KinLinks: Software Toolkit for Kinship Analysis and Pedigree Generation from NGS Datasets

Abstract

The ability to predict familial relationships from source DNA in multiple samples has a number of forensic and medical applications. Kinship testing of suspect DNA profiles against relatives in a law enforcement database can provide valuable investigative leads, determination of familial relationships can inform immigration decisions, and remains identification can provide closure to families of missing individuals. The proliferation of Next-Generation Sequencing technologies allows for enhanced capabilities to accurately predict familial relationships to the third degree and beyond. KinLinks, developed by MIT Lincoln Laboratory, is an open source software tool that predicts pairwise relationships and reconstructs kinship pedigrees for multiple input samples using single-nucleotide polymorphism (SNP) profiles. The software has been trained and evaluated on a set of 175 subjects (30,450 pairwise relationships), consisting of three multi-generational families and 52 geographically diverse subjects. Though a panel of 5396 SNPs was selected for kinship prediction, KinLinks is highly modular, allowing for the substitution of expanded SNP panels and additional training models as sequencing capabilities continue to progress. KinLinks builds on the SNP-calling capabilities of Sherlocks Toolkit, and is fully integrated with the Sherlocks Toolkit pipeline.

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Document Details

Document Type
Technical Report
Publication Date
Apr 21, 2015
Accession Number
AD1034989

Entities

People

  • Anna Shcherbina
  • Christina Zook
  • Darrell O Ricke
  • Edward Wack
  • Eric Schwoebel
  • Johanna Bobrow
  • Martha Petrovick
  • Tara Boettcher

Organizations

  • MIT Lincoln Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Capillary Electrophoresis
  • Cell Line
  • Cells
  • Chromosomes
  • Families (Human)
  • Genetic Algorithms
  • Genetics
  • Genotypes
  • Information Science
  • Machine Learning
  • Nuclear Family
  • Probability
  • Retinal Diseases
  • Supervised Machine Learning
  • United States

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