Genetic Variant in HK1 Is Associated With a Proanemic State and A1C but Not Other Glycemic Control–Related Traits

OBJECTIVE A1C is widely considered the gold standard for monitoring effective blood glucose levels. Recently, a genome-wide association study reported an association between A1C and rs7072268 within HK1 (encoding hexokinase 1), which catalyzes the first step of glycolysis. HK1 deficiency in erythrocytes (red blood cells [RBCs]) causes severe nonspherocytic hemolytic anemia in both humans and mice. RESEARCH DESIGN AND METHODS The contribution of rs7072268 to A1C and the RBC-related traits was assessed in 6,953 nondiabetic European participants. We additionally analyzed the association with hematologic traits in 5,229 nondiabetic European individuals (in whom A1C was not measured) and 1,924 diabetic patients. Glucose control–related markers other than A1C were analyzed in 18,694 nondiabetic European individuals. A type 2 diabetes case-control study included 7,447 French diabetic patients. RESULTS Our study confirms a strong association between the rs7072268–T allele and increased A1C (β = 0.029%; P = 2.22 × 10−7). Surprisingly, despite adequate study power, rs7072268 showed no association with any other markers of glucose control (fasting- and 2-h post-OGTT–related parameters, n = 18,694). In contrast, rs7072268–T allele decreases hemoglobin levels (n = 13,416; β = −0.054 g/dl; P = 3.74 × 10−6) and hematocrit (n = 11,492; β = −0.13%; P = 2.26 × 10−4), suggesting a proanemic effect. The T allele also increases risk for anemia (836 cases; odds ratio 1.13; P = 0.018). CONCLUSIONS HK1 variation, although strongly associated with A1C, does not seem to be involved in blood glucose control. Since HK1 rs7072268 is associated with reduced hemoglobin levels and favors anemia, we propose that HK1 may influence A1C levels through its anemic effect or its effect on glucose metabolism in RBCs. These findings may have implications for type 2 diabetes diagnosis and clinical management because anemia is a frequent complication of the diabetes state

A Genome-Wide Association Study Identifies rs2000999 as a Strong Genetic Determinant of Circulating Haptoglobin Levels

Haptoglobin is an acute phase inflammatory marker. Its main function is to bind hemoglobin released from erythrocytes to aid its elimination, and thereby haptoglobin prevents the generation of reactive oxygen species in the blood. Haptoglobin levels have been repeatedly associated with a variety of inflammation-linked infectious and non-infectious diseases, including malaria, tuberculosis, human immunodeficiency virus, hepatitis C, diabetes, carotid atherosclerosis, and acute myocardial infarction. However, a comprehensive genetic assessment of the inter-individual variability of circulating haptoglobin levels has not been conducted so far

The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs

International audienceBackground: Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more fexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efciency related traits using training sets that combine animals from two diferent, but geneticallyrelated lines. We compared realized prediction accuracy and prediction bias for diferent training set compositions for five production traits.Results: Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly afected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses.Conclusions: Our results show that genomic prediction using a training set that includes animals from geneticallyrelated lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly afected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes

Analyse d'images pour le suivi d'animaux d'élevage

International audienc

Devenir des données brutes stockées : extraction et traitement

Le stockage des données de phénotypage n’est pas une fin en soi. Leur valorisation constitue un réel outil pour le suivi quotidien d’un troupeau d’animaux sur le terrain et est également indispensable pour répondre à des questions scientifiques émergentes. Des outils, plus ou moins complexes d’utilisation, sont disponibles pour, dans un premier temps, extraire les données brutes des bases de données puis assurer leur traitement de façon à transmettre au scientifique des variables d’intérêt. Des bibliothèques logicielles permettent également la représentation graphique de ces données pour une meilleure visibilité

Predicting sow postures from video images: Comparison of convolutional neural networks and segmentation combined with support vector machines under various training and testing setups

International audienceThe use of CNN and segmentation to extract image features for the prediction of four postures for sows kept in crates was examined. The extracted features were used as input variables in an SVM classification method to estimate posture. The possibility of using a posture prediction model with images not necessarily obtained under the same conditions as those used for the training set was explored. As a reference case, the efficacy of the posture prediction model was explored when training and testing datasets were built using the same pool of images. In this case, all the models produced satisfactory results, with a maximum f1-score of 97.7% with CNNs and 93.3% with segmentation. To evaluate the impact of environmental variations, the models were trained and tested on different monitoring days. In this case, the best f1-score dropped to 86.7%. The impact of using the posture prediction model on animals that were not present in the training dataset was then explored. The best f1-score reduced to 63.4% when the posture prediction models were trained on one animal and tested on 11 other different animals. Conversely, when the models were tested on one animal and trained on the 11 others, the f1-score only decreased to 86% with the best model. On average, a decrease of around 17% caused by environmental and individual variations between training and testing was observed.Nous avons comparé l’utilisation des CNN et de la segmentation pour extraire des caractéristiques d’intérêts dans les images (features), afin de prédire la posture de truies allaitantes. Une fois extraite, les features peuvent être utilisées comme variable d’entrée d’une méthode de classification, de type SVM, pour prédire la posture de la truie. Nous avons exploré l’impact de l’ensemble d’apprentissage sur la qualité de la prédiction, notamment lorsque l’ensemble d’apprentissage et de test ne sont pas obtenues dans les mêmes conditions. Nous avons d’abord considéré un cas de référence, où la méthode d’estimation de la posture était entrainée et testée sur des images prises dans les mêmes conditions. Dans ce cas, tous les modèles testés ont fournis des résultats satisfaisants, avec un f1-score de 97.7% pour le meilleur CNN et de 93.3% pour la méthode basée sur la segmentation d’images. Pour évaluer l’impact des changements environnementaux lors de la prise de vue des images, nous avons ensuite entrainé et testé les modèles sur des images provenants de deux jours différents. Dans ce cas, le meilleur f1-score est réduit à 86.7%. Enfin, nous avons considéré le cas où des animaux présent dans l’ensemble test, ne le sont pas dans l’ensemble d’apprentissage. Lorsque le modèle de prediction de posture est entrainé sur une truie, et testé sur les 11 truies, le meilleur f1-score obtenu est de 63.4%. Lorsque le modèle est entrainé sur 11 truies et testé sur 1 truie, le f1-score est de 86%. En moyenne, nous avons observé que les variations environnementales et individuelles faisaient baisser le f1-score de 17%

Utilisation d’une puce très basse densité (1 100 SNP) pour la sélection génomique chez 3 races de porcs françaises

To reduce genotyping costs for genomic selection, a Low-Density SNP (LD) chip, designed in 2016, is now used routinely. This panel is composed of approximately 1 100 equidistant SNPs. The relevance of this chip has been studied in French populations of the Landrace, Large White and Pietrain pig breeds. The quality of imputation was estimated by the correlation between actual and imputed genotypes and error rates. The impact of imputation on the genomic evaluations was estimated by the correlation between the genomic values obtained for the candidates with imputed genotypes, and those obtained with the high-density genotypes. Average error rates of imputation estimated on all the chromosomes were 0.03, 0.11 and 0.14 for Landrace, Large White and Pietrain, respectively. The estimated correlations between actual and imputed genotypes were relatively high at 0.93, 0.92 and 0.88 forLandrace, Large White and Pietrain populations, respectively. Correlations between genomic breeding values predicted with high-density genomic data or imputed genomic data from the LD SNP panel ranged from 0.89-0.97 for Large White and Landrace populations for reproductive traits. They were higher than those obtained for the Pietrain population (0.80 and 0.97 for production traits, r espectively). In conclusion, despite the limited number of SNPs on the low-density panel used in this study, the imputation accuracy is sufficient to use the imputed genotypes in the genomic evaluations. In practice, genotyping candidates with a LD chip isa solution for selecting future breeding pigs at lower cos

Genome wide association study of growth and feed efficiency traits in rabbits

[EN] Feed efficiency is a major production trait in animal genetic breeding schemes. To further investigate the genetic control of feed efficiency in rabbits, we performed a genome-wide association study (GWAS) for growth and feed efficiency on 679 rabbits genotyped with the Affimetrix Axiom Rabbit 200K Genotyping Array. After quality control, 127 847 single-nucleotide polymorphisms (SNP) were retained for association analyses. The GWAS were performed using GEMMA software, applying a mixed univariate animal model with a linear regression on each SNP allele. The traits analysed were weight at weaning and at 63 days of age, average daily gain, total individual feed intake, feed conversion ratio and residual feed intake. No significant SNP was found for growth traits or feed intake. Fifteen genome-wide significant SNPs were detected for feed conversion ratio on OCU7, spanning from 124.8 Mbp to 126.3 Mbp, plus two isolated SNP on OCU2 (77.3 Mbp) and OCU8 (16.5 Mbp). For residual feed intake, a region on OCU18 (46.1-53.0 Mbp) was detected, which contained a putative functional candidate gene, GOT1.This study is part of the Feed-a-Gene Project, funded from the European Union’s H2020 Programme under grant agreement nº 633 531.Garreau, H.; Labrune, Y.; Chapuis, H.; Ruesche, J.; Riquet, J.; Demars, J.; Benitez, F.... (2023). Genome wide association study of growth and feed efficiency traits in rabbits. World Rabbit Science. 31(3):163-169. https://doi.org/10.4995/wrs.2023.18215163169313Aggrey S.E., Lee J., Karnuah A.B., Rekaya R. 2014. Transcriptomic analysis of genes in the nitrogen recycling pathway of meattype chickens divergently selected for feed efficiency. Anim. Genet., 45: 215-222. https://doi.org/10.1111/age.12098Carneiro M., Rubin C.J., Di Palma F., Albert F.W., Alföldi J., Barrio A.M., Pielberg G., Rafati N., Sayyab S., Turner-Maier J., Younis S., Afonso S., Aken B., Alves J.M., Barrell D., Bolet G., Boucher S., Burbano H.A., Campos R., Chang J.L., Duranthon V., Fontanesi L., Garreau H., Heiman D., Johnson J., Mage R.G., Peng Z., Queney G., Rogel Gaillard C., Ruffier M., Searle S., Villafuerte R., Xiong A., Young S., Forsberg-Nilsson K., Good J.M., Lander E.S., Ferrand N., Lindblad-Toh K., Andersson L. 2014. Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication. Science, 345: 1074-1079. https://doi.org/10.1126/science.1253714Delpuech E., Aliakbari A., Labrune Y., Fève K., Billon Y., Gilbert H., Riquet J. 2021. Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency. Genet. Sel. 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