Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations.

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values.

A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population.

Background: Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method).

XSim version 2: simulation of modern breeding programs.

Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results.

Breed of origin of alleles and genomic predictions for crossbred dairy cows.

Background: In dairy cattle, genomic selection has been implemented successfully for purebred populations, but, to date, genomic estimated breeding values (GEBV) for crossbred cows are rarely available, although they are valuable for rotational crossbreeding schemes that are promoted as efficient strategies. An attractive approach to provide GEBV for crossbreds is to use estimated marker effects from the genetic evaluation of purebreds. The effects of each marker allele in crossbreds can depend on the breed of origin of the allele (BOA), thus applying marker effects based on BOA could result in more accurate GEBV than applying only proportional contribution of the purebreds.

Genomic prediction using a reference population of multiple pure breeds and admixed individuals.

Background: In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question.

MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes' regulatory interactions.

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait's realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation.

Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses.

Background: Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step genomic best linear unbiased prediction (ssGBLUP) model are scarce. We investigated the integration of sequencing SNPs selected by association (1262 SNPs) and bioinformatics (2359 SNPs) analyses into the currently used 54K-SNP chip, using three ssGBLUP models which make different assumptions on the distribution of SNP effects: a basic ssGBLUP model, a so-called featured ssGBLUP (ssFGBLUP) model that considered selected sequencing SNPs as a feature genetic component, and a weighted ssGBLUP (ssWGBLUP) model in which the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, with every 1, 30, or 100 adjacent SNPs within a chromosome region sharing the same variance.

Imputation for sequencing variants preselected to a customized low-density chip.

The sequencing variants preselected from association analyses and bioinformatics analyses could improve genomic prediction. In this study, the imputation of sequencing SNPs preselected from major dairy breeds in Denmark-Finland-Sweden (DFS) and France (FRA) was investigated for both contemporary animals and old bulls in Danish Jersey. For contemporary animals, a two-step imputation which first imputed to 54 K and then to 54 K + DFS + FRA SNPs achieved highest accuracy.

Correction: Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Pooled genotyping strategies for the rapid construction of genomic reference populations1.

The growing concern with the environment is making important for livestock producers to focus on selection for efficiency-related traits, which is a challenge for commercial cattle herds due to the lack of pedigree information. To explore a cost-effective opportunity for genomic evaluations of commercial herds, this study compared the accuracy of bulls' genomic estimated breeding values (GEBV) using different pooled genotype strategies. We used ten replicates of previously simulated genomic and phenotypic data for one low (t1) and one moderate (t2) heritability trait of 200 sires and 2,200 progeny.

Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome.

Widely used genomic prediction models may not properly account for heterogeneous (co)variance structure across the genome. Models such as BayesA and BayesB assume locus-specific variance, which are highly influenced by the prior for (co)variance of single nucleotide polymorphism (SNP) effect, regardless of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects.

Improvement of genomic prediction by integrating additional single nucleotide polymorphisms selected from imputed whole genome sequencing data.

The availability of whole genome sequencing (WGS) data enables the discovery of causative single nucleotide polymorphisms (SNPs) or SNPs in high linkage disequilibrium with causative SNPs. This study investigated effects of integrating SNPs selected from imputed WGS data into the data of 54K chip on genomic prediction in Danish Jersey. The WGS SNPs, mainly including peaks of quantitative trait loci, structure variants, regulatory regions of genes, and SNPs within genes with strong effects predicted with variant effect predictor, were selected in previous analyses for dairy breeds in Denmark-Finland-Sweden (DFS) and France (FRA).

Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome.

Implicit assumption of common (co)variance for all loci in multi-trait Genomic Best Linear Unbiased Prediction (GBLUP) results in a genomic relationship matrix () that is common to all traits. When this assumption is violated, Bayesian whole genome regression methods may be superior to GBLUP by accounting for unequal (co)variance for all loci or genome regions. This study aimed to develop a strategy to improve the accuracy of GBLUP for multi-trait genomic prediction, using (co)variance estimates of SNP effects from Bayesian whole genome regression methods.

An Upper Bound for Accuracy of Prediction Using GBLUP.

This study aims at characterizing the asymptotic behavior of genomic prediction R2 as the size of the reference population increases for common or rare QTL alleles through simulations. Haplotypes derived from whole-genome sequence of 85 Caucasian individuals from the 1,000 Genomes Project were used to simulate random mating in a population of 10,000 individuals for at least 100 generations to create the LD structure in humans for a large number of individuals. To reduce computational demands, only SNPs within a 0.

Effects of slaughter age and mass selection on slaughter and carcass characteristics in 2 lines of Japanese quail.

The aim of this study was to evaluate the relationship between slaughter age and slaughter-carcass characteristics in 2 quail lines. With this aim, a Japanese quail flock subjected to mass selection to increase BW for 4 generations and a control flock that randomly mated for 4 generations were used. Birds of both lines were slaughtered at 4, 5, 6, 7, and 8 wk of age.

Genetic parameter estimates of growth curve and reproduction traits in Japanese quail.

The goal of selection studies in broilers is to obtain genetically superior chicks in terms of major economic traits, which are mainly growth rate, meat yield, and feed conversion ratio. Multiple selection schedules for growth and reproduction are used in selection programs within commercial broiler dam lines. Modern genetic improvement methods have not been applied in experimental quail lines.

Nonlinear mixed effects modeling of growth in Japanese quail.

The aim of this study was to examine the use of a nonlinear mixed modeling approach to growth studies of Japanese quail. Weekly BW measurements of 89 female and 89 male quail were used in the study. A well-known logistic growth function was used in the analysis.

Japanese quail meat quality: characteristics, heritabilities, and genetic correlations with some slaughter traits.

The aim of this study was to evaluate the genetic parameters of several breast meat quality traits and their genetic relationships with some slaughter traits [BW, breast yield (BRY), and abdominal fat yield (AFY)]. In total, 1,093 pedigreed quail were slaughtered at 35 d of age to measure BRY, AFY, and breast meat quality traits [ultimate pH (pHU), Commission Internationale d'Eclairage color parameters (L*, lightness; a*, redness; and b*, yellowness), thawing and cooking loss (TL and CL, respectively), and Warner-Bratzler shear value (WB)]. The average pHU, L*, a*, and b* were determined to be 5.

Investigation of nonlinear models to describe long-term egg production in Japanese quail.

In this study, long-term egg production was monitored in a Japanese quail flock, which had not undergone any genetic improvement, for 52 wk as of the age of sexual maturity. The study aimed to detect some traits with respect to egg production, to determine the cumulative hen-housed egg numbers, and to compare goodness of fit of different nonlinear models for the percentage of hen-day egg production. The mean age at first egg was 38.