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Predicting Genetic Relatedness from Low-Coverage Sequencing Data of Human and Animal Genomes Using Various

Xinyi Lin1,2, Shuang Han1,2, Qifan Sun3

  • 1Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China.

Genes
|December 30, 2025
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Summary
This summary is machine-generated.

Low-coverage sequencing offers a cost-effective alternative for genomic analyses. This study validates algorithms for kinship inference using low-coverage data, crucial for paleogenomics and conservation genomics.

Keywords:
animal genomeshuman genomeskinship classificationlow-coverage whole genome sequencingsingle nucleotide polymorphism

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • High-coverage whole genome sequencing is limited by cost and sample requirements.
  • Low-coverage sequencing presents challenges in reconstructing genomic data.

Purpose of the Study:

  • Evaluate algorithms for kinship inference using low-coverage sequencing data.
  • Assess the performance of allele-frequency-independent methods in animal genomics.

Main Methods:

  • Analyzed low- to medium-coverage sequencing data.
  • Performed kinship inference on 33 animal sample pairs across three species.
  • Utilized READ and KIN algorithms.

Main Results:

  • READ and KIN algorithms accurately identified unrelated pairs.
  • The KIN algorithm showed confusion between first- and second-degree relationships.

Conclusions:

  • This study provides a comprehensive evaluation of algorithms on low-coverage sequencing data.
  • Validated methods using authentic human and animal samples with ground truth.