Mining Pure, Strict Epistatic Interactions from High-Dimensional Datasets: Ameliorating the Curse of Dimensionality

Background: The interaction between loci to affect phenotype is called epistasis. It is strict epistasis if no proper subset of the interacting loci exhibits a marginal effect. For many diseases, it is likely that unknown epistatic interactions affect disease susceptibility. A difficulty when mining epistatic interactions from high-dimensional datasets concerns the curse of dimensionality. There are too many combinations of SNPs to perform an exhaustive search. A method that could locate strict epistasis without an exhaustive search can be considered the brass ring of methods for analyzing high-dimensional datasets. Methodology/Findings: A SNP pattern is a Bayesian network representing SNP-disease relationships. The Bayesian score for a SNP pattern is the probability of the data given the pattern, and has been used to learn SNP patterns. We identified a bound for the score of a SNP pattern. The bound provides an upper limit on the Bayesian score of any pattern that could be obtained by expanding a given pattern. We felt that the bound might enable the data to say something about the promise of expanding a 1-SNP pattern even when there are no marginal effects. We tested the bound using simulated datasets and semi-synthetic high-dimensional datasets obtained from GWAS datasets. We found that the bound was able to dramatically reduce the search time for strict epistasis. Using an Alzheimer's dataset, we showed that it is possible to discover an interaction involving the APOE gene based on its score because of its large marginal effect, but that the bound is most effective at discovering interactions without marginal effects. Conclusions/Significance: We conclude that the bound appears to ameliorate the curse of dimensionality in high-dimensional datasets. This is a very consequential result and could be pivotal in our efforts to reveal the dark matter of genetic disease risk from high-dimensional datasets. © 2012 Jiang, Neapolitan

Fabrication and Characterization of Electrostatic Quantum Dots in a Si/SiGe 2D Electron Gas, Including an Integrated Read-out Channel

A new fabrication technique is used to produce quantum dots with read-out channels in silicon/silicon-germanium two-dimensional electron gases. The technique utilizes Schottky gates, placed on the sides of a shallow etched quantum dot, to control the electronic transport process. An adjacent quantum point contact gate is integrated to the side gates to define a read-out channel and thus allow for noninvasive detection of the electronic occupation of the quantum dot. Reproducible and stable Coulomb oscillations and the corresponding jumps in the read-out channel resistance are observed at low temperatures. The fabricated dot combined with the read-out channel represent a step towards the spin-based quantum bit in Si/SiGe heterostructures.Comment: 3 pages, 4 fig

Nano-electromechanical switchable photonic metamaterials

We introduce mechanically reconfigurable electrostatically-driven photonic metamaterials (RPMs) as a generic platform for large-range tuning and switching of photonic metamaterial properties. Here we illustrate this concept with a high-contrast metamaterial electro-optic switch exhibiting relative reflection changes of up to 72% in the optical part of the spectrum

Sputtered Gold as an Effective Schottky Gate for Strained Si/SiGe Nanostructures

Metallization of Schottky surface gates by sputtering Au on strained Si/SiGe heterojunctions enables the depletion of the two dimensional electron gas (2DEG) at a relatively small voltage while maintaining an extremely low level of leakage current. A fabrication process has been developed to enable the formation of sub-micron Au electrodes sputtered onto Si/SiGe without the need of a wetting layer.Comment: 3 pages, 3 figure