Panya Sae-Lim

Defining desired genetic gains for rainbow trout breeding objective using analytic hierarchy process

Distributing animals from a single breeding program to a global market may not satisfy all producers, as they may differ in market objectives and farming environments. Analytic hierarchy process (AHP) is used to estimate preferences, which can be aggregated to consensus preference values using weighted goal programming (WGP). The aim of this study was to use an AHP-WGP based approach to derive desired genetic gains for rainbow trout breeding and to study whether breeding trait preferences vary depending on commercial products and farming environments. Two questionnaires were sent out. Questionnaire-A (Q-A) was distributed to 178 farmers from 5 continents and used to collect information on commercial products and farming environments. In this questionnaire, farmers were asked to rank the 6 most important traits for genetic improvement from a list of 13 traits. Questionnaire B (Q-B) was sent to all farmers who responded to Q-A (53 in total). For Q-B, preferences of the 6 traits were obtained using pairwise comparison. Preference intensity was given to quantify (in % of a trait mean; G%) the degree to which 1 trait is preferred over the other. Individual preferences, social preferences, and consensus preferences (Con-P) were estimated using AHP and WGP. Desired gains were constructed by multiplying Con-P by G%. The analysis revealed that the 6 most important traits were thermal growth coefficient (TGC), survival (Surv), feed conversion ratio (FCR), condition factor (CF), fillet percentage (FIL%), and late maturation (LMat). Ranking of traits based on average Con-P values were Surv (0.271), FCR (0.246), TGC (0.246), LMat (0.090), FIL% (0.081), and CF (0.067). Corresponding desired genetic gains (in % of trait mean) were 1.63, 1.87, 1.67, 1.29, 0.06, and 0.33%, respectively. The results from Con-P values show that trait preferences may vary for different types of commercial production or farming environments. This study demonstrated that combination of AHP and WGP can be used to derive desired gains for a breeding program and to quantify differences due to variations market demand or production environment.

Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): a continental scale study.

Rainbow trout is a globally important fish species for aquaculture. However, fish for most farms worldwide are produced by only a few breeding companies. Selection based solely on fish performance recorded at a nucleus may lead to lower-than-expected genetic gains in other production environments when genotype-by-environment (G × E) interaction exists. The aim was to quantify the magnitude of G × E interaction of growth traits (tagging weight; BWT, harvest weight; BWH, and growth rate; TGC) measured across 4 environments, located in 3 different continents, by estimating genetic correlations between environments. A total of 100 families, of at least 25 in size, were produced from the mating 58 sires and 100 dams. In total, 13,806 offspring were reared at the nucleus (selection environment) in Washington State (NUC) and in 3 other environments: a recirculating aquaculture system in Freshwater Institute (FI), West Virginia; a high-altitude farm in Peru (PE), and a cold-water farm in Germany (GER). To account for selection bias due to selective mortality, a multitrait multienvironment animal mixed model was applied to analyze the performance data in different environments as different traits. Genetic correlation (rg) of a trait measured in different environments and rg of different traits measured in different environments were estimated. The results show that heterogeneity of additive genetic variances was mainly found for BWH measured in FI and PE. Additive genetic coefficient of variation for BWH in NUC, FI, PE, and GER were 7.63, 8.36, 8.64, and 9.75, respectively. Genetic correlations between the same trait in different environments were low, indicating strong reranking (BWT: rg = 0.15 to 0.37, BWH: rg = 0.19 to 0.48, TGC: rg = 0.31 to 0.36) across environments. The rg between BWT in NUC and BWH in both FI (0.31) and GER (0.36) were positive, which was also found between BWT in NUC and TGC in both FI (0.10) and GER (0.20). However, rg were negative between BWT in NUC and both BWH (-0.06) and TGC (-0.20) in PE. Correction for selection bias resulted in higher additive genetic variances. In conclusion, strong G × E interaction was found for BWT, BWH, and TGC. Accounting for G × E interaction in the breeding program, either by using sib information from testing stations or environment-specific breeding programs, would increase genetic gains for environments that differ significantly from NUC.

Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments

BackgroundWhen rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (rg) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and rg between BW and its uniformity were influenced by a scale effect.ResultsAlthough heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (rg = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low rg (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The rg between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively.ConclusionsGenetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative rg between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its rg.

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models

BackgroundIdentifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes.MethodsReaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models.ResultsThe combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model.ConclusionsDay*Degree and photoperiod were identified as environmental variables responsible for the strong GxE interaction for body weight at harvest in rainbow trout across four environments. Both the reaction norm and the factor analytic models can help identify the environmental variables responsible for GxE interaction. A factor analytic model is preferred over a reaction norm model when limited information on differences in environmental variables between farms is available.

Genetics of growth reaction norms in farmed rainbow trout

Rainbow trout is farmed globally under diverse uncontrollable environments. Fish with low macroenvironmental sensitivity (ES) of growth is important to thrive and grow under these uncontrollable environments. The ES may evolve as a correlated response to selection for growth in one environment when the genetic correlation between ES and growth is nonzero. The aims of this study were to quantify additive genetic variance for ES of body weight (BW), defined as the slope of reaction norm across breeding environment (BE) and production environment (PE), and to estimate the genetic correlation (r g(int, sl)) between BW and ES. To estimate heritable variance of ES, the coheritability of ES was derived using selection index theory. The BW records from 43,040 rainbow trout performing either in freshwater or seawater were analysed using a reaction norm model. High additive genetic variance for ES (9584) was observed, inferring that genetic changes in ES can be expected. The coheritability for ES was either -0.06 (intercept at PE) or -0.08 (intercept at BE), suggesting that BW observation in either PE or BE results in low accuracy of selection for ES. Yet, the r g(int, sl) was negative (-0.41 to -0.33) indicating that selection for BW in one environment is expected to result in more sensitive fish. To avoid an increase of ES while selecting for BW, it is possible to have equal genetic gain in BW in both environments so that ES is maintained stable.

A comparison of nonlinear mixed models and response to selection of tick-infestation on lambs

Tick-borne fever (TBF) is stated as one of the main disease challenges in Norwegian sheep farming during the grazing season. TBF is caused by the bacterium Anaplasma phagocytophilum that is transmitted by the tick Ixodes ricinus. A sustainable strategy to control tick-infestation is to breed for genetically robust animals. In order to use selection to genetically improve traits we need reliable estimates of genetic parameters. The standard procedures for estimating variance components assume a Gaussian distribution of the data. However, tick-count data is a discrete variable and, thus, standard procedures using linear models may not be appropriate. Thus, the objectives of this study were twofold: 1) to compare four alternative non-linear models: Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial based on their goodness of fit for quantifying genetic variation, as well as heritability for tick-count and 2) to investigate potential response to selection against tick-count based on truncation selection given the estimated genetic parameters from the best fit model. Our results showed that zero-inflated Poisson was the most parsimonious model for the analysis of tick count data. The resulting estimates of variance components and high heritability (0.32) led us to conclude that genetic determinism is relevant on tick count. A reduction of the breeding values for tick-count by one sire-dam genetic standard deviation on the liability scale will reduce the number of tick counts below an average of 1. An appropriate breeding scheme could control tick-count and, as a consequence, probably reduce TBF in sheep.

Desired genetic gains for a breeding objective: A novel participatory approach

Maternal abilities and piglet vitality were analyzed on 24 Meishan (MS) and 24 Large White (LW) gilts. Females were inseminated with a mixture of semen from both breeds. Three MS and 3 LW boars were used to constitute 3 duos formed by mixing of MS and LW semen in equal proportions. Farrowing events were studied over 5 successive batches. The proportion of purebred and crossbred piglets within the litter varied according to the duo used and the dam breed (p<0.01). The average within-litter percentage of purebred piglets in LW and MS sows was respectively 43% and 50% with use of duo 1, 64% and 23% with duo 2 and 69% and 81% with duo 3. Gestation was shorter in MS than in LW sows (111.6 vs 114.0 days; p<0.05) and litter size tended to be larger in LW than in MS sows (14.6 vs 12.8 total born piglets; p=0.08). Over the three first days of lactation, piglet probability of survival was similar between purebred and crossbred piglets born from LW sows (94.5% vs 95.0%) and higher in purebred than crossbred piglets born from MS sows (96.6% vs 98.7%, p<0.05). In LW sows, crossbred piglets were heavier at birth and more reactive in a novel environment than purebred piglets (1.29 vs 1.21 kg, p<0.10; reactivity score: 1.38 vs 1.03 respectively). In MS sows, purebred piglets had a lower birth weight than crossbred piglets but showed similar vitality (0.86 vs 1.08 kg, p<0.001; reactivity score: 1.00 vs 1.03). Birth process and piglet behavior in early lactation will be analyzed to estimate the interaction between dam breed and piglet genetic type (purebred vs crossbred) on the expression of maternal behavior and piglet vitality (udder activity and survival).Young horses normally live in small year-round stable groups including one stallion, their mothers, a few other mares, their siblings and unrelated peers. On the contrary, most of young domestic horses are generally maintained in same-age and same-sex groups from weaning until training. One has to consider that the absence of adult partners during ontogeny may be a source of behavioral disorders. In a first study, we focused on social conditions at weaning. While it is well known that presence of peers is of high importance to alleviate weaning stress, we investigated here the effects of the introduction of unrelated adult mares in groups of weanlings. Results showed that signs of stress were less pronounced and shorter in time in weanlings housed with adult mares than in weanlings kept in same-age groups (e.g. distress vocalizations: P<0.05; salivary cortisol: P<0.05). Besides, only foals deprived of adult presence exhibited increased aggressiveness towards peers (P<0.05) and abnormal behaviors (P<0.05). In conclusion, the presence of two unrelated adults in groups of weanlings not only alleviated weaning stress, but also favored positive social behavior and limited the emergence of abnormal behaviors. In a second study, we examined the impact of the temporary presence of adult horses on the behavior of 1- and 2-year-old horses. Results showed that young horses reared in homogeneous groups had a reduced behavioral repertoire, no real preferred partner and displayed many agonistic interactions compared to domestic horses reared under more natural conditions. Interestingly, after the introduction of adults, young horses expressed new behaviors (e.g. snapping, lying recumbent), preferential social associations emerged (P<0.05) and positive social behavior increased (P<0.05). Taken together, these results have important implications in terms of husbandry, indicating the importance of keeping young horses with adults.The aim of this work was to characterise the European consumer of pig meat (within ALCASDE project). A total of 822 respondents participated in a survey that was carried out Germany (DE n=132), Spain (ES n=133), France (FR n=139), Italy (IT n=140), Netherlands (NL n=132) and United Kingdom (UK n=146). All of them were selected for consuming pork > 1 time/month and stratified by age and gender, within each country profile. Respondents answered socio-demographic questions and frequency of consumption of different pork products, the most common purchasing place for fresh pork meat, if they were responsible for buying fresh pork at home, if they were responsible for cooking at home, and if they usually eat the pork with the fat. Data was analysed with FREQ procedure of SAS software. In general, over ninety percent of consumers ate fresh pork > 2 times/week (DE 96.2 %; ES 95.5 %; IT 92.9 %; NL 93.9 %; UK 97.3 %) except for FR (34.8 %). The most consumed product was the sausage in DE, dry cured ham in ES and IT; cooked ham in FR, mince meat in NL and sliced bacon in UK. In all the countries, the supermarket was the most common purchasing place of fresh pork with the exception of NL, where it was the traditional market. In general, the percentage of respondents responsible for buying fresh pork in their household was 91.0 %. In all countries, women were more responsible for buying fresh pork than men, and they were mostly between 41-60 years old. Ninety-one percent of respondents were partially responsible for cooking at home. Women were more responsible for cooking at home than men. France was an exception, where 49.6 % women and 50.4 % men cooked at home. Considering all respondents, 44.5 % ate the pork with the fat in all the countries (35.4 % of women and 54.6 % of men). The study showed differences among countries regarding respondents’ traits.