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Efficient Control of Population Structure in Model Organism Association Mapping

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      Most cited references 52

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      Statistical significance for genomewide studies.

      With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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        Principal components analysis corrects for stratification in genome-wide association studies.

        Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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          A Simplex Method for Function Minimization

           J Nelder,  R Mead (1965)
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            Author and article information

            Journal
            Genetics
            Genetics
            Genetics Society of America
            0016-6731
            1943-2631
            April 01 2008
            March 2008
            March 2008
            April 01 2008
            : 178
            : 3
            : 1709-1723
            10.1534/genetics.107.080101
            © 2008

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