On the holobiont ‘predictome’ of immunocompetence in pigs
Author
Calle-García, Joan
Zingaretti, Laura M.
Pérez-Enciso, Miguel
Publication date
2023-05-01ISSN
0999-193X
Abstract
Background
Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available.
Methods
We used phenotypic data on six immunity traits and the relative abundance of gut bacterial communities on 400 Duroc pigs that were genotyped for 70 k SNPs. We compared the predictive accuracy, defined as the correlation between predicted and observed phenotypes, of a wide catalogue of models: reproducing kernel Hilbert space (RKHS), Bayes C, and an ensemble method, using a range of priors and microbial clustering strategies. Combined (holobiont) models that include both genotype and microbiome data were compared with partial models that use one source of variation only.
Results
Overall, holobiont models performed better than partial models. Host genotype was especially relevant for predicting adaptive immunity traits (i.e., concentration of immunoglobulins M and G), whereas microbial composition was important for predicting innate immunity traits (i.e., concentration of haptoglobin and C-reactive protein and lymphocyte phagocytic capacity). None of the models was uniformly best across all traits. We observed a greater variability in predictive accuracies across models when microbiability (the variance explained by the microbiome) was high. Clustering microbial abundances did not necessarily increase predictive accuracy.
Conclusions
Gut microbiota information is useful for predicting immunocompetence traits, especially those related to innate immunity. Modelling microbiome abundances deserves special attention when microbiability is high. Clustering microbial data for prediction is not recommended by default.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
575 - General genetics. General cytogenetics
Pages
12
Publisher
BMC
Is part of
Genetics Selection Evolution
Citation
Calle-García, Joan, Yuliaxis Ramayo-Caldas, Laura M. Zingaretti, Raquel Quintanilla, María Ballester, and Miguel Pérez-Enciso. 2023. "On The Holobiont ‘Predictome’ Of Immunocompetence In Pigs". Genetics Selection Evolution 55 (1). doi:10.1186/s12711-023-00803-4.
Grant agreement number
MICIU/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I y Programa Estatal de I+D+I orientada a los retos de la sociedad/PID2019-108829RB-I00/ES/La belleza de lo profundo: Aplicaciones del deep learning a la predicción genómica/
MINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2016-75432-R/ES/DETERMINACION GENETICA DE LA CAPACIDAD INMUNOLOGICA EN PORCINO: IDENTIFICACION DE VARIANTES GENETICAS FUNCIONALES PARA LA IMPLEMENTACION DE SELECCION GENOMICA/
MICINN/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I y Programa Estatal de I+D+I orientada a los retos de la sociedad/PID2020-112677RB-C21/ES/FISIOLOGIA MOLECULAR DEL INMUNOMETABOLISMO EN PORCINO: BASES PARA LA SELECCION DE POBLACIONES MAS ROBUSTAS/
MINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2017-88849-R/ES/MICROBIOTA INTESTINAL Y GENETICA DEL HUESPED: CONTRIBUCION CONJUNTA A LA EFICIENCIA, EL COMPORTAMIENTO Y LA ROBUSTEZ EN PORCINO/
MICIU/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I/CEX2019-000902-S/ES/ /
MICIU-FEDER/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/RYC2019-027244-I/ES/Metagenomics and integrative biology tools to improve sustainable livestock systems/
Program
Genètica i Millora Animal
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