scholarly journals Group size planning of breedings of gene-modified animals

2021 ◽  
Author(s):  
Thorsten Buch ◽  
Vladislava Milchevskaya ◽  
Philippe Bugnon ◽  
Emiel ten Buren ◽  
Frank Brand ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Litter Size ◽  
Group Size ◽  
R Package ◽  
Animal Breeding ◽  
Successful Outcome ◽  
Careful Planning ◽  
Mendelian Genetics ◽  
Stochastic Events

Abstract Animal breeding is time-consuming, costly, and affected by stochastic events related to Mendelian genetics, fertility, and litter size. Careful planning is mandatory to ensure a successful outcome using the least number of animals, hence adhering to the 3Rs of animal welfare. We have developed an R package, accessible also through an interactive website, that optimizes breeding design and provides a comprehensive report suitable for any breeder of genetically defined traits.

scholarly journals Group size planning of breedings of gene-modified animals

2021 ◽  
Author(s):  
Vladislava Milchevskaya ◽  
Philippe Bugnon ◽  
Emiel B.J. ten Buren ◽  
Frank Brand ◽  
Achim Tresch ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Litter Size ◽  
Group Size ◽  
R Package ◽  
Animal Breeding ◽  
Successful Outcome ◽  
Careful Planning ◽  
Mendelian Genetics ◽  
Stochastic Events

Animal breeding is time-consuming, costly, and affected by stochastic events related to Mendelian genetics, fertility, and litter size. Careful planning is mandatory to ensure a successful outcome using the least number of animals, hence adhering to the 3Rs of animal welfare. We have developed an R package, accessible also through an interactive website, that optimizes breeding design and provides a comprehensive report suitable for any breeder of genetically defined traits.

scholarly journals The multinomial index: a robust measure of reproductive skew

2020 ◽  
Vol 287 (1936) ◽  
pp. 20202025
Author(s):  
Cody T. Ross ◽  
Adrian V. Jaeggi ◽  
Monique Borgerhoff Mulder ◽  
Jennifer E. Smith ◽  
Eric Alden Smith ◽  
...  
Keyword(s):  
Group Size ◽  
Social Evolution ◽  
Natural Populations ◽  
R Package ◽  
Reproductive Skew ◽  
Primate Species ◽  
Highly Sensitive ◽  
Highly Nonlinear ◽  
Population Comparisons ◽  
Negative Effect

Inequality or skew in reproductive success (RS) is common across many animal species and is of long-standing interest to the study of social evolution. However, the measurement of inequality in RS in natural populations has been challenging because existing quantitative measures are highly sensitive to variation in group/sample size, mean RS, and age-structure. This makes comparisons across multiple groups and/or species vulnerable to statistical artefacts and hinders empirical and theoretical progress. Here, we present a new measure of reproductive skew, the multinomial index, M , that is unaffected by many of the structural biases affecting existing indices. M is analytically related to Nonacs’ binomial index, B , and comparably accounts for heterogeneity in age across individuals; in addition, M allows for the possibility of diminishing or even highly nonlinear RS returns to age. Unlike B , however, M is not biased by differences in sample/group size. To demonstrate the value of our index for cross-population comparisons, we conduct a reanalysis of male reproductive skew in 31 primate species. We show that a previously reported negative effect of group size on mating skew was an artefact of structural biases in existing skew measures, which inevitably decline with group size; this bias disappears when using M . Applying phylogenetically controlled, mixed-effects models to the same dataset, we identify key similarities and differences in the inferred within- and between-species predictors of reproductive skew across metrics. Finally, we provide an R package, SkewCalc , to estimate M from empirical data.

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

2018 ◽  
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.

scholarly journals Effects of group-size-floor space allowance during the nursery phase of production on future litter size and retention of sows through three parities1

2019 ◽  
Vol 4 (1) ◽  
pp. 299-306
Author(s):  
Stuart R Callahan ◽  
Amanda J Cross ◽  
Ashley E DeDecker ◽  
Merlin D Lindemann ◽  
Mark J Estienne
Keyword(s):  
Litter Size ◽  
Group Size ◽  
Blood Chemistry ◽  
Abortion Rate ◽  
Linear Effect ◽  
Floor Space ◽  
Large Size ◽  
Breeding Herd ◽  
Kg Medium ◽  
Relationship Of

Abstract We previously reported that reduced floor space allowance caused by increasing the number of gilts per pen decreased growth and affected blood chemistry and immunology. The current objective was to determine effects of nursery group-size-floor space allowance on future litter sizes and retention in the breeding herd through three parities in sows. A 3 × 3 factorial arrangement of treatments was employed with 2,537 gilts classified as large (6.92 ± 0.06 kg), medium (5.60 ± 0.06 kg), or small (4.42 ± 0.06 kg), and placed in nursery pens of 14, 11, or 8 pigs to allow 0.15, 0.19, or 0.27 m2 floor space/pig, respectively. After the nursery and grow-finish periods, 1,453 gilts selected for breeding were relocated to one of 11 sow farms. Total litter size and pigs born alive increased (P < 0.01) with increasing parity and total litter size was 12.94, 13.28, and 13.99 (SE = 0.13) and pigs born alive was 12.21, 12.64, and 13.23 (SE = 0.11) for Parities 1, 2, and 3, respectively. There was a tendency (P = 0.08) for a quadratic relationship of group-size-floor space allowance and total litter size (13.39, 13.54, and 13.27 [SE = 0.13] for gilts allowed 0.15, 0.19, or 0.27 m2 floor space/pig, respectively). A linear effect of size of pig at weaning (P = 0.03) on pigs born dead was detected and was 0.64, 0.75, and 0.75, for small, medium, and large size pigs, respectively. There was no effect of group-size-floor space allowance on the percentages of gilts completing zero (P = 0.36), one (P = 0.35), two (P = 0.32), or three (P = 0.50) parities. In contrast, the percentage of small gilts that failed to complete one parity was greater (P < 0.05) and the percentage completing one parity (P < 0.05) was less than for either large or medium gilts. Abortion rate was greater (P < 0.01) in gilts classified as small (2.51%) or medium (1.36%) at weaning compared with those classified as large (0.20%). Size at weaning did not affect the proportion of gilts completing two (P = 0.88) or three (P = 0.72) parities. Group-size-floor space allowance during the nursery phase of production did not have remarkable effects on future litter sizes or retention in sows. Likewise, size of pig at weaning did not affect litter size and pigs born alive. Compared with larger pigs, however, more pigs classified as small at weaning and entering the breeding herd did not complete a parity and displayed a greater abortion rate.

Guidelines to understand and compute the number needed to treat

2021 ◽  
pp. ebmental-2020-300232
Author(s):  
Valentin Vancak ◽  
Yair Goldberg ◽  
Stephen Z Levine
Keyword(s):  
Web Application ◽  
Cox Regression ◽  
R Package ◽  
Successful Outcome ◽  
Number Needed To Treat ◽  
Explanatory Variables ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Copy And Paste ◽  
User Friendly

ObjectiveWe aim to explain the unadjusted, adjusted and marginal number needed to treat (NNT) and provide software for clinicians to compute them.MethodsThe NNT is an efficacy index that is commonly used in randomised clinical trials. The NNT is the average number of patients needed to treat to obtain one successful outcome (ie, response) due to treatment. We developed the nntcalc R package for desktop use and extended it to a user-friendly web application. We provided users with a user-friendly step-by-step guide. The application calculates the NNT for various models with and without explanatory variables. The implemented models for the adjusted NNT are linear regression and analysis of variance (ANOVA), logistic regression, Kaplan-Meier and Cox regression. If no explanatory variables are available, one can compute the unadjusted Laupacis et al’s NNT, Kraemer and Kupfer’s NNT and the Furukawa and Leucht’s NNT. All NNT estimators are computed with their associated appropriate 95% confidence intervals. All calculations are in R and are replicable.ResultsThe application provides the user with an easy-to-use web application to compute the NNT in different settings and models. We illustrate the use of the application from examples in schizophrenia research based on the Positive and Negative Syndrome Scale. The application is available from https://nntcalc.iem.technion.ac.il. The output is given in a journal compatible text format, which users can copy and paste or download in a comma-separated values format.ConclusionThis application will help researchers and clinicians assess the efficacy of treatment and consequently improve the quality and accuracy of decisions.

scholarly journals 17 Effects of birth traits, physical or fenceline boar exposure and group size on pubertal measures and lifetime fertility of replacement gilts

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 12-12
Author(s):  
Robert V Knox ◽  
Ashley Daniel ◽  
Jenny Patterson ◽  
Lidia S Arend ◽  
George Foxcroft
Keyword(s):  
Growth Rate ◽  
Litter Size ◽  
Group Size ◽  
Delayed Puberty ◽  
Floor Space ◽  
Birth Records ◽  
And Control ◽  
Farrowing Rate

Abstract In experiment 1, prepubertal gilts with (n = 264) and without (n = 43) birth records received Fenceline (FBE) or Physical (PBE) Boar Exposure (BE) in a Boar Exposure Area (BEAR). At 185 d of age, gilts (13/pen) received BE for 15 min/d for 3 wk. At the start of Week 3, anestrual gilts received PG600 or no-PG600 (Control). At estrus, females were moved into stalls and inseminated at 2nd heat. Gilts born in larger litters were lighter (r = -0.26) while heavier pigs grew faster to puberty (r = 0.25). PBE increased estrus in Week 1 (38%) over FBE (28%). In Week 3, PBE-PG600 increased estrus (79.9%) compared with PBE- Control (36.2%), while FBE-PG600 and Control did not differ (52.7 vs. 42.5%). By 6 wk, estrus tended to be greater (P < 0.08) for PBE (91.2%) than FBE (83.2%). Reduced fertility associated with: 1) small birth litter; 2) heaviest birthweight; 3) slower growth rate; 4) delayed puberty and age at 1st service; and 5) abnormal estrus interval. Experiment 2 tested the pubertal response to PBE or FBE with 10 or 20 gilts/pen. Gilts (n = 180) at 168 d with 1.8 m2 floor space received BE once/d for 15 min for 1–3 wk. At the start of Week 3, anestrual gilts received PG600. Estrus in Week 1 (7.3%) did not differ, but a BE x Pen effect occurred in Week 2 (estrus range: 15–34%). In Week 3, PG600 increased estrus (P < 0.03) in Pens of 10 (83.7%) compared to Pens of 20 (64.1%). BE method had no effect and Pens of 10 had greater estrus (P = 0.05) than Pens of 20 (88.3 vs 75.8%). These results indicated that use of PBE, a BEAR, smaller group size, and PG600 can be used in combinations to enhance puberty induction. Birth and pubertal measures influenced service and farrowing rate, litter size, and age at removal.

scholarly journals AlphaSimR: an R package for breeding program simulations

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.

scholarly journals A unifying concept of animal breeding programs

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.

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

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.

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