Genomic inbreeding measures applied to a population of mice divergently selected for birth weight environmental variance

This study aimed to compare different inbreeding measures estimated from pedigree and molecular data from two divergent mouse lines selected for environmental birth weight during 26 generations. Furthermore, the performance of different approaches and both molecular and pedigree data sources for est...

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Detalles Bibliográficos
Autores: Ojeda Marín, Candela, Cervantes Navarro, Isabel, Formoso-Rafferty Castilla, Nora, Gutiérrez García, Juan Pablo
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/103658
Acceso en línea:https://hdl.handle.net/20.500.14352/103658
Access Level:acceso abierto
Palabra clave:636.09
Genomic inbreeding
Effective population size
Divergent selection
Birth weight environmental variability
Increase in inbreeding
Veterinaria
3109.02 Genética
Descripción
Sumario:This study aimed to compare different inbreeding measures estimated from pedigree and molecular data from two divergent mouse lines selected for environmental birth weight during 26 generations. Furthermore, the performance of different approaches and both molecular and pedigree data sources for estimating Ne were tested in this population. A total of 1,699 individuals were genotyped using a high-density genotyping array. Genomic relationship matrices were used to calculate molecular inbreeding: Nejati-Javaremi (FNEJ), Li and Horvitz (FL&H), Van Raden method 1 (FVR1) and method 2 (FVR2), and Yang (FYAN). Inbreeding based on runs of homozygosity (FROH) and pedigree inbreeding (FPED) were also computed. FROH, FNEJ, and FL&H were also adjusted for their average values in the first generation of selection and named FROH0, FNEJ0, and FL&H0. ∆F was calculated from pedigrees as the individual inbreeding rate between the individual and his parents (∆FPEDt) and individual increases in inbreeding (∆FPEDi). Moreover, individual ∆F was calculated from the different molecular inbreeding coefficients (∆FNEJ0, ∆FL&H, ∆FL&H0, ∆FVR1, ∆FVR2, ∆FYAN, and ∆FROH0). The Ne was obtained from different ∆F, such as NePEDt, NePEDi, NeNEJ0, NeL&H, NeL&H0, NeVR1, NeVR2, NeYAN, and NeROH0. Comparing with FPED, FROH, FNEJ and FVR2 overestimated inbreeding while FNEJ0, FL&H, FL&H0, FVR1 and FYAN underestimated inbreeding. Correlations between inbreeding coefficients and ∆F were calculated. FROH had the highest correlation with FPED (0.89); FYAN had correlations >0.95 with all the other molecular inbreeding coefficients. NePEDi was more reliable than NePEDt and presented similar behaviour to NeL&H0 and NeNEJ0. Stable trends in Ne were not observed until the 10th generation. In the 10th generation NePEDi was 42.20, NeL&H0 was 45.04 and NeNEJ0 was 45.05 and in the last generation these Ne were 35.65, 35.94 and 35.93, respectively FROH presented the highest correlation with FPED, which addresses the identity by descent probability (IBD). The evolution of NeL&H0 and NeNEJ0 was the most similar to that of NePEDi. Data from several generations was necessary to reach a stable trend for Ne, both with pedigree and molecular data. This population was useful to test different approaches to computing inbreeding coefficients and Ne using molecular and pedigree data.