NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses

Thiago Peixoto Leal; Vinicius C Furlan; Mateus Henrique Gouveia; Julia Maria Saraiva Duarte; Pablo As Fonseca; Rafael Tou; Marilia de Oliveira Scliar; Gilderlanio Santana de Araujo; Lucas F Costa; Camila Zolini; Maria Gabriela Campolina Diniz Peixoto; Maria Raquel Santos Carvalho; Maria Fernanda Lima-Costa; Robert H Gilman; Eduardo Tarazona-Santos; Maíra Ribeiro Rodrigues

doi:10.1016/j.csbj.2022.04.009

NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses

Comput Struct Biotechnol J. 2022 Apr 9:20:1821-1828. doi: 10.1016/j.csbj.2022.04.009. eCollection 2022.

Authors

Thiago Peixoto Leal^{1

2}, Vinicius C Furlan¹, Mateus Henrique Gouveia^{1

3}, Julia Maria Saraiva Duarte¹, Pablo As Fonseca^{1

4}, Rafael Tou¹, Marilia de Oliveira Scliar⁵, Gilderlanio Santana de Araujo⁶, Lucas F Costa¹, Camila Zolini^{1

7

8}, Maria Gabriela Campolina Diniz Peixoto⁹, Maria Raquel Santos Carvalho¹, Maria Fernanda Lima-Costa¹⁰, Robert H Gilman^{11

12}, Eduardo Tarazona-Santos^{1

8

12}, Maíra Ribeiro Rodrigues^{1

13}

Affiliations

¹ Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
² Lerner Research Institute, Genomic Medicine, Cleveland Clinic, Cleveland, OH, United States.
³ Center for Research on Genomics & Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States.
⁴ Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada.
⁵ Centro de Estudos do Genoma Humano e Células-Tronco, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil.
⁶ Laboratório de Genética Humana e Médica, Programa de Pós-Graduação em Biologia Molecular, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Pará, Brazil.
⁷ Beagle, Belo Horizonte, Minas Gerais, Brazil.
⁸ Mosaico Translational Genomics Initiative, Belo Horizonte, Minas Gerais, Brazil.
⁹ Embrapa Gado de Leite, Embrapa, Juiz de Fora, Minas Gerais, Brazil.
¹⁰ Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brazil.
¹¹ Universidad Peruana Cayetano Heredia, Lima, Lima, Perú.
¹² Dept of International Health, Johns Hopkins School of Public Health Baltimore, Baltimore, MD, USA.
¹³ Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo, Brazil.

Abstract

Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.

Keywords: ARP, All-Relatives Pruning; Complex network theory; DU, Dataset Unrelated; GRM, Genetic Relatedness Matrix; Genealogies simulator; Genetic kinship; KING, Kinship-based INference for Genome-wide association studies; MAF, Minor Allele Frequency; NAToRA, Network Algorithm to Relatedness Analysis; NDC, Node Degree Centrality; Nc, Network with cuts; PCA, Principal Component Analysis; Population genetics; REAP, Relatedness Estimation in Admixed Populations; SNV, Single Nucleotide Variation.