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Application of Machine-Learning Methods to Understand Gene Expression Regulation Read chapter Read first chapter

2015 | OriginalPaper | Chapter

2. Identification of Novel Genetic Models of Glaucoma Using the “EMERGENT” Genetic Programming-Based Artificial Intelligence System

Authors : Jason H. Moore, Casey S. Greene, Douglas P. Hill

Published in: Genetic Programming Theory and Practice XII

Publisher: Springer International Publishing

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Abstract

The genetic basis for primary open-angle glaucoma (POAG) is not yet understood but is likely the result of many interacting genetic variants that influence risk in the context of our local ecology. The complexity of the genotype to phenotype mapping relationship for common diseases like POAG necessitates analytical approaches that move beyond parametric statistical methods such as logistic regression that assume a particular mathematical model. This is particularly important in the era of big data where it is routine to collect and analyze data sets with hundreds of thousands of measured genetic variants in thousands of human subjects. We introduce here the Exploratory Modeling for Extracting Relationships using Genetic and Evolutionary Navigation Techniques (EMERGENT) algorithm as an artificial intelligence approach to the genetic analysis of common human diseases. EMERGENT builds models of genetic variation from lists of mathematical functions using a form of genetic programming called computational evolution. A key feature of the system is the ability to utilize pre-processed expert knowledge giving it the ability to explore model space much as a human would. We describe this system in detail and then apply it to the genetic analysis of POAG in the Glaucoma Gene Environment Initiative (GLAUGEN) study that included approximately 1,272 subjects with the disease and 1057 healthy controls. A total of 657,366 single-nucleotide polymorphisms (SNPs) from across the human genome were measured in these subjects and available for analysis. Analysis using the EMERGENT framework revealed a best model consisting of six SNPs that map to at least six different genes. Two of these genes have previously been associated with POAG in several studies. The others represent new hypotheses about the genetic basis of POAG. All of the SNPs are involved in non-additive gene-gene interactions. Further, the six genes are all directly or indirectly related through biological interactions to the vascular endothelial growth factor (VEGF) gene that is an actively investigated drug target for POAG. This study demonstrates the routine application of an artificial intelligence-based system for the genetic analysis of complex human diseases.

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previous chapter Application of Machine-Learning Methods to Understand Gene Expression Regulation
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Metadata
Title
Identification of Novel Genetic Models of Glaucoma Using the “EMERGENT” Genetic Programming-Based Artificial Intelligence System
Authors
Jason H. Moore
Casey S. Greene
Douglas P. Hill
Copyright Year
2015
Book
Genetic Programming Theory and Practice XII

Print ISBN: 978-3-319-16029-0

Electronic ISBN: 978-3-319-16030-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16030-6_2

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