How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases
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- @Article{Moore:2020:AlifeJ,
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author = "J. H. Moore and R. S. Olson and P. Schmitt and
Y. Chen and E. Manduchi",
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title = "How Computational Experiments Can Improve Our
Understanding of the Genetic Architecture of Common
Human Diseases",
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journal = "Artificial Life",
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year = "2020",
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volume = "26",
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number = "1",
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pages = "23--37",
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month = apr,
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keywords = "genetic algorithms, genetic programming, Genetics,
complexity, epistasis, simulation",
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DOI = "doi:10.1162/artl_a_00308",
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ISSN = "1064-5462",
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abstract = "Susceptibility to common human diseases such as cancer
is influenced by many genetic and environmental factors
that work together in a complex manner. The state of
the art is to perform a genome-wide association study
(GWAS) that measures millions of single-nucleotide
polymorphisms (SNPs) throughout the genome followed by
a one-SNP-at-a-time statistical analysis to detect
univariate associations. This approach has identified
thousands of genetic risk factors for hundreds of
diseases. However, the genetic risk factors detected
have very small effect sizes and collectively explain
very little of the overall heritability of the disease.
Nonetheless, it is assumed that the genetic component
of risk is due to many independent risk factors that
contribute additively. The fact that many genetic risk
factors with small effects can be detected is taken as
evidence to support this notion. It is our working
hypothesis that the genetic architecture of common
diseases is partly driven by non-additive interactions.
To test this hypothesis, we developed a heuristic
simulation-based method for conducting experiments
about the complexity of genetic architecture. We show
that a genetic architecture driven by complex
interactions is highly consistent with the magnitude
and distribution of univariate effects seen in real
data. We compare our results with measures of
univariate and interaction effects from two large-scale
GWASs of sporadic breast cancer and find evidence to
support our hypothesis that is consistent with the
results of our computational experiment.",
-
notes = "Also known as \cite{9082081}",
- }
Genetic Programming entries for
Jason H Moore
R S Olson
Peter Schmitt
Y Chen
Elisabetta Manduchi
Citations