Results 11  20
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51
Interviews and correspondance
 Managed Health Care (SGC) Office, Wright Patterson AFB OH
, 1992
"... Ciliostasis is a key early event during colonization of canine tracheal tissue by Bordetella bronchiseptica ..."
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Ciliostasis is a key early event during colonization of canine tracheal tissue by Bordetella bronchiseptica
C (2010) Theoretical formulation of principal components analysis to detect and correct for population stratification
 PLoS ONE
"... The Eigenstrat method, based on principal components analysis (PCA), is commonly used both to quantify population relationships in population genetics and to correct for population stratification in genomewide association studies. However, it can be difficult to make appropriate inference about pop ..."
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The Eigenstrat method, based on principal components analysis (PCA), is commonly used both to quantify population relationships in population genetics and to correct for population stratification in genomewide association studies. However, it can be difficult to make appropriate inference about population relationships from the principal component (PC) scatter plot. Here, to better understand the working mechanism of the Eigenstrat method, we consider its theoretical or ‘‘population’ ’ formulation. The eigenequation for samples from an arbitrary number (K) of populations is reduced to that of a matrix of dimension K, the elements of which are determined by the variancecovariance matrix for the random vector of the K allele frequencies. Solving the reduced eigenequation is numerically trivial and yields eigenvectors that are the axes of variation required for differentiating the populations. Using the reduced eigenequation, we investigate the withinpopulation fluctuations around the axes of variation on the PC scatter plot for simulated datasets. Specifically, we show that there exists an asymptotically stable pattern of the PC plot for large sample size. Our results provide theoretical guidance for interpreting the pattern of PC plot in terms of population relationships. For applications in genetic association tests, we demonstrate that, as a method of correcting for population stratification, regressing out the theoretical PCs corresponding to the axes of variation is equivalent to simply removing the population mean of allele counts and works as well as or better than the Eigenstrat method.
Principal Components Analysis of Population Admixture
, 2012
"... With the availability of highdensity genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology. An important issue is how to make appropriate and correct inferences ..."
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With the availability of highdensity genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology. An important issue is how to make appropriate and correct inferences about population relationships from the results of PCA, especially when admixed individuals are included in the analysis. We extend our recently developed theoretical formulation of PCA to allow for admixed populations. Because the sampled individuals are treated as features, our generalized formulation of PCA directly relates the pattern of the scatter plot of the top eigenvectors to the admixture proportions and parameters reflecting the population relationships, and thus can provide valuable guidance on how to properly interpret the results of PCA in practice. Using our formulation, we theoretically justify the diagnostic of twoway admixture. More importantly, our theoretical investigations based on the proposed formulation yield a diagnostic of multiway admixture. For instance, we found that admixed individuals with three parental populations are distributed inside the triangle formed by their parental populations and divide the triangle into three smaller triangles whose areas have the same proportions in the big triangle as the corresponding admixture proportions. We tested and illustrated these findings using simulated data and data from HapMap III and the Human Genome Diversity Project.
ARTICLE ROADTRIPS: CaseControl Association Testing with Partially or Completely Unknown Population and Pedigree Structure
"... fy ur g f ag re at ..."
Funding Information No funding.
, 2013
"... Understanding factors regulating hybrid fitness and gene exchange is a major research challenge for evolutionary biology. Genomic cline analysis has been used to evaluate alternative patterns of introgression, but only two models have been used widely and the approach has generally lacked a hypothes ..."
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Understanding factors regulating hybrid fitness and gene exchange is a major research challenge for evolutionary biology. Genomic cline analysis has been used to evaluate alternative patterns of introgression, but only two models have been used widely and the approach has generally lacked a hypothesis testing framework for distinguishing effects of selection and drift. I propose two alternative cline models, implement multivariate outlier detection to identify markers associated with hybrid fitness, and simulate hybrid zone dynamics to evaluate the signatures of different modes of selection. Analysis of simulated data shows that previous approaches are prone to false positives (multinomial regression) or relatively insensitive to outlier loci affected by selection (Barton’s concordance). The new, theorybased logitlogistic cline model is generally best at detecting loci affecting hybrid fitness. Although some generalizations can be made about different modes of selection, there is no onetoone correspondence between pattern and process. These new methods will enhance our ability to extract important information about the genetics of reproductive isolation and hybrid fitness. However, much remains to be done to relate statistical patterns to particular evolutionary processes. The methods described here are implemented in a freely available package “HIest ” for the R statistical software
DOI:10.1093/sysbio/sys038 A Unifying Model for the Analysis of Phenotypic, Genetic, and Geographic Data
"... Abstract.—Recognition of evolutionary units (species, populations) requires integrating several kinds of data, such as genetic or phenotypic markers or spatial information in order to get a comprehensive view concerning the differentiation of the units. We propose a statistical model with a double ..."
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Abstract.—Recognition of evolutionary units (species, populations) requires integrating several kinds of data, such as genetic or phenotypic markers or spatial information in order to get a comprehensive view concerning the differentiation of the units. We propose a statistical model with a double original advantage: (i) it incorporates information about the spatial distribution of the samples, with the aim to increase inference power and to relate more explicitly observed patterns to geography and (ii) it allows one to analyze genetic and phenotypic data within a unified model and inference framework, thus opening the way to robust comparisons between markers and possibly combined analyses. We show from simulated data as well as real data that our method estimates parameters accurately and is an improvement over alternative approaches in many situations. The power of this method is exemplified using an intricate case of inter and intraspecies differentiation based on an original data set of georeferenced genetic and morphometric markers obtained on Myodes voles from Sweden.
Why Some Families of Probability Distributions Are Practically Efficient: A SymmetryBased Explanation
"... Abstract Out of many possible families of probability distributions, some families turned out to be most efficient in practical situations. Why these particular families and not others? To explain this empirical success, we formulate the general problem of selecting a distribution with the largest p ..."
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Abstract Out of many possible families of probability distributions, some families turned out to be most efficient in practical situations. Why these particular families and not others? To explain this empirical success, we formulate the general problem of selecting a distribution with the largest possible utility under appropriate constraints. We then show that if we select the utility functional and the constraints which are invariant under natural symmetries – shift and scaling corresponding to changing the starting point and the measuring unit for describing the corresponding quantity x. then the resulting optimal families of probability distributions indeed include most of the empirically successful families. Thus, we get a symmetrybased explanation for their empirical success.
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"... the mapping of genetic loci that influence complex traits. A problem se ly ..."
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the mapping of genetic loci that influence complex traits. A problem se ly