Few quantitative trait loci (QTL) have been mapped for the expression of partial resistance to Phytophthora sojae in soybean and very little is known about the molecular mechanisms that contribute to this trait. Therefore, the objectives of this study were to identify additional QTL conferring resistance to P. sojae and to identify candidate genes that may contribute to this form of defense. QTL on chromosomes 12, 13, 14, 17, and 19, each explaining 4 to 7% of the phenotypic variation, were identifi ed using 186 RILs from a cross of the partially resistant cultivar 'Conrad' and susceptible cultivar 'Sloan' through composite interval mapping. Microarray analysis identifi ed genes with signifi cant differences in transcript abundances between Conrad and Sloan, both constitutively and following inoculation. Of these genes, 55 mapped to the fi ve QTL regions. Ten genes encoded proteins with unknown functions, while the others encode proteins related to defense or physiological traits. Seventeen genes within the genomic region that encompass the QTL were selected and their transcript abundance was confi rmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR). These results suggest a complex QTL-mediated resistance network. This study will contribute to soybean resistance breeding by providing additional QTL for marker-assisted selection as well as a list of candidate genes which may be manipulated to confer resistance.

The existence of a large number of single nucleotide polymorphisms (SNPs) provides opportunities and challenges to screen DNA variations affecting complex traits using a candidate gene analysis. In this article, four types of epistasis effects of two candidate gene SNPs with Hardy-Weinberg disequilibrium (HWD) and linkage disequilibrium (LD) are considered: additive ϫ additive, additive ϫ dominance, dominance ϫ additive, and dominance ϫ dominance. The Kempthorne genetic model was chosen for its appealing genetic interpretations of the epistasis effects. The method in this study consists of extension of Kempthorne's definitions of 35 individual genetic effects to allow HWD and LD, genetic contrasts of the 35 extended individual genetic effects to define the 4 epistasis effects, and a linear model method for testing epistasis effects. Formulas to predict statistical power (as a function of contrast heritability, sample size, and type I error) and sample size (as a function of contrast heritability, type I error, and type II error) for detecting each epistasis effect were derived, and the theoretical predictions agreed well with simulation studies. The accuracy in estimating each epistasis effect and rates of false positives in the absence of all or three epistasis effects were evaluated using simulations. The method for epistasis testing can be a useful tool to understand the exact mode of epistasis, to assemble genome-wide SNPs into an epistasis network, and to assemble all SNP effects affecting a phenotype using pairwise epistasis tests.single nucleotide polymorphism; quantitative trait; linkage disequilibrium; Hardy-Weinberg disequilibrium THE SIGNIFICANCE OF EPISTASIS in complex traits has been well recognized (3,12,16,17). It has been hypothesized that epistasis is ubiquitous in determining susceptibility to common human diseases. Observations supporting this hypothesis include deviations from Mendelian ratios, molecular interactions in gene regulation and biochemical and metabolic systems, nonrepeatable positive effects of single polymorphisms, and gene interaction effects commonly found when properly investigated (16). Large epistasis networks found in yeast (19, 21) also point to the ubiquitous nature of epistasis. The presence of a large number of single nucleotide polymorphisms (SNPs) provides opportunities and challenges to identify DNA variations associated with complex traits using SNPs as candidate genes. A number of studies on SNP-disease association have been reported (2,7,8,20,24), but most current candidate gene studies focus on single gene effects. Ongoing studies of geneotyping up to 500,000 SNPs on 2,000 individuals have been reported (1). Such large-scale SNP studies would be capable of detecting certain epistasis effects. 1For two bi-allelic genes unaffected by the environment, a total of 512 disease models are possible, but the number of nonredundant models is in the range of 50 -102 (12). For two bi-allelic genes such as two SNP loci with quantitative measures, epistasis effects are typica...

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Abstract

Epistasis refers to gene interaction effect involving two or more genes. Statistical methods for mapping quantitative trait loci (QTL) with epistasis effects have become available recently. However, little is known about the statistical power and sample size requirements for mapping epistatic QTL using genetic markers. In this study, we developed analytical formulae to calculate the statistical power and sample requirement for detecting each epistasis effect under the F-2 design based on crossing inbred lines. Assuming two unlinked interactive QTL and the same absolute value for all epistasis effects, the heritability of additive × additive (a × a) effect is twice as large as that of additive × dominance (a × d) or dominance × additive (d × a) effect, and is four times as large as that of dominance × dominance (d × d) effect. Consequently, among the four types of epistasis effects involving two loci, 'a × a' effect is the easiest to detect whereas 'd × d' effect is the most difficult to detect. The statistical power for detecting 'a × a' effect is similar to that for detecting dominance effect of a single QTL. The sample size requirements for detecting 'a × d', 'd × a' and 'd × d' are highly sensitive to increased distance between the markers and the interacting QTLs. Therefore, using dense marker coverage is critical to detecting those effects.

-Epistasis refers to gene interaction effect involving two or more genes. Statistical methods for mapping quantitative trait loci (QTL) with epistasis effects have become available recently. However, little is known about the statistical power and sample size requirements for mapping epistatic QTL using genetic markers. In this study, we developed analytical formulae to calculate the statistical power and sample requirement for detecting each epistasis effect under the F-2 design based on crossing inbred lines. Assuming two unlinked interactive QTL and the same absolute value for all epistasis effects, the heritability of additive × additive (a × a) effect is twice as large as that of additive × dominance (a × d) or dominance × additive (d × a) effect, and is four times as large as that of dominance × dominance (d × d) effect. Consequently, among the four types of epistasis effects involving two loci, 'a × a' effect is the easiest to detect whereas 'd × d' effect is the most difficult to detect. The statistical power for detecting 'a × a' effect is similar to that for detecting dominance effect of a single QTL. The sample size requirements for detecting 'a × d', 'd × a' and 'd × d' are highly sensitive to increased distance between the markers and the interacting QTLs. Therefore, using dense marker coverage is critical to detecting those effects. epistasis / QTL / statistical power / sample size / F-2