A method for partitioning trends in genetic mean and variance to understand breeding practices.

BACKGROUND: In breeding programmes, the observed genetic change is a sum of the contributions of different selection paths represented by groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual paths due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values. METHODS: First, we extended the partitioning method to quantify the contribution of different paths to genetic variance assuming that the breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme. RESULTS: We show how to quantify the contribution of different groups of individuals to genetic mean and variance and that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed that the partitioning method under the pedigree-based model has some limitations, which suggests the need for a genomic extension. CONCLUSIONS: We presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different selection paths interact within a breeding programme and how they can be optimised.

SIMplyBee: an R package to simulate honeybee populations and breeding programs.

BACKGROUND: The Western honeybee is an economically important species globally, but has been experiencing colony losses that lead to economical damage and decreased genetic variability. This situation is spurring additional interest in honeybee breeding and conservation programs. Stochastic simulators are essential tools for rapid and low-cost testing of breeding programs and methods, yet no existing simulator allows for a detailed simulation of honeybee populations. Here we describe SIMplyBee, a holistic simulator of honeybee populations and breeding programs. SIMplyBee is an R package and hence freely available for installation from CRAN http://cran.r-project.org/package=SIMplyBee . IMPLEMENTATION: SIMplyBee builds upon the stochastic simulator AlphaSimR that simulates individuals with their corresponding genomes and quantitative genetic values. To enable honeybee-specific simulations, we extended AlphaSimR by developing classes for global simulation parameters, SimParamBee, for a honeybee colony, Colony, and multiple colonies, MultiColony. We also developed functions to address major honeybee specificities: honeybee genome, haplodiploid inheritance, social organisation, complementary sex determination, polyandry, colony events, and quantitative genetics at the individual- and colony-levels. RESULTS: We describe its implementation for simulating a honeybee genome, creating a honeybee colony and its members, addressing haplodiploid inheritance and complementary sex determination, simulating colony events, creating and managing multiple colonies at the same time, and obtaining genomic data and honeybee quantitative genetics. Further documentation, available at http://www.SIMplyBee.info , provides details on these operations and describes additional operations related to genomics, quantitative genetics, and other functionalities. DISCUSSION: SIMplyBee is a holistic simulator of honeybee populations and breeding programs. It simulates individual honeybees with their genomes, colonies with colony events, and individual- and colony-level genetic and breeding values. Regarding the latter, SIMplyBee takes a user-defined function to combine individual- into colony-level values and hence allows for modeling any type of interaction within a colony. SIMplyBee provides a research platform for testing breeding and conservation strategies and their effect on future genetic gain and genetic variability. Future developments of SIMplyBee will focus on improving the simulation of honeybee genomes, optimizing the simulator's performance, and including spatial awareness in mating functions and phenotype simulation. We invite the honeybee genetics and breeding community to join us in the future development of SIMplyBee.

AlphaPart-R implementation of the method for partitioning genetic trends.

BACKGROUND: In this paper, we present the AlphaPart R package, an open-source implementation of a method for partitioning breeding values and genetic trends to identify the contribution of selection pathways to genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from estimated breeding values averaged by year of birth. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated stylized multi-tier breeding programme mimicking a pig or poultry breeding programme. RESULTS: The package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by pre-defined selection paths, and a set of functions for handling data and results. The package is freely available from the CRAN repository at http://CRAN.R-project.org/package=AlphaPart . We demonstrate the use of the package by partitioning the nucleus and multiplier genetic gain of the stylized breeding programme by tier-gender paths. For traits measured and selected in the multiplier, the multiplier selection generated additional genetic gain. By using AlphaPart, we show that the additional genetic gain depends on accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. We have proven that AlphaPart is a valuable tool for understanding the sources of genetic gain in the nucleus and especially the multiplier, and the relationship between the sources and parameters that affect them. CONCLUSIONS: AlphaPart implements the method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to improve genetic gain through a better understanding of the key selection points that are driving gains in each trait.

Investigating shared aetiology between type 2 diabetes and major depressive disorder in a population based cohort.

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.

The decalog of long non-coding RNA involvement in cancer diagnosis and monitoring.

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.

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study.

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping.

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

Genetic variability of microRNA regulome in human.

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.

Genetic Variability of MicroRNA Genes in 15 Animal Species.

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.