Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile.

To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (α-CN, α-CN, β-CN, κ-CN, β-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy).

Applying family analyses to electronic health records to facilitate genetic research.

Motivation: Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary.

Results: This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs).

Genome-wide association analysis in dogs implicates 99 loci as risk variants for anterior cruciate ligament rupture.

Anterior cruciate ligament (ACL) rupture is a common condition that can be devastating and life changing, particularly in young adults. A non-contact mechanism is typical. Second ACL ruptures through rupture of the contralateral ACL or rupture of a graft repair is also common.

A predictive assessment of genetic correlations between traits in chickens using markers.

Background: Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection.

Estimation of genetic parameters for longevity considering the cow's age at last calving.

The aim of this study was to estimate heritability and predict breeding values for longevity among cows in herds of Nellore breed, considering the trait cow's age at last calving (ALC), by means of survival analysis methodology. The records of 11,791 animals from 22 farms were used. The variable ALC has been used by a criterion that made it possible to include cows not only at their first calving but also at their ninth calving.

Incorporating parent-of-origin effects in whole-genome prediction of complex traits.

Background: Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits.

Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics.

The goal of this paper is to present an implementation of stochastic search variable selection (SSVS) to multilevel model from item response theory (IRT). As experimental settings get more complex and models are required to integrate multiple (and sometimes massive) sources of information, a model that can jointly summarize and select the most relevant characteristics can provide better interpretation and a deeper insight into the problem. A multilevel IRT model recently proposed in the literature for modeling multifactorial diseases is extended to perform variable selection in the presence of thousands of covariates using SSVS.

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens.

Background: Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations.

Genomic prediction of breeding values for carcass traits in Nellore cattle.

Background: The objective of this study was to evaluate the accuracy of genomic predictions for rib eye area (REA), backfat thickness (BFT), and hot carcass weight (HCW) in Nellore beef cattle from Brazilian commercial herds using different prediction models.

Methods: Phenotypic data from 1756 Nellore steers from ten commercial herds in Brazil were used. Animals were offspring of 294 sires and 1546 dams, reared on pasture, feedlot finished, and slaughtered at approximately 2 years of age.

Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data.

Background: Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Current studies mainly report associations, i.e.

Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data.

Prediction of complex traits using molecular genetic information is an active area in quantitative genetics research. In the postgenomic era, many types of -omic (e.g.

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice.

Background: Genomic imprinting is an epigenetic mechanism that can lead to differential gene expression depending on the parent-of-origin of a received allele. While most studies on imprinting address its underlying molecular mechanisms or attempt at discovering genomic regions that might be subject to imprinting, few have focused on the amount of phenotypic variation contributed by such epigenetic process. In this report, we give a brief review of a one-locus imprinting model in a quantitative genetics framework, and provide a decomposition of the genetic variance according to this model.

Defining window-boundaries for genomic analyses using smoothing spline techniques.

Background: High-density genomic data is often analyzed by combining information over windows of adjacent markers. Interpretation of data grouped in windows versus at individual locations may increase statistical power, simplify computation, reduce sampling noise, and reduce the total number of tests performed. However, use of adjacent marker information can result in over- or under-smoothing, undesirable window boundary specifications, or highly correlated test statistics.

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models.

The term "effect" in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies.

Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes.

The prediction of total egg production (TEP) potential in poultry is an important task to aid optimized management decisions in commercial enterprises. The objective of the present study was to compare different modeling approaches for prediction of TEP in meat type quails (Coturnix coturnix coturnix) using phenotypes such as weight, weight gain, egg production and egg quality measurements. Phenotypic data on 30 traits from two lines (L1, n=180; and L2, n=205) of quail were modeled to predict TEP.

Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data.

Background: Genotype imputation is an important tool for whole-genome prediction as it allows cost reduction of individual genotyping. However, benefits of genotype imputation have been evaluated mostly for linear additive genetic models. In this study we investigated the impact of employing imputed genotypes when using more elaborated models of phenotype prediction.

Maternal nutrition induces gene expression changes in fetal muscle and adipose tissues in sheep.

Background: Maternal nutrition during different stages of pregnancy can induce significant changes in the structure, physiology, and metabolism of the offspring. These changes could have important implications on food animal production especially if these perturbations impact muscle and adipose tissue development. Here, we evaluated the impact of different maternal isoenergetic diets, alfalfa haylage (HY; fiber), corn (CN; starch), and dried corn distillers grains (DG; fiber plus protein plus fat), on the transcriptome of fetal muscle and adipose tissues in sheep.

One hundred years of statistical developments in animal breeding.

Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, and many milestones are examined from historical and critical perspectives.

Meta-analysis of candidate gene effects using bayesian parametric and non-parametric approaches.

Candidate gene (CG) approaches provide a strategy for identification and characterization of major genes underlying complex phenotypes such as production traits and susceptibility to diseases, but the conclusions tend to be inconsistent across individual studies. Meta-analysis approaches can deal with these situations, e.g.

Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context.

Background: Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures.

Liver functional genomics in beef cows on grazing systems: novel genes and pathways revealed.

The adaptation of the liver to periods of negative energy balance is largely unknown in beef cattle on grazing systems. We evaluated liver transcriptome throughout gestation and early lactation of purebred and crossbred beef cows [Angus, Hereford, and their F1 crossbreeds (CR)], grazing high or low herbage allowances (HA) of native grasslands (4 and 2.5 kg dry matter/kg body wt annual mean; n = 16) using an Agilent 4 × 44k bovine array.

Estradiol and progesterone exhibit similar patterns of hepatic gene expression regulation in the bovine model.

Female sex steroid hormones, estradiol-17β (E2-17β) and progesterone (P4) regulate reproductive function and gene expression in a broad range of tissues. Given the central role of the liver in regulating homeostasis including steroid hormone metabolism, we sought to understand how E2-17β and P4 interact to affect global gene expression in liver. Ovariectomized cows (n = 8) were randomly assigned to 4 treatment groups applied in a replicated Latin Square design: 1) No hormone supplementation, 2) E2-17β treatment (ear implant), 3) P4 treatment (intravaginal inserts), and 4) E2-17β combined with P4.

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.

Background: Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation. ANN can capture nonlinear relationships between predictors and responses and can adaptively learn complex functional forms, in particular, for situations where conventional regression models are ineffective. In a previous study, ANN with Bayesian regularization outperformed a benchmark linear model when predicting milk yield in dairy cattle or grain yield of wheat.

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data.

Background: Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose.

Is structural equation modeling advantageous for the genetic improvement of multiple traits?

Structural equation models (SEMs) are multivariate specifications capable of conveying causal relationships among traits. Although these models offer insights into how phenotypic traits relate to each other, it is unclear whether and how they can improve multiple-trait selection. Here, we explored concepts involved in SEMs, seeking for benefits that could be brought to breeding programs, relative to the standard multitrait model (MTM) commonly used.