Conservation Genetics

, Volume 7, Issue 5, pp 783–787 | Cite as

Beyond Bonferroni: Less conservative analyses for conservation genetics

Article

Abstract

Studies in conservation genetics often attempt to determine genetic differentiation between two or more temporally or geographically distinct sample collections. Pairwise p-values from Fisher’s exact tests or contingency Chi-square tests are commonly reported with a Bonferroni correction for multiple tests. While the Bonferroni correction controls the experiment-wise α, this correction is very conservative and results in greatly diminished power to detect differentiation among pairs of sample collections. An alternative is to control the false discovery rate (FDR) that provides increased power, but this method only maintains experiment-wise α when none of the pairwise comparisons are significant. Recent modifications to the FDR method provide a moderate approach to determining significance level. Simulations reveal that critical values of multiple comparison tests with both the Bonferroni method and a modified FDR method approach a minimum asymptote very near zero as the number of tests gets large, but the Bonferroni method approaches zero much more rapidly than the modified FDR method. I compared pairwise significance from three published studies using three critical values corresponding to Bonferroni, FDR, and modified FDR methods. Results suggest that the modified FDR method may provide the most biologically important critical value for evaluating significance of population differentiation in conservation genetics.␣Ultimately, more thorough reporting of statistical significance is needed to allow interpretation of biological significance of genetic differentiation among populations.

Keywords

Bonferroni conservation genetics false discovery rate multiple comparison tests 

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References

  1. Abbot CL, Double MC (2003) Genetic structure, conservation genetics and evidence of speciation by range expansion in shy and white capped albatrosses. Mol. Ecol. 12: 2953–2962PubMedCrossRefGoogle Scholar
  2. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57: 289–300Google Scholar
  3. Benjamini Y, Yekutieli D (2001) The control of false discovery rate under dependency. Ann. Stat 29: 1165–1188CrossRefGoogle Scholar
  4. Bickel D (2004) Degrees of differential gene expression: detecting biologically significant expression differences and estimating their magnitudes. Bioinformatics 20: 682–688PubMedCrossRefGoogle Scholar
  5. Clegg SM, Hale P, Moritz C (1998) Molecular population genetics of the red kangaroo (Macropus rufus): mtDNA variation. Mol. Ecol 7, 679–686PubMedCrossRefGoogle Scholar
  6. Garcia LV (2004) Escaping the Bonferroni iron claw in ecological studies. OIKOS 105: 657–663CrossRefGoogle Scholar
  7. Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical maps in functional neuroimage analysis using the false discovery rate. Neuroimage 15: 870–878PubMedCrossRefGoogle Scholar
  8. Hochberg Y (1988) A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75: 800–803CrossRefGoogle Scholar
  9. Holm S (1979) A simple sequential rejective multiple test procedure. Scand. J. Stat 6: 65–70Google Scholar
  10. Ludbrook J (1991) On making multiple comparisons in clinical and experimental pharmacology and physiology. Clin. Exp. Pharmacol. Physiol 18: 379–392PubMedGoogle Scholar
  11. Ludbrook J (1998) Multiple comparison procedures updated. Clin. Exp. Pharmacol. Physiol 25: 1032–1037PubMedGoogle Scholar
  12. Narum SR, Contor C, Talbot A, Powell M (2004) Genetic divergence of sympatric resident and anadromous forms of Oncorhynchus mykiss in the Walla Walla River and Columbia River Basin,U.S.A.. J.Fish Bio 65: 471–488CrossRefGoogle Scholar
  13. Reiner A, Yekutieli D, Benjamini Y (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19: 368–375PubMedCrossRefGoogle Scholar
  14. Rice WR (1989) Analyzing tables of statistical test. Evolution, 43: 223–225CrossRefGoogle Scholar
  15. Ryman N, Jorde PE (2001) Statistical power when testing for genetic differentiation. Mol. Ecol 10: 2361–2373PubMedCrossRefGoogle Scholar
  16. Storey JD (2002) A direct approach to false discovery rates. J. R. Stat. Soc. B 64: 479–498CrossRefGoogle Scholar
  17. Waples RS (1995) Evolutionary significant units and the conservation of biological diversity under the Endangered Species Act. Am. Fish. Soc. Symp 17: 8–27Google Scholar
  18. Weller JI, Song JZ, Heyen DW, Lewin HA, Ron M (1998) A new approach to the problem of multiple comparisons in the genetic dissection of complex traits. Genetics 150: 1699–1706PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Columbia River Inter-Tribal Fish CommissionHagermanUSA

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