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GLIDE: GPU-Based Linear Regression for Detection of Epistasis

2012

Article

ei


Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year’s time to complete the same task.

Author(s): Kam-Thong, T. and Azencott, C-A. and Cayton, L. and Pütz, B. and Altmann, A. and Karbalai, N. and Sämann, PG. and Schölkopf, B. and Müller-Myhsok, B. and Borgwardt, KM.
Journal: Human Heredity
Volume: 73
Number (issue): 4
Pages: 220-236
Year: 2012
Month: September
Day: 4

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0

Links: Web

BibTex

@article{KamThongACPAKSSMB2012,
  title = {GLIDE: GPU-Based Linear Regression for Detection of Epistasis},
  author = {Kam-Thong, T. and Azencott, C-A. and Cayton, L. and P{\"u}tz, B. and Altmann, A. and Karbalai, N. and S{\"a}mann, PG. and Sch{\"o}lkopf, B. and M{\"u}ller-Myhsok, B. and Borgwardt, KM.},
  journal = {Human Heredity},
  volume = {73},
  number = {4},
  pages = {220-236},
  month = sep,
  year = {2012},
  month_numeric = {9}
}