Reducing cryptic relatedness in genomic datasets 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 analyzes. The present 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 consists 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 affect...隐性亲缘关系是遗传多样性与遗传关联研究中的混杂因素。在样本中降低隐性亲缘关系的策略开发，是后续遗传分析的关键环节。本研究采用基于网络中心性度数的节点选择算法，评估其在遗传多样性与群体分层评估中的适用性及影响。本研究使用Illumina Bovine SNP50 v2基因芯片对1036头古兹拉特瘤牛（Bos indicus）母牛进行基因分型。共对比了四种策略：第一种与第二种策略分别基于PLINK亲缘系数（φ_ij）与VanRaden亲缘系数（φ_ij），通过迭代剔除亲缘关系最强的个体来实施。第三种与第四种策略则基于节点选择算法。其中第四种策略——网络G矩阵（Network G matrix）——从初始样本中保留了更多个体，且具备更优的多样性与代表性。确定最可能的群体数量会直接影响……