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Itsik Pe'er

Publications and source records attributed to Itsik Pe'er.

15 recordsLinked to original sources
PubMed2025

Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles.

In microbiome studies, sample processing errors are frequent and difficult to detect, especially in large studies involving multiple sites, personnel, and sample types. We present two complementary approaches to identify such errors using host DNA profiled via metagenomic sequencing of microbiome samples. The first approach compares host SNPs inferred from metagenomics to independently obtained genotypes (e.g., microarray genotypes) to match samples to their donors, while the second method compares metagenomics-inferred SNPs between samples to identify samples supplied by the same donor. Furthermore, we demonstrate that combining these methods with experimental metadata provides greater confidence in the identification of errors. Analyzing a longitudinal vaginal microbiome dataset, we demonstrate the ability of our approach to identify mislabeled samples. Using subsampling, we further show that our methods are robust to low sequencing coverage. Overall, our analysis highlights the frequency of processing errors in microbiome studies. We therefore recommend applying error-detection methods in all studies with suitable data.

Journal Article↗
PubMed2006

Evaluating and improving power in whole-genome association studies using fixed marker sets.

Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.

Genetic Markers↗
PubMed2006

Biases and reconciliation in estimates of linkage disequilibrium in the human genome.

Genetic association studies of common disease often rely on linkage disequilibrium (LD) along the human genome and in the population under study. Although understanding the characteristics of this correlation has been the focus of many large-scale surveys (culminating in genomewide haplotype maps), the results of different studies have yielded wide-ranging estimates. Since understanding these differences (and whether they can be reconciled) has important implications for whole-genome association studies, in this article we dissect biases in these estimations that are due to known aspects of study design and analytic methodology. In particular, we document in the empirical data that the long-known complicating effects of allele frequency, marker density, and sample size largely reconcile all large-scale surveys. Two exceptions are an underappraisal of redundancy among single-nucleotide polymorphisms (SNPs) when evaluation is limited to short regions (as in candidate-gene resequencing studies) and an inflation in the extent of LD in HapMap phase I, which is likely due to oversampling of specific haplotypes in the creation of the public SNP map. Understanding these factors can guide the understanding of empirical LD surveys and has implications for genetic association studies.

Genome, Human↗
PubMed2006

Evaluating potential for whole-genome studies in Kosrae, an isolated population in Micronesia.

Whole-genome association studies are predicted to be especially powerful in isolated populations owing to increased linkage disequilibrium (LD) and decreased allelic diversity, but this possibility has not been empirically tested. We compared genome-wide data on 113,240 SNPs typed on 30 trios from the Pacific island of Kosrae to the same markers typed in the 270 samples from the International HapMap Project. The extent of LD is longer and haplotype diversity is lower in Kosrae than in the HapMap populations. More than 98% of Kosraen haplotypes are present in HapMap populations, indicating that HapMap will be useful for genetic studies on Kosrae. The long-range LD around common alleles and limited diversity result in improved efficiency in genetic studies in this population and augments the power to detect association of 'hidden SNPs'.

Alleles↗
PubMed2006

Optimal two-stage genotyping designs for genome-wide association scans.

The much-anticipated fixed-array, genome-wide SNP genotyping technologies make large-scale genome-wide association scans now possible for large numbers of subjects. In this paper we reconsider the problem (Satagopan and Elston [2003] Genet Epidemiol 25:149-157) of optimizing a two-stage genotyping design to deal with important new issues that are relevant when studies are expanded from candidate gene size to a genome-wide scale. We investigate how the basic two-stage genotyping approach, in which all markers are genotyped in an initial group of subjects (stage I) and only the promising markers are genotyped in additional subjects (stage II), can be used to reduce genotyping cost in a genome-wide case-control association study even after allowing for much higher per genotype costs using specially designed assays in stage II, compared to the fixed array of SNPs used in stage I. In addition, we consider the problem of using measured SNPs to make (imperfect) prediction of unmeasured SNPs for association tests of all SNPs (measured or unmeasured) genome wide and the utility of expanding genotyping densities in stage II in the regions where significant associations were detected in stage I. Under a set of reasonable but conservative assumptions, we derive optimal two-stage design configurations (sample sizes and the thresholds of significance in both stages) with these optimal designs depending both on the total number of markers tested and upon the ratios of cost in stage II versus stage I. In addition we show how existing software for power and sample size calculations can be used for the purpose of designing two-stage studies, for a wide range of assumptions about the number of markers genotyped and the costs of genotyping in each stage of the study.

Case-Control Studies↗
PubMed2005

Efficiency and power in genetic association studies.

We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.

Algorithms↗
PubMed2005

Trick or treat: the effect of placebo on the power of pharmacogenetic association studies.

The genetic mapping of drug-response traits is often characterised by a poor signal-to-noise ratio that is placebo related and which distinguishes pharmacogenetic association studies from classical case-control studies for disease susceptibility. The goal of this study was to evaluate the statistical power of candidate gene association studies under different pharmacogenetic scenarios, with special emphasis on the placebo effect. Genotype/phenotype data were simulated, mimicking samples from clinical trials, and response to the drug was modelled as a binary trait. Association was evaluated by a logistic regression model. Statistical power was estimated as a function of the number of single nucleotide polymorphisms (SNPs) genotyped, the frequency of the placebo 'response', the genotype relative risk (GRR) of the response polymorphism, the strategy for selecting SNPs for genotyping, the number of individuals in the trial and the ratio of placebo-treated to drug-treated patients. We show that: (i) the placebo 'response' strongly affects the statistical power of association studies--even a highly penetrant drug-response allele requires at least a 500-patient trial in order to reach 80 per cent power, several-fold more than the value estimated by standard tools that are not calibrated to pharmacogenetics; (ii) the power of a pharmacogenetic association study depends primarily on the penetrance of the response genotype and, when this penetrance is fixed, power decreases for larger placebo effects; (iii) power is dramatically increased when adding markers; (iv) an optimal study design includes a similar number of placebo- and drug-treated patients; and (v) in this setting, straightforward haplotype analysis does not seem to have an advantage over single marker analysis.

Clinical Trials as Topic↗
PubMed2004

Proteomic signatures: amino acid and oligopeptide compositions differentiate among phyla.

Availability of complete genome sequences allows in-depth comparison of single-residue and oligopeptide compositions of the corresponding proteomes. We have used principal component analysis (PCA) to study the landscape of compositional motifs across more than 70 genera from all three superkingdoms. Unexpectedly, the first two principal components clearly differentiate archaea, eubacteria, and eukaryota from each other. In particular, we contrast compositional patterns typical of the three superkingdoms and characterize differences between species and phyla, as well as among patterns shared by all compositional proteomic signatures. These species-specific patterns may even extend to subsets of the entire proteome, such as proteins pertaining to individual yeast chromosomes. We identify factors that affect compositional signatures, such as living habitat, and detect strong eukaryotic preference for homopeptides and palindromic tripeptides. We further detect oligopeptides that are either universally over- or underabundant across the whole proteomic landscape, as well as oligopeptides whose over- or underabundance is phylum- or species-specific. Finally, we report that species composition signatures preserve evolutionary memory, providing a new method to compare phylogenetic relationships among species that avoids problems of sequence alignment and ortholog detection.

Amino Acid Motifs↗
PubMed2004

Recovering frequencies of known haplotype blocks from single-nucleotide polymorphism allele frequencies.

Prospects for large-scale association studies rely on economical methods and powerful analysis. Representing available SNPs by small subsets and measuring allele frequencies on pooled DNA samples each improve genotyping cost effectiveness, while haplotype analysis may highlight associations in otherwise underpowered studies. This manuscript provides the mathematical framework to integrate these methodologies.

Gene Frequency↗
PubMed2003

On the applicability of a haplotype map to un-assayed populations.

The rationale for mapping all common haplotypes in our species relies on reports of the conservation of haplotype blocks across human populations. Recent findings indicate that these blocks may, at least in part, be a random artifact of genetic drift. This raises the concern that the latter process may challenge the general applicability of a human haplotype map to case-by-case population-specific association studies. We develop arguments indicating that even stochastic drift-originated blocks will, under many conditions, be shared across populations, supporting the utilization of a panhuman haplotype map.

Alleles↗
PubMed2003

Advanced computational techniques for re-sequencing DNA with polymerase signaling assay arrays.

Re-sequencing, the identification of the specific variants in a sequence of interest compared with a known genomic sequence, is a ubiquitous task in today's biology. Universal arrays, which interrogate all possible oligonucleotides of a certain length in a target sequence, have been suggested for computationally determining a polynucleotide sequence from its oligonucleotide content. We present here new methods that use such arrays for re-sequencing. Our methods are applied to data obtained by the polymerase signaling assay, which arrays single-based primer extension reactions for either universal or partial arrays of pentanucleotides. The computational analysis uses the spectrum alignment algorithm, which is refined and enhanced here in order to overcome noise incurred by the use of such short primers. We present accurate re-sequencing results for both synthetic and amplified DNA molecules.

Algorithms↗
PubMed2002

A computational method for resequencing long DNA targets by universal oligonucleotide arrays.

Universal arrays contain all possible oligonucleotides of a certain length, typically 6-10 bases. They can determine in a single experiment all substrings of that length that occur along a target sequence. That information, also called the spectrum of the sequence, is not sufficient to uniquely reconstruct a sequence longer than a few hundred bases. We have devised a polynomial algorithm that reconstructs the sequence, given the spectrum and an additional reference sequence, homologous to the target sequence. Such a reference is available, for example, in the identification of single-nucleotide polymorphisms. The algorithm can handle errors in the spectrum as well as substitutions, insertions, and deletions in the target sequence. We present extensive simulation results, which show that the algorithm correctly reconstructs target sequences of >2,000 nucleotides from error-prone 8-mer spectra when realistic levels of single-nucleotide polymorphisms are present.

Algorithms↗
PubMed2002

A structural EM algorithm for phylogenetic inference.

A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural Expectation Maximization (EM) for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-step, we use the current tree topology and edge lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a suboptimal one. To escape from such "local optima," we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.

Algorithms↗
PubMed2002

A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: Application to the evolution of five gene families.

MOTIVATION: We developed an algorithm to reconstruct ancestral sequences, taking into account the rate variation among sites of the protein sequences. Our algorithm maximizes the joint probability of the ancestral sequences, assuming that the rate is gamma distributed among sites. Our algorithm probably finds the global maximum. The use of 'joint' reconstruction is motivated by studies that use the sequences at all the internal nodes in a phylogenetic tree, such as, for instance, the inference of patterns of amino-acid replacement, or tracing the biochemical changes that occurred during the evolution of a given protein family. RESULTS: We give an algorithm that guarantees finding the global maximum. The efficient search method makes our method applicable to datasets with large number sequences. We analyze ancestral sequences of five gene families, exploring the effect of the amount of among-site-rate-variation, and the degree of sequence divergence on the resulting ancestral states. AVAILABILITY AND SUPPLEMENTARY INFORMATION: http://evolu3.ism.ac.jp/~tal/ CONTACT: tal@ism.ac.jp

Algorithms↗