Species and Phenotypic Distribution Models Reveal Population Differentiation in Ethiopian Indigenous Chickens

Smallholder poultry production dominated by indigenous chickens is an important source of livelihoods for most rural households in Ethiopia. The long history of domestication and the presence of diverse agroecologies in Ethiopia create unique opportunities to study the effect of environmental selective pressures. Species distribution models (SDMs) and Phenotypic distribution models (PDMs) can be applied to investigate the relationship between environmental variation and phenotypic differentiation in wild animals and domestic populations. In the present study we used SDMs and PDMs to detect environmental variables related with habitat suitability and phenotypic differentiation among nondescript Ethiopian indigenous chicken populations. 34 environmental variables (climatic, soil, and vegetation) and 19 quantitative traits were analyzed for 513 adult chickens from 26 populations. To have high variation in the dataset for phenotypic and ecological parameters, animals were sampled from four spatial gradients (each represented by six to seven populations), located in different climatic zones and geographies. Three different ecotypes are proposed based on correlation test between habitat suitability maps and phenotypic clustering of sample populations. These specific ecotypes show phenotypic differentiation, likely in response to environmental selective pressures. Nine environmental variables with the highest contribution to habitat suitability are identified. The relationship between quantitative traits and a few of the environmental variables associated with habitat suitability is non-linear. Our results highlight the benefits of integrating species and phenotypic distribution modeling approaches in characterization of livestock populations, delineation of suitable habitats for specific breeds, and understanding of the relationship between ecological variables and quantitative traits, and underlying evolutionary processes

Continuous real-time cow identification by reading ear tags from live-stream video

In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements

Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens

Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6–7 populations per gradient). We performed signatures of selection analyses (and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified byand XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations

An analytical framework to predict slaughter traits from images in fish

Accurate measurements of breeding traits on individuals are critical in aquaculture for obtaining breeding values and tracking the progress of the breeding program. Modern breeding programs prioritize not only production traits but also complex traits related to production, product quality, body composition, disease resistance, and fish health, such as slaughter traits. Slaughter traits can be selected indirectly and incorporated into breeding programs. Indirect selection is cost-effective, but there is often little genetic correlation between measured and target traits. Accurate phenotypic prediction of the target traits using modern phenotyping technology can be game-changing in indirect selection. This paper proposes an analytical framework for predicting slaughter traits using images. The framework demonstrated that using images in addition to body weight improved fat percentage prediction accuracy from 0.4 to 0.7 when compared to a model that only used body weight and its numerical derivations. The framework also allowed for the interpretation of the prediction by providing imaginal features. In the case study, the dorsal side, the upper edge of the pectoral fin, and operculum edge were discovered to be the three regions on seabream that have properties that are negatively correlated with fillet fat percentage. The framework showed that both body weight and visceral weight are highly correlated with total fish body area. The framework also revealed that the lower edge of the pectoral fin, operculum edge, and anal fin are the regions with properties that explain variation in the visceral percentage. Future research will be required to segment and quantify each predictive imaginal feature to calculate its heritability. The framework can potentially predict other harvest, post-slaughter, and metabolic traits for aquacultural study

A Stochastic Bio-Economic Farm Model for Brazilian Farrow-to-finish Pig Production System

A stochastic bio-economic farm model was developed to assess the impact of innovations on pig farm performance. The model accounts for emissions of greenhouse gases by using the shadow price of CO2 and for stochastic prices. The model was used to assess the impact of using co-products in pigs’ diets on private and social profits for a typical Brazilian farrow-to-finish pig farm. The results show that social profits are 2.2-3.6% lower than private profits in all the standard and alternative cases. The stochasticity of profits is large (with coefficients of variation 52% to 61%) following from the volatility of prices

Swimming Performance and Oxygen Consumption as Non-lethal Indicators of Production Traits in Atlantic Salmon and Gilthead Seabream

The aim of this study was to investigate swimming performance and oxygen consumption as non−lethal indicator traits of production parameters in Atlantic salmon Salmo salar L. and Gilthead seabream Sparus aurata L. A total of 34 individual fish of each species were subjected to a series of experiments: (1) a critical swimming speed (Ucrit) test in a swim-gutter, followed by (2) two starvation-refeeding periods of 42 days, and (3) swimming performance experiments coupled to respirometry in swim-tunnels. Ucrit was assessed first to test it as a predictor trait. Starvation-refeeding traits included body weight; feed conversion ratio based on dry matter; residual feed intake; average daily weight gain and loss. Swim-tunnel respirometry provided oxygen consumption in rest and while swimming at the different speeds, optimal swim speed and minimal cost of transport (COT). After experiments, fish were dissected and measured for tissue weights and body composition in terms of dry matter, ash, fat, protein and moist, and energy content. The Ucrit test design was able to provide individual Ucrit values in high throughput manner. The residual Ucrit (RUcrit) should be considered in order to remove the size dependency of swimming performance. Most importantly, RUcrit predicted filet yield in both species. The minimal COT, the oxygen consumption when swimming at Uopt, added predictive value to the seabream model for feed intake.</p

Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

After publication of this work [1], we noticed that there was an error: the formula to calculate the standard error of the estimated correlation.</p

Optimizing design to estimate genetic correlations between environments with common environmental effects

Breeding programs for different species aim to improve performance by testing members of full-sib (FS) and half-sib (HS) families in different environments. When genotypes respond differently to changes in the environment, this is defined as genotype by environment (G × E) interaction. The presence of common environmental effects within families generates covariance between siblings, and these effects should be taken into account when estimating a genetic correlation. Therefore, an optimal design should be established to accurately estimate the genetic correlation between environments in the presence of common environmental effects. We used stochastic simulation to find the optimal population structure using a combination of FS and HS groups with different levels of common environmental effects. Results show that in a population with a constant population size of 2,000 individuals per environment, ignoring common environmental effects when they are present in the population will lead to an upward bias in the estimated genetic correlation of on average 0.3 when the true genetic correlation is 0.5. When no common environmental effects are present in the population, the lowest standard error (SE) of the estimated genetic correlation was observed with a mating ratio of one dam per sire, and 10 offspring per sire per environment. When common environmental effects are present in the population and are included in the model, the lowest SE is obtained with mating ratios of at least 5 dams per sire and with a minimum number of 10 offspring per sire per environment. We recommend that studies that aim to estimate the magnitude of G × E in pigs, chicken, and fish should acknowledge the potential presence of common environmental effects and adjust the mating ratio accordingly.</p

The impact of genome editing on the introduction of monogenic traits in livestock

Background: Genome editing technologies provide new tools for genetic improvement and have the potential to become the next game changer in animal and plant breeding. The aim of this study was to investigate how genome editing in combination with genomic selection can accelerate the introduction of a monogenic trait in a livestock population as compared to genomic selection alone. Methods: A breeding population was simulated under genomic selection for a polygenic trait. After reaching Bulmer equilibrium, the selection objective was to increase the allele frequency of a monogenic trait, with or without genome editing, in addition to improving the polygenic trait. Scenarios were compared for time to fixation of the desired allele, selection response for the polygenic trait, and level of inbreeding. The costs, in terms of number of editing procedures, were compared to the benefits of having more animals with the desired phenotype of the monogenic trait. Effects of reduced editing efficiency were investigated. Results: In a population of 20,000 selection candidates per generation, the total number of edited zygotes needed to reach fixation of the desired allele was 22,118, 7072, or 3912 with, no, moderate, or high selection emphasis on the monogenic trait, respectively. Genome editing resulted in up to four-fold faster fixation of the desired allele when efficiency was 100%, while the loss in long-term selection response for the polygenic trait was up to seven-fold less compared to genomic selection alone. With moderate selection emphasis on the monogenic trait, introduction of genome editing led to a four-fold reduction in the total number of animals showing the undesired phenotype before fixation. However, with a currently realistic editing efficiency of 4%, the number of required editing procedures increased by 72% and loss in selection response increased eight-fold compared to 100% efficiency. With low efficiency, loss in selection response was 29% more compared to genomic selection alone. Conclusions: Genome editing strongly decreased the time to fixation for a desired allele compared to genomic selection alone. Reduced editing efficiency had a major impact on the number of editing procedures and on the loss in selection response. In addition to ethical and welfare considerations of genome editing, a careful assessment of its technical costs and benefits is required