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      PSMIX: an R package for population structure inference via maximum likelihood method

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      1 , 2 , 3 , 4 ,
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings.

          Results

          We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture.

          Conclusion

          Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy, but more efficient computations.

          PSMIX and its supplemental documents are freely available at http://bioinformatics.med.yale.edu/PSMIX.

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          Most cited references41

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          Detecting immigration by using multilocus genotypes.

          Immigration is an important force shaping the social structure, evolution, and genetics of populations. A statistical method is presented that uses multilocus genotypes to identify individuals who are immigrants, or have recent immigrant ancestry. The method is appropriate for use with allozymes, microsatellites, or restriction fragment length polymorphisms (RFLPs) and assumes linkage equilibrium among loci. Potential applications include studies of dispersal among natural populations of animals and plants, human evolutionary studies, and typing zoo animals of unknown origin (for use in captive breeding programs). The method is illustrated by analyzing RFLP genotypes in samples of humans from Australian, Japanese, New Guinean, and Senegalese populations. The test has power to detect immigrant ancestors, for these data, up to two generations in the past even though the overall differentiation of allele frequencies among populations is low.
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            Population stratification and spurious allelic association.

            Great efforts and expense have been expended in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, technology for detection and scoring of single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a means for investigation of the genetic causes of complex human diseases. For many diseases, population-based studies of unrelated individuals--in which case-control and cohort studies serve as standard designs for genetic association analysis--can be the most practical and powerful approach. However, extensive debate has arisen about optimum study design, and considerable concern has been expressed that these approaches are prone to population stratification, which can lead to biased or spurious results. Over the past decade, a great shift has been noted, away from case-control and cohort studies, towards family-based association designs. These designs have fewer problems with population stratification but have greater genotyping and sampling requirements, and data can be difficult or impossible to gather. We discuss past evidence for population stratification on genotype-phenotype association studies, review methods to detect and account for it, and present suggestions for future study design and analysis.
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              Searching for genetic determinants in the new millennium.

              N Risch (2000)
              Human genetics is now at a critical juncture. The molecular methods used successfully to identify the genes underlying rare mendelian syndromes are failing to find the numerous genes causing more common, familial, non-mendelian diseases. With the human genome sequence nearing completion, new opportunities are being presented for unravelling the complex genetic basis of non-mendelian disorders based on large-scale genome-wide studies. Considerable debate has arisen regarding the best approach to take. In this review I discuss these issues, together with suggestions for optimal post-genome strategies.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                2006
                22 June 2006
                : 7
                : 317
                Affiliations
                [1 ]Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
                [2 ]Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
                [3 ]Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, Yale University School of Medicine, New Haven, CT, USA
                [4 ]Department of Genetics, Yale University School of Medicine, New Haven, CT, Yale University School of Medicine, New Haven, CT, USA
                Article
                1471-2105-7-317
                10.1186/1471-2105-7-317
                1550430
                16792813
                284f3434-8607-4aad-a83e-4308297f60f0
                Copyright © 2006 Wu et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 January 2006
                : 22 June 2006
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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