A genome-wide association study for morphometric traits in quarter horse

A genome-wide association study for morphometric traits was conducted in 184 Quarter Horses, 120 from a racing population, and 64 from a cutting population, which were genotyped using the Illumina EquineSNP50 chip. Association analysis was performed with 42,058 single-nucleotide polymorphisms (SNPs) (after quality control) using Qxpak5 software. The following traits were measured: weight (W), rump length (RL), and body length (BL). These morphometric traits are important for the best performance in race and cutting events. For weight, three SNPs associated (P < .0001) were found on chromosomes (Equus caballus autosomes [ECA]) 2 and 3. For rump length, eight SNPs associated (P < .0001) were found on ECA 2, 3, 6, 7, 9, 21, and 26. On ECA 3 and ECA 8, two SNPs were associated (P < .0001) with body length. So, a total of 13 important chromosomal regions were identified with Q values of 0.53 (SNPs for W), 0.40 (SNPs for RL), and 0.99 (SNPs for BL). Positional and functional candidate genes emerging from this study were WWOX and AAVPR1A. Further studies are required to confirm these associations in other populations. (c) 2014 Elsevier Inc. All rights reserved

Anti-apoptotic seminal vesicle protein IV inhibits cell-mediated immunity.

The in vitro effect of seminal vesicle protein IV (SV-IV) on the cytotoxic activity of human natural or acquired cellular immunity has been investigated by standard immunological procedures, a 51Cr-release cytotoxicity assay, and labeled-ligand binding experiments. The data obtained demonstrate that: (1) fluoresceinated or [125I]-labeled SV-IV binds specifically to the surface of human purified non-adherent monuclear cells (NA-MNC); (2)SV-IV suppresses the cytotoxicity of natural killer (NK) cells against K562 target cells, that of IL-2-stimulated NK (LAK) cells against DAUDI target cells, and that of VEL antigen-sensitized cytotoxic T lymphocytes (CTLs) against VEL target cells; (3) treatment of K562 target cells alone with SV-IV decreases their susceptibility to NK-induced lysis. These findings indicate that the protein SV-IV has a marked in vitro inhibitory effect on NK, LAK and CTL cytotoxicity, providing a better understanding of its immune regulatory functions

Qualità e grado di conservazione del paesaggio vegetale del litorale sabbioso del Veneto (Italia settentrionale).

Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle

Combining multi-OMICs information to identify key-regulator genes for pleiotropic effect on fertility and production traits in beef cattle

[EN] The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.SIThis research was supported by the Beef Farmers of Ontario, OMAFRA (Ontario Ministry of Agriculture, Food and Rural Affairs), Beef Cattle Research Council (BCRC), NSERC (Natural Sciences and Engineering Research Council) and Ontario Centres of Excellence (OCE). MRSC is supported by a fellowship from the Brazilian National Research Council (CNPq 312068/2015-8) and grants from the Fundac¸ão de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG, APQ APQ-01377-17 and APQ-01377-17). PASF is supported by a fellowship from the Brazilian National Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Association weight matrix: a network-based approach towards functional genome-wide association studies

In this chapter we describe the Association Weight Matrix (AWM), a novel procedure to exploit the results from genome-wide association studies (GWAS) and, in combination with network inference algorithms, generate gene networks with regulatory and functional significance. In simple terms, the AWM is a matrix with rows represented by genes and columns represented by phenotypes. Individual {i, j}th elements in the AWM correspond to the association of the SNP in the ith gene to the jth phenotype. While our main objective is to provide a recipe-like tutorial on how to build and use AWM, we also take the opportunity to briefly reason the logic behind each step in the process. To conclude, we discuss the impact on AWM of issues like the number of phenotypes under scrutiny, the density of the SNP chip and the choice of contrast upon which to infer the cause-effect regulatory interactions

Dynamics of gene co-expression networks in time-series data: a case study in Drosophila melanogaster embryogenesis

Co-expression networks tightly coordinate the spatiotemporal patterns of gene expression unfolding during development. Due to the dynamic nature of developmental processes simply overlaying gene expression patterns onto static representations of co-expression networks may be misleading. Here, we aim to formally quantitate topological changes of co-expression networks during embryonic development using a publicly available Drosophila melanogaster transcriptome data set comprising 14 time points. We deployed a network approach which inferred 10 discrete co-expression networks by smoothly sliding along from early to late development using 5 consecutive time points per window. Such an approach allows changing network structure, including the presence of hubs, modules and other topological parameters to be quantitated. To explore the dynamic aspects of gene expression captured by our approach, we focused on regulator genes with apparent influence over particular aspects of development. Those key regulators were selected using a differential network algorithm to contrast the first 7 (early) with the last 7 (late) developmental time points. This assigns high scores to genes whose connectivity to abundant differentially expressed target genes has changed dramatically between states. We have produced a list of key regulators – some increasing (e.g., Tusp, slbo, Sidpn, DCAF12, and chinmo) and some decreasing (Rfx, bap, Hmx, Awh, and mld) connectivity during development – which reflects their role in different stages of embryogenesis. The networks we have constructed can be explored and interpreted within Cytoscape software and provide a new systems biology approach for the Drosophila research community to better visualize and interpret developmental regulation of gene expression

Variation in genes involved in epigenetic processes offers insights into tropically adapted cattle diversity

We evaluated the relevance of the BovineHD Illumina SNP chip with respect to genes involved in epigenetic processes. Genotypes for 729,068 SNP on two tropical cattle breeds of Australia were used: Brahman (n = 2112) and Tropical Composite (n = 2550). We used data mining approaches to compile a list of bovine protein-coding genes involved in epigenetic processes. These genes represent 9 functional categories that contain between one (histone demethylases) and 99 (chromatin remodeling factors) genes. A total of 3091 SNP mapped to positions within 3000 bp of the 193 coding regions of those genes, including 113 SNP in transcribed regions, 2738 in intronic regions and 240 in up- or down-stream regions. For all these SNP categories, we observed differences in the allelic frequencies between Brahman and Tropical Composite cattle. These differences were larger than those observed for the entire set of 729,068 SNP (P = 1.79 x 10(-5)). A multidimensional scaling analysis using only the 113 SNP in transcribed regions allowed for the separation of the two populations and this separation was comparable to the one obtained with a random set of 113 SNP (Principal Component 1 r (2) > 0.84). To further characterize the differences between the breeds we defined a gene-differentiation metric based on the average genotypic frequencies of SNP connected to each gene and compared both cattle populations. The 10% most differentiated genes were distributed across 10 chromosomes, with significant (P < 0.05) enrichment on BTA 3 and 10. The 10% most conserved genes were located in 12 chromosomes. We conclude that there is variation between cattle populations in genes connected to epigenetic processes, and this variation can be used to differentiate cattle breeds. More research is needed to fully characterize the use of these SNP and its potential as means to further our understanding of biological variation and epigenetic processes