Selection for environmental variance of litter size in rabbits

[EN] Background: In recent years, there has been an increasing interest in the genetic determination of environmental variance. In the case of litter size, environmental variance can be related to the capacity of animals to adapt to new environmental conditions, which can improve animal welfare. Results: We developed a ten-generation divergent selection experiment on environmental variance. We selected one line of rabbits for litter size homogeneity and one line for litter size heterogeneity by measuring intra-doe phenotypic variance. We proved that environmental variance of litter size is genetically determined and can be modified by selection. Response to selection was 4.5% of the original environmental variance per generation. Litter size was consistently higher in the Low line than in the High line during the entire experiment. Conclusions: We conclude that environmental variance of litter size is genetically determined based on the results of our divergent selection experiment. This has implications for animal welfare, since animals that cope better with their environment have better welfare than more sensitive animals. We also conclude that selection for reduced environmental variance of litter size does not depress litter size.This research was funded by the Ministerio de Economía y Competitividad (Spain), Projects AGL2014-55921, C2-1-P and C2-2-P. Marina Martínez-Alvaro has a Grant from the same funding source, BES-2012-052655.Blasco Mateu, A.; Martínez Álvaro, M.; García Pardo, MDLL.; Ibáñez Escriche, N.; Argente, MJ. (2017). Selection for environmental variance of litter size in rabbits. Genetics Selection Evolution. 49(48):1-8. https://doi.org/10.1186/s12711-017-0323-4S184948Morgante F, Sørensen P, Sorensen DA, Maltecca C, Mackay TFC. Genetic architecture of micro-environmental plasticity in Drosophila melanogaster. Sci Rep. 2015;5:9785.Sørensen P, de los Campos G, Morgante F, Mackay TFC, Sorensen D. Genetic control of environmental variation of two quantitative traits of Drosophila melanogaster revealed by whole-genome sequencing. Genetics. 2015;201:487–97.Zhang XS, Hill WG. Evolution of the environmental component of the phenotypic variance: stabilizing selection in changing environments and the homogeneity cost. Evolution. 2005;59:1237–44.Mulder HA, Bijma P, Hill WG. Selection for uniformity in livestock by exploiting genetic heterogeneity of residual variance. Genet Sel Evol. 2008;40:37–59.Bodin L, Bolet G, Garcia M, Garreau H, Larzul C, David I. Robustesse et canalisation, vision de généticiens. INRA Prod Anim. 2010;23:11–22.García ML, Argente MJ, Muelas R, Birlanga V, Blasco A. Effect of divergent selection for residual variance of litter size on health status and welfare. In: Proceedings of the 10th World Rabbit Congress. Sharm El-Sheikh; 2012. p. 103–6.Argente MJ, García ML, Zbynovska K, Petruska P, Capcarova M, Blasco A. Effect of selection for residual variance of litter size on hematology parameters as immunology indicators in rabbits. In: Proceedings of the 10th World Congress on genetics applied to livestock production. Vancouver; 2014.García ML, Zbynovska K, Petruska P, Bovdisová I, Kalafová A, Capcarova M, et al. Effect of selection for residual variance of litter size on biochemical parameters in rabbits. In: Proceedings of the 67th annual meeting of the European Federation of Animal Science. Belfast; 2016.Broom DM. Welfare assessment and relevant ethical decisions: key concepts. Annu Rev Biomed Sci. 2008;20:79–90.SanCristobal-Gaudy M, Bodin L, Elsen JM, Chevalet C. Genetic components of litter size variability in sheep. Genet Sel Evol. 2001;33:249–71.Sorensen D, Waagepetersen R. Normal linear models with genetically structured residual variance heterogeneity: a case study. Genet Res. 2003;82:207–22.Mulder HA, Hill WG, Knol EF. Heritable environmental variance causes nonlinear relationships between traits: application to birth weight and stillbirth of pigs. Genetics. 2015;199:1255–69.Ros M, Sorensen D, Waagepetersen R, Dupont-Nivet M, San Cristobal M, Bonnet JC. Evidence for genetic control of adult weight plasticity in the snail Helix aspersa. Genetics. 2004;168:2089–97.Gutiérrez JP, Nieto B, Piqueras P, Ibáñez N, Salgado C. Genetic parameters for components analysis of litter size and litter weight traits at birth in mice. Genet Sel Evol. 2006;38:445–62.Ibáñez-Escriche N, Sorensen D, Waagepetersen R, Blasco A. Selection for environmental variation: a statistical analysis and power calculations to detect response. Genetics. 2008;180:2209–26.Wolc A, White IM, Avendano S, Hill WG. Genetic variability in residual variation of body weight and conformation scores in broiler chickens. Poult Sci. 2009;88:1156–61.Fina M, Ibáñez-Escriche N, Piedrafita J, Casellas J. Canalization analysis of birth weight in Bruna dels Pirineus beef cattle. J Anim Sci. 2013;91:3070–8.Mulder HA, Rönnegård L, Fikse WF, Veerkamp RF, Strandberg E. Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models. Genet Sel Evol. 2013;45:23.Janhunen M, Kause A, Vehviläinen H, Järvisalom O. Genetics of microenvironmental sensitivity of body weight in rainbow trout (Oncorhynchus mykiss) selected for improved growth. PLoS One. 2012;7:e38766.Sonesson AK, Ødegård J, Rönnegård L. Genetic heterogeneity of within-family variance of body weight in Atlantic salmon (Salmo salar). Genet Sel Evol. 2013;45:41.Garreau H, Bolet G, Larzul C, Robert-Granie C, Saleil G, SanCristobal M, et al. Results of four generations of a canalising selection for rabbit birth weight. Livest Sci. 2008;119:55–62.Pun A, Cervantes I, Nieto B, Salgado C, Pérez-Cabal MA, Ibáñez-Escriche N, et al. Genetic parameters for birth weight environmental variability in mice. J Anim Breed Genet. 2012;130:404–14.Hill WG, Mulder HA. Genetic analysis of environmental variation. Genet Res (Camb). 2010;92:381–95.Yang Y, Christensen OF, Sorensen D. Analysis of a genetically structured variance heterogeneity model using the Box–Cox transformation. Genet Res (Camb). 2011;93:33–46.Piles M, Garcia ML, Rafel O, Ramon J, Baselga M. Genetics of litter size in three maternal lines of rabbits: repeatability versus multiple-trait models. J Anim Sci. 2006;84:2309–15.Estany J, Baselga M, Blasco A, Camacho J. Mixed model methodology for the estimation of genetic response to selection in litter size of rabbits. Livest Prod Sci. 1989;21:67–75.Box GEP, Tiao GC. Bayesian inference in statistical analysis. New York: Wiley; 1973.Searle SR. Matrix algebra useful for statistics. Toronto: Wiley; 1982.Sorensen D, Gianola D. Likelihood, Bayesian and MCMC methods in quantitative genetics. New York: Springer; 2002.Geyer CM. Practical Markov chain Monte Carlo (with discussion). Stat Sci. 1992;7:467–511.Legarra A. TM threshold model. 2008. http://genoweb.toulouse.inra.fr/~alegarra/tm_folder/ . Accessed 02 May 2017.Blasco A. Bayesian data analysis for animal scientists. New York: Springer; 2017.Rönnegård L, Felleki M, Fikse F, Mulder HA, Strandberg E. Genetic heterogeneity of residual variance—estimation of variance components using double hierarchical generalized linear models. Genet Sel Evol. 2010;42:8.Felleki M, Lee D, Lee Y, Gilmour AR, Rönnegård L. Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models. Genet Res (Camb). 2012;94:307–17.Thompson R. Estimation of realized heritability in a selected population using mixed model methods. Genet Sel Evol. 1986;18:475–84.Sorensen DA, Johansson K. Estimation of direct and correlated responses to selection using univariate animal models. J Anim Sci. 1992;70:2038–44.Popper K. The logic of scientific discovery. London: Hutchinson & Co; 1959.Falconer DS, MacKay TFC. An introduction to quantitative genetics. 4th ed. Harlow: Longman Group Ltd; 1996.Formoso-Rafferty N, Cervantes I, Ibáñez-Escriche N, Gutiérrez JP. Correlated genetic trends for production and welfare traits in a mouse population divergently selected for birth weight environmental variability. Animal. 2016;10:1770–7.Ibáñez-Escriche N, Moreno A, Nieto B, Piqueras P, Salgado C, Gutiérrez JP. Genetic parameters related to environmental variability of weight traits in a selection experiment for weight gain in mice; signs of correlated canalised response. Genet Sel Evol. 2008;40:279–93.Mulder HA, Hill WG, Vereijken A, Veerkamp RF. Estimation of genetic variation in residual variance in female and male broiler chickens. Animal. 2009;3:1673–80.Ibáñez-Escriche N, Varona L, Sorensen D, Noguera JL. A study of heterogeneity of environmental variance for slaughter weight in pigs. Animal. 2008;2:19–26.Bolet G, Garreau H, Hurtaud J, Saleil G, Esparbié J, Falieres J. Canalising selection on within litter variability of birth weight in rabbits: responses to selection and characteristics of the uterus of the does. In: Proceedings of the 9th World Rabbit Congress. Verona; 2008. p. 51–6.San Cristobal-Gaudy M, Elsen JM, Bodin L, Chevalet C. Prediction of the response to a selection for canalisation of a continuous trait in animal breeding. Genet Sel Evol. 1998;30:423–51.Argente MJ, Santacreu MA, Climent A, Blasco A. Genetic correlations between litter size and uterine capacity. In: Proceeding of the 8th World Rabbit Congress. Valencia; 2000. p. 333–38.Rauw WM. Immune response from a resource allocation perspective. Front Genet. 2012;3:267

Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Methods Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. Results In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Conclusions Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available

Association of genetic variants of the histamine H1 and muscarinic M3 receptors with BMI and HbA1c values in patients on antipsychotic medication

Rationale: Antipsychotic affinity for the histamine H1 receptor and the muscarinic M3 receptor have been associated with the side effects weight gain, and development of diabetes, respectively. Objectives: We investigated polymorphisms of the histamine H1 (HRH1) and muscarinic acetylcholine receptor M3 (CHRM3) receptor genes for an association with body mass index (BMI) and glycated hemoglobin (HbA1c). Methods: We included 430 Caucasian patients with a non-affective psychotic disorder using antipsychotics for at least 3 months. Primary endpoints of the study were cross-sectionally measured BMI and HbA1c; secondary endpoints were obesity and hyperglycaemia. Two single-nucleotide polymorphisms (SNPs) in the HRH1 gene, rs346074 and rs346070, and one SNP in the CHRM3 gene, rs3738435, were genotyped. Our primary hypothesis in this study was an interaction between genotype on BMI and antipsychotic affinity for the H1 and M3 receptor. Results: A significant association of interaction between haplotype rs346074-rs346070 and BMI (p value 0.025) and obesity (p value 0.005) in patients using high-H1 affinity antipsychotics versus patients using low-H1 affinity antipsychotics was found. There was no association of CHRM3 gene variant rs3738435 with BMI, and we observed no association with HbA1c or hyperglycaemia in any of the variants. Conclusions: This study, for the first time, demonstrates a significant association between HRH1 variants and BMI in patients with a psychotic disorder using antipsychotics. In future, genotyping of HRH1 variants may help predicting weight gain in patients using antipsychotics

Genetics of Microenvironmental Sensitivity of Body Weight in Rainbow Trout (Oncorhynchus mykiss) Selected for Improved Growth

Microenvironmental sensitivity of a genotype refers to the ability to buffer against non-specific environmental factors, and it can be quantified by the amount of residual variation in a trait expressed by the genotype’s offspring within a (macro)environment. Due to the high degree of polymorphism in behavioral, growth and life-history traits, both farmed and wild salmonids are highly susceptible to microenvironmental variation, yet the heritable basis of this characteristic remains unknown. We estimated the genetic (co)variance of body weight and its residual variation in 2-year-old rainbow trout (Oncorhynchus mykiss) using a multigenerational data of 45,900 individuals from the Finnish national breeding programme. We also tested whether or not microenvironmental sensitivity has been changed as a correlated genetic response when genetic improvement for growth has been practiced over five generations. The animal model analysis revealed the presence of genetic heterogeneity both in body weight and its residual variation. Heritability of residual variation was remarkably lower (0.02) than that for body weight (0.35). However, genetic coefficient of variation was notable in both body weight (14%) and its residual variation (37%), suggesting a substantial potential for selection responses in both traits. Furthermore, a significant negative genetic correlation (−0.16) was found between body weight and its residual variation, i.e., rapidly growing genotypes are also more tolerant to perturbations in microenvironment. The genetic trends showed that fish growth was successfully increased by selective breeding (an average of 6% per generation), whereas no genetic change occurred in residual variation during the same period. The results imply that genetic improvement for body weight does not cause a concomitant increase in microenvironmental sensitivity. For commercial production, however, there may be high potential to simultaneously improve weight gain and increase its uniformity if both criteria are included in a selection index

Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001)

Heat or Insulation: Behavioral Titration of Mouse Preference for Warmth or Access to a Nest

In laboratories, mice are housed at 20–24°C, which is below their lower critical temperature (≈30°C). This increased thermal stress has the potential to alter scientific outcomes. Nesting material should allow for improved behavioral thermoregulation and thus alleviate this thermal stress. Nesting behavior should change with temperature and material, and the choice between nesting or thermotaxis (movement in response to temperature) should also depend on the balance of these factors, such that mice titrate nesting material against temperature. Naïve CD-1, BALB/c, and C57BL/6 mice (36 male and 36 female/strain in groups of 3) were housed in a set of 2 connected cages, each maintained at a different temperature using a water bath. One cage in each set was 20°C (Nesting cage; NC) while the other was one of 6 temperatures (Temperature cage; TC: 20, 23, 26, 29, 32, or 35°C). The NC contained one of 6 nesting provisions (0, 2, 4, 6, 8, or 10g), changed daily. Food intake and nest scores were measured in both cages. As the difference in temperature between paired cages increased, feed consumption in NC increased. Nesting provision altered differences in nest scores between the 2 paired temperatures. Nest scores in NC increased with increasing provision. In addition, temperature pairings altered the difference in nest scores with the smallest difference between locations at 26°C and 29°C. Mice transferred material from NC to TC but the likelihood of transfer decreased with increasing provision. Overall, mice of different strains and sexes prefer temperatures between 26–29°C and the shift from thermotaxis to nest building is seen between 6 and 10 g of material. Our results suggest that under normal laboratory temperatures, mice should be provided with no less than 6 grams of nesting material, but up to 10 grams may be needed to alleviate thermal distress under typical temperatures

The plasticity of Plasmodium falciparum gametocytaemia in relation to age in Burkina Faso

BACKGROUND: Malaria transmission depends on the presence of gametocytes in the peripheral blood. In this study, the age-dependency of gametocytaemia was examined by microscopy and molecular tools. METHODS: A total of 5,383 blood samples from individuals of all ages were collected over six cross sectional surveys in Burkina Faso. One cross-sectional study used quantitative nucleic acid sequence based amplification (QT-NASBA) for parasite quantification (n = 412). The proportion of infections with concurrent gametocytaemia and median proportion of gametocytes among all parasites were calculated. RESULTS: Asexual parasite prevalence and gametocyte prevalence decreased with age. Gametocytes made up 1.8% of the total parasite population detected by microscopy in the youngest age group. This proportion gradually increased to 18.2% in adults (p < 0.001). Similarly, gametocytes made up 0.2% of the total parasite population detected by QT-NASBA in the youngest age group, increasing to 5.7% in adults (p < 0.001). This age pattern in gametocytaemia was also evident in the proportion of gametocyte positive slides without concomitant asexual parasites which increased from 13.4% (17/127) in children to 45.6% (52/114) in adults (OR 1.55, 95% CI 1.38-1.74, p < 0.001). CONCLUSIONS: The findings of this study suggest that although gametocytes are most commonly detected in children, the proportion of asexual parasites that is committed to develop into gametocytes may increase with age. These findings underscore the importance of adults for the human infectious reservoir for malaria