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Simon Boitard - One of the best experts on this subject based on the ideXlab platform.
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linkage disequilibrium interval mapping of quantitative trait loci
BMC Genomics, 2006Co-Authors: Christine Ciercoayrolles, J M Abdallah, Simon Boitard, Hubert De Rochambeau, Brigitte ManginAbstract:Background For many years Gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of Gene Location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods.
Brigitte Mangin - One of the best experts on this subject based on the ideXlab platform.
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linkage disequilibrium interval mapping of quantitative trait loci
BMC Genomics, 2006Co-Authors: Christine Ciercoayrolles, J M Abdallah, Simon Boitard, Hubert De Rochambeau, Brigitte ManginAbstract:Background For many years Gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of Gene Location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods.
Christine Ciercoayrolles - One of the best experts on this subject based on the ideXlab platform.
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linkage disequilibrium interval mapping of quantitative trait loci
BMC Genomics, 2006Co-Authors: Christine Ciercoayrolles, J M Abdallah, Simon Boitard, Hubert De Rochambeau, Brigitte ManginAbstract:Background For many years Gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of Gene Location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods.
Edward C. Cox - One of the best experts on this subject based on the ideXlab platform.
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Gene Location and DNA Density Determine Transcription Factor Distributions in E. Coli
Biophysical Journal, 2013Co-Authors: Thomas E. Kuhlman, Edward C. CoxAbstract:The diffusion coefficient of the canonical transcription factor Lac Repressor, LacI, within living Escherichia coli has been measured directly by in vivo tracking to be D = 0.4 μm∧2/s. At this rate, simple models of diffusion lead to the expectation that LacI and other proteins will rapidly homogenize throughout the cell.We have tested this expectation of spatial homoGeneity by high-throughput single molecule visualization of LacI molecules non-specifically bound to DNA in fixed cells to Generate an ensemble average of the steady-state distribution of protein in the cell. Contrary to expectation, we find that the distribution of LacI depends on the spatial Location of its encoding Gene. We demonstrate that the spatial distribution of LacI is also determined by the local state of DNA compaction, and that E. coli can dynamically redistribute proteins by modifying the state of its nucleoid. We then show that LacI inhomoGeneity increases the strength with which targets located proximally to the LacI Gene are regulated. Finally, we propose a model for intranucleoid diffusion which can reconcile these results with previous measurements of LacI diffusion.
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Gene Location and DNA density determine transcription factor distributions in Escherichia coli
Molecular systems biology, 2012Co-Authors: Thomas E. Kuhlman, Edward C. CoxAbstract:The diffusion coefficient of the transcription factor LacI within living Escherichia coli has been measured directly by in vivo tracking to be D=0.4 μm2/s. At this rate, simple models of diffusion lead to the expectation that LacI and other proteins will rapidly homogenize throughout the cell. Here, we test this expectation of spatial homoGeneity by single-molecule visualization of LacI molecules non-specifically bound to DNA in fixed cells. Contrary to expectation, we find that the distribution depends on the spatial Location of its encoding Gene. We demonstrate that the spatial distribution of LacI is also determined by the local state of DNA compaction, and that E. coli can dynamically redistribute proteins by modifying the state of its nucleoid. Finally, we show that LacI inhomoGeneity increases the strength with which targets located proximally to the LacI Gene are regulated. We propose a model for intranucleoid diffusion that can reconcile these results with previous measurements of LacI diffusion, and we discuss the implications of these findings for Gene regulation in bacteria and eukaryotes.
B S Weir - One of the best experts on this subject based on the ideXlab platform.
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measures of human population structure show heteroGeneity among genomic regions
Genome Research, 2005Co-Authors: B S Weir, Lon R Cardon, Amy D Anderson, Dahlia M Nielsen, William G HillAbstract:Publication of the Perlegen SNP data set (Hinds et al. 2005) and completion of Phase I of the International HapMap Project (The International HapMap Consortium 2005) have allowed a new perspective on the Genetic structure of human populations. These two whole-genome data sets allow population Genetic analyses at an unprecedented scale: Previous estimates of Genetic population structure (for review, see Garte 2003) have been based on a limited number of loci and provided only average figures of quantities such as FST (Wright 1951) across the whole genome. The precision of previous estimates is not high, and they relate only to specific Genes rather than to the region in which the markers are located. We can expect there to be some diversity in the magnitude of population structure between regions of the genome because the precise Genealogy is not the same for each chromosome or part thereof, with values becoming increasingly similar the more closely linked are the regions. The Genealogy can differ both by random events and by non-random events such as selection. Strong selection at a locus will induce hitch-hiking of nearby regions (Maynard Smith and Haigh 1974), leading to both a reduction in heterozygosity within populations and an increase in diversity between populations as measured by FST. Examination of the differences in diversity between regions therefore provides an opportunity to identify those that cannot be explained solely in terms of random sampling of the Genealogy due to Mendelian segregation, variation in family size, migration, and recombination between Genetic sites. Methods for estimating FST from samples of a group of populations are well established (e.g., Weir and Cockerham 1984). More recently they have been discussed for estimating values separately for each of a set of populations assumed to come from a common founder, but which may differ both in their times of divergence from each other and in the sizes of the populations (Weir and Hill 2002; Shriver et al. 2004). The stochastic nature of evolution means that the actual allele frequencies in a population differ from the expected values, and the population-specific FST describes the variance of allele frequencies about the means for that population. Because there is only one realization of the population, the variance is estimated from the allele frequencies of that population and at least one other population. The average of the population-specific values is the usual (population-average) FST, and its estimate is proportional to the sample variance in allele frequencies among the sampled populations. It serves as a measure of Genetic differentiation of the populations, and, in the case of population divergence being due to Genetic drift, the value for each pair of populations serves as a measure of time since diverging from an ancestral population. Because there is not replication of each of the populations studied, the population-specific and population-average values are relative to the value in their ancestral population. In this paper we compute values of FST from all autosomes in the Perlegen and HapMap data sets, but we use only those SNPs that were found to be segregating in all population samples within each data set. Our estimates are calculated for all markers separately and also for all markers in all the 5-Mb windows centered on each SNP in the autosomal genome. The numbers of markers used are shown in Table 1. We find substantial diversity in these measures, and we attempt to explain how much of this can be attributed to sampling of different kinds. We consider the data as a function of the number and choice of sites in the region, and as a function of the individuals that comprise the sample. We predict the variation in identity at individual regions and their covariance with other regions expected from the sampling in Genealogy of the population. Further, we examine the results to reveal regions associated with known Genes that have been under selection in one or more of the populations so as to consider the utility of FST measures in Gene Location or in detecting signatures of past selective events. Table 1. Chromosome lengths and numbers of markers segregating in all samples within a data set
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maximum likelihood estimation of Gene Location by linkage disequilibrium
American Journal of Human Genetics, 1994Co-Authors: William G Hill, B S WeirAbstract:Linkage disequilibrium, D, between a polymorphic disease and mapped markers can, in principle, be used to help find the map position of the disease Gene. Likelihoods are therefore derived for the value of D conditional on the observed number of haplotypes in the sample and on the population parameter Nc, where N is the effective population size and c the recombination fraction between the disease and marker loci. The likelihood is computed explicitly for the case of two loci with heterozygote superiority and, more Generally, by computer simulations assuming a steady state of constant population size and selective pressures or neutrality. It is found that the likelihood is, in General, not very dependent on the degree of selection at the loci and is very flat. This suggests that precise information on map position will not be obtained from estimates of linkage disequilibrium. 15 refs., 5 figs., 21 tabs.
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maximum likelihood estimation of Gene Location by linkage disequilibrium
American Journal of Human Genetics, 1994Co-Authors: William G Hill, B S WeirAbstract:Linkage disequilibrium, D, between a polymorphic disease and mapped markers can, in principle, be used to help find the map position of the disease Gene. Likelihoods are therefore derived for the value of D conditional on the observed number of haplotypes in the sample and on the population parameter Nc, where N is the effective population size and c the recombination fraction between the disease and marker loci. The likelihood is computed explicitly for the case of two loci with heterozygote superiority and, more Generally, by computer simulations assuming a steady state of constant population size and selective pressures or neutrality. It is found that the likelihood is, in General, not very dependent on the degree of selection at the loci and is very flat. This suggests that precise information on map position will not be obtained from estimates of linkage disequilibrium.