Computational Method for Estimating DNA Copy Numbers in Normal Samples, Cancer Cell Lines, and Solid Tumors Using Array Comparative Genomic Hybridization

Genomic copy number variations are a typical feature of cancer. These variations may influence cancer outcomes as well as effectiveness of treatment. There are many computational methods developed to detect regions with deletions and amplifications without estimating actual copy numbers (CN) in these regions. We have developed a computational method capable of detecting regions with deletions and amplifications as well as estimating actual copy numbers in these regions. The method is based on determining how signal intensity from different probes is related to CN, taking into account changes in the total genome size, and incorporating into analysis contamination of the solid tumors with benign tissue. Hidden Markov Model is used to obtain the most likely CN solution. The method has been implemented for Affymetrix 500K GeneChip arrays and Agilent 244K oligonucleotide arrays. The results of CN analysis for normal cell lines, cancer cell lines, and tumor samples are presented. The method is capable of detecting copy number alterations in tumor samples with up to 80% contamination with benign tissue. Analysis of 178 cancer cell lines reveals multiple regions of common homozygous deletions and strong amplifications encompassing known tumor suppressor genes and oncogenes as well as novel cancer related genes

Meta‐Analysis of Genome‐wide Linkage Studies in BMI and Obesity

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI‐defined obesity using a nonparametric genome scan meta‐analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome‐wide logarithm of the odds (LOD) scores, non‐parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI‐defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Results: Bins at chromosome 13q13.2‐ q33.1, 12q23‐q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3‐22.3 were also observed for BMI‐defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1‐qter and 12p11.21‐q23 (p < 0.01). Conclusion: Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93663/1/oby.2007.269.pd

Universality and Diversity of the Protein Folding Scenarios: A Comprehensive Analysis With the Aid of a Lattice Model

this paper, we have presented a comprehensive analysis of the role of intermediates for folding of lattice model proteins. In previous instances, lattice models have been successful in identifying such key elements of the protein folding mechanism as a specific nucleus [10,12,53]. The important question remaining is to what extent are present results on the role of intermediates applicable to real proteins? In other words, how do the simulation results compare with experimental data? Experimentally, both scenarios described in this work (and summarized in Fig. 13) were observed for different proteins. Important examples of folding proceeding via intermediates are myoglobin [16], hen egg white lysozyme (HEWL) [18,58], barnase [17], cytochrome c at pH 7 [49], [59], and RNase A [50,51]. On the other hand, there are a growing number of examples where folding of a protein has been shown to follow a simple two-state scenario both in thermodynamics and in kinetics: chymotrypsin inhibitor 2 [19], ubiquitin at 8°C [22], cytochrome c at pH 5 [25], Ig-binding domain of staphylococcal protein G [20], SH3 [21], E. coli cold-shock protein [23], and acylcoenzyme A binding protein [24]. Moreover, the same protein can exhibit, at different temperatures, either scenario [22,53], or a single mutation can cause a switch between folding mechanisms (e.g. mutation I96®A in barnase, see [17]). This is in accord with our findings for the lattice model in which we have shown that the mechanism is not determined by the native structure but by features of the sequence, as comparison betwee