Detecting Higher Order Genomic Variant Interactions with Spectral Analysis. Uminsky, D., Banuelos, M., González-Albino, L., Garza, R., & Nwakanma, S. A. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019.
Detecting Higher Order Genomic Variant Interactions with Spectral Analysis [pdf]Paper  doi  abstract   bibtex   
Genomic variations among a species consisting of one nucleotide change are known as single nucleotide polymorphisms (SNPs). Often these mutations result in a change in phenotype, but detecting higher order interaction of multiple SNPs remains a challenging problem. Common approaches to find groups of interacting SNPs associated with a phenotypic response, a problem under the umbrella of epistasis, often suffers from a combinatorial explosion and require Bonferroni or similar corrections. In this work, we develop and apply a novel Fourier transformation on the symmetric group to uncover higher order interactions of SNPs associated with a quantitative phenotypic response. We present results for simulated data and then apply our method to previously published data to detect, for the first time using a signal processing approach, new and statistically significant higher order SNP interaction phenotypes related to muscle mice genomic variants.
@InProceedings{8902725,
  author = {D. Uminsky and M. Banuelos and L. González-Albino and R. Garza and S. A. Nwakanma},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Detecting Higher Order Genomic Variant Interactions with Spectral Analysis},
  year = {2019},
  pages = {1-5},
  abstract = {Genomic variations among a species consisting of one nucleotide change are known as single nucleotide polymorphisms (SNPs). Often these mutations result in a change in phenotype, but detecting higher order interaction of multiple SNPs remains a challenging problem. Common approaches to find groups of interacting SNPs associated with a phenotypic response, a problem under the umbrella of epistasis, often suffers from a combinatorial explosion and require Bonferroni or similar corrections. In this work, we develop and apply a novel Fourier transformation on the symmetric group to uncover higher order interactions of SNPs associated with a quantitative phenotypic response. We present results for simulated data and then apply our method to previously published data to detect, for the first time using a signal processing approach, new and statistically significant higher order SNP interaction phenotypes related to muscle mice genomic variants.},
  keywords = {bioinformatics;cellular biophysics;genetics;genomics;molecular biophysics;polymorphism;spectral analysis;statistical analysis;higher order genomic variant interactions;spectral analysis;genomic variations;nucleotide change;single nucleotide polymorphisms;phenotype;higher order interaction;multiple SNPs;interacting SNPs;quantitative phenotypic response;statistically significant higher order SNP interaction phenotypes;muscle mice genomic variants;Fourier transforms;Signal processing;Genomics;Bioinformatics;Mice;Europe;Spectral analysis;Fourier transform;algebraic signal processing;epistasis;genomic variation},
  doi = {10.23919/EUSIPCO.2019.8902725},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570534029.pdf},
}
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