Biomedical subjects

S Dudoit

Publications and source records attributed to S Dudoit.

5 recordsLinked to original sources

PubMed2006

Prognosis of stage II colon cancer by non-neoplastic mucosa gene expression profiling.

We have assessed the possibility to build a prognosis predictor (PP), based on non-neoplastic mucosa microarray gene expression measures, for stage II colon cancer patients. Non-neoplastic colonic mucosa mRNA samples from 24 patients (10 with a metachronous metastasis, 14 with no recurrence) were profiled using the Affymetrix HGU133A GeneChip. Patients were repeatedly and randomly divided into 1000 training sets (TSs) of size 16 and validation sets (VS) of size 8. For each TS/VS split, a 70-gene PP, identified on the TS by selecting the 70 most differentially expressed genes and applying diagonal linear discriminant analysis, was used to predict the prognoses of VS patients. Mean prognosis prediction performances of the 70-gene PP were 81.8% for accuracy, 73.0% for sensitivity and 87.1% for specificity. Informative genes suggested branching signal-transduction pathways with possible extensive networks between individual pathways. They also included genes coding for proteins involved in immune surveillance. In conclusion, our study suggests that one can build an accurate PP for stage II colon cancer patients, based on non-neoplastic mucosa microarray gene expression measures.

Adenocarcinoma↗

PubMed2001

Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairs.

Identification of genes involved in complex traits by traditional (lod score) linkage analysis is difficult due to many complicating factors. An unfortunate drawback of non-parametric procedures in general, though, is their low power to detect genetic effects. Recently, Dudoit and Speed [2000] proposed using a (likelihood-based) score test for detecting linkage with IBD data on sib pairs. This method uses the likelihood for theta, the recombination fraction between a trait locus and a marker locus, conditional on the phenotypes of the two sibs to test the null hypothesis of no linkage (theta = (1/2)). Although a genetic model must be specified, the approach offers several advantages. This paper presents results of simulation studies characterizing the power and robustness properties of this score test for linkage, and compares the power of the test to the Haseman-Elston and modified Haseman-Elston tests. The score test is seen to have impressively high power across a broad range of true and assumed models, particularly under multiple ascertainment. Assuming an additive model with a moderate allele frequency, in the range of p = 0.2 to 0.5, along with heritability H = 0.3 and a moderate residual correlation rho = 0.2 resulted in a very good overall performance across a wide range of trait-generating models. Generally, our results indicate that this score test for linkage offers a high degree of protection against wrong assumptions due to its strong robustness when used with the recommended additive model.

Computer Simulation↗

PubMed2000

Power of a score test for quantitative trait linkage analysis of relative pairs.

The score test of Dudoit and Speed [(2000) Biostatistics 1:1-26] to detect linkage between a trait locus and a marker locus, using identity by descent data on sib pairs, is extended to other types of relative pairs (grandparent/grandchild, avuncular, and half-sib relationships). The test is based on the likelihood of the recombination fraction theta between trait and marker loci, conditional on phenotypes of the relatives. We present results of simulation studies characterizing power and robustness properties of this linkage score test, and compare the power of the score test to that of the classical and modified Haseman-Elston tests. The score test has considerable power, particularly under sampling schemes where selection is on double probands. Use of a generic additive model [Goldstein et al., submitted] with allele frequency p = 0.2, heritability H = 0.3, and a moderate residual correlation of rho = 0.2 resulted in a very good overall performance across a wide range of trait-generating models.

Computer Simulation↗

PubMed2000

A score test for the linkage analysis of qualitative and quantitative traits based on identity by descent data from sib-pairs.

We propose a general likelihood-based approach to the linkage analysis of qualitative and quantitative traits using identity by descent (IBD) data from sib-pairs. We consider the likelihood of IBD data conditional on phenotypes and test the null hypothesis of no linkage between a marker locus and a gene influencing the trait using a score test in the recombination fraction theta between the two loci. This method unifies the linkage analysis of qualitative and quantitative traits into a single inferential framework, yielding a simple and intuitive test statistic. Conditioning on phenotypes avoids unrealistic random sampling assumptions and allows sib-pairs from differing ascertainment mechanisms to be incorporated into a single likelihood analysis. In particular, it allows the selection of sib-pairs based on their trait values and the analysis of only those pairs having the most informative phenotypes. The score test is based on the full likelihood, i.e. the likelihood based on all phenotype data rather than just differences of sib-pair phenotypes. Considering only phenotype differences, as in Haseman and Elston (1972) and Kruglyak and Lander (1995), may result in important losses in power. The linkage score test is derived under general genetic models for the trait, which may include multiple unlinked genes. Population genetic assumptions, such as random mating or linkage equilibrium at the trait loci, are not required. This score test is thus particularly promising for the analysis of complex human traits. The score statistic readily extends to accommodate incomplete IBD data at the test locus, by using the hidden Markov model implemented in the programs MAPMAKER/SIBS and GENEHUNTER (Kruglyak and Lander, 1995; Kruglyak et al., 1996). Preliminary simulation studies indicate that the linkage score test generally matches or outperforms the Haseman-Elston test, the largest gains in power being for selected samples of sib-pairs with extreme phenotypes.

Journal Article↗

PubMed2000

Microarray expression profiling identifies genes with altered expression in HDL-deficient mice.

Based on the assumption that severe alterations in the expression of genes known to be involved in high-density lipoprotein (HDL) metabolism may affect the expression of other genes, we screened an array of >5000 mouse expressed sequence tags for altered gene expression in the livers of two lines of mice with dramatic decreases in HDL plasma concentrations. Labeled cDNA from livers of apolipoprotein AI (apoAI)-knockout mice, scavenger receptor BI (SR-BI) transgenic mice, and control mice were cohybridized to microarrays. Two-sample t statistics were used to identify genes with altered expression levels in the knockout or transgenic mice compared with control mice. In the SR-BI group we found nine array elements representing at least five genes that were significantly altered on the basis of an adjusted P value < 0.05. In the apoAI-knockout group, eight array elements representing four genes were altered compared with the control group (adjusted P < 0.05). Several of the genes identified in the SR-BI transgenic suggest altered sterol metabolism and oxidative processes. These studies illustrate the use of multiple-testing methods for the identification of genes with altered expression in replicated microarray experiments.

Animals↗