Reducing cryptic relatedness in genomic data sets via a central node exclusion algorithm
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,...
Saved in:
Published in | Molecular ecology resources Vol. 18; no. 3; pp. 435 - 447 |
---|---|
Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Wiley Subscription Services, Inc
01.05.2018
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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 Guzerá (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. |
---|---|
Bibliography: | Funding information This study was supported by funding from Fundação de Amparo a Pesquisa de Minas Gerais (FAPEMIG), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Empresa Brasileira de Pesquisa Agropecuária (Embrapa). MG was supported by FAPEMIG‐CVZ APQ 01353 and CVZ APQ 3182‐5.04/07. MR has a fellowship from CNPq—312068/2015‐8 and was supported by CNPq—505338/2008‐A and 481018/2008‐5 projects. MG, RV and MA have fellowships from FAPEMIG. PA, FC and IC have CAPES fellowships. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1755-098X 1755-0998 |
DOI: | 10.1111/1755-0998.12746 |