On the holobiont 'predictome' of immunocompetence in pigs.

Background: Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available.

Modeling microRNA-driven post-transcriptional regulation using exon-intron split analysis in pigs.

The contribution of microRNAs (miRNAs) to mRNA post-transcriptional regulation has often been explored by the post hoc selection of downregulated genes and determining whether they harbor binding sites for miRNAs of interest. This approach, however, does not discriminate whether these mRNAs are also downregulated at the transcriptional level. Here, we have characterized the transcriptional and post-transcriptional changes in mRNA expression in two porcine tissues: gluteus medius muscle of fasted and fed Duroc gilts and adipose tissue of lean and obese Duroc-Göttingen minipigs.

Corrigendum to "Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry".

[This corrects the article DOI: 10.34133/2021/9812910.].

Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs.

Background: The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs.

Results: To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs.

Opportunities and limits of combining microbiome and genome data for complex trait prediction.

Background: Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host's genome, microbiome, and phenome be recovered?

Methods: Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data.

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry.

Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained.

Gut eukaryotic communities in pigs: diversity, composition and host genetics contribution.

Background: The pig gut microbiome harbors thousands of species of archaea, bacteria, viruses and eukaryotes such as protists and fungi. However, since the majority of published studies have been focused on prokaryotes, little is known about the diversity, host-genetic control, and contributions to host performance of the gut eukaryotic counterparts. Here we report the first study that aims at characterizing the diversity and composition of gut commensal eukaryotes in pigs, exploring their putative control by host genetics, and analyzing their association with piglets body weight.

A pilot RNA-seq study in 40 pietrain ejaculates to characterize the porcine sperm microbiome.

The microbiome plays a key role in homeostasis and health and it has been also linked to fertility and semen quality in several animal species including swine. Despite the more than likely importance of sperm bacteria on the boar's reproductive ability and the dissemination of pathogens and antimicrobial resistance genes, the high throughput characterization of the swine sperm microbiome remains scarce. We carried RNA-seq on 40 ejaculates each from a different Pietrain boar and found that a proportion of the sequencing reads did not map to the Sus scrofa genome.

Proteomic Profiling and Monitoring of Training Distress and Illness in University Swimmers During a 25-Week Competitive Season.

Purpose: To evaluate relationships of proteomics data, athlete-reported illness, athlete training distress (TDS), and coaches' ratings of distress and performance over the course of the competitive season.

Methods: Thirty-five NCAA Division II swimmers were recruited to the study (male = 19, female = 16; age 19.1 ± 1.

Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach.

Assessing conformation features in an accurate and rapid manner remains a challenge in the dairy industry. While recent developments in computer vision has greatly improved automated background removal, these methods have not been fully translated to biological studies. Here, we present a composite method (DeepAPS) that combines two readily available algorithms in order to create a precise mask for an animal image.

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species.

Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks.

SeqBreed: a python tool to evaluate genomic prediction in complex scenarios.

Background: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3.

Link-HD: a versatile framework to explore and integrate heterogeneous microbial communities.

Motivation: We present Link-HD, an approach to integrate multiple datasets. Link-HD is a generalization of 'Structuration des Tableaux A Trois Indices de la Statistique-Analyse Conjointe de Tableaux', a family of methods designed to integrate information from heterogeneous data. Here, we extend the classical approach to deal with broader datasets (e.

A Guide for Using Deep Learning for Complex Trait Genomic Prediction.

Deep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoretical foundations of DL and provide a generic code that can be easily modified to suit specific needs.