The Innocent v The Fickle Feww: How Jurors Understand Random-Match-Probabilities and Judges' Directions when Reasoning about DNA and Refuting Evidence

Given the enhanced discriminatory power of the mitochondrial DNA (mtDNA) genome (mitogenome) over the commonly sequenced control region (CR) portion, the scientific merit of mitogenome sequencing is generally accepted. However, many laboratories remain beholden to CR sequencing due to privacy policies and legal requirements restricting the use of disease information or coding region (codR) information. In this report, we present an approach to obviate the reporting of sensitive codR data in forensic haplotypes. We consulted the MitoMap database to identify 92 mtDNA codR variants with confirmed pathogenicity. We determined the frequencies of these pathogenic variants in literature-quality and forensic-quality databases to be very low, at 1.2% and 0.36%, respectively. The observed effect of pathogenic variant filtering on random match statistics in 2488 forensic-quality mitogenome haplotypes from four populations was nil. We propose that pathogenic variant filtering should be incorporated into variant calling algorithms for mitogenome haplotype reporting to maximize the discriminatory power of the locus while minimizing the reveal of sensitive genetic information.

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An algorithm for random match probability calculation from peptide sequences

Forensic Science International Genetics ◽

10.1016/j.fsigen.2020.102295 ◽

2020 ◽

Vol 47 ◽

pp. 102295

Author(s):

August E. Woerner ◽

F. Curtis Hewitt ◽

Myles W. Gardner ◽

Michael A. Freitas ◽

Kathleen Q. Schulte ◽

...

Keyword(s):

Match Probability ◽

Random Match ◽

Random Match Probability ◽

Probability Calculation

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Apprentice Labor and Firm Output: Evidence from a random match experiment

AEA Randomized Controlled Trials ◽

10.1257/rct.297 ◽

2014 ◽

Author(s):

Jamie McCasland

Keyword(s):

Random Match

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Stability of the Bipartite Matching Model

Advances in Applied Probability ◽

10.1239/aap/1370870122 ◽

2013 ◽

Vol 45 (2) ◽

pp. 351-378 ◽

Cited By ~ 8

Author(s):

Ana Bušić ◽

Varun Gupta ◽

Jean Mairesse

Keyword(s):

Bipartite Graph ◽

Stability Region ◽

Joint Probability ◽

Matching Model ◽

Time Step ◽

Random Match ◽

Bipartite Matching ◽

Discrete Time Markov Chain ◽

The Stability ◽

Maximal Stability

We consider the bipartite matching model of customers and servers introduced by Caldentey, Kaplan and Weiss (2009). Customers and servers play symmetrical roles. There are finite sets C and S of customer and server classes, respectively. Time is discrete and at each time step one customer and one server arrive in the system according to a joint probability measure μ on C× S, independently of the past. Also, at each time step, pairs of matched customers and servers, if they exist, depart from the system. Authorized matchings are given by a fixed bipartite graph (C, S, E⊂ C × S). A matching policy is chosen, which decides how to match when there are several possibilities. Customers/servers that cannot be matched are stored in a buffer. The evolution of the model can be described by a discrete-time Markov chain. We study its stability under various admissible matching policies, including ML (match the longest), MS (match the shortest), FIFO (match the oldest), RANDOM (match uniformly), and PRIORITY. There exist natural necessary conditions for stability (independent of the matching policy) defining the maximal possible stability region. For some bipartite graphs, we prove that the stability region is indeed maximal for any admissible matching policy. For the ML policy, we prove that the stability region is maximal for any bipartite graph. For the MS and PRIORITY policies, we exhibit a bipartite graph with a non-maximal stability region.

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