Principal component approach in variance component estimation for international sire evaluation

<p>Abstract</p> <p>Background</p> <p>The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.</p> <p>Methods</p> <p>This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.</p> <p>Results</p> <p>Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.</p> <p>Conclusions</p> <p>In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.</p

A practical approach to detect ancestral haplotypes in livestock populations

Background The effects of different evolutionary forces are expected to lead to the conservation, over many generations, of particular genomic regions (haplotypes) due to the development of linkage disequilibrium (LD). The detection and identification of early (ancestral) haplotypes can be used to clarify the evolutionary dynamics of different populations as well as identify selection signatures and genomic regions of interest to be used both in conservation and breeding programs. The aims of this study were to develop a simple procedure to identify ancestral haplotypes segregating across several generations both within and between populations with genetic links based on whole-genome scanning. This procedure was tested with simulated and then applied to real data from different genotyped populations of Spanish, Fleckvieh, Simmental and Brown-Swiss cattle. Results The identification of ancestral haplotypes has shown coincident patterns of selection across different breeds, allowing the detection of common regions of interest on different bovine chromosomes and mirroring the evolutionary dynamics of the studied populations. These regions, mainly located on chromosomes BTA5, BTA6, BTA7 and BTA21 are related with certain animal traits such as coat colour and milk protein and fat content. Conclusion In agreement with previous studies, the detection of ancestral haplotypes provides useful information for the development and comparison of breeding and conservation programs both through the identification of selection signatures and other regions of interest, and as indicator of the general genetic status of the populations

Mapping targets for small nucleolar RNAs in yeast

Background: Recent analyses implicate changes in the expression of the box C/D class of small nucleolar RNAs (snoRNAs) in several human diseases. Methods: Here we report the identification of potential novel RNA targets for box C/D snoRNAs in budding yeast, using the approach of UV crosslinking and sequencing of hybrids (CLASH) with the snoRNP proteins Nop1, Nop56 and Nop58. We also developed a bioinformatics approach to filter snoRNA-target interactions for bona fide methylation guide interactions. Results: We recovered 241,420 hybrids, out of which 190,597 were classed as reproducible, high energy hybrids. As expected, the majority of snoRNA interactions were with the ribosomal RNAs (rRNAs). Following filtering, 117,047 reproducible hybrids included 51 of the 55 reported rRNA methylation sites. The majority of interactions at methylation sites were predicted to guide methylation. However, competing, potentially regulatory, binding was also identified. In marked contrast, following CLASH performed with the RNA helicase Mtr4 only 7% of snoRNA-rRNA interactions recovered were predicted to guide methylation. We propose that Mtr4 functions in dissociating inappropriate snoRNA-target interactions. Numerous snoRNA-snoRNA interactions were recovered, indicating potential cross regulation. The snoRNAs snR4 and snR45 were recently implicated in site-directed rRNA acetylation, and hybrids were identified adjacent to the acetylation sites. We also identified 1,368 reproducible snoRNA-mRNA interactions, representing 448 sites of interaction involving 39 snoRNAs and 382 mRNAs. Depletion of the snoRNAs U3, U14 or snR4 each altered the levels of numerous mRNAs. Targets identified by CLASH were over-represented among these species, but causality has yet to be established. Conclusions: Systematic mapping of snoRNA-target binding provides a catalogue of high-confidence binding sites and indicates numerous potential regulatory interactions

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Controlling sugar beet mortality disease by application of new bioformulations

There is growing interests in the use of biological approaches to replace or reduce the application of chemical pesticides in modern agriculture. In this regard, antagonistic fungi and particularly bacteria have proved to be potential candidates. In the search for efficient alternative biofungicides, eight new Bioformulations were developed and prepared using two strains of Pseudomonas fluorescens (B1) and Bacillus coagulans (B2) isolated from different rhizospheric soils and plant roots of Iranian sugar beet fields. Bioformulations were developed using procedures described in the literature. Bioformulations included a talc-based powder and bentonite-based powder as inorganic carriers, and peat and rice bran as organic carriers. The results of our greenhouse experiment, where these bioformulations were applied to sugar beet seeds to control seedling mortality disease, showed that most of the treatments at different intervals (15, 30, 45 and 60 days after sowing) were effective in reducing the disease (compared to the untreated control). According to the results, six out of eight of the developed bioformulations, including Peat-B1, Peat-B2, R.B.-B2, Bent.-B1, Talc-B1 and Talc-B2, were more effective than commonly used fungicides (Carboxin-thiram) in controlling sugar beet mortality disease. Yet, two bioformulations (R.B.-B1 and Bent.-B2) were less effective than carboxin-thiram in the reduction of the disease incidence

Expression patterns of ERα66 and its novel variant isoform ERα36 in lactotroph pituitary adenomas and associations with clinicopathological characteristics

Purpose: The regulatory effects of estradiol on pituitary homeostasis have been well documented. However, the expression patterns of ERα66 and ERα36 and their correlations with the clinical course of postoperative prolactinoma tumors remain unclear. Methods: The expression of ERα36, ERα66, Ki67, p53, and CD31 were determined by immunohistochemistry in 62 prolactinoma patients. Snap-frozen tumors and normal pituitaries were also examined by western blotting for estrogen receptor detection. Results: A broad expression of ERα36 was identified in normal pituitaries. The median scores of ERα36 and ERα66 expression were 8 and 6 in normal pituitaries and 4 and 0 in tumors, respectively. Four phenotypes of ERα36 and ERα66 expression were explored in tumors with regard to sex, invasiveness, dopamine resistance, and recurrence. Low ERα36 expression was associated with tumor invasion and increased Ki67. Low ERα66 expression was associated with tumor invasion, dopamine-agonist resistance, and enhanced tumor size. Multivariable logistic regression analysis showed that low ERα36 expression is an independent risk factor for invasiveness. The significant inverse association of ERα66 with invasiveness, dopamine resistance, and tumor size remained significant after adjustment for sex as a potential confounder. After controlling for sex, the low ERα66/low ERα36 phenotype was 6.24 times more prevalent in invasive tumors than in noninvasive tumors. Although the decreasing trend of CD31 expression from surrounding nontumoral lactotroph adenomas to tumors was similar to that of the estrogen receptors, a significant correlation was not observed here. Conclusion: The decreasing trends of ERα36 and ERα66 expression from normal pituitaries to tumors are associated with aggressive clinical behavior. © 2020, Springer Science+Business Media, LLC, part of Springer Nature