Carsten Rosenow

Global analysis of small RNA and mRNA targets of Hfq

Hfq, a bacterial member of the Sm family of RNA‐binding proteins, is required for the action of many small regulatory RNAs that act by basepairing with target mRNAs. Hfq binds this family of small RNAs efficiently. We have used co‐immunoprecipitation with Hfq and direct detection of the bound RNAs on genomic microarrays to identify members of this small RNA family. This approach was extremely sensitive; even Hfq‐binding small RNAs expressed at low levels were readily detected. At least 15 of 46 known small RNAs in E. coli interact with Hfq. In addition, high signals in other intergenic regions suggested up to 20 previously unidentified small RNAs bind Hfq; five were confirmed by Northern analysis. Strong signals within genes and operons also were detected, some of which correspond to known Hfq targets. Within the argX‐hisR‐leuT‐proM operon, Hfq appears to compete with RNase E and modulate RNA processing and degradation. Thus Hfq immunoprecipitation followed by microarray analysis is a highly effective method for detecting a major class of small RNAs as well as identifying new Hfq functions.

Monitoring gene expression using DNA microarrays

The concurrent development of high-density array technologies and the complete sequencing of a number of microbial genomes is providing the opportunity to comprehensively and efficiently survey the transcription profile of microorganisms under different conditions and well-defined genotypes. Microarray-based studies are uncovering broad patterns of genetic activity, providing new understanding of gene functions and, in some cases, generating unexpected insight into transcriptional processes and biological mechanisms. One topic that has come to the forefront is how best to effectively manage and interpret the large data sets being generated. Although progress has been made, this remains a challenging opportunity for functional genomics research.

Genomic screening and replication using the same data set in family-based association testing

The Human Genome Project and its spin-offs are making it increasingly feasible to determine the genetic basis of complex traits using genome-wide association studies. The statistical challenge of analyzing such studies stems from the severe multiple-comparison problem resulting from the analysis of thousands of SNPs. Our methodology for genome-wide family-based association studies, using single SNPs or haplotypes, can identify associations that achieve genome-wide significance. In relation to developing guidelines for our screening tools, we determined lower bounds for the estimated power to detect the gene underlying the disease-susceptibility locus, which hold regardless of the linkage disequilibrium structure present in the data. We also assessed the power of our approach in the presence of multiple disease-susceptibility loci. Our screening tools accommodate genomic control and use the concept of haplotype-tagging SNPs. Our methods use the entire sample and do not require separate screening and validation samples to establish genome-wide significance, as population-based designs do.

Comparison of Microsatellites Versus Single-Nucleotide Polymorphisms in a Genome Linkage Screen for Prostate Cancer–Susceptibility Loci

Prostate cancer is one of the most common cancers among men and has long been recognized to occur in familial clusters. Brothers and sons of affected men have a 2-3-fold increased risk of developing prostate cancer. However, identification of genetic susceptibility loci for prostate cancer has been extremely difficult. Although the suggestion of linkage has been reported for many chromosomes, the most promising regions have been difficult to replicate. In this study, we compare genome linkage scans using microsatellites with those using single-nucleotide polymorphisms (SNPs), performed in 467 men with prostate cancer from 167 families. For the microsatellites, the ABI Prism Linkage Mapping Set version 2, with 402 microsatellite markers, was used, and, for the SNPs, the Early Access Affymetrix Mapping 10K array was used. Our results show that the presence of linkage disequilibrium (LD) among SNPs can lead to inflated LOD scores, and this seems to be an artifact due to the assumption of linkage equilibrium that is required by the current genetic-linkage software. After excluding SNPs with high LD, we found a number of new LOD-score peaks with values of at least 2.0 that were not found by the microsatellite markers: chromosome 8, with a maximum model-free LOD score of 2.2; chromosome 2, with a LOD score of 2.1; chromosome 6, with a LOD score of 4.2; and chromosome 12, with a LOD score of 3.9. The LOD scores for chromosomes 6 and 12 are difficult to interpret, because they occurred only at the extreme ends of the chromosomes. The greatest gain provided by the SNP markers was a large increase in the linkage information content, with an average information content of 61% for the SNPs, versus an average of 41% for the microsatellite markers. The strengths and weaknesses of microsatellite versus SNP markers are illustrated by the results of our genome linkage scans.

Detect and adjust for population stratification in population-based association study using genomic control markers: an application of Affymetrix Genechip Human Mapping 10K array.

Population-based association design is often compromised by false or nonreplicable findings, partially due to population stratification. Genomic control (GC) approaches were proposed to detect and adjust for this confounder. To date, the performance of this strategy has not been extensively evaluated on real data. More than 10 000 single-nucleotide polymorphisms (SNPs) were genotyped on subjects from four populations (including an Asian, an African-American and two Caucasian populations) using GeneChip® Mapping 10 K array. On these data, we tested the performance of two GC approaches in different scenarios including various numbers of GC markers and different degrees of population stratification. In the scenario of substantial population stratification, both GC approaches are sensitive using only 20–50 random SNPs, and the mixed subjects can be separated into homogeneous subgroups. In the scenario of moderate stratification, both GC approaches have poor sensitivities. However, the bias in association test can still be corrected even when no statistical significant population stratification is detected. We conducted extensive benchmark analyses on GC approaches using SNPs over the whole human genome. We found GC method can cluster subjects to homogeneous subgroups if there is a substantial difference in genetic background. The inflation factor, estimated by GC markers, can effectively adjust for the confounding effect of population stratification regardless of its extent. We also suggest that as low as 50 random SNPs with heterozygosity >40% should be sufficient as genomic controls.