Evaluating the utility of identity-by-descent segment numbers for relatedness inference via information theory and classification

Abstract

Despite decades of methods development for classifying relatives in genetic studies, pairwise relatedness methods’ recalls are above 90% only for first through third degree relatives. The top-performing approaches, which leverage identity-by-descent (IBD) segments, often use only kinship coefficients, while others, including ERSA, use the number of segments relatives share. To quantify the potential for using segment numbers in relatedness inference, we leveraged information theory measures to analyze exact (i.e., produced by a simulator) IBD segments from simulated relatives. Over a range of settings, we found that the mutual information between the relatives’ degree of relatedness and a tuple of their kinship coefficient and segment number is on average 4.6% larger than between the degree and the kinship coefficient alone. We further evaluated IBD segment number utility by building a Bayes classifier to predict first through sixth degree relationships using different feature sets. When trained and tested with exact segments, the inclusion of segment numbers improves the recall by between 0.28% and 3% for second through sixth degree relatives. However, the recalls improve by less than 1.8% per degree when using inferred segments, suggesting limitations due to IBD detection accuracy. Lastly, we compared our Bayes classifier that includes segment numbers with both ERSA and IBIS and found comparable recalls, with the Bayes classifier and ERSA slightly outperforming each other across different degrees. Overall, this study shows that IBD segment numbers can improve relatedness inference, but errors from current SNP array-based detection methods yield dampened signals in practice.

Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption

“…• Identity by Descent (IBD) Analysis by considering shared alleles across the entire genome, provides insights into relatedness at different temporal scales and levels of relatedness. Smith et al (2022) use mutual information between the relatives' degree of relatedness and a tuple of their kinship coefficient to build a Bayes classifier to predict first through sixth-degree relationships. Seidman et al (2020) developed IBIS, an IBD detector that locates long regions of allele sharing between unphased individuals.…”

Parent‐offspring inference in inbred populations

“…Typical genotyping error levels in human studies can also have a large impact on pedigree inference [20,21] and these errors might be more prevalent in less well-studied species and populations [22][23][24]. Genotyping errors can result in parent-offspring relationships appearing as less related than expected, obfuscating the distinction between parent-offspring and other types of relationships.…”

“…Genotyping errors can result in parent-offspring relationships appearing as less related than expected, obfuscating the distinction between parent-offspring and other types of relationships. Furthermore, these errors can interrupt inferred IBD segments, limiting the utility of segment number and length on genealogical inference [21].…”