1.02−1.24) for lower white-collar workers compared with blue-collar workers. Among women in these higher SES groups, 40% of the excess OASIS risk was explained by age, non-smoking, birthweight and mode of delivery. Despite the large effect of SES on OASIS, inclusion of SES in multivariable models caused only small changes in estimated adjusted effects for other established risk factors.

Conclusions

OASIS at the first vaginal delivery demonstrates a strong positive social gradient. Higher SES is associated with a number of risk factors for OASIS, including higher birthweight and non-smoking, but only 40% of the excess incidence is explained by these known risk factors. Further research should address other underlying causes including differences in lifestyle or environmental factors, and inequalities in healthcare provision.     

Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference

Background

An important goal of Zebu breeding programs is to improve reproductive performance. A major problem faced with the genetic improvement of reproductive traits is that recording the time for an animal to reach sexual maturity is costly. Another issue is that accurate estimates of breeding values are obtained only a long time after the young bulls have gone through selection. An alternative to overcome these problems is to use traits that are indicators of the reproductive efficiency of the herd and are easier to measure, such as age at first calving. Another problem is that heifers that have conceived once may fail to conceive in the next breeding season, which increases production costs. Thus, increasing heifer’s rebreeding rates should improve the economic efficiency of the herd. Response to selection for these traits tends to be slow, since they have a low heritability and phenotypic information is provided only later in the life of the animal. Genome-wide association studies (GWAS) are useful to investigate the genetic mechanisms that underlie these traits by identifying the genes and metabolic pathways involved.

Results

Data from 1853 females belonging to the Agricultural Jacarezinho LTDA were used. Genotyping was performed using the BovineHD BeadChip (777 962 single nucleotide polymorphisms (SNPs)) according to the protocol of Illumina - Infinium Assay II ® Multi-Sample HiScan with the unit SQ ™ System. After quality control, 305 348 SNPs were used for GWAS. Forty-two and 19 SNPs had a Bayes factor greater than 150 for heifer rebreeding and age at first calving, respectively. All significant SNPs for age at first calving were significant for heifer rebreeding. These 42 SNPs were next or within 35 genes that were distributed over 18 chromosomes and comprised 27 protein-encoding genes, six pseudogenes and two miscellaneous noncoding RNAs.

Conclusions

The use of Bayes factor to determine the significance of SNPs allowed us to identify two sets of 42 and 19 significant SNPs for heifer rebreeding and age at first calving, respectively, which explain 11.35 % and 6.42 % of their phenotypic variance, respectively. These SNPs provide relevant information to help elucidate which genes affect these traits.     

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

Background

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction.

Methods

Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values.

Results

Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied.

Conclusions

These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.     

Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

Background

Many studies have provided evidence of the existence of genetic heterogeneity of environmental variance, suggesting that it could be exploited to improve robustness and uniformity of livestock by selection. However, little is known about the perspectives of such a selection strategy in beef cattle.

Methods

A two-step approach was applied to study the genetic heterogeneity of residual variance of weight gain from birth to weaning and long-yearling weight in a Nellore beef cattle population. First, an animal model was fitted to the data and second, the influence of additive and environmental effects on the residual variance of these traits was investigated with different models, in which the log squared estimated residuals for each phenotypic record were analyzed using the restricted maximum likelihood method. Monte Carlo simulation was performed to assess the reliability of variance component estimates from the second step and the accuracy of estimated breeding values for residual variation.

Results

The results suggest that both genetic and environmental factors have an effect on the residual variance of weight gain from birth to weaning and long-yearling in Nellore beef cattle and that uniformity of these traits could be improved by selecting for lower residual variance, when considering a large amount of information to predict genetic merit for this criterion. Simulations suggested that using the two-step approach would lead to biased estimates of variance components, such that more adequate methods are needed to study the genetic heterogeneity of residual variance in beef cattle.     

Risk Assessment of Adverse Birth Outcomes in Relation to Maternal Age

Background

Although a number of studies have investigated correlations of maternal age with birth outcomes, an extensive assessment using age as a continuous variable is lacking. In the current study, we estimated age-specific risks of adverse birth outcomes in childbearing women.

Method

National population-based data containing maternal and neonatal information were derived from the Health Promotion Administration, Taiwan. A composite adverse birth outcome was defined as at least anyone of stillbirth, preterm birth, low birth weight, macrosomia, neonatal death, congenital anomaly, and small for gestational age (SGA). Singletons were further analyzed for outcomes of live birth in relation to each year of maternal age. A log-binomial model was used to adjust for possible confounders of maternal and neonatal factors.

Results

In total, 2,123,751 births between 2001 and 2010 were utilized in the analysis. The risk of a composite adverse birth outcome was significantly higher at extreme maternal ages. In specific, risks of stillbirth, neonatal death, preterm birth, congenital anomaly, and low birth weight were higher at the extremes of maternal age. Furthermore, risk of macrosomia rose proportionally with an increasing maternal age. In contrast, risk of SGA declined proportionally with an increasing maternal age. The log-binomial model showed greater risks at the maternal ages of <26 and > 30 years for a composite adverse birth outcome.

Conclusions

Infants born to teenagers and women at advanced age possess greater risks for stillbirth, preterm birth, neonatal death, congenital anomaly, and low birth weight. Pregnancies at advanced age carry an additional risk for macrosomia, while teenage pregnancies carry an additional risk for SGA. The data suggest that the optimal maternal ages to minimize adverse birth outcomes are 26∼30 years.