Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

Background

Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis.

Methods

Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations.

Results

Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process.

Conclusions

The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health.     

A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses

Background

To obtain predictions that are not biased by selection, the conditional mean of the breeding values must be computed given the data that were used for selection. When single nucleotide polymorphism (SNP) effects have a normal distribution, it can be argued that single-step best linear unbiased prediction (SS-BLUP) yields a conditional mean of the breeding values. Obtaining SS-BLUP, however, requires computing the inverse of the dense matrix G of genomic relationships, which will become infeasible as the number of genotyped animals increases. Also, computing G requires the frequencies of SNP alleles in the founders, which are not available in most situations. Furthermore, SS-BLUP is expected to perform poorly relative to variable selection models such as BayesB and BayesC as marker densities increase.

Methods

A strategy is presented for Bayesian regression models (SSBR) that combines all available data from genotyped and non-genotyped animals, as in SS-BLUP, but accommodates a wider class of models. Our strategy uses imputed marker covariates for animals that are not genotyped, together with an appropriate residual genetic effect to accommodate deviations between true and imputed genotypes. Under normality, one formulation of SSBR yields results identical to SS-BLUP, but does not require computing G or its inverse and provides richer inferences. At present, Bayesian regression analyses are used with a few thousand genotyped individuals. However, when SSBR is applied to all animals in a breeding program, there will be a 100 to 200-fold increase in the number of animals and an associated 100 to 200-fold increase in computing time. Parallel computing strategies can be used to reduce computing time. In one such strategy, a 58-fold speedup was achieved using 120 cores.

Discussion

In SSBR and SS-BLUP, phenotype, genotype and pedigree information are combined in a single-step. Unlike SS-BLUP, SSBR is not limited to normally distributed marker effects; it can be used when marker effects have a t distribution, as in BayesA, or mixture distributions, as in BayesB or BayesC π. Furthermore, it has the advantage that matrix inversion is not required. We have investigated parallel computing to speedup SSBR analyses so they can be used for routine applications.

Electronic supplementary material

The online version of this article (doi:10.1186/1297-9686-46-50) contains supplementary material, which is available to authorized users.     

The distance between Mars and Venus: measuring global sex differences in personality

Background

Sex differences in personality are believed to be comparatively small. However, research in this area has suffered from significant methodological limitations. We advance a set of guidelines for overcoming those limitations: (a) measure personality with a higher resolution than that afforded by the Big Five; (b) estimate sex differences on latent factors; and (c) assess global sex differences with multivariate effect sizes. We then apply these guidelines to a large, representative adult sample, and obtain what is presently the best estimate of global sex differences in personality.

Methodology/Principal Findings

Personality measures were obtained from a large US sample (N = 10,261) with the 16PF Questionnaire. Multigroup latent variable modeling was used to estimate sex differences on individual personality dimensions, which were then aggregated to yield a multivariate effect size (Mahalanobis D). We found a global effect size D = 2.71, corresponding to an overlap of only 10% between the male and female distributions. Even excluding the factor showing the largest univariate ES, the global effect size was D = 1.71 (24% overlap). These are extremely large differences by psychological standards.

Significance

The idea that there are only minor differences between the personality profiles of males and females should be rejected as based on inadequate methodology.     

Improving the computational efficiency of fully Bayes inference and assessing the effect of misspecification of hyperparameters in whole-genome prediction models

Background

The reliability of whole-genome prediction models (WGP) based on using high-density single nucleotide polymorphism (SNP) panels critically depends on proper specification of key hyperparameters. A currently popular WGP model labeled BayesB specifies a hyperparameter π, that is `loosely used to describe the proportion of SNPs that are in linkage disequilibrium (LD) with causal variants. The remaining markers are specified to be random draws from a Student t distribution with key hyperparameters being degrees of freedom v and scale s².

Methods

We consider three alternative Markov chain Monte Carlo (MCMC) approaches based on the use of Metropolis-Hastings (MH) to estimate these key hyperparameters. The first approach, termed DFMH, is based on a previously published strategy for which s² is drawn by a Gibbs step and v is drawn by a MH step. The second strategy, termed UNIMH, substitutes MH for Gibbs when drawing s² and further collapses or marginalizes the full conditional density of v. The third strategy, termed BIVMH, is based on jointly drawing the two hyperparameters in a bivariate MH step. We also tested the effect of misspecification of s² for its effect on accuracy of genomic estimated breeding values (GEBV), yet allowing for inference on the other hyperparameters.

Results

The UNIMH and BIVMH strategies had significantly greater (P < 0.05) computational efficiencies for estimating v and s² than DFMH in BayesA (π = 1) and BayesB implementations. We drew similar conclusions based on an analysis of the public domain heterogeneous stock mice data. We also determined significant drops (P < 0.01) in accuracies of GEBV under BayesA by overspecifying s², whereas BayesB was more robust to such misspecifications. However, understating s² was compensated by counterbalancing inferences on v in BayesA and BayesB, and on π in BayesB.

Conclusions

Sampling strategies based solely on MH updates of v and s², and collapsed representations of full conditional densities can improve the computational efficiency of MCMC relative to the use of Gibbs updates. We believe that proper inferences on s², v and π are vital to ensure that the accuracy of GEBV is maximized when using parametric WGP models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0092-x) contains supplementary material, which is available to authorized users.     

A genetic validation study reveals a role of vitamin D metabolism in the response to interferon-alfa-based therapy of chronic hepatitis C

Background

To perform a comprehensive study on the relationship between vitamin D metabolism and the response to interferon-α-based therapy of chronic hepatitis C.

Methodology/Principal Findings

Associations between a functionally relevant polymorphism in the gene encoding the vitamin D 1α-hydroxylase (CYP27B1-1260 rs10877012) and the response to treatment with pegylated interferon-α (PEG-IFN-α) and ribavirin were determined in 701 patients with chronic hepatitis C. In addition, associations between serum concentrations of 25-hydroxyvitamin D₃ (25[OH]D₃) and treatment outcome were analysed. CYP27B1-1260 rs10877012 was found to be an independent predictor of sustained virologic response (SVR) in patients with poor-response IL28B genotypes (15% difference in SVR for rs10877012 genotype AA vs. CC, p = 0.02, OR = 1.52, 95% CI = 1.061–2.188), but not in patients with favourable IL28B genotype. Patients with chronic hepatitis C showed a high prevalence of vitamin D insufficiency (25[OH]D₃<20 ng/mL) during all seasons, but 25(OH)D₃ serum levels were not associated with treatment outcome.

Conclusions/Significance

Our study suggests a role of bioactive vitamin D (1,25[OH]₂D₃, calcitriol) in the response to treatment of chronic hepatitis C. However, serum concentration of the calcitriol precursor 25(OH)D₃ is not a suitable predictor of treatment outcome.     

Effectiveness of genomic prediction on milk flow traits in dairy cattle

Background

Milkability, primarily evaluated by measurements of milking speed and time, has an economic impact in milk production of dairy cattle. Recently the Italian Brown Swiss Breeders Association has included milking speed in genetic evaluations. The main objective of this study was to investigate the possibility of implementing genomic selection for milk flow traits in the Italian Brown Swiss population and thereby evaluate the potential of genomic selection for novel traits in medium-sized populations. Predicted breeding values and reliabilities based on genomic information were compared with those obtained from traditional breeding values.

Methods

Milk flow measures for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow were collected on 37 213 Italian Brown Swiss cows. Breeding values for genotyped sires (n = 1351) were obtained from standard BLUP and genome-enhanced breeding value techniques utilizing two-stage and single-step methods. Reliabilities from a validation dataset were estimated and used to compare accuracies obtained from parental averages with genome-enhanced predictions.

Results

Genome-enhanced breeding values evaluated using two-stage methods had similar reliabilities with values ranging from 0.34 to 0.49 for the different traits. Across two-stage methods, the average increase in reliability from parental average was approximately 0.17 for all traits, with the exception of descending time, for which reliability increased to 0.11. Combining genomic and pedigree information in a single-step produced the largest increases in reliability over parent averages: 0.20, 0.24, 0.21, 0.14, 0.20 and 0.21 for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow, respectively.

Conclusions

Using genomic models increased the accuracy of prediction compared to traditional BLUP methods. Our results show that, among the methods used to predict genome-enhanced breeding values, the single-step method was the most successful at increasing the reliability for most traits. The single-step method takes advantage of all the data available, including phenotypes from non-genotyped animals, and can easily be incorporated into current breeding evaluations.     

Horizontal gene transfers link a human MRSA pathogen to contagious bovine mastitis bacteria

Background

Acquisition of virulence factors and antibiotic resistance by many clinically important bacteria can be traced to horizontal gene transfer (HGT) between related or evolutionarily distant microflora. Comparative genomic analysis has become an important tool for identifying HGT DNA in emerging pathogens. We have adapted the multi-genome alignment tool EvoPrinter to facilitate discovery of HGT DNA sequences within bacterial genomes and within their mobile genetic elements.

Principal Findings

EvoPrinter analysis of 13 different Staphylococcus aureus genomes revealed that one of the human isolates, the hospital epidemic methicillin-resistant MRSA252 strain, uniquely shares multiple putative HGT DNA sequences with different causative agents of bovine mastitis that are not found in the other human S. aureus isolates. MRSA252 shares over 14 different DNA sequence blocks with the bovine mastitis ET3 S. aureus strain RF122, and many of the HGT DNAs encode virulence factors. EvoPrinter analysis of the MRSA252 chromosome also uncovered virulence-factor encoding HGT events with the genome of Listeria monocytogenes and a Staphylococcus saprophyticus associated plasmid. Both bacteria are also causal agents of contagious bovine mastitis.

Conclusions

EvoPrinter analysis reveals that the human MRSA252 strain uniquely shares multiple DNA sequence blocks with different causative agents of bovine mastitis, suggesting that HGT events may be occurring between these pathogens. These findings have important implications with regard to animal husbandry practices that inadvertently enhance the contact of human and livestock bacterial pathogens.     

Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population

Background

A single-step blending approach allows genomic prediction using information of genotyped and non-genotyped animals simultaneously. However, the combined relationship matrix in a single-step method may need to be adjusted because marker-based and pedigree-based relationship matrices may not be on the same scale. The same may apply when a GBLUP model includes both genomic breeding values and residual polygenic effects. The objective of this study was to compare single-step blending methods and GBLUP methods with and without adjustment of the genomic relationship matrix for genomic prediction of 16 traits in the Nordic Holstein population.

Methods

The data consisted of de-regressed proofs (DRP) for 5 214 genotyped and 9 374 non-genotyped bulls. The bulls were divided into a training and a validation population by birth date, October 1, 2001. Five approaches for genomic prediction were used: 1) a simple GBLUP method, 2) a GBLUP method with a polygenic effect, 3) an adjusted GBLUP method with a polygenic effect, 4) a single-step blending method, and 5) an adjusted single-step blending method. In the adjusted GBLUP and single-step methods, the genomic relationship matrix was adjusted for the difference of scale between the genomic and the pedigree relationship matrices. A set of weights on the pedigree relationship matrix (ranging from 0.05 to 0.40) was used to build the combined relationship matrix in the single-step blending method and the GBLUP method with a polygenetic effect.

Results

Averaged over the 16 traits, reliabilities of genomic breeding values predicted using the GBLUP method with a polygenic effect (relative weight of 0.20) were 0.3% higher than reliabilities from the simple GBLUP method (without a polygenic effect). The adjusted single-step blending and original single-step blending methods (relative weight of 0.20) had average reliabilities that were 2.1% and 1.8% higher than the simple GBLUP method, respectively. In addition, the GBLUP method with a polygenic effect led to less bias of genomic predictions than the simple GBLUP method, and both single-step blending methods yielded less bias of predictions than all GBLUP methods.

Conclusions

The single-step blending method is an appealing approach for practical genomic prediction in dairy cattle. Genomic prediction from the single-step blending method can be improved by adjusting the scale of the genomic relationship matrix.     

Accuracy of Specific BIVA for the Assessment of Body Composition in the United States Population

Background

Bioelectrical impedance vector analysis (BIVA) is a technique for the assessment of hydration and nutritional status, used in the clinical practice. Specific BIVA is an analytical variant, recently proposed for the Italian elderly population, that adjusts bioelectrical values for body geometry.

Objective

Evaluating the accuracy of specific BIVA in the adult U.S. population, compared to the ‘classic’ BIVA procedure, using DXA as the reference technique, in order to obtain an interpretative model of body composition.

Design

A cross-sectional sample of 1590 adult individuals (836 men and 754 women, 21–49 years old) derived from the NHANES 2003–2004 was considered. Classic and specific BIVA were applied. The sensitivity and specificity in recognizing individuals below the 5^th and above the 95^th percentiles of percent fat (FM_DXA%) and extracellular/intracellular water (ECW/ICW) ratio were evaluated by receiver operating characteristic (ROC) curves. Classic and specific BIVA results were compared by a probit multiple-regression.

Results

Specific BIVA was significantly more accurate than classic BIVA in evaluating FM_DXA% (ROC areas: 0.84–0.92 and 0.49–0.61 respectively; p = 0.002). The evaluation of ECW/ICW was accurate (ROC areas between 0.83 and 0.96) and similarly performed by the two procedures (p = 0.829). The accuracy of specific BIVA was similar in the two sexes (p = 0.144) and in FM_DXA% and ECW/ICW (p = 0.869).

Conclusions

Specific BIVA showed to be an accurate technique. The tolerance ellipses of specific BIVA can be used for evaluating FM% and ECW/ICW in the U.S. adult population.     

Hepatitis C Virus Infection Influences the S-Methadone Metabolite Plasma Concentration

Background and Objectives

Heroin-dependent patients typically contract hepatitis C virus (HCV) at a disproportionately high level due to needle exchange. The liver is the primary target organ of HCV infection and also the main organ responsible for drug metabolism. Methadone maintenance treatment (MMT) is a major treatment regimen for opioid dependence. HCV infection may affect methadone metabolism but this has rarely been studied. In our current study, we aimed to test the hypothesis that HCV may influence the methadone dosage and its plasma metabolite concentrations in a MMT cohort from Taiwan.

Methods

A total of 366 MMT patients were recruited. The levels of plasma hepatitis B virus (HBV), HCV, human immunodeficiency virus (HIV) antibodies (Ab), liver aspartate aminotransferase (AST) and alanine aminotransferase (ALT), as well as methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP) were measured along with the urine morphine concentration and amphetamine screening.

Results

Of the 352 subjects in our cohort with HCV test records, 95% were found to be positive for plasma anti-HCV antibody. The liver functional parameters of AST (Wilcoxon Rank-Sum test, P = 0.02) and ALT (Wilcoxon Rank-Sum test, P = 0.04), the plasma methadone concentrations (Wilcoxon Rank-Sum test, P = 0.043) and the R-enantiomer of methadone concentrations (Wilcoxon Rank-Sum test, P = 0.032) were significantly higher in the HCV antibody-positive subjects than in the HCV antibody-negative patients, but not the S-EDDP/methadone dose ratio. The HCV levels correlated with the methadone dose ( = 14.65 and 14.13; P = 0.029 and 0.03) and the S-EDDP/methadone dose ratio ( = −0.41 and −0.40; P = 0.00084 and 0.002) in both univariate and multivariate regression analyses.

Conclusions

We conclude that HCV may influence the methadone dose and plasma S-EDDP/methadone dose ratio in MMT patients in this preliminary study.     

Structural equation models to estimate risk of infection and tolerance to bovine mastitis

Background

One method to improve durably animal welfare is to select, as reproducers, animals with the highest ability to resist or tolerate infection. To do so, it is necessary to distinguish direct and indirect mechanisms of resistance and tolerance because selection on these traits is believed to have different epidemiological and evolutionary consequences.

Methods

We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, among the many potential mediators of infection, the few ones that influence it significantly and (2) to estimate direct and indirect levels of tolerance of animals infected naturally with pathogens. We applied the method to two surveys of bovine mastitis in the Walloon region of Belgium, in which we recorded herd management practices, mastitis frequency, and results of bacteriological analyses of milk samples.

Results and discussion

Structural equation models suggested that, among more than 35 surveyed herd characteristics, only nine (age, addition of urea in the rations, treatment of subclinical mastitis, presence of dirty liner, cows with hyperkeratotic teats, machine stripping, pre- and post-milking teat disinfection, and housing of milking cows in cubicles) were directly and significantly related to a latent measure of bovine mastitis, and that treatment of subclinical mastitis was involved in the pathway between post-milking teat disinfection and latent mastitis. These models also allowed the separation of direct and indirect effects of bacterial infection on milk productivity. Results suggested that infected cows were tolerant but not resistant to mastitis pathogens.

Conclusions

We revealed the advantages of structural equation models, compared to classical models, for dissecting measurements of resistance and tolerance to infectious diseases, here bovine mastitis. Using our method, we identified nine major risk factors that were directly associated with an increased risk of mastitis and suggested that cows were tolerant but not resistant to mastitis. Selection should aim at improved resistance to infection by mastitis pathogens, although further investigations are needed due to the limitations of the data used in this study.     

The Prognostic Value of SOX2 Expression in Non-Small Cell Lung Cancer: A Meta-Analysis

Objective

To investigate the association of SOX2 expression in tumor with clinicopathological features and survival of non-small-cell lung carcinoma (NSCLC) patients.

Methods

Publications assessing the clinicopathological characteristics and prognostic significance of SOX2 in NSCLC were identified up to May 2013. A meta-analysis of eligible studies was performed using standard statistical methods to clarify the association between SOX2 expression and these clinical parameters.

Results

A total of eight studies met the inclusion criteria. Analysis of these data showed that SOX2 expression was positively associated with squamous histology, (pooled OR = 5.26, 95% CI: 1.08–25.6, P = 0.040). Simultaneously, we also found that SOX2 expression was positively associated with overall survival (pooled HR = 0.65, 95% CI: 0.47–0.89, P = 0.007, random-effect).

Conclusions

SOX2 expression in tumor is a candidate positive prognostic biomarker for NSCLC patients.     

Association of L-Ficolin Levels and FCN2 Genotypes with Chronic Chagas Disease

Background

L-ficolin (encoded by FCN2) binds to acetylated sugar moieties of many pathogens, including Trypanosoma cruzi, promoting their phagocytosis and lysis by the complement system.

Methods

We investigated L-ficolin levels in 160 T. cruzi infected patients with chronic Chagas disease and 71 healthy individuals, and FCN2 polymorphisms (−986 G>A, −602 G>A, and −4 A>G in the promoter and A258S in exon 8) in 243 patients, being 88 indeterminate (asymptomatic), 96 with cardiac, 23 with digestive and 33 with cardiodigestive manifestations (two were unspecified) and 305 controls (135 for A258S).

Results

Patients presented lower L-ficolin plasma levels than controls (p<0.0001). Among the different groups of cardiac commitment, individuals with moderate forms had higher L-ficolin levels than the severe forms (P = 0.039). Lower L-ficolin levels were found associated with the 258S variant in the patients (P = 0.034). We found less −4A/G heterozygotes in the cardiac patients, than in the controls (OR = 0.56 [95% CI = 0.33–0.94], P = 0.034). Heterozygote −4A/G genotypes with the 258S variant and 258SS homozygotes were nevertheless more frequent among cardiodigestive patients than in controls (OR = 14.1 [95% CI = 3.5–56.8], P = 0.0001) and in indeterminate patients (OR = 3.2 [95% CI = 1.1–9.4], P = 0.037). We also found an association of the allelic frequency of the 258S variant with cardiodigestive Chagas disease compared to controls (OR = 2.24 [95% CI = 1.1–4.5], P = 0.037). Thus, decreased patient levels of L-ficolin reflect not only protein consumption due to the disease process, but also the higher frequency of the 258S variant in patients with cardiodigestive symptoms.

Conclusion

The very first study on Brazilian cohort associates both L-ficolin plasma levels and FCN2 variants to Chagas disease and subsequent disease progression. The prognostic value of L-ficolin levels and the FCN2*A258S polymorphism should be further evaluated in other settings.     

Comparative expression profiling of E. coli and S. aureus inoculated primary mammary gland cells sampled from cows with different genetic predispositions for somatic cell score

Background

During the past ten years many quantitative trait loci (QTL) affecting mastitis incidence and mastitis related traits like somatic cell score (SCS) were identified in cattle. However, little is known about the molecular architecture of QTL affecting mastitis susceptibility and the underlying physiological mechanisms and genes causing mastitis susceptibility. Here, a genome-wide expression analysis was conducted to analyze molecular mechanisms of mastitis susceptibility that are affected by a specific QTL for SCS on Bos taurus autosome 18 (BTA18). Thereby, some first insights were sought into the genetically determined mechanisms of mammary gland epithelial cells influencing the course of infection.

Methods

Primary bovine mammary gland epithelial cells (pbMEC) were sampled from the udder parenchyma of cows selected for high and low mastitis susceptibility by applying a marker-assisted selection strategy considering QTL and molecular marker information of a confirmed QTL for SCS in the telomeric region of BTA18. The cells were cultured and subsequently inoculated with heat-inactivated mastitis pathogens Escherichia coli and Staphylococcus aureus, respectively. After 1, 6 and 24 h, the cells were harvested and analyzed using the microarray expression chip technology to identify differences in mRNA expression profiles attributed to genetic predisposition, inoculation and cell culture.

Results

Comparative analysis of co-expression profiles clearly showed a faster and stronger response after pathogen challenge in pbMEC from less susceptible animals that inherited the favorable QTL allele ''Q'' than in pbMEC from more susceptible animals that inherited the unfavorable QTL allele ''q''. Furthermore, the results highlighted RELB as a functional and positional candidate gene and related non-canonical Nf-kappaB signaling as a functional mechanism affected by the QTL. However, in both groups, inoculation resulted in up-regulation of genes associated with the Ingenuity pathways ''dendritic cell maturation'' and ''acute phase response signaling'', whereas cell culture affected biological processes involved in ''cellular development''.

Conclusions

The results indicate that the complex expression profiling of pathogen challenged pbMEC sampled from cows inheriting alternative QTL alleles is suitable to study genetically determined molecular mechanisms of mastitis susceptibility in mammary epithelial cells in vitro and to highlight the most likely functional pathways and candidate genes underlying the QTL effect.     

Estimation of heritability, evolvability and genetic correlations of two pollen and pistil traits involved in a sexual conflict over timing of stigma receptivity in Collinsia heterophylla (Plantaginaceae)

Background and Aims

Heritable genetic variation is crucial for selection to operate, yet there is a paucity of studies quantifying such variation in interactive male/female sexual traits, especially those of plants. Previous work on the annual plant Collinsia heterophylla, a mixed-mating species, suggests that delayed stigma receptivity is involved in a sexual conflict: pollen from certain donors fertilize ovules earlier than others at the expense of reduced maternal seed set and lower levels of pollen competition.

Methods

Parent–offspring regressions and sib analyses were performed to test for heritable genetic variation and co-variation in male and female interactive traits related to the sexual conflict.

Key Results

Some heritable variation and evolvability were found for the female trait (delayed stigma receptivity in presence of pollen), but no evidence was found for genetic variation in the male trait (ability to fertilize ovules early). The results further indicated a marginally significant correlation between a male''s ability to fertilize early and early stigma receptivity in offspring. However, despite potential indirect selection of these traits, antagonistic co-evolution may not occur given the lack of heritability of the male trait.

Conclusions

To our knowledge, this is the first study of a plant or any hermaphrodite that examines patterns of genetic correlation between two interactive sexual traits, and also the first to assess heritabilities of plant traits putatively involved in a sexual conflict. It is concluded that the ability to delay fertilization in presence of pollen can respond to selection, while the pollen trait has lower evolutionary potential.     

Salt-Restriction-Spoon Improved the Salt Intake among Residents in China

Objective

To evaluate the effect of an improved salt-restriction spoon on the attitude of salt-restriction, the using rate of salt-restriction-spoon, the actual salt intake, and 24-hour urinary sodium excretion (24HUNa).

Design

A community intervention study.

Setting

Two villages in Beijing.

Participants

403 local adult residents being responsible for home cooking.

Intervention

Participants were randomly assigned to the intervention group or the control group. Those in the intervention group were provided with an improved salt-restriction-spoon and health education, and were informed of their actual salt intake and 24HUNa. Not any intervention was given to those in the control group.

Main Outcome Measures

The scores on the variables of Health Belief Model, the using rate of salt-restriction-spoon, the actual salt intake, and 24HUNa.

Analysis

Covariance analyses, Chi-square tests, Student’s t tests, and repeated measures analyses of variance.

Results

After 6 months of intervention, the intervention group felt significantly less objective barriers, and got access to significantly more cues to action as compared to the control group. The using rate and the correctly using rate of salt-restriction-spoon were significantly higher in the intervention group. The daily salt intake decreased by 1.42 g in the intervention group and by 0.28 g in the control group, and repeated measures analysis of variance showed significant change over time (F = 7.044, P<0.001) and significant difference between groups by time (F = 2.589, P = 0.041). The 24HUNa decreased by 34.84 mmol in the intervention group and by 33.65 mmol in the control group, and repeated measures analysis of variance showed significant change over time (F = 14.648, P<0.001) without significant difference between groups by time (F = 0.222, P = 0.870).

Conclusions

The intervention effect was acceptable, therefore, the improved salt-restriction-spoon and corresponding health education could be considered as an alternative for salt reduction strategy in China and other countries where salt intake comes mainly from home cooking.     

Genomic prediction of genetic merit using LD-based haplotypes in the Nordic Holstein population

Background

A haplotype approach to genomic prediction using high density data in dairy cattle as an alternative to single-marker methods is presented. With the assumption that haplotypes are in stronger linkage disequilibrium (LD) with quantitative trait loci (QTL) than single markers, this study focuses on the use of haplotype blocks (haploblocks) as explanatory variables for genomic prediction. Haploblocks were built based on the LD between markers, which allowed variable reduction. The haploblocks were then used to predict three economically important traits (milk protein, fertility and mastitis) in the Nordic Holstein population.

Results

The haploblock approach improved prediction accuracy compared with the commonly used individual single nucleotide polymorphism (SNP) approach. Furthermore, using an average LD threshold to define the haploblocks (LD≥0.45 between any two markers) increased the prediction accuracies for all three traits, although the improvement was most significant for milk protein (up to 3.1 % improvement in prediction accuracy, compared with the individual SNP approach). Hotelling’s t-tests were performed, confirming the improvement in prediction accuracy for milk protein. Because the phenotypic values were in the form of de-regressed proofs, the improved accuracy for milk protein may be due to higher reliability of the data for this trait compared with the reliability of the mastitis and fertility data. Comparisons between best linear unbiased prediction (BLUP) and Bayesian mixture models also indicated that the Bayesian model produced the most accurate predictions in every scenario for the milk protein trait, and in some scenarios for fertility.

Conclusions

The haploblock approach to genomic prediction is a promising method for genomic selection in animal breeding. Building haploblocks based on LD reduced the number of variables without the loss of information. This method may play an important role in the future genomic prediction involving while genome sequences.