The recent development of Affymetrix chips designed from assembled EST sequences has spawned considerable interest in identifying single-feature polymorphisms (SFPs) from transcriptome data. SFPs are valuable genetic markers that potentially offer a physical link to the structural genes themselves. However, most current SFP prediction methodologies were developed for sequenced species although SFPs are particularly valuable for species with complex and unsequenced genomes. To establish the sensitivity and specificity of prediction, we explored four methods for identifying SFPs from experiments involving two tissues in two commercial barleys and their doubled-haploid progeny. The methods were compared in terms of numbers of SFPs predicted and their ability to identify known sequence polymorphisms in the features, to confirm existing SNP genotypes and to match existing maps and individual haplotypes.We identified.4000 separate SFPs that accurately predicted the SNP genotype of.98% of the doubled-haploid (DH) lines. They were highly enriched for features containing sequence polymorphisms but all methods uniformly identified a majority of SFPs (-64%) in features for which there was no sequence polymorphism while 5% mapped to different locations, indicating that "SFPs" mainly represent polymorphism in cis-acting regulators. All methods are efficient and robust at predicting markers for gene mapping.

Allozyme and PCR-based molecular markers have been widely used to investigate genetic diversity and population genetic structure in autotetraploid species. However, an empirical but inaccurate approach was often used to infer marker genotype from the pattern and intensity of gel bands. Obviously, this introduces serious errors in prediction of the marker genotypes and severely biases the data analysis. This paper developed a theoretical model to characterize genetic segregation of alleles at genetic marker loci, in autotetraploid populations and a novel likelihood-based method to estimate the model parameters. The model properly accounts for segregation complexities due to multiple alleles and double reduction at autotetrasomic loci in natural populations, and the method takes appropriate account for incomplete marker phenotype information with respect to genotype due to multiple dosage allele segregation at marker loci in tetraploids. The theoretical analyses were validated by making use of a computer simulation study and their utility is demonstrated by analyzing microsatellite marker data collected from two populations of sycamore maple (Acer pseudoplatanus L.), an economically important autotetraploid tree species. Numerical analyses based on simulation data indicate that the model parameters can be adequately estimated and double reduction is detected with good power using reasonable sample size.

Linkage analysis in autotetraploid species has been an historical challenge in quantitative genetics theory and is a stumbling block that urgently needs to be removed in the rapidly emerging genome research on this species, such as cultivated potato. This article presents theory of a full model of tetrasomic linkage and develops a statistical framework for the linkage analysis. The model considers both double reduction and recombination, the most essential features of tetrasomic inheritance with linked loci, whereas the statistical method takes appropriate account of the major complexities in analyzing both dominant and codominant molecular marker data during map reconstruction in tetraploid species. These complexities include the problems arising from multiple dosage of allelic inheritance, the null allele, allelic segregation distortion, mixed bivalent and quadrivalent pairing in meiosis, and incomplete information of marker phenotype data. The theoretical analysis established the relationship between the coefficients of double reduction at linked loci, which is essential in the present tetrasomiclinkage analysis and in assessing the impact of double reduction on the evolution of tetraploid populations. The statistical method, based on the combination of theoretical analysis and a computerbased algorithm, provided analytical tools for predicting the maximum-likelihood estimates of the model parameters. A simulation study showed the feasibility of a practical implementation of the method, detailed the procedure of the analysis, validated the power and reliability in the parameter estimation, and compared the present method with those proposed in the current literature.

Purpose: Reduced expression of the transforming growth factor ? receptor type II (TGF ? RII), a key inhibitor of epithelial cell growth and tumor suppressor gene, was reported frequently in many types of tumors including nonsmall cell lung cancer (NSCLC). This study explored the significance of the TGF RII gene in NSCLC carcinogenesis. Experimental Design: With 43 independent pairs of tumor and paracarcinoma tissue samples from patients with primary NSCLC, we carried out PCR-denaturing gradient gel electrophoresis screening for DNA variants over the coding sequence of the TGF ? RII gene, immunohistochemical assay of TGF ? RII expression, methylation-specific PCR analysis, and semiquantitative reverse transcription-PCR.