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Related Experiment Video

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Infinium Assay for Large-scale SNP Genotyping Applications
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Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Quickly identifying identical and closely related subjects in large databases using genotype data.

Yumi Jin1, Alejandro A Schäffer1, Stephen T Sherry1

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America.

Plos One
|June 14, 2017
PubMed
Summary

A new method, Genetic Relationship and Fingerprinting (GRAF), rapidly identifies duplicate and related individuals in large genomic datasets, even with differing genotyping platforms. This tool aids in curating large repositories like dbGaP, ensuring data integrity for genetic studies.

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Published on: August 15, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Genome-wide association studies (GWAS) require non-related samples, but combining datasets is challenging due to differing platforms and potential duplicates.
  • Large repositories like dbGaP (Database of Genotypes and Phenotypes) contain millions of samples with undetected duplicates and related individuals across studies.
  • Accurate sample identification is crucial for robust genetic analyses and data integrity in large-scale genomic research.

Purpose of the Study:

  • To develop and validate a rapid, statistically-driven method for detecting duplicate and closely related individuals within and across large genomic datasets.
  • To address the computational and statistical challenges of identifying sample relationships when genotype data comes from different platforms and marker sets.
  • To enhance the curation and reliability of large genetic repositories such as dbGaP.

Main Methods:

  • Developed Genetic Relationship and Fingerprinting (GRAF), a method that extracts 10,000 informative SNPs and uses rapid algorithms to detect sample relationships.
  • Implemented statistical metrics All Genotype Mismatch Rate (AGMR) and Homozygous Genotype Mismatch Rate (HGMR) to assess subject relationships directly from genotypes, bypassing IBD or kinship calculations.
  • Validated GRAF's performance on over 880,000 samples from the dbGaP repository, achieving detection in under two hours.

Main Results:

  • GRAF successfully identified duplicate and closely related samples across diverse datasets within the dbGaP repository.
  • The method demonstrated high speed and accuracy, processing over 880,000 samples in less than two hours, even with differing marker sets and unknown DNA strand orientations.
  • GRAF's direct genotype comparison metrics (AGMR, HGMR) effectively determined subject relationships, aligning with reported pedigree data.

Conclusions:

  • GRAF provides a computationally efficient and statistically robust solution for identifying sample relationships in large-scale genomic data.
  • The freely available C++ program enables researchers to curate their own datasets and integrate them with public repositories like dbGaP.
  • GRAF significantly improves data quality and reliability for genetic studies by ensuring sample accuracy.