Fractioned DNA Pooling: A New Cost-Effective Strategy for Fine Mapping of Quantitative Trait Loci

Abstract

Selective DNA pooling (SDP) is a cost-effective means for an initial scan for linkage between marker and quantitative trait loci (QTL) in suitable populations. The method is based on scoring marker allele frequencies in DNA pools from the tails of the population trait distribution. Various analytical approaches have been proposed for QTL detection using data on multiple families with SDP analysis. This article presents a new experimental procedure, fractioned-pool design (FPD), aimed to increase the reliability of SDP mapping results, by ''fractioning'' the tails of the population distribution into independent subpools. FPD is a conceptual and structural modification of SDP that allows for the first time the use of permutation tests for QTL detection rather than relying on presumed asymptotic distributions of the test statistics. For situations of family and cross mapping design we propose a spectrum of new tools for QTL mapping in FPD that were previously possible only with individual genotyping. These include: joint analysis of multiple families and multiple markers across a chromosome, even when the marker loci are only partly shared among families; detection of families segregating (heterozygous) for the QTL; estimation of confidence intervals for the QTL position; and analysis of multiple-linked QTL. These new advantages are of special importance for pooling analysis with SNP chips. Combining SNP microarray analysis with DNA pooling can dramatically reduce the cost of screening large numbers of SNPs on large samples, making chip technology readily applicable for genomewide association mapping in humans and farm animals. This extension, however, will require additional, nontrivial, development of FPD analytical tools.

High-Throughput Genetic Mapping of Mutants via Quantitative Single Nucleotide Polymorphism Typing

“…SNP-based BSA of maize mutants in a mixed B73-and Mo17-derived genetic background: The quantitative nature of the Sequenom platform provides the potential to map mutants via BSA (Michelmore et al 1991;Korol et al 2007;Lambreghts et al 2009). A series of 40 recessive mutants (Table S7 and Figure S3) generated via EMS mutagenesis (Till et al 2004) of B73 was used to demonstrate the utility of combining Sequenom-based quantitative SNP detection with BSA.…”

The Genetic Basis of Transgressive Ovary Size in Honeybee Workers

“…Specifically, for each chromosome, we created a function relating recombination distance to physical distance using a least squares fit of a six-term polynomial of physical distance to recombination distance; R 2 . While standard permutation tests (between subpools, ignoring whether subpools come from the high or low phenotypic pools) can be used to establish genomewide thresholds in the fractioned pool design (Korol et al 2007), our low number of subpools (3) was not sufficient (only 10 total permutations per backcross). Instead, as suggested by Wang et al (2007), we simulated allele frequencies of subpools with a binomial distribution using the observed allele frequency estimates and the technical error variance estimates for each locus.…”

“…The DNA was resuspended in 100 ml TE and quality and quantity was assessed with a Nanodrop spectrophotometer. DNA samples of sufficient purity (A 260/280 $ 1.8) and quantity (.100 ng/ml) were diluted to 100 ng/ml and used for subsequent analyses.We used a selective pooled DNA QTL approach (Darvasi and Soller 1994;Dekkers 2000;Korol et al 2007;Wang et al 2007) to map the genomic location in the two AHB backcross populations with the most extreme worker ovary size (ABC3 and ABC5, Figure 1). DNA from individuals in the high and low tails of the phenotypic distribution of ovariole number was pooled into high and low pools for both ABC3 and ABC5.…”

“…DNA from individuals in the high and low tails of the phenotypic distribution of ovariole number was pooled into high and low pools for both ABC3 and ABC5. From each cross and each phenotypic tail, three DNA subpools were created as biological replicates with 8 individuals each (fractioned DNA pool design; Korol et al 2007). Workers composing the high ovariole pools had a median total ovariole number of 37.5 (range, 31-54) (ABC3) and 42 (39-58) (ABC5), workers in the low ovariole pools 14 (5-17) and 13.5 (8-15) For each locus we estimated the allele frequency in each DNA pool from the normalized signal intensity of the alternative alleles.…”

“…We also compared our simulated genomewide threshold with a LOD-3 genomewide threshold previously established for QTL mapping in A. mellifera Rueppell et al 2004). Bootstrap resampling (10,000 random samples) from within high and low subpools was used to estimate the 95% confidence intervals of QTL position (Korol et al 2007). Because our sample size was very limited we also calculated 1.5-LOD support intervals for QTL position (Lander and Botstein 1989;Dupuis and Siegmund 1999).…”