69
views
0
recommends
+1 Recommend
0 collections
    3
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Mixed linear model approach adapted for genome-wide association studies.

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.

          Related collections

          Author and article information

          Journal
          Nat Genet
          Nature genetics
          Springer Science and Business Media LLC
          1546-1718
          1061-4036
          Apr 2010
          : 42
          : 4
          Affiliations
          [1 ] Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. zz19@cornell.edu
          Article
          ng.546 NIHMS212982
          10.1038/ng.546
          2931336
          20208535
          5c1737d4-1d8c-4f15-9c65-516d0c3ccda7
          History

          Comments

          Comment on this article