Efficient SNP-based tests of association for quantitative phenotypes using pooled DNA

Abstract

Introduction Genetic factors underlying complex diseases are difficult to identify: many polymorphisms may contribute, each having a small effect and low penetrance. These factors may be identified by association studies of large populations, an alternative to family-based linkage studies. Allele frequency measurements of pooled DNA selected from population-level DNA repositories can reduce the costs of these studies. We provide guidance for selecting unrelated individuals for pooling and for comparing the power of studies based on pooled measurements to the power of individual genotyping, particularly for studies using single-nucleotide polymorphism (SNP) markers.

Materials and methods We used exact numerical calculations to set pooling criteria that maximized the power to detect association as a function of marker frequency, inheritance mode, and additive variance. Analytical approximations are also provided.

Results and discussion Power estimates are provided for two pooled DNA designs: the classification of individuals as affected or unaffected, analogous to a case-control design, and the optimized selection of individuals with extreme phenotypic values. Optimized selection is approximately fourfold more efficient than affected/unaffected classification. The optimal design for most markers is to pool the top and bottom 27% of individuals. Neglecting experimental measurement error, this design requires a population only 1.24-fold larger than that required for individual genotyping. When measurement error is included, the pooled DNA association test serves better as a pre-screen to identify candidate markers which then proceed to individual genotyping. This strategy can still provide a 100-fold savings over individual genotyping.