Inferring the Number of Contributors to Mixed DNA Profiles

David R. Paoletti; Dan E. Krane; Travis E. Doom; Michael L. Raymer

doi:10.1109/TCBB.2011.76

Inferring the Number of Contributors to Mixed DNA Profiles

David R. Paoletti, Dan E. Krane, Travis E. Doom, Michael L. Raymer

Research output: Contribution to journal › Article › peer-review

Abstract

Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.

Original language	American English
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume	9
DOIs	https://doi.org/10.1109/TCBB.2011.76
State	Published - Jan 1 2012

Keywords

DNA
Mixture
Probabilistic Computation
Optimization
Bioinformatics

Disciplines

Bioinformatics
Communication
Communication Technology and New Media
Computer Sciences
Databases and Information Systems
Life Sciences
OS and Networks
Physical Sciences and Mathematics
Science and Technology Studies
Social and Behavioral Sciences

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10.1109/TCBB.2011.76License: CC BY

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title = "Inferring the Number of Contributors to Mixed DNA Profiles",

abstract = " Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.",

keywords = "DNA, Mixture, Probabilistic Computation, Optimization, Bioinformatics",

author = "Paoletti, {David R.} and Krane, {Dan E.} and Doom, {Travis E.} and Raymer, {Michael L.}",

year = "2012",

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doi = "10.1109/TCBB.2011.76",

language = "American English",

volume = "9",

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T1 - Inferring the Number of Contributors to Mixed DNA Profiles

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AU - Krane, Dan E.

AU - Doom, Travis E.

AU - Raymer, Michael L.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.

AB - Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.

KW - DNA

KW - Mixture

KW - Probabilistic Computation

KW - Optimization

KW - Bioinformatics

UR - https://corescholar.libraries.wright.edu/knoesis/943

U2 - 10.1109/TCBB.2011.76

DO - 10.1109/TCBB.2011.76

M3 - Article

C2 - 21519119

VL - 9

JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics

JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics

ER -

Inferring the Number of Contributors to Mixed DNA Profiles

Abstract

Keywords

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