An optimal DNA pooling strategy for progressive fine mapping

Abstract

We present a cost-effective DNA pooling strategy for fine mapping of a single Mendelian gene in controlled crosses. The theoretical argument suggests that it is potentially possible for a single-stage pooling approach to reduce the overall experimental expense considerably by balancing costs for genotyping and sample collection. Further, the genotyping burden can be reduced through multi-stage pooling. Numerical results are provided for practical guidelines. For example, the genotyping effort can be reduced to only a small fraction of that needed for individual genotyping at a small loss of estimation accuracy or at a cost of increasing sample sizes slightly when recombination rates are 0.5% or less. An optimal two-stage pooling scheme can reduce the amount of genotyping to 19.5%, 14.5% and 6.4% of individual genotyping efforts for identifying a gene within 1, 0.5, and 0.1 cM, respectively. Finally, we use a genetic data set for mapping the rice xl(t) gene to demonstrate the feasibility and efficiency of the DNA pooling strategy. Taken together, the results demonstrate that this DNA pooling strategy can greatly reduce the genotyping burden and the overall cost in fine mapping experiments.

Combining DNA pooling with selective recombinant genotyping for increased efficiency in fine mapping

“…Only the sub-pools in those positive pools are required to individually genotype for identifying which contains the allele and which does not. Thus, the expected number of genotypings is (Chi et al 2009),…”

“…For a given π , we can compute optimal pool and subpool allocations that minimize the expected number of genotypings by setting the partial derivative equal to zero and solving the resulting equations. In most cases, the analytical solution is not immediately obvious and a numerical solution may be used (Chi et al 2009). …”

“…Assuming that any disparate marker alleles can be detected in a small pool (e.g., 20 individuals) and that the relative frequencies of alleles can be reasonably well estimated in a large bulked sample (e.g., hundreds of individuals), we advocate the use of two pooled genotyping designs for reducing the numbers of PCR reactions and genotyping assays: two-stage pooling (Chi et al 2009) and bulked-sample analysis (BA) (Michelmore et al 1991; Sham et al 2002). The applications include (1) a two-stage pooling for identifying recombinants between a pair of flanking markers, and (2) a combined use of bulked DNA analysis and two-stage pooling for genotyping recombinants.…”

Regression analysis for multiple‐disease group testing data

“…This has led to the adoption of group testing in a number of infectious disease applications, including blood donation screening by the American Red Cross [1], opportunistic chlamydia and gonorrhea testing in medical clinics [2], and detecting influenza viruses in humans [3]. Group testing has also proven to be beneficial in other areas including drug discovery [4], genetics [5], animal ecology [6], and food contamination testing [7]. …”

Reexamining the Pooled Sampling Approach for Estimating Prevalence of Infected Insect Vectors

“…Some recent examples include beet leafhopper, Circulifer tenellus (Baker), transmitting a phytoplasma (Crosslin et al 2005); potato purple top phytoplasma transmitted by leafhoppers (Munyaneza et al 2007); black ßy vector of onchocerciasis in West Africa (Yameogo et al 1999); psyllid vector of phytoplasmas (Carraro et al 2004, Garcia-Chapa et al 2005; and mosquitoes vectoring a viral pathogen in deer (Andreadis et al 2008). The technique also was used in medicine (Novack et al 2008), animal health (Rovira et al 2008), Þsheries (Wallace et al 2008), plant health (Geng et al 1983, Coutts et al 2009), and DNA mapping (Chi et al 2009). …”