The question of what significance threshold is appropriate for genomewide association studies is somewhat unresolved. Previous theoretical suggestions have yet to be validated in practice, whereas permutation testing does not resolve a discrepancy between the genomewide multiplicity of the experiment and the subset of markers actually tested. We used genotypes from the Wellcome Trust Case-Control Consortium to estimate a genomewide significance threshold for the UK Caucasian population. We subsampled the genotypes at increasing densities, using permutation to estimate the nominal P-value for 5% family-wise error. By extrapolating to infinite density, we estimated the genomewide significance threshold to be about 7.2 Â 10 À8 . To reduce the computation time, we considered Patterson's eigenvalue estimator of the effective number of tests, but found it to be an order of magnitude too low for multiplicity correction. However, by fitting a Beta distribution to the minimum P-value from permutation replicates, we showed that the effective number is a useful heuristic and suggest that its estimation in this context is an open problem. We conclude that permutation is still needed to obtain genomewide significance thresholds, but with subsampling, extrapolation and estimation of an effective number of tests, the threshold can be standardized for all studies of the same population. Genet. Epidemiol. 32: 227-234, 2008.

We use a mixture model, involving differentially expressed (DE) and non-DE genes, that captures the most common problem of finding DE genes. Factors determining FDR are (1) the proportion of truly differentially expressed genes, (2) the distribution of the true differences, (3) measurement variability and (4) sample size. Many current small microarray studies are plagued with large FDR, but controlling FDR alone can lead to unacceptably large FNR. In evaluating a design of a microarray study, sensitivity or FNR curves should be computed routinely together with FDR curves. Under certain assumptions, the FDR and FNR curves coincide, thus simplifying the choice of sample size for controlling the FDR and FNR jointly.

Hematopoiesis is a carefully controlled process that is regulated by complex networks of transcription factors that are, in part, controlled by signals resulting from ligand binding to cell-surface receptors. To further understand hematopoiesis, we have compared gene expression profiles of human erythroblasts, megakaryocytes, B cells, cytotoxic and helper T cells, natural killer cells, granulocytes, and monocytes using whole genome microarrays. A bioinformatics analysis of these data was performed focusing on transcription factors, immunoglobulin superfamily members, and lineage-specific transcripts. We observed that the numbers of lineage-specific genes varies by 2 orders of magnitude, ranging from 5 for cytotoxic T cells to 878 for granulocytes. In addition, we have identified novel coexpression patterns for key transcription factors involved in hematopoiesis (eg, GATA3-GFI1 and GATA2-KLF1). This study represents the most comprehensive analysis of gene expression in hematopoietic cells to date and has identified genes that play key roles in lineage commitment and cell function. The data, which are freely accessible, will be invaluable for future studies on hematopoiesis and the role of specific genes and will also aid the understanding of the recent genome-wide association studies. (Blood. 2009;113:e1-e9) IntroductionThe hematopoietic system represents one of the best-studied cellular differentiation processes in mammals. The differentiation of the hematopoietic stem cell (HSC) into the blood cell lineages, which is depicted as a stepwise process, generates diverse types of cells that perform many different functions. Historical observations of the blood, made in the late 18th century using some of the first microscopes, revealed that blood is composed of a heterogeneous population of cells that are distinct in number, morphology, and function. Since these early studies, the application of both technologic and methodologic advances to the investigation of blood has led to an ever-increasing understanding of the nature and function of the different types of blood cells. For example, the use of monoclonal antibodies (mAbs) and the designation of the cluster of differentiation (CD) markers, of which there are now more than 300, 1 allows hematologists to assign detailed phenotypes to malignant blood cells, which form the basis of decisions on therapeutic intervention.The value of the current understanding of the hematopoietic system to patient care is perhaps best illustrated in the field of malignancy where gene and protein expression profiles permit rapid and routine patient stratification. It is now possible to stratify patients with leukemia and lymphoma with unprecedented accuracy using gene expression profiles. Signature gene expression profiles may be used for diagnosis and predicting disease prognosis. In addition to studies in patients, gene expression profiles are available for a wide range of healthy tissue types. However, many of these resources, although broad in tissue coverage, are limited in the nu...

Supplementary data are available at Bioinformatics online.