The rate of de novo structural variation is increased in in vitro-produced offspring and preferentially affects the paternal genome.

Assisted reproductive technologies (ARTs), including in vitro maturation and fertilization (IVF), are increasingly used in human and animal reproduction. Whether these technologies directly affect the rate of de novo mutation (DNM), and to what extent, has been a matter of debate. Here we take advantage of domestic cattle, characterized by complex pedigrees that are ideally suited to detect DNMs and by the systematic use of ART, to study the rate of de novo structural variation (dnSV) in this species and how it is impacted by IVF.

Integration of beef cattle international pedigree and genomic estimated breeding values into national evaluations, with an application to the Italian Limousin population.

Background: International evaluations combine data from different countries allowing breeders to have access to larger panels of elite bulls and to increase the accuracy of estimated breeding values (EBV). However, international and national evaluations can use different sources of information to compute EBV (EBV and EBV, respectively), leading to differences between them. Choosing one of these EBV results in losing the information that is contained only in the discarded EBV.

Classifying aneuploidy in genotype intensity data using deep learning.

Aneuploidy is the loss or gain of one or more chromosomes. Although it is a rare phenomenon in liveborn individuals, it is observed in livestock breeding populations. These breeding populations are often routinely genotyped and the genotype intensity data from single nucleotide polymorphism (SNP) arrays can be exploited to identify aneuploidy cases.

Impact of genomic preselection on subsequent ssGBLUP evaluation of preselected animals for scarcely recorded feed intake in pigs.

We have previously shown that single-step genomic best linear unbiased prediction (ssGBLUP) estimates breeding values of genomically preselected animals without preselection bias for widely recorded traits, that is traits recorded for the majority of animals in the breeding population. This study investigated the impact of genomic preselection (GPS) on accuracy and bias in ssGBLUP evaluation of genomically preselected animals for a scarcely recorded trait, that is a trait recorded for only a small proportion of the animals, which generally has a lower prediction accuracy than widely recorded traits, mainly due to having a much smaller number of phenotypes available. We used data from a commercial pig breeding program, considering feed intake as a scarcely recorded target trait, being available for ~30% of the animals with phenotypes for any trait, and average daily gain, backfat thickness and loin depth as widely recorded predictor traits, being available for >95% of the animals with phenotypes for any trait.

International single-step SNPBLUP beef cattle evaluations for Limousin weaning weight.

Background: Compared to national evaluations, international collaboration projects further improve accuracies of estimated breeding values (EBV) by building larger reference populations or performing a joint evaluation using data (or proxy of them) from different countries. Genomic selection is increasingly adopted in beef cattle, but, to date, the benefits of including genomic information in international evaluations have not been explored. Our objective was to develop an international beef cattle single-step genomic evaluation and investigate its impact on the accuracy and bias of genomic evaluations compared to current pedigree-based evaluations.

Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs.

Background: Empirically assessing the impact of preselection on genetic evaluation of preselected animals requires comparing scenarios that take different approaches into account, including scenarios without preselection. However, preselection is almost always performed in animal breeding programs, so it is difficult to have a dataset without preselection. Hence, most studies on preselection have used simulated datasets, and have concluded that genomic estimated breeding values (GEBV) from subsequent single-step genomic best linear unbiased prediction (ssGBLUP) evaluations are unbiased.

Development of resilience indicator traits based on daily step count data for dairy cattle breeding.

Background: Resilient animals are minimally affected by disturbances, such as diseases and heat stress, and quickly recover. Daily activity data can potentially indicate resilience, because resilient animals likely keep variations due to disturbances that threat animal homeostasis at a low magnitude. We used daily step count of cows to define resilience indicators based on theory, exploratory analysis and literature, and then investigated if they can be used to genetically improve resilience by estimating heritability and repeatability, and genetic associations with other resilience-related traits, i.

The impact of direct-maternal genetic correlations on international beef cattle evaluations for Limousin weaning weight.

In beef cattle maternally influenced traits, estimates of direct-maternal genetic correlations (rdm) are usually reported to be negative. In international evaluations, rdm can differ both within countries (rdm_WC) and between countries (rdm_BC). The rdm_BC are difficult to estimate and are assumed to be zero in the current model for international beef cattle evaluations (Interbeef).

Breeding goal traits accounting for feed intake capacity and roughage or concentrate intake separately.

Current breeding tools aiming to improve feed efficiency use definitions based on total dry matter intake (DMI); for example, residual feed intake or feed saved. This research aimed to define alternative traits using existing data that differentiate between feed intake capacity and roughage or concentrate intake, and to investigate the phenotypic and genetic relationships among these traits. The data set contained 39,017 weekly milk yield, live weight, and DMI records of 3,164 cows.

Identification of genomic regions that exhibit sexual dimorphism for size and muscularity in cattle.

Sexual dimorphism, the phenomenon whereby males and females of the same species are distinctive in some aspect of appearance or size, has previously been documented in cattle for traits such as growth rate and carcass merit using a quantitative genetics approach. No previous study in cattle has attempted to document sexual dimorphism at a genome level; therefore, the objective of the present study was to determine whether genomic regions associated with size and muscularity in cattle exhibited signs of sexual dimorphism. Analyses were undertaken on 10 linear-type traits that describe the muscular and skeletal characteristics of both males and females of five beef cattle breeds: 1,444 Angus (AA), 6,433 Charolais (CH), 1,129 Hereford, 8,745 Limousin (LM), and 1,698 Simmental.

Avoiding preselection bias in subsequent single-step genomic BLUP evaluations of genomically preselected animals.

In animal breeding, parents of the next generation are usually selected in multiple stages, and the initial stages of this selection are called preselection. Preselection reduces the information available for subsequent evaluation of preselected animals and this sometimes leads to bias. The objective of this study was to establish the minimum information required to subsequently evaluate genomically preselected animals without bias arising from preselection, with single-step genomic best linear unbiased prediction (ssGBLUP).

Inbreeding depression across the genome of Dutch Holstein Friesian dairy cattle.

Background: Inbreeding depression refers to the decrease in mean performance due to inbreeding. Inbreeding depression is caused by an increase in homozygosity and reduced expression of (on average) favourable dominance effects. Dominance effects and allele frequencies differ across loci, and consequently inbreeding depression is expected to differ along the genome.

Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock.

Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively.

Heritability estimates of the novel trait 'suppressed in ovo virus infection' in honey bees (Apis mellifera).

Honey bees are under pressure due to abnormal high colony death rates, especially during the winter. The infestation by the Varroa destructor mite and the viruses that this ectoparasite transmits are generally considered as the bees' most important biological threats. Almost all efforts to remedy this dual infection have so far focused on the control of the Varroa mite alone and not on the viruses it transmits.

Using short read sequencing to characterise balanced reciprocal translocations in pigs.

Background: A balanced constitutional reciprocal translocation (RT) is a mutual exchange of terminal segments of two non-homologous chromosomes without any loss or gain of DNA in germline cells. Carriers of balanced RTs are viable individuals with no apparent phenotypical consequences. These animals produce, however, unbalanced gametes and show therefore reduced fertility and offspring with congenital abnormalities.

Investigating the impact of preselection on subsequent single-step genomic BLUP evaluation of preselected animals.

Background: Preselection of candidates, hereafter referred to as preselection, is a common practice in breeding programs. Preselection can cause bias and accuracy loss in subsequent pedigree-based best linear unbiased prediction (PBLUP). However, the impact of preselection on subsequent single-step genomic BLUP (ssGBLUP) is not completely clear yet.

Impact of sub-setting the data of the main Limousin beef cattle population on the estimates of across-country genetic correlations.

Background: Cattle international genetic evaluations allow the comparison of estimated breeding values (EBV) across different environments, i.e. countries.

A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices.

Background: A multi-population genomic prediction (GP) model in which important pre-selected single nucleotide polymorphisms (SNPs) are differentially weighted (MPMG) has been shown to result in better prediction accuracy than a multi-population, single genomic relationship matrix ([Formula: see text]) GP model (MPSG) in which all SNPs are weighted equally. Our objective was to underpin theoretically the advantages and limits of the MPMG model over the MPSG model, by deriving and validating a deterministic prediction equation for its accuracy.

Methods: Using selection index theory, we derived an equation to predict the accuracy of estimated total genomic values of selection candidates from population [Formula: see text] ([Formula: see text]), when individuals from two populations, [Formula: see text] and [Formula: see text], are combined in the training population and two [Formula: see text], made respectively from pre-selected and remaining SNPs, are fitted simultaneously in MPMG.

Genomic Regions Associated With Skeletal Type Traits in Beef and Dairy Cattle Are Common to Regions Associated With Carcass Traits, Feed Intake and Calving Difficulty.

Linear type traits describing the skeletal characteristics of an animal are moderately to strongly genetically correlated with a range of other performance traits in cattle including feed intake, reproduction traits and carcass merit; thus, type traits could also provide useful insights into the morphological differences among animals underpinning phenotypic differences in these complex traits. The objective of the present study was to identify genomic regions associated with five subjectively scored skeletal linear traits, to determine if these associated regions are common in multiple beef and dairy breeds, and also to determine if these regions overlap with those proposed elsewhere to be associated with correlated performance traits. Analyses were carried out using linear mixed models on imputed whole genome sequence data separately in 1,444 Angus, 1,129 Hereford, 6,433 Charolais, 8,745 Limousin, 1,698 Simmental, and 4,494 Holstein-Friesian cattle, all scored for the linear type traits.

Genomic regions associated with muscularity in beef cattle differ in five contrasting cattle breeds.

Background: Linear type traits, which reflect the muscular characteristics of an animal, could provide insight into how, in some cases, morphologically very different animals can yield the same carcass weight. Such variability may contribute to differences in the overall value of the carcass since primal cuts vary greatly in price; such variability may also hinder successful genome-based association studies. Therefore, the objective of our study was to identify genomic regions that are associated with five muscularity linear type traits and to determine if these significant regions are common across five different breeds.

Machine learning to further improve the decision which boar ejaculates to process into artificial insemination doses.

Current artificial insemination (AI) laboratory practices assess semen quality of each boar ejaculate to decide which ones to process into AI doses. This decision is aided with two, world-wide used, motility parameters that come available through computer assisted semen analysis (CASA). This decision process, however, still results in AI doses with variable and sometimes suboptimal fertility outcomes (e.

Inbreeding depression due to recent and ancient inbreeding in Dutch Holstein-Friesian dairy cattle.

Background: Inbreeding decreases animal performance (inbreeding depression), but not all inbreeding is expected to be equally harmful. Recent inbreeding is expected to be more harmful than ancient inbreeding, because selection decreases the frequency of deleterious alleles over time. Selection efficiency is increased by inbreeding, a process called purging.