Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 67,043 bioRxiv papers from 295,115 authors.

Fast and Powerful Genome Wide Association Analysis of Dense Genetic Data with High Dimensional Imaging Phenotypes

By Habib Ganjgahi, Anderson M. Winkler, David C Glahn, John Blangero, Brian Donohue, Peter Kochunov, Thomas E Nichols

Posted 21 Aug 2017
bioRxiv DOI: 10.1101/179150 (published DOI: 10.1038/s41467-018-05444-6)

Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.

Download data

  • Downloaded 513 times
  • Download rankings, all-time:
    • Site-wide: 20,292 out of 67,043
    • In genetics: 1,365 out of 3,773
  • Year to date:
    • Site-wide: 47,177 out of 67,043
  • Since beginning of last month:
    • Site-wide: 30,660 out of 67,043

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News