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ISSN: 2157-7145

Journal of Forensic Research
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Research Article

Prediction Model Validation: Normal Human Pigmentation Variation

Robert K. Valenzuela1,2, Shosuke Ito3, Kazumasa Wakamatsu3 and Murray H. Brilliant1,2*

1Department of Pediatrics, College of Medicine, University of Arizona, Tucson, AZ 85724, USA

2Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA

3Department of Chemistry, Fujita Health University School of Health Sciences, Toyoake, Aichi, Japan

*Corresponding Author:

Dr. Murray H. Brilliant
Center for Human Genetics Marshfield Clinic Research Foundation Marshfield
WI 54449, USA
Tel:
+1 715-207-9493
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+1 715-389- 4950
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[email protected]

Received date: September 24, 2011; Accepted date: October 28, 2011; Published date: October 29, 2011

Citation: Valenzuela RK, Ito S, Wakamatsu K, Brilliant MH (2011) Prediction Model Validation: Normal Human Pigmentation Variation. J Forensic Res 2:139. doi: 10.4172/2157-7145.1000139

Copyright: © 2011 Valenzuela RK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

In a past study, we developed multiple linear regression (MLR) models that employed three single nucleotide polymorphisms (SNPs) that predicted a significant proportion of variation in pigmentation phenotypes from a large population cohort (n=789, training sample). Multiple linear regression models were developed for skin reflectance, eye color, and two aspects of hair color (log of the ratio of eumelanin-to-pheomelanin and total melanin). In this report, using an independent cohort (n=242 , test sample), we 1) externally cross-validated the prediction models, and 2) tested and refined the algorithm presented in the study by Valenzuela and colleagues, (2010). Relative shrinkage was moderate for skin reflectance (23.4%), eye color (19.4%), and the log of the ratio of eumelanin-to-pheomelanin in hair (37.3%), and largest for total melanin (67%) in hair. Independent construction of predictive models using our algorithm for the test sample set yielded the same or similar models as the training sample set. Two of the three SNPs composing the models were the same, with some variability in the third SNP of the model.

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