High-density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further preprocessing and data reduction occurs after the image-processing step. Statistical procedures developed by academic groups have been successful in improving the default algorithms provided by the Affymetrix system. In this article we present a solution to one of the preprocessing steps—background adjustment—based on a formal statistical framework. Our solution greatly improves the performance of the technology in various practical applications. These arrays use short oligonucleotides to probe for genes in an RNA sample. Typically, each gene is represented by 11–20 pairs of oligonucleotide probes. The first component of these pairs is referred to as a perfect match probe and is designed to hybridize only with transcripts from the intended gene (i. e., specific hybridization). However, hybridization by other...

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A Model-Based Background Adjustment for Oligonucleotide Expression Arrays

Cultured human embryonic stem cell (hESC) lines are an invaluable resource because they provide a uniform and stable genetic system for functional analyses and therapeutic applications. Nevertheless, these dividing cells, like other cells, probably undergo spontaneous mutation at a rate of 10(-9) per nucleotide. Because each mutant has only a few progeny, the overall biological properties of the cell culture are not altered unless a mutation provides a survival or growth advantage. Clonal evolution that leads to emergence of a dominant mutant genotype may potentially affect cellular phenotype as well. We assessed the genomic fidelity of paired early- and late-passage hESC lines in the course of tissue culture. Relative to early-passage lines, eight of nine late-passage hESC lines had one or more genomic alterations commonly observed in human cancers, including aberrations in copy number (45%), mitochondrial DNA sequence (22%) and gene promoter methylation (90%), although the latter was essentially restricted to 2 of 14 promoters examined. The observation that hESC lines maintained in vitro develop genetic and epigenetic alterations implies that periodic monitoring of these lines will be required before they are used in in vivo applications and that some late-passage hESC lines may be unusable for therapeutic purposes.

Genomic alterations in cultured human embryonic stem cells

Nearly three decades have passed since the invention of electrophoretic methods for DNA sequencing. The exponential growth in the cost-effectiveness of sequencing has been driven by automation and by numerous creative refinements of Sanger sequencing, rather than through the invention of entirely new methods. Various novel sequencing technologies are being developed, each aspiring to reduce costs to the point at which the genomes of individual humans could be sequenced as part of routine health care. Here, we review these technologies, and discuss the potential impact of such a 'personal genome project' on both the research community and on society.

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Advanced sequencing technologies: methods and goals.

Genome-wide association studies with SNP markers are expected to allow identification of genes that underlie complex disorders. Hundreds of thousands of SNP markers will be required for comprehensive genome-wide association studies. The development of microarray-based methods for SNP genotyping on this scale remains a demanding task, despite many recent advances in technology for the production of high-density microarrays. A key technical obstacle is the PCR amplification step, which is required to reduce the complexity of and gain sufficient sensitivity for genotyping SNPs in large, diploid genomes. The multiplexing level that can be achieved in PCR does not match that of current microarray-based methods, making PCR the limiting step in the assays. Highly multiplexed microarray systems for SNP genotyping have recently been developed by combining well-known reaction principles for DNA amplification and SNP genotyping in clever ways. These new methods offer the potential of genome-wide SNP mapping of genes involved in complex diseases in the foreseeable future, provided that issues related to selection of the optimal SNP markers, sample throughput and the cost of the assays can be addressed.

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Toward genome-wide SNP genotyping

The identification of common variants that contribute to the genesis of human inherited disorders remains a significant challenge. Hirschsprung disease (HSCR) is a multifactorial, non-mendelian disorder in which rare high-penetrance coding sequence mutations in the receptor tyrosine kinase RET contribute to risk in combination with mutations at other genes. We have used family-based association studies to identify a disease interval, and integrated this with comparative and functional genomic analysis to prioritize conserved and functional elements within which mutations can be sought. We now show that a common non-coding RET variant within a conserved enhancer-like sequence in intron 1 is significantly associated with HSCR susceptibility and makes a 20-fold greater contribution to risk than rare alleles do. This mutation reduces in vitro enhancer activity markedly, has low penetrance, has different genetic effects in males and females, and explains several features of the complex inheritance pattern of HSCR. Thus, common low-penetrance variants, identified by association studies, can underlie both common and rare diseases.

A common sex-dependent mutation in a RET enhancer underlies Hirschsprung disease risk.

The central goal of human genetics is to identify, characterize and ultimately understand the specific DNA variants that contribute to human phenotypes in general, and human disease in particular (Lander and Schork 1994; Chakravarti 1999; Zwick et al. 2000, 2001; On-line Mendelian Inheritance in Man 2001). The genetic approach to this problem is, in principle, straightforward. First, we identify individuals showing phenotypic variation for the trait of interest. Second, we genotype genetic variants, such as microsatellites or SNPs, in all of the individuals in a study. Third, we perform appropriate statistical tests to identify any genetic variants correlated with variation in the phenotype. Finally, if such variants are found, we perform additional experiments to demonstrate a causal relationship.

Step two poses a question: What genetic variants should be examined? The answer to this question must balance technological and practical considerations. Nevertheless, in the best of all worlds, a researcher would be able to determine the genotype of every base in every sample, that is, a complete resequencing of the entire genome of all individuals under study. No technology currently exists to do this in an economical manner. Moreover, any technology used for this purpose must be capable of extraordinary resequencing accuracy.

Nucleotide diversity in the general human population is ∼8 × 10−4 per site (Cargill et al. 1999; Halushka et al. 1999; The International SNP Map Working [TISMW] Group 2001; Venter et al. 2001; this study). This implies that a randomly selected chromosome will differ from the human reference sequence at ∼8 of every 10,000 bases. Now, imagine a technology that allowed one to rapidly and inexpensively determine the genotype of an individual at every nucleotide site of interest with an accuracy of 99.9%. Such a technology would be remarkable, but insufficient. The problem with only 99.9% accuracy is that this implies 10 errors for every 10,000 bases. Because the true rate of variation is eight in 10,000, 55.5% of the identified variants will be errors. This is unacceptably high. The error rate needs to be much lower.

Microarrays are inherently parallel devices that offer the promise of determining the genotypes of individuals at every site of interest with a limited level of effort (Fodor et al. 1991; Southern et al. 1992; Pease et al. 1994; McGall et al. 1996; Lipshutz et al. 1999). Variation Detection Arrays (VDAs) manufactured by Affymetrix have been used to such an end with success (Chee et al. 1996; Hacia et al. 1996, 1998a,b, 1999, 2000; Hacia and Collins 1999; Halushka et al. 1999; Wang et al. 1998). Unfortunately, it has also been reported that between 12% and 45% of the detected variants are false (Cargill et al. 1999; Halushka et al. 1999; Wang et al. 1998). This indicates that VDAs are, on average, between 99.99% and 99.93% accurate.

Although microarrays may be, on average, insufficiently accurate, it is certainly possible that a large fraction of genotype calls are, in fact, much more accurate than 99.9% and a smaller fraction are much less than 99.9% accurate. The approach used here is to construct an objective statistical framework to distinguish genotype calls that can be made with extraordinary accuracy from those less reliable. The need to build such a framework for microarrays is not a new idea (Southern et al. 1992) and the objectives are to strive for some of the accomplishments that Green and colleagues (Nickerson et al. 1997; Ewing and Green 1998; Ewing et al. 1998; Gordon et al. 1998; Rieder et al. 1998) have made for automated sequencing, namely the assignment to individual genotype calls of a quality score that is larger for calls more likely to be accurate. Green and colleagues, in fact, have done even more; phred provides not only a quality score that increases with increasing accuracy, but also a direct estimate of the probability that a base call is correct.

Researchers performing automated sequencing routinely rely on these phred scores (Ewing and Green 1998; Ewing et al. 1998), and in conjunction with certain other neighborhood quality rules (Altshuler et al. 2000; Mullikin et al. 2000), can achieve an extremely high level of accuracy for SNP discovery (T.I.S.M.W. Group 2001). This work attempts the same task. An objective statistical framework is developed to assign to each VDA genotype call a quality score. Certain simple neighborhood rules are applied, and sites in which extraordinarily high confidence can be placed are distinguished from those less reliable sites. In contrast to automated sequencing experiments that employ only haploid targets (Altshuler et al. 2000; Mullikin et al. 2000), this statistical method can be applied to both haploid and diploid targets. We call the system ABACUS (from Adaptive Background genotype Calling Scheme, see below) and will show that, in general, greater than 99.9999% accuracy can be achieved on >80% of the genotype calls on a VDA.

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Katherine P. Tobin

Papers

High-Throughput Variation Detection and Genotyping Using Microarrays