Most pooling variation in array-based DNA pooling is attributable to array error rather than pool construction error

Abstract

Genome-wide association (GWA) approaches are important in complex disease gene mapping studies but are often prohibitively expensive. Array-based DNA pooling has been shown to offer substantial cost savings compared with individual genotyping. This reduced cost potentially brings well-powered GWA studies well within the reach of most laboratories. The main factor, which affects the efficiency of pooling compared with individual genotyping is the magnitude of the pooling error variance. By examining variation between and within pools it is shown that most of the error associated with pooling is attributable to array variation not pooling construction variation (assuming the pools are not small and the pools are accurately constructed). With Affymetrix HindIII 50K arrays used here the array-specific variance is seven times the pooling construction variance. This has important implications for optimal study design for array-based pooling. Given carefully constructed pools, resources should be allocated to increasing the number of arrays per sample rather than to constructing multiple pools.

Rapid inexpensive genome-wide association using pooled whole blood

“…À5 is 5-10 times smaller than the variance due to random sampling of individuals from the population (for fuller discussion, see Macgregor 2007;Macgregor et al 2008). The arrays used here (1M) differed from those we have reported previously HumanHap300), but HumanHap300 and 1M arrays have similar arrangements of beadscores on the arrays so similar performance for different array types is expected.…”

A Comparison of Association Statistics between Pooled and Individual Genotypes

“…For example, the current costs of Illumina technology mean the cost of a custom chip with 4000 markers is the same as the cost of the 300K chip, hence pooling is only cost effective when significantly fewer markers are followed up. If pooling is to be undertaken, there is a lot of information available on how to do it most efficiently, including a review by Sham et al [23] and a recent paper by MacGregor [24] . The latter looks at issues of variability, suggesting that although multiple pools are useful, there are diminishing returns when one gets to a certain point.…”

The Application of Pooled DNA Sequencing in Disease Association Study

“…Combined Z-test (Abraham et al, 2008) This method combines chi-square statistic andstatistic for testing the differences in mean allele frequencies between cases and controls. The general description of this statistic has been presented in (Sham et al, 2002;Macgregor, 2007;Kirov et al, 2009):…”

“…This method considers sampling error and experimental error, which is equivalent to a simplified version of the complex regression model suggested by Macgregor (2007).…”