Submit to Animals Review for Animals Propose a Special Issue

Journal Browser

Modeling Genotype by Environment Interaction for Precision Farming and Improved Animal Welfare

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal Genetics and Genomics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 11767

Share This Special Issue

Special Issue Editors

Prof. Francesco Tiezzi

E-Mail Website
Guest Editor

Department of Animal Science, North Carolina State University, Raleigh, NC 27695, USA
Interests: genomic selection; genotyping strategies; genotype by environment interaction; meat quality; selection index

Dr. Luiz F. Brito

E-Mail Website
Guest Editor

Department of Animal Sciences, Purdue University, 270 S Russell St, West Lafayette, IN 47970, USA
Interests: livestock genomics; quantitative genetics; physiological genomics; behavior; welfare; resilience; small ruminants; cattle; pigs; environmental efficiency
Special Issues, Collections and Topics in MDPI journals

Prof. Breno Fragomeni

E-Mail Website
Guest Editor

Department of Animal Science, University of Connecticut, Storrs, CT 06269, USA
Interests: genomic selection; genotype-by-environment interaction; heat stress; genomic prediction; genetics of disease resistance

Special Issue Information

Dear Colleagues,

Livestock genetic improvement is experiencing a new phase of increased relevance, where novel traits, sophisticated statistical methods and high-throughput technologies are constantly being proposed or refined. Such advancements are resulting in faster rates of genetic progress compared to the pre-genomics era. The large majority of livestock breeding programs have primarily focused on productive traits, but more recently, worldwide selection indexes are being refined to incorporate indicators of reproductive performance, animal resilience, adaptation to changing environments, and health traits. Meanwhile, agriculture in general is moving to data-based decision making, where a wealth of data and heavy use of computational tools are employed every day for planning, monitoring, and managing livestock production and breeding.

Large datasets were necessary for performing genetic improvement of livestock before they became popular for decision making on other contexts of production. Now, genetic improvement is moving towards the incorporation of large and comprehensive datasets, that include phenotypic, genomic, physiological and environmental variables into statistical genomic models. This expansion in tools available happens contemporarily to the raising importance of welfare traits as breeding goals, driven by market demand and its increased economic importance (e.g. heat stress tolerance). The evaluation of animal welfare involves a complete assessment of the animal’s physiological, behavioral, physical, and emotional state. Therefore, animal welfare cannot be reduced to a single trait, but is composed of a wide spectrum of variables, which are probably determined by interactions between the genotype and environmental effects. Precision livestock farming and selection for animal welfare show an inherent advantage of including genotype by environment interactions. This involves scouting for new data sources (e.g. sensors), testing or refining statistical models (e.g. machine learning), unravelling genomic regions associated with such interactions, and engaging livestock industry stakeholders about the potential of these new methods and approaches (e.g. interactive selection index composition).

We invite original research papers, literature reviews and technical notes that address the topic of selection for novel and innovative traits incorporating the modelling of genotype by environment interactions. List of topics includes, but is not limited to: use of sensors in measuring phenotypes or determining condition, selection and genomic basis of tolerance to thermal stress, modelling of longitudinal data, behavioral genomics, and genetic by environment interaction in the determination of relevant breeding goals and product quality traits. Papers having any livestock species as subject are welcome.

Prof. Francesco Tiezzi
Prof. Luiz F. Brito
Prof. Breno Fragomeni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Genomic selection
Genotype by environment interaction
Heat tolerance
Animal welfare
Precision livestock farming
Animal resilience
Longitudinal data

Published Papers (5 papers)

Download All Papers

Research

16 pages, 1316 KiB

Open AccessArticle

Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein

by Francesco Tiezzi, Allison Fleming and Francesca Malchiodi

Animals 2022, 12(9), 1189; https://0-doi-org.brum.beds.ac.uk/10.3390/ani12091189 - 06 May 2022

Viewed by 1354

Abstract

The purpose of this study was to provide a procedure for the inclusion of milk spectral information into genomic prediction models. Spectral data were considered a set of covariates, in addition to genomic covariates. Milk yield and somatic cell score were used as traits to investigate. A cross-validation was employed, making a distinction for predicting new individuals’ performance under known environments, known individuals’ performance under new environments, and new individuals’ performance under new environments. We found an advantage of including spectral data as environmental covariates when the genomic predictions had to be extrapolated to new environments. This was valid for both observed and, even more, unobserved families (genotypes). Overall, prediction accuracy was larger for milk yield than somatic cell score. Fourier-transformed infrared spectral data can be used as a source of information for the calculation of the ‘environmental coordinates’ of a given farm in a given time, extrapolating predictions to new environments. This procedure could serve as an example of integration of genomic and phenomic data. This could help using spectral data for traits that present poor predictability at the phenotypic level, such as disease incidence and behavior traits. The strength of the model is the ability to couple genomic with high-throughput phenomic information. Full article

(This article belongs to the Special Issue Modeling Genotype by Environment Interaction for Precision Farming and Improved Animal Welfare)

► Show Figures

Figure 1

22 pages, 1449 KiB

Open AccessArticle

Genotype by Environment Interaction and Selection Response for Milk Yield Traits and Conformation in a Local Cattle Breed Using a Reaction Norm Approach

by Cristina Sartori, Francesco Tiezzi, Nadia Guzzo, Enrico Mancin, Beniamino Tuliozi and Roberto Mantovani

Animals 2022, 12(7), 839; https://0-doi-org.brum.beds.ac.uk/10.3390/ani12070839 - 26 Mar 2022

Cited by 2 | Viewed by 2441

Abstract

Local breeds are often reared in various environmental conditions (EC), suggesting that genotype by environment interaction (GxE) could influence genetic progress. This study aimed at investigating GxE and response to selection (R) in Rendena cattle under diverse EC. Traits included milk, fat, and protein yields, fat and protein percentage, and somatic cell score, three-factor scores and 24 linear type traits. The traits belonged to 11,085 cows (615 sires). Variance components were estimated in a two-step reaction norm model (RNM). A single trait animal model was run to obtain the solutions of herd-EC effect, then included in a random regression sire model. A multivariate response to selection (R) in different EC was computed for traits under selection including beef traits from a performance test. GxE accounted on average for 10% of phenotypic variance, and an average rank correlation of over 0.97 was found between bull estimated breeding values (EBVs) by either including or not including GxE, with changing top ranks. For various traits, significantly greater genetic components and R were observed in plain farms, loose housing rearing system, feeding total mixed ration, and without summer pasture. Conversely, for beef traits, a greater R was found for mountain farms, loose housing, hay-based feeding and summer pasture. Full article

(This article belongs to the Special Issue Modeling Genotype by Environment Interaction for Precision Farming and Improved Animal Welfare)

► Show Figures

Figure 1

14 pages, 2037 KiB

Open AccessArticle

Single-Step GBLUP and GWAS Analyses Suggests Implementation of Unweighted Two Trait Approach for Heat Stress in Swine

by Gabriella Roby Dodd, Kent Gray, Yijian Huang and Breno Fragomeni

Animals 2022, 12(3), 388; https://0-doi-org.brum.beds.ac.uk/10.3390/ani12030388 - 05 Feb 2022

Cited by 3 | Viewed by 2963

Abstract

The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri, U.S. Individuals were from a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8232 animals with 33,581 SNPs. The pedigree file contained a total of 553,448 animals. A threshold of 78 on the Temperature Humidity Index (THI) was used to signify heat stress. A two-trait analysis was used with the phenotypes heat stress (Trait One) and non-heat stress (Trait Two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for Traits One and Two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The benchmark was the GEBV with complete data, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies. Both methods showed a maximum accuracy of 0.32 for Trait One and 0.54 for Trait Two. Manhattan Plots for Trait One, Trait Two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining more than 0.8% of the genetic variance were isolated. Chromosomes 1 and 14 showed peaks in the difference between the two traits. The genetic correlation suggests a different mechanism for Hot Carcass Weight under heat stress. The GWAS results show that both traits are highly polygenic, with only a few genomic regions explaining more than 1% of variance. Full article

(This article belongs to the Special Issue Modeling Genotype by Environment Interaction for Precision Farming and Improved Animal Welfare)

► Show Figures

Figure 1

15 pages, 8431 KiB

Open AccessArticle

Use of Principal Component Analysis to Combine Genetic Merit for Heat Stress and for Fat and Protein Yield in Spanish Autochthonous Dairy Goat Breeds

by Alberto Menéndez-Buxadera, Eva Muñoz-Mejías, Manuel Sánchez, Juan Manuel Serradilla and Antonio Molina

Animals 2021, 11(3), 736; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11030736 - 08 Mar 2021

Cited by 3 | Viewed by 1775

Abstract

We studied the effect of the Temperature Humidity Index (THI) (i.e., the average of temperature and relative humidity registered at meteorological stations) closest to the farms taken during the test day (TD), for total daily protein and fat yields (fpy) of the three main Spanish dairy goats. The data were from Florida (11,244 animals and 126,825 TD), Malagueña (12,215 animals and 141,856 TD) and Murciano Granadina (5162 animals and 62,834 TD) breeding programs and were studied by different linear models to estimate the nature of the fpy response throughout the THI and the weeks of lactation (Days in Milk, DIM) trajectories. The results showed an antagonism between THI and DIM, with a marked depression in the fpy level in animals kept in the hot zone of the THI values (THI > 25) compared with those in the cold zone (THI ≤ 16), with a negative impact equivalent to production of 13 to 30 days. We used a Reaction Norm model (RN), including THI and DIM as fixed covariates and a Test Day Model (TDM), to estimate the genetic (co)variance components. The heritability and genetic correlations estimated with RN and TDM showed a decreased pattern along the scale of THI and DIM, with slight differences between breeds, meaning that there was significant genetic variability in the animal’s ability to react to different levels of THI, which is not constant throughout the DIM, showing the existence of genotype-environment interaction. The breeding values (BV) of all animals for each level of THI and DIM were subject to a principal component analysis, and the results showed that 89 to 98% of the variance between the BV was explained by the two first eigenvalues. The standardized BV were weighted with the corresponding eigenvector coefficients to construct an index that showed, in a single indicator, the most complete expression of the existing genetic variability in the animals’ ability to produce fpy along the trajectories of THI and DIM. This new option will make it easier to select animals which are more productive, and with better adaptability to heat stress, as well as enabling us to exploit genetic variations in the form of the response to heat stress to be adapted to different production systems. Full article

(This article belongs to the Special Issue Modeling Genotype by Environment Interaction for Precision Farming and Improved Animal Welfare)

► Show Figures

Figure 1

13 pages, 1830 KiB

Open AccessArticle

Selection for Test-Day Milk Yield and Thermotolerance in Brazilian Holstein Cattle

by Renata Negri, Ignacio Aguilar, Giovani Luis Feltes and Jaime Araújo Cobuci

Animals 2021, 11(1), 128; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11010128 - 08 Jan 2021

Cited by 13 | Viewed by 2218

Abstract

Intense selection for milk yield has increased environmental sensitivity in animals, and currently, heat stress is an expensive problem in dairy farming. The objectives were to identify the best model for characterizing environmental sensitivity in Holstein cattle, using the test-day milk yield (TDMY) combined with the temperature–humidity index (THI), and identify sires genetically superior for heat-stress (HS) tolerance and milk yield, through random regression. The data comprised 94,549 TDMYs of 11,294 first-parity Holstein cows in Brazil, collected from 1997 to 2013. The yield data were fitted to Legendre orthogonal polynomials, linear splines and the Wilmink function. The THI (the average of two days before the dairy control) was used as an environmental gradient. An animal model that fitted production using a Legendre polynomials of quartic order for the days in milk and quadratic equations for the THI presented a better quality of fit (Akaike’s information criterion (AIC) and Bayesian information criterion (BIC)). The Spearman correlation coefficient of greatest impact was 0.54, between the top 1% for TDMY and top 1% for HS. Only 9% of the sires showed plasticity and an aptitude for joint selection. Thus, despite the small population fraction allowed for joint selection, sufficient genetic variability for selecting more resilient sires was found, which promoted concomitant genetic gains in milk yield and thermotolerance. Full article

(This article belongs to the Special Issue Modeling Genotype by Environment Interaction for Precision Farming and Improved Animal Welfare)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Modeling Genotype by Environment Interaction for Precision Farming and Improved Animal Welfare

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI