Biomedical subjects

Gad Kimmel

Publications and source records attributed to Gad Kimmel.

6 recordsLinked to original sources

PubMed2006

A fast method for computing high-significance disease association in large population-based studies.

Because of rapid progress in genotyping techniques, many large-scale, genomewide disease-association studies are now under way. Typically, the disorders examined are multifactorial, and, therefore, researchers seeking association must consider interactions among loci and between loci and other factors. One of the challenges of large disease-association studies is obtaining accurate estimates of the significance of discovered associations. The linkage disequilibrium between SNPs makes the tests highly dependent, and dependency worsens when interactions are tested. The standard way of assigning significance (P value) is by a permutation test. Unfortunately, in large studies, it is prohibitively slow to compute low P values by this method. We present here a faster algorithm for accurately calculating low P values in case-control association studies. Unlike with several previous methods, we do not assume a specific distribution of the traits, given the genotypes. Our method is based on importance sampling and on accounting for the decay in linkage disequilibrium along the chromosome. The algorithm is dramatically faster than the standard permutation test. On data sets mimicking medium-to-large association studies, it speeds up computation by a factor of 5,000-100,000, sometimes reducing running times from years to minutes. Thus, our method significantly increases the problem-size range for which accurate, meaningful association results are attainable.

Algorithms↗

PubMed2006

A specific RAD51 haplotype increases breast cancer risk in Jewish non-Ashkenazi high-risk women.

While the precise genes involved in determining familial breast cancer risk in addition to BRCA1/2 are mostly unknown, one strong candidate is RAD51. Jewish non-Ashkenazi women at high-risk for breast/ovarian cancer and ethnically matched controls were genotyped using four single nucleotide polymorphisms spanning the RAD51 genomic region, and the resulting haplotypes were constructed using the GERBIL algorithm. A total of 314 individuals were genotyped: 184 non-Ashkenazi high-risk women (119 with breast cancer), and 130 unaffected, average-risk ethnically matched controls. Using GEBRIL, three frequent haplotypes were constructed. One of the haplotypes (TGTA - coined haplotype 3) was present in 7.3% (19/260 haplotypes) of controls (n=130) and in 16.8% (40/238 haplotypes) of high-risk breast cancer patients (n=119, P=0.001). A specific RAD51 haplotype is more prevalent among non-Ashkenazi Jewish high-risk women than in average-risk population.

Breast Neoplasms↗

PubMed2005

A block-free hidden Markov model for genotypes and its application to disease association.

We present a new stochastic model for genotype generation. The model offers a compromise between rigid block structure and no structure altogether: It reflects a general blocky structure of haplotypes, but also allows for "exchange" of haplotypes at nonboundary SNP sites; it also accommodates rare haplotypes and mutations. We use a hidden Markov model and infer its parameters by an expectation-maximization algorithm. The algorithm was implemented in a software package called HINT (haplotype inference tool) and tested on 58 datasets of genotypes. To evaluate the utility of the model in association studies, we used biological human data to create a simple disease association search scenario. When comparing HINT to three other models, HINT predicted association most accurately.

Algorithms↗

PubMed2005

Tag SNP selection in genotype data for maximizing SNP prediction accuracy.

MOTIVATION: The search for genetic regions associated with complex diseases, such as cancer or Alzheimer's disease, is an important challenge that may lead to better diagnosis and treatment. The existence of millions of DNA variations, primarily single nucleotide polymorphisms (SNPs), may allow the fine dissection of such associations. However, studies seeking disease association are limited by the cost of genotyping SNPs. Therefore, it is essential to find a small subset of informative SNPs (tag SNPs) that may be used as good representatives of the rest of the SNPs. RESULTS: We define a new natural measure for evaluating the prediction accuracy of a set of tag SNPs, and use it to develop a new method for tag SNPs selection. Our method is based on a novel algorithm that predicts the values of the rest of the SNPs given the tag SNPs. In contrast to most previous methods, our prediction algorithm uses the genotype information and not the haplotype information of the tag SNPs. Our method is very efficient, and it does not rely on having a block partition of the genomic region. We compared our method with two state-of-the-art tag SNP selection algorithms on 58 different genotype datasets from four different sources. Our method consistently found tag SNPs with considerably better prediction ability than the other methods. AVAILABILITY: The software is available from the authors on request.

Algorithms↗

PubMed2005

The incomplete perfect phylogeny haplotype problem.

The problem of resolving genotypes into haplotypes, under the perfect phylogeny model, has been under intensive study recently. All studies so far handled missing data entries in a heuristic manner. We prove that the perfect phylogeny haplotype problem is NP-complete when some of the data entries are missing, even when the phylogeny is rooted. We define a biologically motivated probabilistic model for genotype generation and for the way missing data occur. Under this model, we provide an algorithm, which takes an expected polynomial time. In tests on simulated data, our algorithm quickly resolves the genotypes under high rates of missing entries.

Algorithms↗

PubMed2004

GERBIL: Genotype resolution and block identification using likelihood.

The abundance of genotype data generated by individual and international efforts carries the promise of revolutionizing disease studies and the association of phenotypes with individual polymorphisms. A key challenge is providing an accurate resolution (phasing) of the genotypes into haplotypes. We present here results on a method for genotype phasing in the presence of recombination. Our analysis is based on a stochastic model for recombination-poor regions ("blocks"), in which haplotypes are generated from a small number of core haplotypes, allowing for mutations, rare recombinations, and errors. We formulate genotype resolution and block partitioning as a maximum-likelihood problem and solve it by an expectation-maximization algorithm. The algorithm was implemented in a software package called GERBIL (genotype resolution and block identification using likelihood), which is efficient and simple to use. We tested GERBIL on four large-scale sets of genotypes. It outperformed two state-of-the-art phasing algorithms. The phase algorithm was slightly more accurate than GERBIL when allowed to run with default parameters, but required two orders of magnitude more time. When using comparable running times, GERBIL was consistently more accurate. For data sets with hundreds of genotypes, the time required by phase becomes prohibitive. We conclude that GERBIL has a clear advantage for studies that include many hundreds of genotypes and, in particular, for large-scale disease studies.

Chromosome Mapping↗