Distribution of miRNA genes in the pig genome.

BACKGROUND: Recent completion of swine genome may simplify the production of swine as a large biomedical model. Here we studied sequence and location of known swine miRNA genes, key regulators of protein-coding genes at the level of RNA, and compared them to human and mouse data to prioritize future molecular studies. RESULTS: Distribution of miRNA genes in pig genome shows no particular relation to different genomic features including protein coding genes - proportions of miRNA genes in intergenic regions, introns and exons roughly agree with the size of these regions in the pig genome. Our analyses indicate that host genes harbouring intragenic miRNAs are longer from other protein-coding genes, however, no important GO enrichment was found. Swine mature miRNAs show high sequence similarity to their human and mouse orthologues. Location of miRNA genes relative to protein-coding genes is also similar among studied species, however, there are differences in the precise position in particular intergenic regions and within particular hosts. The most prominent difference between pig and human miRNAs is a large group of pig-specific sequences (53% of swine miRNAs). We found no evidence that this group of evolutionary new pig miRNAs is different from old miRNAs genes with respect to genomic location except that they are less likely to be clustered. CONCLUSIONS: There are differences in precise location of orthologues miRNA genes in particular intergenic regions and within particular hosts, and their meaning for coexpression with protein-coding genes deserves experimental studies. Functional studies of a large group of pig-specific sequences in future may reveal limits of the pig as a model organism to study human gene expression.

QTLMAS 2010: simulated dataset.

BACKGROUND: Objective was to simulate the data for the QTLMAS 2010 Workshop under a model that includes major additive, epistatic and parent-of-origin effects. RESULTS: Data were simulated for 3226 individuals in 5 generations. Genomic data for 5 chromosomes were simulated using coalescent model. In total, the data included 10,031 SNPs, 30 additive QTLs, 2 interacting QTL pairs, and 3 imprinted loci. The density was 20 SNPs/1Mb, whereas mean linkage disequilibrium between adjacent SNPs was 0.1. One quantitative and one binary trait were simulated with heritability of 0.39-0.52 and additive correlation of 0.59. The data can be used as a benchmark for comparison of QTL mapping methods and models for genomic breeding value estimation under complex genetic architecture.

Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis.

BACKGROUND: A quantitative and a binary trait for the 14th QTLMAS 2010 workshop were simulated under a model which combined additive inheritance, epistasis and imprinting. This paper aimed to compare results submitted by the participants of the workshop. METHODS: The results were compared according to three criteria: the success rate (ratio of mapped QTL to the total number of simulated QTL), and the error rate (ratio of false positives to the number of reported positions), and mean distance between a true mapped QTL and the nearest submitted position. RESULTS: Seven groups submitted results for the quantitative trait and five for the binary trait. Among the 37 simulated QTL 17 remained undetected. Success rate ranged from 0.05 to 0.43, error rate was between 0.00 and 0.92, and the mean distance ranged from 0.26 to 0.77 Mb. CONCLUSIONS: Our comparison shows that differences among methods used by the participants increases with the complexity of genetic architecture. It was particularly visible for the quantitative trait which was determined partly by non-additive QTL. Furthermore, an imprinted QTL with a large effect may remain undetected if the applied model tests only for Mendelian genes.

Polymorphisms in 5'-flanking regions of genes encoding adiponectin, leptin, and resistin are not associated with obesity of Polish children and adolescents.

Genes encoding adipokines are important functional candidates for development of obesity. In this study we screened for polymorphism 5'-flanking regions of the adiponectin (ADIPOQ), leptin (LEP) and resistin (RETN) genes in a cohort of Polish obese children and adolescents (n = 243) and a control group of non-obese adults (n = 100). Altogether 13 SNPs (single nucleotide polymorphisms) and 1 InDel (insertion/deletion polymorphism) were found. Among them five polymorphisms, localized in the LEP gene, turned out to be novel, but their distribution was insufficient for association studies. We found no consistent evidence for association between obesity and the SNPs demonstrating minor allele frequency (MAF) above 0.2 (ADIPOQ: -11377C>G, LEP: -2548C>T, 19A>G, RETN: -1300G>A, -1258C>T, -420C>G). Comparison of polymorphisms distribution in patients and control group suggested association with ADIPOQ -11377C>G (Pearson test P = 2.76 × 10(-11)), however, we did not observe any effect of this polymorphism on BMI or relative BMI (RBMI) within obese patients (P = 0.41). We conclude that the tested SNPs are not useful markers of childhood and adolescence obesity in Polish population.