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We began analyzing https://www.nature.com/articles/6800717, but it redirected us to https://www.nature.com/articles/6800717. The analysis below is for the second page.

Title[redir]:
Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix | Heredity
Description:
Correlated multiple testing is widely performed in genetic research, particularly in multilocus analyses of complex diseases. Failure to control appropriately for the effect of multiple testing will either result in a flood of false-positive claims or in true hits being overlooked. Cheverud proposed the idea of adjusting correlated tests as if they were independent, according to an ‘effective number’ (Meff) of independent tests. However, our experience has indicated that Cheverud

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Keywords {🔍}

meff, tests, equation, article, analyses, data, google, scholar, pubmed, test, multilocus, fdr, correlated, independent, correlation, method, control, cas, multiple, number, testing, loci, procedure, nature, permutation, ewsl, cheverud, estimate, rate, snps, hypotheses, table, genetic, proposed, methods, rejected, singlelocus, benjamini, applied, genet, eigenvalues, discovery, equal, stepup, study, eigenvalue, central, analysis, false, step,

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nature portfolio permissions reprints privacy policy [18f]fe-pe2i dat correlates genetic research experiment-wise significant level nature point-wise significant level advertising social media author information authors fractionation/mass spectrometry platform published schizophrenia data author correspondence multifactor dimensionality reduction mathematical multi-locus approaches suggested resampling-based methods false discovery rate dual-task test verbal angiotensin-i-converting enzyme false discovery rates employed resampling-based methods data-driven algorithm based detecting gene-gene interactions permissions generous sharing china genome-wise type design meff-based procedures quantitative trait mapping simulated case–control data adjusting multiple testing data-driven meff method false-positive claims correlated multiple testing environmental risk factors privacy resampling-based methods single-locus analysis keavney data set explore content similar content k-locus analysis performance single-locus analyses journals search log low replication rate computing approximate thresholds composite interval mapping schizophrenia data set nonintegral part represents

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         description:Correlated multiple testing is widely performed in genetic research, particularly in multilocus analyses of complex diseases. Failure to control appropriately for the effect of multiple testing will either result in a flood of false-positive claims or in true hits being overlooked. Cheverud proposed the idea of adjusting correlated tests as if they were independent, according to an âeffective numberâ (Meff) of independent tests. However, our experience has indicated that Cheverud's estimate of the Meff is overly large and will lead to excessively conservative results. We propose a more accurate estimate of the Meff, and design Meff-based procedures to control the experiment-wise significant level and the false discovery rate. In an evaluation, based on both real and simulated data, the Meff-based procedures were able to control the error rate accurately and consequently resulted in a power increase, especially in multilocus analyses. The results confirm that the Meff is a useful concept in the error-rate control of correlated tests. With its efficiency and accuracy, the Meff method provides an alternative to computationally intensive methods such as the permutation test.
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