Estimation of Recent Shared Ancestry (ERSA)

Download ERSA 2.1

Register to download the most recent version of ERSA here.

Maximum-likelihood estimation of recent shared ancestry (ERSA)

Chad D. Huff, David J. Witherspoon, Tatum S. Simonson, Jinchuan Xing, W. Scott Watkins, Yuhua Zhang, Therese M. Tuohy, Deborah W. Neklason, Randall W. Burt, Stephen L. Guthery, Scott R. Woodward, Lynn B. Jorde (2011): Maximum-likelihood estimation of recent shared ancestry (ERSA). In: Genome Research, 21 (5), pp. 768–774, 2011, ISSN: 1549-5469.

ERSA (Estimation of Recent Shared Ancestry) estimates recent shared ancestry between pairs of individuals based on the number and lengths of chromosomal segments that they share identically-by-descent through common ancestors (IBD segments). Prior to ERSA, established methods were capable of accurately estimating kinship for first-degree through third-degree relatives. ERSA is accurate to within one degree of relationship for 97% of first-degree through fifth-degree relatives and 80% of sixth-degree and seventh-degree relatives, greatly expanding the range of relationships that can be estimated from genetic data.

Relationship Estimation from Whole-Genome Sequence Data (ERSA 2.0)

Hao Hu, Chad D. Huff (2014): Detecting statistical interaction between somatic mutational events and germline variation from next-generation sequence data. In: Pac Symp Biocomput, pp. 51–62, 2014.

Performance of relationship estimation in 30 families with whole-genome sequencing using ERSA 2.0
Performance of relationship estimation in 30 families with whole-genome sequencing using ERSA 2.0
Previously, we and others have demonstrated that IBD information generated from high-density SNP data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing marks the final step in increasing genetic marker density by assaying all SNVs, and thus has the potential to further improve relationship detection. However, whole-genome sequencing introduces new complexities that must be addressed in order to achieve these improvements. We developed new methods in ERSA 2.0 to identify and mask genomic regions with excess IBD information in whole-genome sequencing data. With ERSA 2.0, whole-genome sequencing provides a 5% to 15% increase in relationship detection power relative to high-density microarray data. We also introduced improvements in ERSA 2.0 to increase relationship detection accuracy for full sibling, avuncular, and direct ancestor-descendant relationships and to provide support for detecting consanguinity.

Licensing and Download

Software implementing the ERSA algorithm is now available. Register to download the most recent version (2.0) here.

Release Notes

Version 2.1

  1. Added a new parameter: model_output_file. When this parameter is specified, a new file is created that reports the likelihood for every possible pairwise relationship.
  2. Fixed an issue in which pairs of individuals that shared no segments IBD of the minimum length or greater would be absent in the output file. Now, all possible pairs of individuals are included in the output file, regardless of whether they share any segments IBD. Note that individuals sharing no IBD segments will always be reported as unrelated.

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