Nature Communications (Jan 2022)

DeepNull models non-linear covariate effects to improve phenotypic prediction and association power

  • Zachary R. McCaw,
  • Thomas Colthurst,
  • Taedong Yun,
  • Nicholas A. Furlotte,
  • Andrew Carroll,
  • Babak Alipanahi,
  • Cory Y. McLean,
  • Farhad Hormozdiari

DOI
https://doi.org/10.1038/s41467-021-27930-0
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 10

Abstract

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GWAS often assume a linear phenotype-covariate relationship which may not hold in practice. Here the authors present DeepNull, in which they apply deep learning to identify and adjust for complex non-linear relationships, improving phenotypic prediction and GWAS power.