Long non-coding RNAs (lncRNAs) are transcripts without protein-coding capacity; initially regarded as "transcriptional noise", lately they have emerged as essential factors in both cell biology and mechanisms of disease. In this article, we present basic knowledge of lncRNA molecular mechanisms, associated physiological processes and cancer association, as well as their diagnostic and therapeutic value in the form of a decalog: (1) Non-coding RNAs (ncRNAs) are transcripts without protein-coding capacity divided by size (short and long ncRNAs), function (housekeeping RNA and regulatory RNA) and direction of transcription (sense/antisense, bidirectional, intronic and intergenic), containing a broad range of molecules with diverse properties and functions, such as messenger RNA, transfer RNA, microRNA and long non-coding RNAs. (2) Long non-coding RNAs are implicated in many molecular mechanisms, such as transcriptional regulation, post-transcriptional regulation and processing of other short ncRNAs. (3) Long non-coding RNAs play an important role in many physiological processes such as X-chromosome inactivation, cell differentiation, immune response and apoptosis. (4) Long non-coding RNAs have been linked to hallmarks of cancer: (a) sustaining proliferative signaling; (b) evading growth suppressors; (c) enabling replicative immortality; (d) activating invasion and metastasis; (e) inducing angiogenesis; (f) resisting cell death; and (g) reprogramming energy metabolism. (5) Regarding their impact on cancer cells, lncRNAs are divided into two groups: oncogenic and tumor-suppressor lncRNAs. (6) Studies of lncRNA involvement in cancer usually analyze deregulated expression patterns at the RNA level as well as the effects of single nucleotide polymorphisms and copy number variations at the DNA level. (7) Long non-coding RNAs have potential as novel biomarkers due to tissue-specific expression patterns, efficient detection in body fluids and high stability. (8) LncRNAs serve as novel biomarkers for diagnostic, prognostic and monitoring purposes. (9) Tissue specificity of lncRNAs enables the development of selective therapeutic options. (10) Long non-coding RNAs are emerging as commercial biomarkers and therapeutic agents.

Type II diabetes (T2D) and major depressive disorder (MDD) are often co‐morbid. The reasons for this co‐morbidity are unclear. Some studies have highlighted the importance of environmental factors and a causal relationship between T2D and MDD has also been postulated. In the present study we set out to investigate the shared aetiology between T2D and MDD using Mendelian randomization in a population based sample, Generation Scotland: the Scottish Family Health Study (N = 21,516). Eleven SNPs found to be associated with T2D were tested for association with MDD and psychological distress (General Health Questionnaire scores). We also assessed causality and genetic overlap between T2D and MDD using polygenic risk scores (PRS) assembled from the largest available GWAS summary statistics to date. No single T2D risk SNP was associated with MDD in the MR analyses and we did not find consistent evidence of genetic overlap between MDD and T2D in the PRS analyses. Linkage disequilibrium score regression analyses supported these findings as no genetic correlation was observed between T2D and MDD (rG = 0.0278 (S.E. 0.11), P‐value = 0.79). As suggested by previous studies, T2D and MDD covariance may be better explained by environmental factors. Future studies would benefit from analyses in larger cohorts where stratifying by sex and looking more closely at MDD cases demonstrating metabolic dysregulation is possible. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

MicroRNAs (miRNA) are a class of non-coding RNAs important in posttranscriptional regulation of target genes. Previous studies have proven that genetic variability of miRNA genes (miR-SNP) has an impact on phenotypic variation and disease susceptibility in human, mice and some livestock species. MicroRNA gene polymorphisms could therefore represent biomarkers for phenotypic traits also in other animal species. We upgraded our previously developed tool miRNA SNiPer to the version 4.0 which enables the search of miRNA genetic variability in 15 animal genomes: http://www.integratomics-time.com/miRNA-SNiPer. Genome-wide in silico screening (GWISS) of 15 genomes revealed that based on the current database releases, miRNA genes are most polymorphic in cattle, followed by human, fruitfly, mouse, chicken, pig, horse, and sheep. The difference in the number of miRNA gene polymorphisms between species is most probably not due to a biological reason and lack of genetic variability in some species, but to different stage of sequencing projects and differences in development of genomic resource databases in different species. Genome screening revealed several interesting genomic hotspots. For instance, several multiple nucleotide polymorphisms (MNPs) are present within mature seed region in cattle. Among miR-SNPs 46 are present on commercial whole-genome SNP chips: 16 in cattle, 26 in chicken, two in sheep and two in pig. The update of the miRNA SNiPer tool and the generated catalogs will serve researchers as a starting point in designing projects dealing with the effects of genetic variability of miRNA genes.

MicroRNAs are currently being extensively studied due to their important role as post-transcriptional regulators. During miRNA biogenesis, precursors undergo two cleavage steps performed by Drosha-DGCR8 (Microprocessor) cleaving of pri-miRNA to produce pre-miRNA and Dicer-mediated cleaving to create mature miRNA. Genetic variants within human miRNA regulome have been shown to influence miRNA expression, target interaction and to affect the phenotype. In this study, we reviewed the literature, existing bioinformatics tools and catalogs associated with polymorphic miRNA regulome, and organized them into four categories: (1) polymorphisms located within miRNA genes (miR-SNPs), (2) transcription factor-binding sites/miRNA regulatory regions (miR-rSNPs), (3) miRNA target sites (miR-TS-SNPs), and 4. miRNA silencing machinery (miR-SM-SNPs). Since the miR-SM-SNPs have not been systematically studied yet, we have collected polymorphisms associated with miRNA silencing machinery. We have developed two catalogs containing genetic variability within: (1) genes encoding three main catalytic components of the silencing machinery, DROSHA, DGCR8, and DICER1; (2) miRNA genes itself, overlapping Drosha and Dicer cleavage sites. The developed resource of polymorphisms is available online (http://www.integratomics-time.com/miRNA-regulome) and will be useful for further functional studies and development of biomarkers associated with diseases and phenotypic traits.

In this study, we compared genetic gain, genetic variation, and the efficiency of converting variation into gain under different genomic selection scenarios with truncation or optimum contribution selection in a small dairy population by simulation. Breeding programs have to maximize genetic gain but also ensure sustainability by maintaining genetic variation. Numerous studies have shown that genomic selection increases genetic gain. Although genomic selection is a well-established method, small populations still struggle with choosing the most sustainable strategy to adopt this type of selection. We developed a simulator of a dairy population and simulated a model after the Slovenian Brown Swiss population with ~10,500 cows. We compared different truncation selection scenarios by varying (1) the method of sire selection and their use on cows or bull-dams, and (2) selection intensity and the number of years a sire is in use. Furthermore, we compared different optimum contribution selection scenarios with optimization of sire selection and their usage. We compared scenarios in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, rate of coancestry, effective population size, and conversion efficiency. The results showed that early use of genomically tested sires increased genetic gain compared with progeny testing, as expected from changes in selection accuracy and generation interval. A faster turnover of sires from year to year and higher intensity increased the genetic gain even further but increased the loss of genetic variation per year. Although maximizing intensity gave the lowest conversion efficiency, faster turnover of sires gave an intermediate conversion efficiency. The largest conversion efficiency was achieved with the simultaneous use of genomically and progeny-tested sires that were used over several years. Compared with truncation selection, optimizing sire selection and their usage increased the conversion efficiency by achieving either comparable genetic gain for a smaller loss of genetic variation or higher genetic gain for a comparable loss of genetic variation. Our results will help breeding organizations implement sustainable genomic selection.