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Combining probability from independent tests: the weighted Z ‐method is superior to Fisher's approach
Abstract
The most commonly used method in evolutionary biology for combining information across multiple tests of the same null hypothesis is Fisher's combined probability test. This note shows that an alternative method called the weighted Z ‐test has more power and more precision than does Fisher's test. Furthermore, in contrast to some statements in the literature, the weighted Z‐ method is superior to the unweighted Z ‐transform approach. The results in this note show that, when combining P ‐values from multiple tests of the same hypothesis, the weighted Z ‐method should be preferred.
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Cites
Analyzing Tables of Statistical Tests
Rice, W. R. (1989) · Evolution · doi:10.2307/2409177 · Cited by 14,571
meta-analysis bias in location and selection of studies
Egger, M., & Smith, G. D. (1998) · BMJ · doi:10.1136/bmj.316.7124.61 · Cited by 1,374
Cited by
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425
Systematic identification of trans eQTLs as putative drivers of known disease associations
Westra, H.-J., Peters, M. J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M. W., Fairfax, B. P., Schramm, K., Powell, J. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2756 · Cited by 1,658
Phenotype molding of stromal cells in the lung tumor microenvironment
Lambrechts, D., Wauters, E., Boeckx, B., Aibar, S., Nittner, D., Burton, O., Bassez, A., Decaluwé, H., Pircher, A., Van den Eynde, K., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0096-5 · Cited by 1,648
MAGIC, Heid, I. M., Jackson, A. U., Randall, J. C., Winkler, T. W., Qi, L., Steinthorsdottir, V., Thorleifsson, G., Zillikens, M. C., Speliotes, E. K., et al. (2010) · Nature Genetics · doi:10.1038/ng.685 · Cited by 931
Blount, Z. D., Borland, C. Z., & Lenski, R. E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803151105 · Cited by 840
The effect of host genetics on the gut microbiome
Bonder, M. J., Kurilshikov, A., Tigchelaar, E. F., Mujagic, Z., Imhann, F., Vila, A. V., Deelen, P., Vatanen, T., Schirmer, M., Smeekens, S. P., et al. (2016) · Nature Genetics · doi:10.1038/ng.3663 · Cited by 819
ITPA gene variants protect against anaemia in patients treated for chronic hepatitis C
Fellay, J., Thompson, A. J., Ge, D., Gumbs, C. E., Urban, T. J., Shianna, K. V., Little, L. D., Qiu, P., Bertelsen, A. H., Watson, M., et al. (2010) · Nature · doi:10.1038/nature08825 · Cited by 441
AGEMAP: A Gene Expression Database for Aging in Mice
Zahn, J. M., Poosala, S., Owen, A. B., Ingram, D. K., Lustig, A., Carter, A., Weeraratna, A. T., Taub, D. D., Gorospe, M., Mazan-Mamczarz, K., et al. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030201 · Cited by 431
Guilloux, J.-P., Douillard-Guilloux, G., Kota, R., Wang, X., Gardier, A. M., Martinowich, K., Tseng, G. C., Lewis, D. A., & Sibille, E. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.113 · Cited by 378
Defining the biological basis of radiomic phenotypes in lung cancer
Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife · doi:10.7554/elife.23421 · Cited by 365
Demerath, E. W., Guan, W., Grove, M. L., Aslibekyan, S., Mendelson, M., Zhou, Y.-H., Hedman, Å. K., Sandling, J. K., Li, L.-A., Irvin, M. R., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv161 · Cited by 355
When is hub gene selection better than standard meta-analysis?
Langfelder, P., Mischel, P. S., & Horvath, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061505 · Cited by 313
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Sibille, E., Morris, H. M., Kota, R. S., & Lewis, D. A. (2011) · International Journal of Neuropsychopharmacology · doi:10.1017/s1461145710001616 · Cited by 217
Guerra, P. A. (2011) · Biological Reviews · doi:10.1111/j.1469-185x.2010.00172.x · Cited by 210
Comprehensive literature review and statistical considerations for GWAS meta-analysis
Begum, F., Ghosh, D., Tseng, G. C., & Feingold, E. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1255 · Cited by 179
Reduced somatostatin in subgenual anterior cingulate cortex in major depression
Tripp, A., Kota, R. S., Lewis, D. A., & Sibille, E. (2011) · Neurobiology of Disease · doi:10.1016/j.nbd.2011.01.014 · Cited by 190
A genome‐wide approach to children's aggressive behavior: The EAGLE consortium
Pappa, I., St Pourcain, B., Benke, K., Cavadino, A., Hakulinen, C., Nivard, M. G., Nolte, I. M., Tiesler, C. M. T., Bakermans‐Kranenburg, M. J., Davies, G. E., et al. (2016) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32333 · Cited by 195
Kodama, K., Horikoshi, M., Toda, K., Yamada, S., Hara, K., Irie, J., Sirota, M., Morgan, A. A., Chen, R., Ohtsu, H., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114513109 · Cited by 171
multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data
(2020) · doi:10.1186/s12859-020-03910-x · Cited by 170
Functional gene group analysis identifies synaptic gene groups as risk factor for schizophrenia
the International Schizophrenia Consortium, Lips, E. S., Cornelisse, L. N., Toonen, R. F., Min, J. L., Hultman, C. M., Holmans, P. A., O'Donovan, M. C., Purcell, S. M., Smit, A. B., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.117 · Cited by 167
Deelen, P., van Dam, S., Herkert, J. C., Karjalainen, J. M., Brugge, H., Abbott, K. M., van Diemen, C. C., van der Zwaag, P. A., Gerkes, E. H., Zonneveld-Huijssoon, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10649-4 · Cited by 138
Using transfer learning to detect galaxy mergers
Ackermann, S., Schawinski, K., Zhang, C., Weigel, A. K., & Turp, M. D. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1398 · Cited by 137
PFAS Exposures and the Human Metabolome: A Systematic Review of Epidemiological Studies
India-Aldana, S., Yao, M., Midya, V., Colicino, E., Chatzi, L., Chu, J., Gennings, C., Jones, D. P., Loos, R. J. F., Setiawan, V. W., et al. (2023) · Current Pollution Reports · doi:10.1007/s40726-023-00269-4 · Cited by 114
Alzheimer disease pathology in cognitively healthy elderly: A genome-wide study
Kramer, P. L., Xu, H., Woltjer, R. L., Westaway, S. K., Clark, D., Erten-Lyons, D., Kaye, J. A., Welsh-Bohmer, K. A., Troncoso, J. C., Markesbery, W. R., et al. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.01.010 · Cited by 108
Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets
Xiong, Q., Ancona, N., Hauser, E. R., Mukherjee, S., & Furey, T. S. (2012) · Genome Research · doi:10.1101/gr.124370.111 · Cited by 103
Cook, C. J., & Crewther, B. T. (2012) · Hormones and Behavior · doi:10.1016/j.yhbeh.2011.09.006 · Cited by 86
The Role of Genetic Sex in Affect Regulation and Expression of GABA-Related Genes Across Species
Seney, M. L., Chang, L.-C., Oh, H., Wang, X., Tseng, G. C., Lewis, D. A., & Sibille, E. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00104 · Cited by 78
Ding, Y., Chang, L.-C., Wang, X., Guilloux, J.-P., Parrish, J., Oh, H., French, B. J., Lewis, D. A., Tseng, G. C., & Sibille, E. (2015) · Complex Psychiatry · doi:10.1159/000369974 · Cited by 61
Hernandez Cordero, A. I., Gonzales, N. M., Parker, C. C., Sokolof, G., Vandenbergh, D. J., Cheng, R., Abney, M., Sko, A., Douglas, A., Palmer, A. A., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.10.014 · Cited by 59
Afroz, S., Giddaluru, J., Vishwakarma, S., Naz, S., Khan, A. A., & Khan, N. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00074 · Cited by 50
Vélez, J. I., Chandrasekharappa, S. C., Henao, E., Martinez, A. F., Harper, U., Jones, M., Solomon, B. D., Lopez, L., Garcia, G., Aguirre-Acevedo, D. C., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.81 · Cited by 44
(2020) · doi:10.1038/s41398-020-0817-7 · Cited by 42
Being fat and smart: A comparative analysis of the fat-brain trade-off in mammals
Heldstab, S. A., van Schaik, C. P., & Isler, K. (2016) · Journal of Human Evolution · doi:10.1016/j.jhevol.2016.09.001 · Cited by 37
The genetic code constrains yet facilitates Darwinian evolution
Firnberg, E., & Ostermeier, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt536 · Cited by 36
Distinct sex-specific DNA methylation differences in Alzheimer’s disease
C. Silva, T., Zhang, W., Young, J. I., Gomez, L., Schmidt, M. A., Varma, A., Chen, X. S., Martin, E. R., & Wang, L. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-01070-z · Cited by 33
“We used Stouffer’s Method [50], as implemented in sumz() function of R package metap, to combine weighted z-scores (transformed from P-values) in these six datasets.”
MethodGranseth, B., Andersson, F. K., & Lindström, S. H. (2015) · Genes, Brain and Behavior · doi:10.1111/gbb.12230 · Cited by 23
Genes essential for embryonic stem cells are associated with neurodevelopmental disorders
Shohat, S., & Shifman, S. (2019) · Genome Research · doi:10.1101/gr.250019.119 · Cited by 21
Genes essential for embryonic stem cells are associated with neurodevelopmental disorders
Shohat, S., & Shifman, S. (2019) · doi:10.1101/567073 · Cited by 1
Metadata
- Authors
- WHITLOCK, M. C.
- Venue
- Journal of Evolutionary Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-08-25
- Volume
- 18
- Issue
- 5
- Pages
- 1368-1373
- ISSN
- 1010-061X (ISSN-L)
- 1420-9101
- OpenAlex
- W1587447328
- Cited by
- 752
- References
- 2
- FWCI
- 18.80
- Type
- journal article
- Language
- en
- Open access
- closed
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