Optimum contribution selection for animal breeding and conservation: the R package optiSel

The present study is a transversal analysis of the interest in genomic selection for plant and animal species. It focuses on the arguments that may convince breeders to switch to genomic selection. The arguments are classified into three different “bricks.” The first brick considers the addition of genotyping to improve the accuracy of the prediction of breeding values. The second consists of saving costs and/or shortening the breeding cycle by replacing all or a portion of the phenotyping effort with genotyping. The third concerns population management to improve the choice of parents to either optimize crossbreeding or maintain genetic diversity. We analyse the relevance of these different bricks for a wide range of animal and plant species and sought to explain the differences between species according to their biological specificities and the organization of breeding programs.

Download Full-text

Phenotypic and genetic parameters for lamb weights in a synthetic line of sheep

Animal Science ◽

10.1017/s0003356100024041 ◽

1980 ◽

Vol 30 (2) ◽

pp. 261-269 ◽

Cited By ~ 22

Author(s):

T. G. Martin ◽

D. I. Sales ◽

C. Smith ◽

D. Nicholson

Keyword(s):

Least Squares ◽

Genetic Parameters ◽

Genetic Correlations ◽

Animal Breeding ◽

Research Organization ◽

Correction Factors ◽

Component Method ◽

Breeding Research ◽

Selection For ◽

Synthetic Line

ABSTRACTData on 2120 lambs produced over 7 years in the Animal Breeding Research Organization synthetic Dam Line (49% Finn, 22% East Friesian, 16 % Border Leicester and 13 % Dorset Horn) were analysed by least squares. The effects of sex, age of dam, year of birth, birthrearing class, and age of lamb on weights at birth and at 4, 8, 12 and 16 weeks of age were all appreciable. However, pooled within-year correction factors should be adequate in adjusting records for selection. Heritability estimates for weights at the five ages ranged from 0·17 to 0·24 by the sire component method and from 0·18 to 0·28 by regression of offspring on dam. Previous reports of differences in heritability in singles and in twins were not confirmed. Estimates of the genetic correlations among the various lamb weights were high (0·62 to 1·04) indicating that selection for weight at one age should result in increased weights at all ages.

Download Full-text

Multiple sire mating in swine; evidence of natural selection for mating efficiency

The Journal of Agricultural Science ◽

10.1017/s0021859600023777 ◽

1961 ◽

Vol 56 (1) ◽

pp. 31-37 ◽

Cited By ~ 7

Author(s):

Lavon J. Sumption

Keyword(s):

Natural Selection ◽

Mating Behaviour ◽

Animal Breeding ◽

Evolutionary Significance ◽

Mating Frequency ◽

Combined Effects ◽

Male And Female ◽

Female Mating ◽

Mating Efficiency ◽

Selection For

Evidence of natural selection for certain aspects of mating efficiency in swine are advanced based on preliminary studies with thirty-one sires, fiftyeight dams and their progeny. Selective fertilization was conclusively demonstrated. Variations in male and female mating behaviour were sufficiently large to indicate considerable non-randomness of mating frequency under the conditions of multiple sire mating (i.e. group exposure of dams to selected sires). The combined effects of the separate phenomena of selective fertilization and mating behaviour are discussed in relation to their evolutionary significance in animal breeding.

Download Full-text

CVRMS: Cross-validated Rank-based Marker Selection for Genome-wide Prediction of Low Heritability

10.1101/756130 ◽

2019 ◽

Author(s):

Seongmun Jeong ◽

Jae-Yoon Kim ◽

Namshin Kim

Keyword(s):

Genetic Information ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Marker Selection ◽

Genome Wide ◽

Precise Prediction ◽

Human Animal ◽

Selection For

AbstractCVRMS is an R package designed to extract marker subsets from repeated rank-based marker datasets generated from genome-wide association studies or marker effects for genome-wide prediction (https://github.com/lovemun/CVRMS). CVRMS provides an optimized genome-wide biomarker set with the best predictability of phenotype by implemented ridge regression using genetic information. Applying our method to human, animal, and plant datasets with wide heritability (zero to one), we selected hundreds to thousands of biomarkers for precise prediction.

Download Full-text

Software update: Moving the R package sommer to multivariate mixed models for genome-assisted prediction

10.1101/354639 ◽

2018 ◽

Cited By ~ 4

Author(s):

Giovanny Covarrubias-Pazaran

Keyword(s):

Random Effects ◽

Mixed Models ◽

Mixed Model ◽

R Package ◽

Animal Breeding ◽

Breeding Programs ◽

Incidence Matrices ◽

Software Update ◽

Trial Analysis ◽

User Friendly

AbstractIn the last decade the use of mixed models has become a pivotal part in the implementation of genome-assisted prediction in plant and animal breeding programs. Exploiting the use genetic correlation among traits through multivariate predictions has been proposed in recent years as a way to boost prediction accuracy and understand pleiotropy and other genetic and ecological phenomena better. Multiple mixed model solvers able to use relationship matrices or deal with marker-based incidence matrices have been released in the last years but multivariate versions are scarse. Such solvers have become quite popular in plant and animal breeding thanks to user-friendly platforms such as R. Among such software one of the most recent and popular is the sommer package. In this short communication we discuss the update of the package that is able to run multivariate mixed models with multiple random effects and different covariance structures at the level of random effects and trait-to-trait covariance along with other functionalities for genetic analysis and field trial analysis to enhance the genome-assisted prediction capabilities of researchers.

Download Full-text

Use of the Syrian hamster, Mesocricetus auratus, in selection for fertility in animal breeding

The Journal of Animal Genetics ◽

10.5924/abgri2000.32.2_65 ◽

2005 ◽

Vol 32 (2) ◽

pp. 65-79

Author(s):

Masahiro SATOH ◽

Kazuo ISHII ◽

Tsutomu FURUKAWA

Keyword(s):

Syrian Hamster ◽

Mesocricetus Auratus ◽

Animal Breeding ◽

Selection For

Download Full-text

M3Drop: dropout-based feature selection for scRNASeq

Bioinformatics ◽

10.1093/bioinformatics/bty1044 ◽

2018 ◽

Vol 35 (16) ◽

pp. 2865-2867 ◽

Cited By ~ 61

Author(s):

Tallulah S Andrews ◽

Martin Hemberg

Keyword(s):

Feature Selection ◽

Single Cell ◽

R Package ◽

Supplementary Information ◽

Supplementary Data ◽

Selection Methods ◽

Functional Responses ◽

Technical Noise ◽

New Methods ◽

Selection For

Abstract Motivation Most genomes contain thousands of genes, but for most functional responses, only a subset of those genes are relevant. To facilitate many single-cell RNASeq (scRNASeq) analyses the set of genes is often reduced through feature selection, i.e. by removing genes only subject to technical noise. Results We present M3Drop, an R package that implements popular existing feature selection methods and two novel methods which take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features. We show these new methods outperform existing methods on simulated and real datasets. Availability and implementation M3Drop is freely available on github as an R package and is compatible with other popular scRNASeq tools: https://github.com/tallulandrews/M3Drop. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

AlphaSimR: an R package for breeding program simulations

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkaa017 ◽

2020 ◽

Vol 11 (2) ◽

Author(s):

R Chris Gaynor ◽

Gregor Gorjanc ◽

John M Hickey

Keyword(s):

Software Package ◽

R Package ◽

Animal Breeding ◽

Breeding Program ◽

Stochastic Simulations ◽

Breeding Programs ◽

Detailed Design ◽

Building Simulations ◽

Wide Range ◽

Commercial Breeding

Abstract This paper introduces AlphaSimR, an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. This paper lists the main features of AlphaSimR and provides a brief example simulation to show how to use the software.

Download Full-text

A unifying concept of animal breeding programs

10.1101/2020.07.08.193201 ◽

2020 ◽

Author(s):

H. Simianer ◽

A. Ganesan ◽

L. Buettgen ◽

N.T. Ha ◽

T. Pook

Keyword(s):

R Package ◽

Companion Paper ◽

Animal Breeding ◽

Breeding Program ◽

General Definition ◽

Full Description ◽

Genetic Progress ◽

Breeding Programs ◽

Definition Of ◽

Transgenic Technologies

ABSTRACTModern animal breeding programs are constantly evolving with advances in breeding theory, biotechnology and genetics. Surprisingly, there seems to be no generally accepted succinct definition of what exactly a breeding program is, neither is there a unified language to describe breeding programs in a comprehensive, unambiguous and reproducible way. In this work, we try to fill this gap by suggesting a general definition of breeding programs that also pertains to cases where genetic progress is not achieved through selection, but e.g. through transgenic technologies, or the aim is not to generate genetic progress, but e.g. to maintain genetic diversity. The key idea of the underlying concept is to represent a breeding program in modular form as a directed graph that is composed of nodes and edges, where nodes represent cohorts of breeding units, usually individuals, and edges represent breeding activities, like ‘selection’ or ‘reproduction’. We claim, that by defining a comprehensive set of nodes and edges it is possible to represent any breeding program of arbitrary complexity by such a graph, which thus comprises a full description of the breeding program. This concept is implemented in a web-based tool (MoBPSweb, available at www.mobps.de) which is described in a companion paper, and has a link to the R-package MoBPS (Modular Breeding Program Simulator) to simulate the described breeding programs. The approach is illustrated by showcasing three different breeding programs of increasing complexity. Finally, potential limitations of the concept are indicated and extensions to other fields, like plant breeding, are discussed.

Download Full-text

AFEchidna is a R package for genetic evaluation of plant and animal breeding datasets

10.1101/2021.06.24.449740 ◽

2021 ◽

Author(s):

Weihua Zhang ◽

Ruiyan Wei ◽

Yan Liu ◽

Yuanzhen Lin

Keyword(s):

Random Effects ◽

Variance Components ◽

Fixed Effects ◽

Genetic Parameters ◽

Linear Models ◽

R Package ◽

Animal Breeding ◽

Mixed Linear Models ◽

Best Linear Unbiased ◽

Genetic Assessment

Progeny tests play important roles in plant and animal breeding programs, and mixed linear models are usually performed to estimate variance components of random effects, estimate the fixed effects (Best Linear Unbiased Estimates, BLUEs) and predict the random effects (Best Linear Unbiased Predictions, BLUPs) via restricted maximum likehood (REML) methods in progeny test datasets. The current pioneer software for genetic assessment is ASReml, but it is commercial and expensive. Although there is free software such as Echidna or the R package sommer, the Echidna syntax is complex and the R package functionality is limited. Therefore, this study aims to develop a R package named AFEchidna based on Echidna software. The mixed linear models are conveniently implemented for users through the AFEchidna package to solve variance components, genetic parameters and the BLUP values of random effects, and the batch analysis of multiple traits, multiple variance structures and multiple genetic parameters can be also performed, as well as comparison between different models and genomic BLUP analysis. The AFEchidna package is free, please email us ([email protected]) to get a copy if one is interested for it. The AFEchidna package is developed to expand free genetic assessment software with the expectation that its efficiency could be close to the commercial software.

Download Full-text