scholarly journals 260 Genetic prediction for first-service conception rate in Angus heifers using a random regression model

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 197-198
Author(s):  
Miguel A Sánchez-Castro ◽  
Milt Thomas ◽  
Mark Enns ◽  
Scott Speidel
Keyword(s):  
Conception Rate ◽  
Random Regression ◽  
State University ◽  
Base Function ◽  
Genetic Prediction ◽  
Colorado State University ◽  
Random Regression Model ◽  
Service Conception ◽  
Available Information ◽  
Angus Heifers

Abstract First-service conception rate (FSCR) can be defined as the probability of a heifer conceiving in response to her first artificial insemination (AI). Given the binary nature of its phenotypes, FSCR has been typically evaluated using animal threshold models (ATM). However, susceptibility of these models to the extreme-category problem (ECP) limits their ability to use all available information to calculate Expected Progeny Differences (EPD). Random regression models (RRM) represent an alternative method to evaluate binary traits, and they are not affected by ECP. Nevertheless, RRM were originally developed to analyze longitudinal traits, so their usefulness to evaluate traits with singly observed phenotypes remains unclear. Therefore, objectives herein were to evaluate the feasibility of a RRM genetic prediction for heifer FSCR by comparing its resulting EPD and genetic parameters to those obtained with a traditional ATM. Breeding and ultrasound records of 4,334 Angus heifers (progeny of 354 sires and 1,626 dams) collected between 1992 to 2019 at the Colorado State University Beef Improvement Center were utilized. Observations for FSCR (1, successful; 0, unsuccessful) were defined by fetal age at pregnancy inspections performed approximately 130 d post-AI. Traditional FSCR evaluation was performed using a univariate BLUP threshold animal model, whereas an alternative evaluation was performed by regressing FSCR on age at AI using a linear RRM with Legendre Polynomials as the base function. Heritability estimates were 0.03 ± 0.02 for the ATM and 0.005 ± 0.001 for the average age at AI with the RRM, respectively. Pearson and rank correlations between EPD obtained with each method were 0.63 and 0.60, respectively. The regression coefficient of RRM predictions on those obtained with the ATM was 0.095. In conclusion, these results suggested that although a RRM genetic prediction for FSCR was feasible, a considerable degree of re-ranking occurred between the two methodologies.

scholarly journals Genetic prediction for first-service conception rate in Angus heifers using a random regression model

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. S43-S47
Author(s):  
Miguel A Sánchez-Castro ◽  
Milton G Thomas ◽  
R Mark Enns ◽  
Scott E Speidel
Keyword(s):  
Regression Model ◽  
Conception Rate ◽  
Random Regression ◽  
Genetic Prediction ◽  
Random Regression Model ◽  
Service Conception ◽  
Angus Heifers

A MODEL FOR DEVELOPING HIGH-QUALITY ONLINE COURSES: INTEGRATING A SYSTEMS APPROACH WITH LEARNING THEORY

2008 ◽  
Vol 12 (3) ◽  
Author(s):  
Maria Jean Puzziferro ◽  
Kaye Shelton
Keyword(s):  
Online Education ◽  
Design Theory ◽  
Systems Approach ◽  
Online Courses ◽  
Process Models ◽  
State University ◽  
High Quality ◽  
Colorado State University ◽  
Online Instructional Design ◽  
Online Course Development

As the demand for online education continues to increase, institutions are faced with developing process models for efficient, high-quality online course development. This paper describes a systems, team-based, approach that centers on an online instructional design theory (Active Mastery Learning) implemented at Colorado State University-Global Campus.

scholarly journals Effects of management decisions on genetic evaluation of simulated calving records using random regression

2021 ◽  
Author(s):  
M D MacNeil ◽  
J W Buchanan ◽  
M L Spangler ◽  
E Hay
Keyword(s):  
Reproductive Success ◽  
Simulated Data ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Management Decisions ◽  
Third Order ◽  
Data Set ◽  
Binary Phenotype ◽  
Random Regression Model ◽  
Missing Observation

Abstract The objective of this study was to evaluate the effects of various data structures on the genetic evaluation for the binary phenotype of reproductive success. The data were simulated based on an existing pedigree and an underlying fertility phenotype with a heritability of 0.10. A data set of complete observations was generated for all cows. This data set was then modified mimicking the culling of cows when they first failed to reproduce, cows having a missing observation at either their second or fifth opportunity to reproduce as if they had been selected as donors for embryo transfer, and censoring records following the sixth opportunity to reproduce as in a cull-for-age strategy. The data were analyzed using a third order polynomial random regression model. The EBV of interest for each animal was the sum of the age-specific EBV over the first 10 observations (reproductive success at ages 2-11). Thus, the EBV might be interpreted as the genetic expectation of number of calves produced when a female is given ten opportunities to calve. Culling open cows resulted in the EBV for 3 year-old cows being reduced from 8.27 ± 0.03 when open cows were retained to 7.60 ± 0.02 when they were culled. The magnitude of this effect decreased as cows grew older when they first failed to reproduce and were subsequently culled. Cows that did not fail over the 11 years of simulated data had an EBV of 9.43 ± 0.01 and 9.35 ± 0.01 based on analyses of the complete data and the data in which cows that failed to reproduce were culled, respectively. Cows that had a missing observation for their second record had a significantly reduced EBV, but the corresponding effect at the fifth record was negligible. The current study illustrates that culling and management decisions, and particularly those that impact the beginning of the trajectory of sustained reproductive success, can influence both the magnitude and accuracy of resulting EBV.

scholarly journals Preface: Modern Heterocycle Synthesis and Functionalization

Synlett ◽  
2021 ◽  
Vol 32 (02) ◽  
pp. 140-141
Author(s):  
Louis-Charles Campeau ◽  
Tomislav Rovis
Keyword(s):  
Active Pharmaceutical Ingredient ◽  
Associate Professor ◽  
Columbia University ◽  
State University ◽  
Heterocycle Synthesis ◽  
Colorado State University ◽  
University Of Toronto ◽  
Process Chemistry ◽  
The University ◽  
Small Molecule Therapeutics

obtained his PhD degree in 2008 with the late Professor Keith Fagnou at the University of Ottawa in Canada as an NSERC Doctoral Fellow. He then joined Merck Research Laboratories at Merck-Frosst in Montreal in 2007, making key contributions to the discovery of Doravirine (MK-1439) for which he received a Merck Special Achievement Award. In 2010, he moved from Quebec to New Jersey, where he has served in roles of increasing responsibility with Merck ever since. L.-C. is currently Executive Director and the Head of Process Chemistry and Discovery Process Chemistry organizations, leading a team of smart creative scientists developing innovative chemistry solutions in support of all discovery, pre-clinical and clinical active pharmaceutical ingredient deliveries for the entire Merck portfolio for small-molecule therapeutics. Over his tenure at Merck, L.-C. and his team have made important contributions to >40 clinical candidates and 4 commercial products to date. Tom Rovis was born in Zagreb in former Yugoslavia but was largely raised in southern Ontario, Canada. He earned his PhD degree at the University of Toronto (Canada) in 1998 under the direction of Professor Mark Lautens. From 1998–2000, he was an NSERC Postdoctoral Fellow at Harvard University (USA) with Professor David A. Evans. In 2000, he began his independent career at Colorado State University and was promoted in 2005 to Associate Professor and in 2008 to Professor. His group’s accomplishments have been recognized by a number of awards including an Arthur C. Cope Scholar, an NSF CAREER Award, a Fellow of the American Association for the Advancement of Science and a ­Katritzky Young Investigator in Heterocyclic Chemistry. In 2016, he moved to Columbia University where he is currently the Samuel Latham Mitchill Professor of Chemistry.

scholarly journals Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle

2011 ◽  
Vol 50 (No. 4) ◽  
pp. 142-154 ◽  
Author(s):  
L. Zavadilová ◽  
J. Jamrozik ◽  
Schaeffer LR
Keyword(s):  
Milk Fat ◽  
Sampling Method ◽  
Random Regression ◽  
Model Parameters ◽  
Residual Variance ◽  
Posterior Distributions ◽  
Production Traits ◽  
Multiple Trait ◽  
Holstein Breed ◽  
Random Regression Model

Multiple-lactation random regression model was applied to test-day records of milk, fat and protein yields in the first three lactations of the Czech Holstein breed. Data included 9 583 cows, 89 584, 44 207 and 11 266 test-day records in the first, second and third lactation, respectively. Milk, fat and protein in the first three lactations were analysed separately and in a multiple-trait analysis. Linear model included herd-test date, fixed regressions within age-season class and two random effects: animal genetic and permanent environment modelled by regressions. Gibbs sampling method was used to generate samples from marginal posterior distributions of the model parameters. The single- and multiple-trait models provided similar results. Genetic and permanent environmental variances and heritability for particular days in milk were high at the beginning and at the end of lactation. The residual variance decreased throughout the lactation. The resulting heritability ranged from 0.13 to 0.52 and increased with parity.  

The Coaxium® Wheat Production System: A New Herbicide-Resistant System for Annual Grass Weed Control and Integrated Weed Management

2021 ◽  
Vol 32 (4) ◽  
pp. 151-157
Author(s):  
Raven A. Bough ◽  
Phillip Westra ◽  
Todd A. Gaines ◽  
Eric P. Westra ◽  
Scott Haley ◽  
...  
Keyword(s):  
Weed Management ◽  
Production System ◽  
Integrated Weed Management ◽  
State University ◽  
Wheat Production ◽  
Colorado State University ◽  
Herbicide Resistant ◽  
System A ◽  
Grass Weed ◽  
Global Food

The authors discuss the importance of wheat as a global food source and describe a novel multi-institutional, public-private partnership between Colorado State University, the Colorado Wheat Research Foundation, and private chemical and seed companies that resulted in the development of a new herbicide-resistant wheat production system.

Hierarchical mixed-model expedites genome-wide longitudinal association analysis

2021 ◽  
Author(s):  
Ying Zhang ◽  
Yuxin Song ◽  
Jin Gao ◽  
Hengyu Zhang ◽  
Ning Yang ◽  
...  
Keyword(s):  
Association Analysis ◽  
Mixed Model ◽  
Repeated Measurements ◽  
Random Regression ◽  
Association Analyses ◽  
Genome Wide ◽  
Random Regression Model ◽  
A Genome ◽  
Longitudinal Association ◽  
Poultry Data

AbstractA hierarchical random regression model (Hi-RRM) was extended into a genome-wide association analysis for longitudinal data, which significantly reduced the dimensionality of repeated measurements. The Hi-RRM first modeled the phenotypic trajectory of each individual using a RRM and then associated phenotypic regressions with genetic markers using a multivariate mixed model (mvLMM). By spectral decomposition of genomic relationship and regression covariance matrices, the mvLMM was transformed into a multiple linear regression, which improved computing efficiency while implementing mvLMM associations in efficient mixed-model association expedited (EMMAX). Compared with the existing RRM-based association analyses, the statistical utility of Hi-RRM was demonstrated by simulation experiments. The method proposed here was also applied to find the quantitative trait nucleotides controlling the growth pattern of egg weights in poultry data.

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