Predictive Modeling for Complex Traits: Normal Human Pigmentation Variation

Persistent Link:
http://hdl.handle.net/10150/145309
Title:
Predictive Modeling for Complex Traits: Normal Human Pigmentation Variation
Author:
Valenzuela, Robert Keams
Issue Date:
2011
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Melanin pigmentation is a complex trait governed by many genes. Variation in melanin pigmentation within, and between, populations makes it an important trait for assisting in physical identification of an individual in forensic investigations. Utilizing a training sample (n=789) comprised of various ethnicities and SNPs (75) in 24 genes previously implicated in human or animal pigmentation studies, I determined three-SNP multiple linear regression models that accounted for large proportions of pigmentation variation in skin (45.7%), eye color (76.4%), and hair [eumelanin-to-pheomelanin (43.2%) and total melanin (76.3%)], independent of ethnic origin. Rather than implementing stepwise regression, to ascertain the three-SNP predictive models, I devised an algorithm that is likely more robust than stepwise regression. The algorithm consisted of two steps: the first step reduced the pool of 75 SNPs to a pool of 40 by selection of SNPs that were significant (p<0.05) by one-way ANOVA; the second step enabled selection of SNPs for model incorporation based on their frequency in the best-fitted models of all possible combinations of three-SNP models (i.e., 40 choose 3).Prediction models were validated utilizing an independent cohort (n=242, test sample) that was very similar in ethnic composition to the training sample. Relative shrinkage was moderate for skin reflectance (23.4%), eye color (19.4%), and eumelanin-to-pheomelanin (37.3%) of hair, and largest for total melanin (67%) of hair. Additionally, we refined our model-building algorithm, enabling visual comparison of the frequency and co-linearity due to linkage or co-inheritance of SNPs of the best-fitted models. Application of our algorithm to the test sample yielded the same or similar models as the training sample. Two of the three SNPs composing the models were the same, with some variability in the third SNP of the model.
Type:
Electronic Dissertation; text
Keywords:
Forensic Science; Genetics; Human; Pigmentation; Prediction Models; QTL
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Genetics
Degree Grantor:
University of Arizona
Advisor:
Brilliant, Murray H.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titlePredictive Modeling for Complex Traits: Normal Human Pigmentation Variationen_US
dc.creatorValenzuela, Robert Keamsen_US
dc.contributor.authorValenzuela, Robert Keamsen_US
dc.date.issued2011en
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractMelanin pigmentation is a complex trait governed by many genes. Variation in melanin pigmentation within, and between, populations makes it an important trait for assisting in physical identification of an individual in forensic investigations. Utilizing a training sample (n=789) comprised of various ethnicities and SNPs (75) in 24 genes previously implicated in human or animal pigmentation studies, I determined three-SNP multiple linear regression models that accounted for large proportions of pigmentation variation in skin (45.7%), eye color (76.4%), and hair [eumelanin-to-pheomelanin (43.2%) and total melanin (76.3%)], independent of ethnic origin. Rather than implementing stepwise regression, to ascertain the three-SNP predictive models, I devised an algorithm that is likely more robust than stepwise regression. The algorithm consisted of two steps: the first step reduced the pool of 75 SNPs to a pool of 40 by selection of SNPs that were significant (p<0.05) by one-way ANOVA; the second step enabled selection of SNPs for model incorporation based on their frequency in the best-fitted models of all possible combinations of three-SNP models (i.e., 40 choose 3).Prediction models were validated utilizing an independent cohort (n=242, test sample) that was very similar in ethnic composition to the training sample. Relative shrinkage was moderate for skin reflectance (23.4%), eye color (19.4%), and eumelanin-to-pheomelanin (37.3%) of hair, and largest for total melanin (67%) of hair. Additionally, we refined our model-building algorithm, enabling visual comparison of the frequency and co-linearity due to linkage or co-inheritance of SNPs of the best-fitted models. Application of our algorithm to the test sample yielded the same or similar models as the training sample. Two of the three SNPs composing the models were the same, with some variability in the third SNP of the model.en_US
dc.typeElectronic Dissertationen_US
dc.typetexten_US
dc.subjectForensic Scienceen_US
dc.subjectGeneticsen_US
dc.subjectHumanen_US
dc.subjectPigmentationen_US
dc.subjectPrediction Modelsen_US
dc.subjectQTLen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineGeneticsen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBrilliant, Murray H.en_US
dc.contributor.committeememberWalsh, Bruceen_US
dc.contributor.committeememberBaier, Leslie J.en_US
dc.contributor.committeememberTsao, Tsu-Shuenen_US
dc.identifier.proquest11477en
dc.identifier.oclc752261346en
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