Genomics. Genetic association by whole-genome analysis?

Pharmacogenetics in acute lymphoblastic leukemia

Progress in the treatment of acute lymphoblastic leukemia (ALL) in children has been remarkable, from a disease being lethal four decades ago to current cure rates exceeding 80%. This exemplary progress is largely due to the optimization of existing treatment modalities rather than the discovery of new antileukemic agents. However, despite these high cure rates, the annual number of children whose leukemia relapses after their initial therapy remains greater than that of new cases of most types of childhood cancers. The aim of pharmacogenetics is to develop strategies to personalize treatment and tailor therapy to individual patients, with the goal of optimizing efficacy and safety through better understanding of human genome variability and its influence on drug response. In this review, we summarize recent pharmacogenomic studies related to the treatment of pediatric ALL. These studies illustrate the promise of pharmacogenomics to further advance the treatment of human cancers, with childhood leukemia serving as a paradigm.

Computational problems in perfect phylogeny haplotyping: typing without calling the allele

A haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics, and is a variant of the perfect phylogeny haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is "full" genotypes, here we assume less informative input, and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time, by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by that tree they map onto, and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes, and present a linear algorithm for identifying those genotypes.

Multiple hypothesis testing strategies for genetic case-control association studies

The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature.

Identification of coding single nucleotide polymorphisms and mutations by combination of genome tiling arrays and enrichment/depletion of mismatch cDNAs

Genome tiling array technology combined with a method for both enrichment and depletion of mismatch-containing cDNA fragments offers a useful approach for detecting coding single nucleotide polymorphisms (cSNPs) and mutations in pooled cDNA samples. Enriched mismatch and perfect match cDNA samples from human primary melanoma cells and normal melanocytes were obtained by selection using mismatch repair thymine DNA glycosylase-bound beads. These cDNA samples were then labeled and hybridized to Encyclopedia of DNA Elements genome tiling arrays. The results revealed that the hybridization intensity values of potential cDNA variation regions of the enriched mismatch samples increased, whereas the hybridization intensity values of corresponding regions of the enriched perfect match samples decreased. Six potential mutations were confirmed by polymerase chain reaction product sequencing, including two novel heterozygous mutations in melanoma cells. We suggest that this strategy should increase the efficiency of both cSNP and mutation detection throughout the entire human genome and decrease the cost and complexity of genomewide analysis of cDNA variations.

PHARMACOGENOMICS OF ACUTE LEUKEMIA

Over the past four decades, treatment of acute leukemia in children has made remarkable progress, from this disease being lethal to now achieving cure rates of 80% for acute lymphoblastic leukemia and 45% for acute myeloid leukemia. This progress is largely owed to the optimization of existing treatment modalities rather than the discovery of new agents. However, the annual number of patients with leukemia who experience relapse after initial therapy remains greater than that of new cases of most childhood cancers. The aim of pharmacogenetics is to develop strategies to personalize medications and tailor treatment regimens to individual patients, with the goal of enhancing efficacy and safety through better understanding of the person's genetic makeup. In this review, we summarize recent pharmacogenomic studies related to the treatment of pediatric acute leukemia. These include work using candidate-gene approaches, as well as genome-wide studies using haplotype mapping and gene expression profiling. These strategies illustrate the promise of pharmacogenomics to further advance the treatment of human cancers, with childhood leukemia serving as a paradigm.

Proline homozygosity in codon 72 of p53 is a factor of susceptibility for thyroid cancer

A common germline polymorphism of p53 gene produces an Arginine to Proline change at aminoacid position 72. The resulting codon 72 variants have been reported associated with tumor susceptibility since they reduce p53 ability to activate apoptosis. Codon 72 polymorphism may play a role in subside vulnerability to different carcinogens and might account for ethnic variations in cancer frequency. Using an allele-specific polymerase chain reaction (PCR), we tested peripheral blood samples from 98 patients with thyroid cancer, including 21 follicular (FC) and 77 papillary carcinomas (PC), 44 patients with benign nodules, including 14 follicular adenomas and 30 goiters and 153 healthy individuals from the same geographical region. Data on lifetime occupational history, smoking history, general health conditions, previous diseases and other anamnestic data were obtained through interviews. Patients with FC (Pro/Pro = 19.0%, Arg/Arg = 42.9%, Arg/Pro = 38%) and with PC (Pro/Pro = 10.3%, Arg/Arg = 36.36%, Arg/Pro = 53.24%) showed a significant overrepresentation of codon 72 variants compared to the control population (Pro/Pro = 1.9%, Arg/Arg = 33.3%, Arg/Pro = 64.7%) (P = 0.0015). The Pro/Pro genotype, after adjusting for gender, age, tobacco and drugs, was associated with a markedly higher risk of FC (OR=9.714; CI: 2.334-40.436) and of PC (OR=5.299; CI: 2.334-40.436). These results provide evidence that p53 polymorphism is implicated in thyroid carcinogenesis and that individuals harboring the Pro/Pro genotype have an increased risk of developing thyroid cancer.

Single nucleotide polymorphism genotyping: biochemistry, protocol, cost and throughput

The large number of single nucleotide polymorphism (SNP) markers available in the public databases makes studies of association and fine mapping of disease loci very practical. To provide information for researchers who do not follow SNP genotyping technologies but need to use them for their research, we review here recent developments in the fields. We start with a general description of SNP typing protocols and follow this with a summary of current methods for each step of the protocol and point out the unique features and weaknesses of these techniques as well as comparing the cost and throughput structures of the technologies. Finally, we describe some popular techniques and the applications that are suitable for these techniques.

Polymorphism in clinical immunology - From HLA typing to immunogenetic profiling

The pathology of humans, in contrast to that of inbred laboratory animals faces the challenge of diversity addressed in genetic terms as polymorphism. Thus, unsurprisingly, treatment modalities that successfully can be applied to carefully-selected pre-clinical models only sporadically succeed in the clinical arena. Indeed, pre-fabricated experimental models purposefully avoid the basic essence of human pathology: the uncontrollable complexity of disease heterogeneity and the intrinsic diversity of human beings. Far from pontificating on this obvious point, this review presents emerging evidence that the study of complex system such as the cytokine network is further complicated by inter-individual differences dictated by increasingly recognized polymorphisms. Polymorphism appears widespread among genes of the immune system possibly resulting from an evolutionary adaptation of the organism facing an ever evolving environment. We will refer to this high variability of immune-related genes as immune polymorphism. In this review we will briefly highlight the possible clinical relevance of immune polymorphism and suggest a change in the approach to the study of human pathology, from the targeted study of individual systems to a broader view of the organism as a whole through immunogenetic profiling.

A strategy for detection of known and unknown SNP using a minimum number of oligonucleotides applicable in the clinical settings

Detection of unknown single nucleotide polymorphism (SNP) relies on large scale sequencing expeditions of genomic fragments or complex high-throughput chip technology. We describe a simplified strategy for fluorimetric detection of known and unknown SNP by proportional hybridization to oligonucleotide arrays based on optimization of the established principle of signal loss or gain that requires a drastically reduced number of matched or mismatched probes. The array consists of two sets of 18-mer oligonucleotide probes. One set includes overlapping oligos with 4-nucleotide tiling representing an arbitrarily selected "consensus" sequence (consensus-oligos), the other includes oligos specific for known SNP within the same genomic region (variant-oligos). Fluorescence-labeled DNA amplified from a homozygous source identical to the consensus represents the reference target and is co-hybridized with a differentially-labeled test sample. Lack of hybridization of the test sample to consensus- with simultaneous hybridization to variant-oligos designates a known allele. Lack of hybridization to consensus- and variant-oligos indicates a new allele. Detection of unknown variants in heterozygous samples depends upon fluorimetric analysis of signal intensity based on the principle that homozygous samples generate twice the amount of signal. This method can identify unknown SNP in heterozygous conditions with a sensitivity of 82% and specificity of 90%. This strategy should dramatically increase the efficiency of SNP detection throughout the human genome and will decrease the cost and complexity of applying genomic wide analysis in the context of clinical trials.

Microarrays in pharmacogenomics--advances and future promise

With their ability to provide global views of genome sequence and gene activity, microarrays have emerged as key analytical tools in the field of pharmacogenomics. Vast amounts of data must be collected and analyzed to meet pharmacogenomics' ambitious goals, ranging from identifying markers that predict individuals' responses to therapy to discovering new drug targets. Microarrays will be instrumental to these efforts because they provide bountiful sources of gene expression and genotypic data. Attesting to their productivity, microarrays have been the central technology used in thousands of peer-reviewed publications and have also become important contributors to many databases including PharmGKB, the Cancer Microarray Database and the database of single nucleotide polymorphisms (dbSNP). Microarrays are also making more focused contributions, however, in helping pursue hypothesis-driven inquiries that extend or complement broad genomic surveys. In addition, their potential as clinical tools is being increasingly recognized. This review identifies some of the varied and changing needs of pharmacogenomics research and discusses the ways in which microarrays are tending to these demands. The technique's strongpoints and limitations are examined, as well as its future potential.

Integrating pharmacogenomics into drug development

The first observations of inherited differences in drug effects in the 1950s led to the recognition of a genetic basis for drug response. With the development of genetics and molecular biology, it became clear that certain drug responses could be associated with specific genetic variations or polymorphisms. There are now examples of polymorphisms that affect response to drugs ranging from common analgesics to chemotherapeutics. The goal of pharmacogenetics is to identify polymorphisms that can serve as predictive markers of drug response. This review summarizes how existing pharmacogenomic technologies can be applied advantageously throughout drug development to bring drugs successfully to market along with diagnostic tests that ensure their appropriate use.

High-throughput genotyping of single nucleotide polymorphisms using new biplex invader technology

The feasibility of large-scale genome-wide association studies of complex human disorders depends on the availability of accurate and efficient genotyping methods for single nucleotide polymorphisms (SNPs). We describe a new platform of the invader assay, a biplex assay, where both alleles are interrogated in a single reaction tube. The assay was evaluated on over 50 different SNPs, with over 20 SNPs genotyped in study cohorts of over 1500 individuals. We assessed the usefulness of the new platform in high-throughput genotyping and compared its accuracy to genotyping results obtained by the traditional monoplex invader assay, TaqMan genotyping and sequencing data. We present representative data for two SNPs in different genes (CD36 and protein tyrosine phosphatase 1beta) from a study cohort comprising over 1500 individuals with high or low-normal blood pressure. In this high-throughput application, the biplex invader assay is very accurate, with an error rate of <0.3% and a failure rate of 1.64%. The set-up of the assay is highly automated, facilitating the processing of large numbers of samples simultaneously. We present new analysis tools for the assignment of genotypes that further improve genotyping success. The biplex invader assay with its automated set-up and analysis offers a new efficient high-throughput genotyping platform that is suitable for association studies in large study cohorts.

Microarrays and genetic epidemiology: a multipurpose tool for a multifaceted field

The advent of molecular technologies that allow the collection and analysis of large amounts of genetic data is rapidly transforming the field of genetic epidemiology. Whether monitoring infectious outbreaks or identifying genotypic variations that underlie disease susceptibility, genetic epidemiology relies heavily on the analysis of multiple, independently derived results. By allowing the simultaneous monitoring of thousands of genetic or expression data points, microarrays are emerging as particularly powerful tools. Several recent reviews have described array manufacturing and the types of scientific questions that can exploit this technology, but few have addressed how the intended use of an array can dictate its design. This review will focus on this latter issue, with particular emphasis on the genetic epidemiology of infectious disease. The design of arrays for genotyping, expression profiling, and fingerprinting are presented, and examples of recent epidemiological studies are used to illustrate the applications' strong points and limitations. In addition to discussing arrays' ability to provide global views of gene identity or function, the review will describe design options for creating arrays that detect multiple genetic variations. It will also examine the reliability of array-generated fingerprints, assay accessibility, and possibilities for sharing and comparing data across studies. Although many challenges lie ahead, microarrays' multiple abilities appear uniquely poised to accelerate the advance of genetic epidemiology's multiple fronts.

How many SNPs does a genome-wide haplotype map require?

We derive and compare several estimates of the number of SNPs that would be required to form the basis of a complete haplotype survey of the human genome. Our estimates make use of reports published by Stephens et al. [1], Patil et al. [2] and Daly et al. [3]. The estimated number of SNPs required for a genome-wide haplotype survey ranges from 180K (based on a European sample of 16 chromosomes) to 600K (based on an ethnically diverse sample of 164 chromosomes). We discuss the implications of using cohorts of different size and ethnic composition and the usefulness of public SNP databases for this effort. Finally, we estimate the experimental effort and cost required to complete a genome-wide haplotype survey.

Candidate-gene approaches for studying complex genetic traits: practical considerations

Association studies with candidate genes have been widely used for the study of complex diseases. However, this approach has been criticized because of non-replication of results and limits on its ability to include all possible causative genes and polymorphisms. These challenges have led to pessimism about the candidate-gene approach and about the genetic analysis of complex diseases in general. We believe that these criticisms can be usefully countered with an appeal to the principles of epidemiological investigation.