Copyright © 2008 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 82, Issue 4, 903-915, 20 March 2008
doi:10.1016/j.ajhg.2008.01.012
Article
SNP Arrays in Heterogeneous Tissue: Highly Accurate Collection of Both Germline and Somatic Genetic Information from Unpaired Single Tumor Samples
Guillaume Assié1, Thomas LaFramboise1, 3, Petra Platzer1, Jérôme Bertherat5, Constantine A. Stratakis6 and Charis Eng1, 2, 3, 4, 
, 
1 Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio 44195, USA
2 Taussig Cancer Institute, Cleveland Clinic Foundation, Cleveland, Ohio 44195, USA
3 Department of Genetics, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
4 Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
5 INSERM U567, Institut Cochin and Département d'Endocrinologie, Hôpital Cochin, 75014 Paris, France
6 Section of Endocrinology and Genetics, Program on Developmental Endocrinology and Genetics, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
Corresponding authorAbstract
SNP arrays provide reliable genotypes and can detect chromosomal aberrations at a high resolution. However, tissue heterogeneity is currently a major limitation for somatic tissue analysis. We have developed SOMATICs, an original program for accurate analysis of heterogeneous tissue samples. Fifty-four samples (42 tumors and 12 normal tissues) were processed through Illumina Beadarrays and then analyzed with SOMATICs. We demonstrate that tissue heterogeneity-related limitations not only can be overcome but can also be turned into an advantage. First, admixture of normal cells with tumor can be used as an internal reference, thereby enabling highly sensitive detection of somatic deletions without having corresponding normal tissue. Second, the presence of normal cells allows for discrimination of somatic from germline aberrations, and the proportion of cells in the tissue sample that are harboring the somatic events can be assessed. Third, relatively early versus late somatic events can also be distinguished, assuming that late events occur only in subsets of cancer cells. Finally, admixture by normal cells allows inference of germline genotypes from a cancer sample. All this information can be obtained from any cancer sample containing a proportion of 40–75% of cancer cells. SOMATICs is a ready-to-use open-source program that integrates all of these features into a simple format, comprehensively describing each chromosomal event.
Article Information
PubMed
Related Articles
- Simple and Efficient Analysis of Disease Association with Mi...Simple and Efficient Analysis of Disease Association with Missing Genotype Data
The American Journal of Human Genetics, Volume 82, Issue 2, 8 February 2008, Pages 444-452
D.Y. Lin, Y. Hu and B.E. Huang
AbstractMissing genotype data arise in association studies when the single-nucleotide polymorphisms (SNPs) on the genotying platform are not assayed successfully, when the SNPs of interest are not on the platform, or when total sequence variation is determined only on a small fraction of individuals. We present a simple and flexible likelihood framework to study SNP-disease associations with such missing genotype data. Our likelihood makes full use of all available data in case-control studies and reference panels (e.g., the HapMap), and it properly accounts for the biased nature of the case-control sampling as well as the uncertainty in inferring unknown variants. The corresponding maximum-likelihood estimators for genetic effects and gene-environment interactions are unbiased and statistically efficient. We developed fast and stable numerical algorithms to calculate the maximum-likelihood estimators and their variances, and we implemented these algorithms in a freely available computer program. Simulation studies demonstrated that the new approach is more powerful than existing methods while providing accurate control of the type I error. An application to a case-control study on rheumatoid arthritis revealed several loci that deserve further investigations.
Abstract | | - Renal Aplasia in Humans Is Associated with RET MutationsRenal Aplasia in Humans Is Associated with RET Mutations
The American Journal of Human Genetics, Volume 82, Issue 2, 8 February 2008, Pages 344-351
Michael A. Skinner, Shawn D. Safford, Justin G. Reeves, Margaret E. Jackson and Alex J. Freemerman
AbstractIn animal models, kidney formation is known to be controlled by the proteins RET, GDNF, and GFRA1; however, no human studies to date have shown an association between abnormal kidney development and mutation of these genes. We hypothesized that stillborn fetuses with congenital renal agenesis or severe dysplasia would possess mutations in RET, GDNF, or GFRA1. We assayed for mutations in these genes in 33 stillborn fetuses that had bilateral or unilateral renal agenesis (29 subjects) or severe congenital renal dysplasia (4 subjects). Mutations in RET were found in 7 of 19 fetuses with bilateral renal agenesis (37%) and 2 of 10 fetuses (20%) with unilateral agenesis. In two fetuses, there were two different RET mutations found, and a total of ten different sequence variations were identified. We also investigated whether these mutations affected RET activation; in each case, RET phosphorylation was either absent or constitutively activated. A GNDF mutation was identified in only one fetus with unilateral agenesis; this subject also had two RET mutations. No GFRA1 mutations were seen in any fetuses. These data suggest that in humans, mutations in RET and GDNF may contribute significantly to abnormal kidney development.
Abstract | | - A Statistical Method for Predicting Classical HLA Alleles fr...A Statistical Method for Predicting Classical HLA Alleles from SNP Data
The American Journal of Human Genetics, Volume 82, Issue 1, 10 January 2008, Pages 48-56
Stephen Leslie, Peter Donnelly and Gil McVean
AbstractGenetic variation at classical HLA alleles is a crucial determinant of transplant success and susceptibility to a large number of infectious and autoimmune diseases. However, large-scale studies involving classical type I and type II HLA alleles might be limited by the cost of allele-typing technologies. Although recent studies have shown that some common HLA alleles can be tagged with small numbers of markers, SNP-based tagging does not offer a complete solution to predicting HLA alleles. We have developed a new statistical methodology to use SNP variation within the region to predict alleles at key class I (HLA-A, HLA-B, and HLA-C) and class II (HLA-DRB1, HLA-DQA1, and HLA-DQB1) loci. Our results indicate that a single panel of ∼100 SNPs typed across the region is sufficient for predicting both rare and common HLA alleles with up to 95% accuracy in both African and non-African populations. Furthermore, we show that HLA alleles can be successfully predicted by using previously genotyped SNPs that are within the MHC and that had not been chosen for their ability to predict HLA alleles, such as those included on genome-wide products. These results indicate that our methodology, combined with an extended database of reference haplotypes, will facilitate large-scale experiments, including disease-association studies and vaccine trials, in which detailed information about HLA type is valuable.
Abstract | | - …more
