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A performance assessment of relatedness inference methods using genome-wide data from thousands of relatives

Monica D. Ramstetter, Thomas D. Dyer, Donna M. Lehman, Joanne E. Curran, Ravindranath Duggirala, John Blangero, Jason G. Mezey, Amy L. Williams
doi: https://doi.org/10.1101/106013
Monica D. Ramstetter
1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA
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  • For correspondence: mdr232@cornell.edu alw289@cornell.edu
Thomas D. Dyer
2South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA and Edinburg, TX 78539, USA
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Donna M. Lehman
2South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA and Edinburg, TX 78539, USA
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Joanne E. Curran
2South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA and Edinburg, TX 78539, USA
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Ravindranath Duggirala
2South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA and Edinburg, TX 78539, USA
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John Blangero
2South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA and Edinburg, TX 78539, USA
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Jason G. Mezey
1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA
3Department of Genetic Medicine, Weill Cornell Medicine, New York, NY 10065, USA
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Amy L. Williams
1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA
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  • For correspondence: mdr232@cornell.edu alw289@cornell.edu
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Abstract

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these methods in real data has been lacking. Here, we report an assessment of 11 state-of-the-art relatedness inference methods using a dataset with 2,485 individuals contained in several large pedigrees that span up to six generations. We nd that all methods have high accuracy (~93% – 99%) when reporting first and second degree relationships, but their accuracy dwindles to less than 60% for fifth degree relationships. However, the inferred relationships were correct to within one relatedness degree at a rate of 83% – 99% across all methods and considered relationship degrees. Furthermore, most methods infer unrelated individuals correctly at a rate of ~99%, suggesting a low rate of false positives. Overall, the most accurate methods were ERSA 2.0 and approaches that classify relationships using the IBD segments inferred by Refined IBD and IBDseq. Combining results from the most accurate methods provides little accuracy improvement, indicating that novel approaches for relatedness inference may be needed to achieve a sizeable jump in performance.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 04, 2017.
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A performance assessment of relatedness inference methods using genome-wide data from thousands of relatives
Monica D. Ramstetter, Thomas D. Dyer, Donna M. Lehman, Joanne E. Curran, Ravindranath Duggirala, John Blangero, Jason G. Mezey, Amy L. Williams
bioRxiv 106013; doi: https://doi.org/10.1101/106013
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A performance assessment of relatedness inference methods using genome-wide data from thousands of relatives
Monica D. Ramstetter, Thomas D. Dyer, Donna M. Lehman, Joanne E. Curran, Ravindranath Duggirala, John Blangero, Jason G. Mezey, Amy L. Williams
bioRxiv 106013; doi: https://doi.org/10.1101/106013

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