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                            Library & Multimedia
 A machine learning approach for missing persons cases with high genotyping errors
NCJ Number
              305511
Journal

*Frontiers in Genetics* Volume: Online Dated: October 2022
Author(s)
          Meng Huang;
                        Muyi Liu;
                        Hongmin Li;
                        Jonathan King;
                        Amy Smuts;
                        Bruce Budowle;
                        Jianye Ge
Date Published

October 2022
Length
              14 pages
Annotation
              Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship estimation could provide valuable information for missing persons cases.
Abstract
              The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.(Publisher abstract provided)
      
  
          Additional Details
Grant Number(s)
        2019-DU-BX-0046


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Dense DNA Data for Enhanced Missing Persons Identification
    
  
  
Date Published: October 1, 2022
    
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