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Biomedical subjects

L Kruglyak

Publications and source records attributed to L Kruglyak.

At least 19 recordsLinked to original sources
PubMed2000

Precise gene localization by phenotypic assay of radiation hybrid cells.

A high resolution map of the human genome previously has been constructed by using the G3 panel of human/hamster radiation hybrid cell lines and >15,000 unique human genetic markers. By determining whether human DNA sequences are present or absent in each of the hybrids, localization of single genes may routinely be achieved at approximately 250-kb resolution. In this paper we have tested whether similarly precise localization might be achieved by phenotypic screening of the hybrids to facilitate positional cloning of unknown genes. We assayed the susceptibility of each of the hybrid cell lines to transduction by retroviral vectors bearing different retroviral envelope proteins that recognize receptors present on human but not on hamster cells. The results for each of the retroviral vectors were informative and allowed precise localization of the receptor genes for the RD114 cat endogenous retrovirus, xenotropic murine leukemia virus, and type C feline leukemia virus. After cloning of the receptors for these retroviruses, we found that standard genotypic mapping by PCR gave results that were nearly identical to those from phenotypic mapping. These experiments show that precise gene localization by phenotypic assay of radiation hybrids is practical and was not appreciably impacted by the known instability of such hybrid cells. This technique should be applicable to many other human genes having discernible phenotypes in hamster cells and, with completion of the human genome project, will allow rapid identification of unknown genes on the basis of phenotype.

Animals↗
PubMed2000

An analysis of strategies for discovery of single-nucleotide polymorphisms.

Strategies for the discovery of single-nucleotide polymorphisms (SNPs) can be characterized by the number of individuals in the discovery sample, and by the minimal required number of observations of each allele. We examine the effect of different strategies on two key properties of the resulting SNP collection: (1) the probability that a SNP with a given population allele frequency is detected; and (2) the allele-frequency distribution of the discovered SNPs. We show that strategies that accept all polymorphic sites lead to collections with a high fraction of SNPs with rare minor alleles, particularly in expanded populations. Such SNPs have a low probability of replication in a second sample. We discuss how to tailor a discovery strategy to the desired properties of a SNP collection.

Alleles↗
PubMed2000

Sampling SNPs.

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Alleles↗
PubMed2000

Full-genome scan for linkage in 50 families segregating the bipolar affective disease phenotype.

A genome scan of approximately 12-cM initial resolution was done on 50 of a set of 51 carefully ascertained unilineal multiplex families segregating the bipolar affective disorder phenotype. In addition to standard multipoint linkage analysis methods, a simultaneous-search algorithm was applied in an attempt to surmount the problem of genetic heterogeneity. The results revealed no linkage across the genome. The results exclude monogenic models and make it unlikely that two genes account for the disease in this sample. These results support the conclusion that at least several hundred kindreds will be required in order to establish linkage of susceptibility loci to bipolar disorder in heterogeneous populations.

Bipolar Disorder↗
PubMed2000

Exact multipoint quantitative-trait linkage analysis in pedigrees by variance components.

Methods based on variance components are powerful tools for linkage analysis of quantitative traits, because they allow simultaneous consideration of all pedigree members. The central idea is to identify loci making a significant contribution to the population variance of a trait, by use of allele-sharing probabilities derived from genotyped marker loci. The technique is only as powerful as the methods used to infer these probabilities, but, to date, no implementation has made full use of the inheritance information in mapping data. Here we present a new implementation that uses an exact multipoint algorithm to extract the full probability distribution of allele sharing at every point in a mapped region. At each locus in the region, the program fits a model that partitions total phenotypic variance into components due to environmental factors, a major gene at the locus, and other unlinked genes. Numerical methods are used to derive maximum-likelihood estimates of the variance components, under the assumption of multivariate normality. A likelihood-ratio test is then applied to detect any significant effect of the hypothesized major gene. Simulations show the method to have greater power than does traditional sib-pair analysis. The method is freely available in a new release of the software package GENEHUNTER.

Algorithms↗
PubMed1999

Prospects for whole-genome linkage disequilibrium mapping of common disease genes.

Recently, attention has focused on the use of whole-genome linkage disequilibrium (LD) studies to map common disease genes. Such studies would employ a dense map of single nucleotide polymorphisms (SNPs) to detect association between a marker and disease. Construction of SNP maps is currently underway. An essential issue yet to be settled is the required marker density of such maps. Here, I use population simulations to estimate the extent of LD surrounding common gene variants in the general human population as well as in isolated populations. Two main conclusions emerge from these investigations. First, a useful level of LD is unlikely to extend beyond an average distance of roughly 3 kb in the general population, which implies that approximately 500,000 SNPs will be required for whole-genome studies. Second, the extent of LD is similar in isolated populations unless the founding bottleneck is very narrow or the frequency of the variant is low (<5%).

Chromosome Mapping↗
PubMed1998

Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome.

Single-nucleotide polymorphisms (SNPs) are the most frequent type of variation in the human genome, and they provide powerful tools for a variety of medical genetic studies. In a large-scale survey for SNPs, 2.3 megabases of human genomic DNA was examined by a combination of gel-based sequencing and high-density variation-detection DNA chips. A total of 3241 candidate SNPs were identified. A genetic map was constructed showing the location of 2227 of these SNPs. Prototype genotyping chips were developed that allow simultaneous genotyping of 500 SNPs. The results provide a characterization of human diversity at the nucleotide level and demonstrate the feasibility of large-scale identification of human SNPs.

Algorithms↗
PubMed1998

Sib-pair collection strategies for complex diseases.

When planning an affected sib pair collection for use in a genomewide search for complex trait loci, researchers must ask: (a) Which family structures will yield the most informative pairs? and (b) Should recruitment extend beyond the index sib pair? The optimal collection strategy will depend on the trait's genetic architecture, but this is rarely known for non-Mendelian diseases. In the present report, we study the consequences of collecting only those sib pairs arising from pedigrees with a precisely specified structure as opposed to a strategy that collects all affected sib pairs at random (i.e., blind to the affection status of first-degree relatives). The former approach turns out to be risky because the power of specific pedigree structures can vary dramatically even among models producing identical observable parameters (such as population prevalence and sibling recurrence rate). In contrast, the latter approach typically involves only a modest loss of power as compared with the optimal (but unknowable) design. Further, we compare the strategy of collecting all affected sib pairs at random with the alternative of imposing some modest limitations on family structure (e.g., presence of at least one unaffected sib or parent). The latter approach generally provides some increase in power but entails additional effort to contact and phenotype relatives: the overall merit of imposing such requirements needs to be evaluated in the context of the specific disease to be studied and of the clinical and analytical resources available. In addition, these findings suggest that a further explanation for failure to replicate positive complex trait linkages lies in differences in ascertainment strategy between data sets.

Data Collection↗
PubMed1998

Faster multipoint linkage analysis using Fourier transforms.

Genetic linkage analysis of human pedigrees using many linked markers simultaneously is a difficult computational problem. We have previously described an approach to this problem that uses hidden Markov models (HMMs) and is quite efficient for pedigrees of moderate size. Here, we describe a new, faster algorithm for the key step in the HMM calculation. The algorithm employs a fast Fourier transform on the group of pedigree inheritance patterns. It substantially improves the overall performance of the software package GENEHUNTER for performing linkage analysis. The Fourier representation opens up new research directions for pedigree analysis.

Algorithms↗
PubMed1998

Genome scan of schizophrenia.

OBJECTIVE: The goal of this study was to identify chromosomal regions likely to contain schizophrenia susceptibility genes. METHOD: A genomewide map of 310 microsatellite DNA markers with average spacing of 11 centimorgans was genotyped in 269 individuals--126 of them with schizophrenia-related psychoses--from 43 pedigrees. Nonparametric linkage analysis was used to assess the pattern of allele sharing at each marker locus relative to the presence of disease. RESULTS: Nonparametric linkage scores did not reach a genomewide level of statistical significance for any marker. There were five chromosomal regions in which empirically derived p values reached nominal levels of significance at eight marker locations. There were p values less than 0.01 at chromosomes 2q (with the peak value in this region at D2S410) and 10q (D10S1239), and there were p values less than 0.05 at chromosomes 4q (D4S2623), 9q (D9S257), and 11q (D11S2002). CONCLUSIONS: The results do not support the hypothesis that a single gene causes a large increase in the risk of schizophrenia. The sample (like most others being studied for psychiatric disorders) has limited power to detect genes of small effect or those that are determinants of risk in a small proportion of families. All of the most positive results could be due to chance, or some could reflect weak linkage (genes of small effect). Multicenter studies may be useful in the effort to identify chromosomal regions most likely to contain schizophrenia susceptibility genes.

Chromosome Mapping↗