RT info:eu-repo/semantics/article
T1 Reducing cryptic relatedness in genomic data sets via a central node exclusion algorithm
A1 De Souza Fonseca, Pablo Augusto
A1 Leal, Thiago P.
A1 Dos Santos, Fernanda Caroline
A1 Gouveia, Mateus H.
A1 Id-Lahoucine, Samir
A1 Da Cruz Rosse, Izinara
A1 Ventura, Ricardo V.
A1 Tomita Bruneli, Frank Angelo
A1 Machado, Marco A.
A1 Diniz Peixoto, Maria Gabriela Campolina
A1 Tarazona Santos, Eduardo
A1 Santos Carvalho, Maria Raquel
A2 Genetica
K1 Genética
K1 bovine
K1 cryptic relatedness
K1 genetic diversity
K1 inbreeding
K1 population genetic structure
K1 3109.02 Genética
AB [EN] Cryptic relatedness is a confounding factor in genetic diversity and genetic association studies. Development of strategies to reduce cryptic relatedness in a sample is a crucial step for downstream genetic analyses. This study uses a node selection algorithm, based on network degrees of centrality, to evaluate its applicability and impact on evaluation of genetic diversity and population stratification. 1,036 Guzer a (Bos indicus) females were genotyped using Illumina Bovine SNP50 v2 BeadChip. Four strategies were compared. The first and second strategies consist on a iterative exclusion of most related individuals based on PLINK kinship coefficient (φij) and VanRaden’s φij, respectively. The third and fourth strategies were based on a node selection algorithm. The fourth strategy, Network G matrix, preserved the larger number of individuals with a better diversity and representation from the initial sample. Determining the most probable number of populations was directly affected by the kinship metric. Network G matrix was the better strategy for reducing relatedness due to producing a larger sample, with more distant individuals, a more similar distribution when compared with the full data set in the MDS plots and keeping a better representation of the population structure. Resampling strategies using VanRaden’s φij as a relationship metric was better to infer the relationships among individuals. Moreover, the resampling strategies directly impact the genomic inflation values in genomewide association studies. The use of the node selection algorithm also implies better selection of the most central individuals to be removed, providing a more representative sample.
PB Wiley
SN 1755-098X
LK https://hdl.handle.net/10612/18236
UL https://hdl.handle.net/10612/18236
NO Fonseca, P. A. S., Leal, T. P., Santos, F. C., Gouveia, M. H., Id-Lahoucine, S., Rosse, I. C., Ventura, R. V., Bruneli, F. A. T., Machado, M. A., Peixoto, M. G. C. D., Tarazona-Santos, E., & Carvalho, M. R. S. (2018). Reducing cryptic relatedness in genomic data sets via a central node exclusion algorithm. Molecular Ecology Resources, 18(3), 435-447. https://doi.org/10.1111/1755-0998.12746
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 20-may-2024