Tomasz Suchocki

Effects of intravenous iron therapy in iron-deficient patients with systolic heart failure: a meta-analysis of randomized controlled trials.

The aim of this study was to assess the net clinical and prognostic effects of intravenous (i.v.) iron therapy in patients with systolic heart failure (HF) and iron deficiency (ID).

Iron Status and Survival in Diabetic Patients With Coronary Artery Disease

OBJECTIVE To investigate the impact of iron status on survival in patients with type 2 diabetes and coronary artery disease (CAD). RESEARCH DESIGN AND METHODS Serum ferritin, transferrin saturation (Tsat), and soluble transferrin receptor (sTfR) were measured in 287 patients with type 2 diabetes and stable CAD (65 ± 9 years of age, 78% men). RESULTS During a mean follow-up of 45 ± 19 months, there were 59 (21%) deaths and 60 (21%) cardiovascular hospitalizations. Both serum ferritin and sTfR strongly predicted 5-year all-cause mortality rates, independently of other variables (including hemoglobin, measures of renal function, inflammation, and neurohormonal activation). There was an exponential relationship between sTfR and mortality (adjusted hazard ratio [HR] per 1 log mg/L: 4.24 [95% CI 1.43–12.58], P = 0.01), whereas the relationship between ferritin and mortality was U-shaped (for the lowest and the highest quintiles vs. the middle quintile [reference group], respectively: adjusted HR 7.18 [95% CI 2.03–25.46], P = 0.002, and adjusted HR 5.12 [1.48–17.73], P = 0.01). Similar patterns were observed for the composite outcome of all-cause mortality or cardiovascular hospitalization, and in these multivariable models, low Tsat was related to unfavorable outcome. CONCLUSIONS Both low and high serum ferritin (possibly reflecting depleted and excessive iron stores, respectively) along with high serum sTfR (reflecting reduced metabolically available iron) identify patients with type 2 diabetes and CAD who have a poor prognosis.

Testing candidate gene effects on milk production traits in dairy cattle under various parameterizations and modes of inheritance

The major objectives of this study were 1) to assess the statistical properties of models commonly used for the estimation of single nucleotide polymorphism (SNP) effects under the assumption of various modes of inheritance and various parameterizations of SNP genotypes using simulated data, and 2) to compare effects of the selected polymorphisms located within butyrophilin (BTN1A1), diacylglycerol acyltransferase 1 (DGAT1), leptin (LEP), and leptin receptor (LEPR) candidate genes on milk production traits using data from 2 dairy cattle breeds (190 Jersey cows and 475 Polish Holstein-Friesian cows). Simulation results showed that type I error and power were not dependent on the assumed parameterization, but differences were observed regarding confidence intervals of estimated SNP effects. In the presence of epistasis, correct confidence intervals for all (epistatic and nonepistatic) SNP and all modes of inheritance were provided only by the parameterization proposed by C. H. Kao and Z. B. Zeng in 2002. However, if no dominance effect was included in the model, confidence intervals for SNP effects were correct for all parameterizations. Results based on real data showed that for both breeds the additive effects of polymorphisms were generally similar, except for LEPR, which had a different allele associated with increased fat content in Holstein-Friesians than in Jerseys. In both breeds, DGAT1 had the largest additive effect of the polymorphisms considered, but its effect on most milk traits was more pronounced in Jerseys than in Holstein-Friesians. Evidence of epistasis was found between LEPR and DGAT1, as well as between LEPR and BTN1A1, but only for milk content traits and only in the Holstein-Friesian breed. There was also more evidence for dominance in the Holstein-Friesian breed than in the Jersey breed.

Genome-wide association study for semen production traits in Holstein-Friesian bulls

Identifying genomic regions, particularly individual genes associated with semen quality traits, may be very important for improving sire fertility via selective breeding. The aim of the study was to estimate (co)variance components and effects of single nucleotide polymorphisms (SNP) from the Illumina BovineSNP50 BeadChip (Illumina, San Diego, CA) on semen production traits and to find candidate genes for these traits. The analyzed data set originates from the Polish Holstein-Friesian dairy cattle population and consists of 1,212 bulls kept at 4 artificial insemination stations. For each bull, 5 semen production traits were collected: sperm concentration, semen volume, number of spermatozoa, motility, and motility score. A multitrait mixed model was used to estimate genetic parameters. The parameters obtained were used to estimate SNP effects for each trait separately by the mixed model, which is used in the Polish direct genomic value project. Additionally, genes located in the vicinity of significant SNP were selected as candidate genes. For motility, 20 genome-wide significant SNP, located on 12 autosomes, were identified. For sperm concentration, we found 7 significant SNP: 3 on chromosome X, and 1 on chromosomes 1, 6, 23, and 24. For semen volume and motility score, 3 and 1 significant SNP were detected, respectively. All these SNP were located on chromosome X. For the number of spermatozoa, 12 significant SNP were observed. Six SNP were located on chromosome X, 3 on chromosome 8, and 1 on chromosomes 2, 7, and 16. This study clearly indicated a key role of the X chromosome in the determination of semen quality and emphasized that including such traits into genetic evaluation should be strongly considered.

The impact of single nucleotide polymorphism selection on prediction of genomewide breeding values

The study focuses on the impact of different sets of single nucleotide polymorphisms (SNPs) selected from the available data set on prediction of genomewide breeding values (GBVs) of animals. Correlations between breeding values estimated as additive polygenic effects (EBVs) and GBVs as well as correlations between true breeding values (TBVs) and GBVs are used as major criteria for the comparison of different SNP selection schemes and GBV estimation models.The analysed data is the simulated data set from the XII QTL Workshop. In the analysis five different SNP data sets are considered. For prediction of EBVs a standard mixed animal model is applied, whereas GBVs are defined as the sum of additive effects of SNPs estimated for the different SNP data sets using model 1 with fixed SNPs effects, model 2 with fixed SNPs effects and a random additive polygenic effect, model 3 with a random effects of uncorrelated SNP genotypes.The additive polygenic and residual variance components estimated by the EBV model amount to 1.36 and 3.12, respectively. Differences between models are expressed by comparing the ranking of individuals based on EBV and on GBV and by correlations. Among 100 individuals with the highest EBVs, depending on a model and a data set, there are only between 11 and 37 individuals with the highest GBVs. The highest correlation between GBV and EBV amounts to 0.787 and is observed for model 3 with 3,328 SNPs selected based on their minor allele frequency, the lowest correlation of 0.519 is attributed to model 2 with 300 SNPs. Correlations between GBV estimates obtained from different models with the same number of SNPs range between 0.916 and 0. 998, whereas correlations between different SNP data sets using the same model fall under 0.850.These results indicate that successful application of high throughoutput SNP genotyping technologies for prediction of breeding values is a very promising approach, but before the method can be routinely applied further methodological improvements regarding model construction and SNP selection are required.

Additive effects of 19 porcine SNPs on growth rate, meat content and selection index.

A total of 306 boars (108 Large White and 198 Landrace) were genotyped for 52 candidate SNPs to determine which of the polymorphisms influence growth rate, meat content and selection index. The effects of SNPs were estimated by a mixed linear model including a random additive polygenic animal effect, fixed effects of SNPs including additive, and pairwise additive-by-additive epistases, year*season of birth, breed and RYR1 genotype. In order to estimate all possible pairwise SNP combinations without overparameterising the model a stochastic approach was adopted. A total of 1 350 replications of the model were generated, each containing five randomly selected SNPs. The final estimates of the fixed effects of the model equaled an average out of the replications. The hypothesis of a nonzero effect of SNP was tested by the Wald test. Among 4 257 estimates calculated, many significant (P<0.01), but mostly minor effects (below 1 phenotypic standard deviation) were recorded. The selected SNPs will be further investigated to determine which may be used in MAS.

Statistical modelling of growth using a mixed model with orthogonal polynomials

In statistical modelling, the effects of single-nucleotide polymorphisms (SNPs) are often regarded as time-independent. However, for traits recorded repeatedly, it is very interesting to investigate the behaviour of gene effects over time. In the analysis, simulated data from the 13th QTL-MAS Workshop (Wageningen, The Netherlands, April 2009) was used and the major goal was the modelling of genetic effects as time-dependent. For this purpose, a mixed model which describes each effect using the third-order Legendre orthogonal polynomials, in order to account for the correlation between consecutive measurements, is fitted. In this model, SNPs are modelled as fixed, while the environment is modelled as random effects. The maximum likelihood estimates of model parameters are obtained by the expectation–maximisation (EM) algorithm and the significance of the additive SNP effects is based on the likelihood ratio test, with p-values corrected for multiple testing. For each significant SNP, the percentage of the total variance contributed by this SNP is calculated. Moreover, by using a model which simultaneously incorporates effects of all of the SNPs, the prediction of future yields is conducted. As a result, 179 from the total of 453 SNPs covering 16 out of 18 true quantitative trait loci (QTL) were selected. The correlation between predicted and true breeding values was 0.73 for the data set with all SNPs and 0.84 for the data set with selected SNPs. In conclusion, we showed that a longitudinal approach allows for estimating changes of the variance contributed by each SNP over time and demonstrated that, for prediction, the pre-selection of SNPs plays an important role.

Modelling QTL effect on BTA06 using random regression test day models

In statistical models, a quantitative trait locus (QTL) effect has been incorporated either as a fixed or as a random term, but, up to now, it has been mainly considered as a time-independent variable. However, for traits recorded repeatedly, it is very interesting to investigate the variation of QTL over time. The major goal of this study was to estimate the position and effect of QTL for milk, fat, protein yields and for somatic cell score based on test day records, while testing whether the effects are constant or variable throughout lactation. The analysed data consisted of 23 paternal half-sib families (716 daughters of 23 sires) of Chinese Holstein-Friesian cattle genotyped at 14 microsatellites located in the area of the casein loci on BTA6. A sequence of three models was used: (i) a lactation model, (ii) a random regression model with a QTL constant in time and (iii) a random regression model with a QTL variable in time. The results showed that, for each production trait, at least one significant QTL exists. For milk and protein yields, the QTL effect was variable in time, while for fat yield, each of the three models resulted in a significant QTL effect. When a QTL is incorporated into a model as a constant over time, its effect is averaged over lactation stages and may, thereby, be difficult or even impossible to be detected. Our results showed that, in such a situation, only a longitudinal model is able to identify loci significantly influencing trait variation.

Using gene networks to identify genes and pathways involved in milk production traits in Polish Holstein dairy cattle

When analyzing phenotypes undergoing a complex mode of inheritance, it is of great interest to switch the scope from single genes to gene pathways, which form better defined functional units. We used gene networks to search for physiological processes and underlying genes responsible for complex traits recorded in dairy cattle. Major problems addressed included loss of information from multiple single nucleotide polymorphisms (SNPs) located within or close to the same gene, ignoring information on linkage disequilibrium and validation of the obtained gene network. 2601 bulls genotyped by the Illumina BovineSNP50 BeadChip were used. SNP effects were estimated using a mixed model, then underlying gene effects were estimated and tested for significance, subsequently a gene network was constructed and the functional information represented by the network was retrieved. The networks were validated by repeating the above-mentioned analyses after permutation of bulls’ pseudophenotypes. Effects of 4345 genes were estimated, what makes 16.4% of all genes mapped to the UMD3.1 reference genome. Assuming the maximum 10% type I error rate, for milk yield 50 different gene ontology (GO) terms and three pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) were significantly overrepresented in the real data as compared to the permuted data sets, while for fat yield nine of the GO terms were significantly overrepresented in the real data network, although none of the KEGG pathways reached the significance level. In turn, for protein yield 28 of the GO terms and six KEGG pathways were significantly overrepresented in the real data. Based on the physiological information we identified sets of loci involved in the determination of milk yield (224 genes), fat yield (72 genes), and protein yield (546 genes). Among the genes some have large effects and have already been reported in previous studies, whereas some others represent novel discoveries and thus most probably genes with medium or small effects on trait variation.

Assessing the degree of stratification between closely related Holstein-Friesian populations

Genomic information is an important part of the routine evaluation of dairy cattle and provides the wide availability of animals genotyped using single nucleotide polymorphism (SNP) microarrays. We analyzed 2243 Polish and 2294 German Holstein-Friesian bulls genotyped using the Illumina BovineSNP50 BeadChip. For each bull, estimated breeding values (EBVs) calculated from national routine genetic evaluation were available for production traits and for somatic cell score (SCS). Separately for each population, we estimated SNP haplotypes, pairwise linkage disequilibrium (LD), and SNP effects. The SNP genetic covariance between both populations was estimated using a bivariate mixed model. The average LD was lower in the Polish than in the German population and, with increasing genomic distance, LD decays 1.7 times more rapidly in German than in Polish cattle. The comparison of SNP allele frequencies for base populations estimated separately using Polish and German data revealed a very good agreement. The comparison of genetic effects corresponding to various window lengths defined in bp emerged a systematic pattern: regardless of the length of the compared region, few significant differences were found for production traits, while many were observed for SCS. For each trait, the German population had much higher SNP variances than the Polish population and the genetic covariance estimates were all positive. Depending on traits’ inheritance mode, the additive genetic variation can be stored in many genes following the infinitesimal model (like for SCS) or distributed between genes with high effects and the polygenic “background” (like for production traits). Accounting for those differences has implications on the prospective international genomic evaluation.