The influence of network topology on reverse-engineering of gene-regulatory networks

AbstractModeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern computational biology investigations into gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from time-course gene expression data. Common mathematical formalisms used to represent such models capture both the relative weight or strength of a regulator gene and the type of the regulator (activator, repressor) with a single model parameter. The goal of this study is to quantify the role this parameter plays in terms of the computational performance of the reverse-engineering process and the predictive power of the inferred GRN models. We carried out three sets of computational experiments on a GRN system consisting of 22 genes. While more comprehensive studies of this kind are ultimately required, this computational study demonstrates that models with similar training (reverse-engineering) error that have been inferred under varying degrees of a priori known topology information, exhibit considerably different predictive performance. This study was performed with a newly developed multiscale modeling and simulation tool called MultiGrain/MAPPER

Polymorphism of the Prolactin (PRL) Gene and Its Effect on Milk Production Traits in Romanian Cattle Breeds

In the present study, we investigated one polymorphism of the PRL gene (rs211032652 SNP) and assessed its influence on milk production and chemical composition in two Romanian cattle breeds. A total of 119 cattle from two breeds reared in Western Romania (64 Romanian Spotted and 55 Romanian Brown) were included in the research herd. A PCR-RFLP genotyping assay was used for the identification of the rs211032652 SNP variants. Shapiro’s test and Levene’s test were used to verify ANOVA assumptions and ANOVA and Tukey’s test were employed to test the associations between PRL genotypes and five milk traits. Among the studied breeds, our results showed that PRL genotypes were significantly associated (p < 0.05) with fat and protein percentage in the milk of Romanian Brown cattle. The AA genotype was associated with a higher fat percentage in milk (4.76 ± 0.28) compared to the GG genotype (4.04 ± 0.22, p = 0.048), as well as a higher protein percentage (3.96 ± 0.32% vs. 3.43 ± 0.15%, p = 0.027) in Romanian Brown cattle. Moreover, the PRL locus favored a significantly higher fat (p = 0.021) and protein (p = 0.028) percentage in the milk of Romanian Brown cattle compared to the Romanian Spotted breed, with a difference of 0.263% and 0.170%, respectively

Comparative Analysis of Romanian and Swiss Bovine Populations Using Whole Genome Sequencing and SNP Microarrays

The use of SNP microarrays has gained distinguished attention in recent years and the identification of numerous SNPs has proven valuable for genetic evaluation and selection in farm animals. In the current study we compared several bovine populations from Romania and Switzerland at the level of SNPs. Romanian Brown (N=39) and Romanian Spotted (N=245) cattle were genotyped using the Axiom Bovine BovMDv3 SNP microarray. For the Swiss population, we acquired sequencing data from the NCBI SRA database for 80 individuals from three breeds: Brown Swiss (N=20), Original Braunvieh (N=20) and Simmental (N=40). Sequencing data were processed using the Bcbio-nextgen data analysis pipeline and variants were called based on the UMD3.1 reference genome. Common SNPs found from both microarray and sequencing data were retained and genotypes from all the Romanian and Swiss animals were pooled together, resulting in a combined dataset of 48,291 SNPs for 364 individuals. Pairwise comparisons were assessed on the five subpopulations according to Weir and Cockerham’s FST index. Small genetic differences (FST < 0.05) were found between the Romanian Brown and Swiss Brown Swiss subpopulations and between Romanian Spotted and Swiss Simmental subpopulations. For all the other pairwise comparisons, FST values were between 0.05 and 0.1, indicating a moderate level of genetic difference among the corresponding subpopulations. The results of fastSTRUCTURE indicated that the most likely number (K) of subpopulations from the pooled dataset was between 8 and 12. Bar plots for K = 5, 8 and 12 confirmed that Romanian Brown and Swiss Brown Swiss subpopulations were genetically similar. However, they also revealed a surprisingly high level of heterogeneity among the Romanian Spotted individuals. As such, future research is required to zoom in on the genetic make-up and explain the most likely sources of heterogeneity for the Romanian Spotted breed. Current results will facilitate a better understanding of genomic selection and its application for improved breeding programs in Romanian cattle breeds

Single Nucleotide Polymorphisms in Bison bison Identified by the GGP Bovine 50K SNP Assay

The vulnerable populations of bison had gone through a drastic reduction in population size, have undergone a very high level of inbreeding and have been through severe bottlenecks. Using a panel of Single Nucleotide Polymorphisms (GGP Bovine 50K SNP arrays, Neogen) developed across the entire bovine (Bos taurus) genome, we have carried out a genome variability screening on a bison (Bison bison) population in Romania. Eight males were included in the analysis. As part of SNP quality control filtering, one individual with a call rate below 80% was removed from the study. From a total of 47,843 SNPs only 4474 were polymorphic (9.35% from the total) and 7 individuals (out of a total of 8) were left after PLINK's quality control filtering. The total call rate of genotyped samples was 90.11% for the filtered dataset. A secondary PLINK run was performed on the 4474 filtered SNPs to find the ones whose HWE p-value fell below 0.05 and 100 markers were highlighted in this way. The results showed a larger number of polymorphic SNPs compared with previous studies from the literature. In addition, the data obtained using the GGP Bovine 50K SNP arrays may facilitate the design of breeding strategies that can be applied for decreasing unwanted inbreeding effects in the vulnerable bison populations