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A simple correction for multiple comparisons in interval mapping genome scans
Open accessAbstract
Several approaches have been proposed to correct point-wise significance thresholds used in interval-mapping genome scans. A method for significance threshold correction based on the Bonferroni test is presented. This test involves calculating the effective number of independent comparisons performed in a genome scan from the variance of the eigenvalues of the observed marker correlation matrix. The more highly correlated the markers, the higher the variance of the eigenvalues and the lower the number of independent tests performed on a chromosome. This approach was evaluated by mapping 1000 normally distributed phenotypes along chromosomes of varying length and marker density in a population size of 500. Experiment-wise significance thresholds obtained from the simulation are compared to those calculated using the Bonferroni criterion and the newly developed measure of the effective number of independent tests in a genome scan. The Bonferroni calculation produced significance thresholds very similar to those obtained by simulation. The threshold levels for both Bonferroni and simulation analysis depended strongly on the marker density and size of chromosomes. There was a slight bias of about 1% in the thresholds obtained at the 5% and 10% point-wise significance levels. The method introduced here provides a relatively simple correction for multiple comparisons that can be easily calculated using standard statistics packages.
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“Modifications to the Bonferroni correction in the context of QTL mapping have been proposed by Cheverud (2001), and further modifications proposed by Li and Ji (2005).”
From The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis (2013) · cites this paper as Cheverud (2001)
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Cites
Genetics and Analysis of Quantitative Traits
(2002) · Journal of Animal Breeding and Genetics · doi:10.1046/j.1439-0388.2002.00356.x · Cited by 6,176
Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.
Lander, E. S., & Botstein, D. (1989) · Genetics · doi:10.1093/genetics/121.1.185 · Cited by 5,236
Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results
Lander, E. S., & Kruglyak, L. (1995) · Nature Genetics · doi:10.1038/ng1195-241 · Cited by 5,236
Empirical threshold values for quantitative trait mapping.
Churchill, G. A., & Doerge, R. W. (1994) · Genetics · doi:10.1093/genetics/138.3.963 · Cited by 4,935
Precision mapping of quantitative trait loci.
Zeng, Z. B. (1994) · Genetics · doi:10.1093/genetics/136.4.1457 · Cited by 3,182
Weir, B. S. (1997) · Biometrics · doi:10.2307/2533134 · Cited by 2,263
Haley, C. S., & Knott, S. A. (1992) · Heredity · doi:10.1038/hdy.1992.131 · Cited by 1,991
Genetic dissection of complex traits
Witte, J., Elston, R., & Schork, N. (1996) · Nature Genetics · doi:10.1038/ng0496-355 · Cited by 1,883
Hill, W. G. (1996) · Genetics Research · doi:10.1017/s0016672300034121 · Cited by 921
Multiple trait analysis of genetic mapping for quantitative trait loci.
Jiang, C., & Zeng, Z. (1995) · Genetics · doi:10.1093/genetics/140.3.1111 · Cited by 831
Cited by
Sorted by citations
Items 201–300 of 421
Association tests through combining -values for case control genome-wide association studies
Chen, Z. (2013) · Statistics & Probability Letters · doi:10.1016/j.spl.2013.04.021 · Cited by 18
Tong, J., Cummins, T. D. R., Johnson, B. P., McKinley, L., Pickering, H. E., Fanning, P. A. J., Stefanac, N., Newman, D. P., Hawi, Z., & Bellgrove, M. A. (2015) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32283 · Cited by 18
Cox, L. A., Birnbaum, S., Mahaney, M. C., Rainwater, D. L., Williams, J. T., & VandeBerg, J. L. (2007) · Circulation · doi:10.1161/circulationaha.107.704346 · Cited by 18
Beyond the one-way ANOVA for ’omics data
Hassall, K. L., & Mead, A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2173-7 · Cited by 18
Considering dependence among genes and markers for false discovery control in eQTL mapping
Chen, L., Tong, T., & Zhao, H. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn373 · Cited by 18
First mitochondrial genome-wide association study with metabolomics
Aboulmaouahib, B., Kastenmüller, G., Suhre, K., Zöllner, S. K., Weißensteiner, H., Prehn, C. P., Adamski, J., Gieger, C., Wang‐Sattler, R., Lichtner, P., et al. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab312 · Cited by 16
Haines, G. E., Moisan, L., Derry, A. M., & Hendry, A. P. (2022) · The American Naturalist · doi:10.1086/722483 · Cited by 17
Wang, X., Liu, X., Sim, X., Xu, H., Khor, C. C., Ong, R. T., Tay, W.-T., Suo, C., Poh, W.-T., Ng, D. P. K., et al. (2011) · European Journal of Human Genetics · doi:10.1038/ejhg.2011.219 · Cited by 17
Wen, S., & Lu, Z.-S. (2011) · Journal of Human Genetics · doi:10.1038/jhg.2011.34 · Cited by 17
Semiparametric nonlinear regression for detecting gene and environment interactions
Ma, S., & Xu, S. (2014) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2014.08.005 · Cited by 16
Suominen, T. H., Alén, M., Törmäkangas, T., Degens, H., Rittweger, J., Heinonen, A. O., Suominen, H., & Korhonen, M. T. (2021) · JBMR Plus · doi:10.1002/jbm4.10513 · Cited by 17
Carvajal‐Rodríguez, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175944 · Cited by 16
Kalous, J. R., Martin, J. M., Sherman, J., Blake, N. K., Lanning, S. P., & Talbert, L. E. (2011) · Crop Science · doi:10.2135/cropsci2011.03.0172 · Cited by 16
Improved two‐step testing of genome‐wide gene–environment interactions
Kawaguchi, E. S., Kim, A. E., Lewinger, J. P., & Gauderman, W. J. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22509 · Cited by 14
Design and analysis of multiple diseases genome-wide association studies without controls
Chen, Z., Huang, H., & Ng, H. K. T. (2012) · Gene · doi:10.1016/j.gene.2012.07.089 · Cited by 15
HSPA5 promoter polymorphisms and risk of Parkinson's disease in Taiwan
Chen, C., Wu, Y., Hu, F.-J., Chen, Y.-C., Chuang, T.-J., Cheng, Y.-F., & Lee‐Chen, G. (2008) · Neuroscience Letters · doi:10.1016/j.neulet.2008.02.039 · Cited by 15
Conservation of Distinct Genetically-Mediated Human Cortical Pattern
Peng, Q., Schork, A. J., Bartsch, H., Lo, M., Panizzon, M. S., Pediatric Imaging, N. A. G. S., Initiative, A. D. N., Westlye, L. T., Kremen, W. S., Jernigan, T. L., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006143 · Cited by 15
HisCoM-GGI: Hierarchical structural component analysis of gene–gene interactions
Choi, S., Lee, S., Kim, Y., Hwang, H., & Park, T. (2018) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720018400267 · Cited by 15
Right Temporoparietal Gray Matter Predicts Accuracy of Social Perception in the Autism Spectrum
David, N., Schultz, J., Milne, E., Schunke, O., Schöttle, D., Münchau, A. M., Siegel, M., Vogeley, K. T., & Engel, A. K. (2013) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-013-2008-3 · Cited by 16
Steiß, V., Letschert, T., Schäfer, H., & Pahl, R. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts086 · Cited by 15
Ibarra-Lopez, J. J., Duarte, P., Antonio-Véjar, V., Calderon-Aranda, E. S., Huerta‐Beristain, G., Flores‐Alfaro, E., & Moreno‐Godínez, M. E. (2013) · Journal of Investigative Medicine · doi:10.2310/jim.0b013e31829a7e7e · Cited by 15
Keller, J. M., Allen, D. E., Davis, C. R., & Leamy, L. J. (2007) · Heredity · doi:10.1038/sj.hdy.6800928 · Cited by 15
A Comprehensive Method for Genome Scans
Malley, J. D., Naiman, D. Q., & Wilson, J. (2002) · Human Heredity · doi:10.1159/000070663 · Cited by 15
Geurts, S., Mens, M. M. J., Bos, M. M., Ikram, M. A., Ghanbari, M., & Kavousi, M. (2021) · Genes · doi:10.3390/genes13010011 · Cited by 14
On Optimal Gene‐Based Analysis of Genome Scans
Bacanu, S. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21625 · Cited by 14
Chromosomal loci important for cotyledon opening under UV-B in Arabidopsis thaliana
Conte, M., de Simone, S., Simmons, S. J., Ballaré, C. L., & Stapleton, A. E. (2010) · BMC Plant Biology · doi:10.1186/1471-2229-10-112 · Cited by 14
Duan, Q., Tait, R. G., Mayes, M. S., Garrick, D. J., Liu, Q., Eenennaam, A. L. V., Mateescu, R. G., Overbeke, D. L. V., Garmyn, A. J., Beitz, D. C., et al. (2011) · Animal Genetics · doi:10.1111/j.1365-2052.2011.02224.x · Cited by 14
Multiple testing in the genomics era: Findings from Genetic Analysis Workshop 15, Group 15
Martin, L. J., Woo, J. G., Avery, C. L., Chen, H., North, K. E., & on behalf of Group 15 (2007) · Genetic Epidemiology · doi:10.1002/gepi.20289 · Cited by 14
Post-Genomic Update on a Classical Candidate Gene for Coronary Artery Disease: ESR1
Lucas, G., Lluís-Ganella, C., Subirana, I., Sentí, M., Willenborg, C., Musameh, M. D., Schwartz, S. M., O’Donnell, C. J., Melander, O., Salomaa, V. V., et al. (2011) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.111.960583 · Cited by 14
Zhang, X., Huang, S., Sun, W., & Wang, W. W. (2012) · Genetics · doi:10.1534/genetics.111.137737 · Cited by 12
Kunkels, Y. K., Smit, A. C., Minaeva, O., Snippe, E., George, S. V., van Roon, A. M., Wichers, M., & Riese, H. (2023) · Clinical Psychological Science · doi:10.1177/21677026221148101 · Cited by 15
Márquez, G. C., Enns, R. M., Grosz, M. D., Alexander, L. J., & MacNeil, M. D. (2009) · Animal Genetics · doi:10.1111/j.1365-2052.2009.01946.x · Cited by 13
Foley, B. R., Saltz, J. B., Nuzhdin, S. V., & Marjoram, P. (2015) · The American Naturalist · doi:10.1086/681084 · Cited by 13
Pope, R., Visconti, A., Zhang, X., Louca, P., Baleanu, A.-F., Lin, Y., Asnicar, F., Bermingham, K. M., Wong, K. E., Michelotti, G. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66046-7 · Cited by 15
Freytag, S., Bickeböller, H., Amos, C. I., Kneib, T., & Schlather, M. (2012) · Human Heredity · doi:10.1159/000347188 · Cited by 12
Pouyan, M. B., Jindal, V., Birjandtalab, J., & Nourani, M. (2016) · BMC Medical Genomics · doi:10.1186/s12920-016-0201-x · Cited by 12
Multiple Testing Correction in Medical Image Analysis
Machado, A. M. C. (2007) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-007-0034-5 · Cited by 12
Jones, D. C., Bernstein, M. C., & German, R. Z. (2011) · Journal of Morphology · doi:10.1002/jmor.10927 · Cited by 12
Joo, J., Omae, Y., Hitomi, Y., Park, B., Shin, H., Yoon, K., Sawai, H., Tsuiji, M., Hayashi, T., Kong, S., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49523-0 · Cited by 12
Marcos, B., Gou, P., Guárdia, M. D., Hortos, M., Colleo, M., Mach, N., PAS, M. F. W. T., Keuning, E., Kruijt, L., Tibau, J., et al. (2012) · Meat Science · doi:10.1016/j.meatsci.2012.10.014 · Cited by 12
Russo, D., Hanel, P. H. P., & van Berkel, N. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3638244 · Cited by 27
Free-living monitoring of ALS progression in upper limbs using wearable accelerometers
Strączkiewicz, M., Burke, K. M., Calcagno, N., Premasiri, A. S., Vieira, F. G., Onnela, J.-P. “., & Berry, J. D. (2024) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-024-01514-7 · Cited by 11
Leamy, L. J., Pomp, D., & Lightfoot, J. T. (2011) · Genetica · doi:10.1007/s10709-011-9586-9 · Cited by 11
Yoskovitz, G., García‐Giralt, N., Rodríguez-Sanz, M., Urreizti, R., Güerri‐Fernández, R., Ariño-Ballester, S., Prieto‐Alhambra, D., Mellibovsky, L., Grinberg, D., Nogués, X., et al. (2013) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.2001 · Cited by 12
Vsevolozhskaya, O. A., Greenwood, M., Powell, S. L., & Zaykin, D. V. (2014) · Environmental and Ecological Statistics · doi:10.1007/s10651-014-0282-7 · Cited by 11
Jarvis, J. P., & Cheverud, J. M. (2009) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2009.01776.x · Cited by 11
Self-citationSokolova, I. M., Oliver, J. D., & Leamy, L. J. (2006) · Journal of Shellfish Research · doi:10.2983/0730-8000(2006)25[95:aaatig]2.0.co;2 · Cited by 11
Shendre, A., Irvin, M. R., Aouizerat, B. E., Wiener, H. W., Vazquez, A. I., Anastos, K., Lazar, J., Liu, C., Karim, R., Limdi, N. A., et al. (2014) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2014.01.035 · Cited by 11
AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies
Emily, M. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2015-0074 · Cited by 13
Gao, X., Liu, J., Gong, P., Wang, J., Wan, F., Yan, H., Zhu, L., & Zhou, X. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx062 · Cited by 11
Paire, L., McCabe, C., & McCabe, T. (2024) · Euphytica · doi:10.1007/s10681-024-03374-7 · Cited by 10
Rondelli, C. M., El‐Zein, R. A., Wickliffe, J. K., Etzel, C. J., & Abdel‐Rahman, S. Z. (2010) · Toxicological Sciences · doi:10.1093/toxsci/kfq027 · Cited by 10
Partial linear varying multi-index coefficient model for integrative gene-environment interactions
Liu, X., Cui, Y., & Li, R. (2015) · Statistica Sinica · doi:10.5705/ss.202015.0114 · Cited by 10
Environmental conditions synchronize waterbird mortality events in the Great Lakes
Princé, K., Chipault, J. G., White, C. L., & Zuckerberg, B. (2018) · Journal of Applied Ecology · doi:10.1111/1365-2664.13063 · Cited by 10
Holliday, E., Mowry, B. J., Chant, D. C., & Nyholt, D. R. (2005) · Human Genetics · doi:10.1007/s00439-005-1282-3 · Cited by 10
A Comparison of Methods for Gene-Based Testing That Account for Linkage Disequilibrium
Cinar, O., & Viechtbauer, W. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.867724 · Cited by 10
The general goodness-of-fit tests for correlated data
Zhang, H., & Wu, Z. (2021) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2021.107379 · Cited by 9
Ott, J., Liu, Z., & Shen, Y. (2012) · Human Heredity · doi:10.1159/000343752 · Cited by 9
Identification of QTLs associated with male strobilus abundance in Cryptomeria japonica
Ujino‐Ihara, T., Iwata, H., Taguchi, Y., & Tsumura, Y. (2012) · Tree Genetics & Genomes · doi:10.1007/s11295-012-0518-3 · Cited by 9
Multiple phenotypes in genome-wide genetic mapping studies
Ott, J., & Wang, J. (2011) · Protein & Cell · doi:10.1007/s13238-011-1059-5 · Cited by 9
Impact of character correlation and variable groupings on modern human population tree resolution
Athreya, S. G., & Glantz, M. M. (2003) · American Journal of Physical Anthropology · doi:10.1002/ajpa.10220 · Cited by 9
Correction for Multiple Testing in Candidate-Gene Methylation Studies
Zhou, Z., Lunetta, K. L., Smith, A. K., Wolf, E. J., Stone, A., Schichman, S. A., McGlinchey, R. E., Milberg, W., Miller, M. W., & Logue, M. W. (2019) · Epigenomics · doi:10.2217/epi-2018-0204 · Cited by 9
Cardiovascular Risk Factors and Ischemic Heart Disease
Elosúa, R., Lluís-Ganella, C., Subirana, I., Havulinna, A. S., Läll, K., Lucas, G., Sayols-Baixeras, S., Pietilä, A., Alver, M., de León, A. C., et al. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.115.001255 · Cited by 8
Strain survey and genetic analysis of vasoreactivity in mouse aorta
Kim, S. K., Avila, J. J., & Massett, M. P. (2016) · Physiological Genomics · doi:10.1152/physiolgenomics.00054.2016 · Cited by 8
Facal, F., Arrojo, M., Paz, E., Páramo, M., & Costas, J. (2022) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13444 · Cited by 8
Analysis of raw hams using SELDI-TOF-MS to predict the final quality of dry-cured hams
Marcos, B., Gou, P., Serra, X., Guárdia, M. D., Zhen, Z., Hortos, M., Mach, N., PAS, M. F. W. T., Keuning, E., Kruijt, L., et al. (2012) · Meat Science · doi:10.1016/j.meatsci.2012.08.026 · Cited by 8
A GABBR2 gene variant modifies pathophysiology in Huntington’s disease
Philpott, A. L., Fitzgerald, P. B., Bailey, N. W., Churchyard, A., Georgiou‐Karistianis, N., & Cummins, T. D. R. (2016) · Neuroscience Letters · doi:10.1016/j.neulet.2016.03.038 · Cited by 8
Schrodi, S. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154472 · Cited by 8
Misawa, K., Fujii, S., Yamazaki, T., Takahashi, A., Takasaki, J., Yanagisawa, M., Ohnishi, Y., Nakamura, Y., & Kamatani, N. (2008) · Journal of Human Genetics · doi:10.1007/s10038-008-0312-0 · Cited by 8
A two-stage clustering technique for automatic biaxial gating of flow cytometry data
Pouyan, M. B., Jindal, V., Birjandtalab, J., & Nourani, M. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359736 · Cited by 7
Murphy, S. E., Guillermo, C., Thomson, N. M., Carmella, S. G., Wittmann, M., Aldrich, M. C., Cai, Q., Sullivan, S. M., Stram, D. O., Marchand, L. L., et al. (2024) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-23-1362 · Cited by 7
A New Correction for Multiple Testing in Gene–Gene Interaction Studies
Babron, M., Etcheto, A., & Dizier, M. (2015) · Annals of Human Genetics · doi:10.1111/ahg.12113 · Cited by 7
Speeding up Permutation Testing in Neuroimaging
Chris Hinrichs, A., Ithapu, V. K., Sun, Q., Johnson, S. C., & Singh, V. (2015) · PubMed · doi:10.48550/arxiv.1502.03536 · Cited by 8
Overview of frequent pattern mining
Ott, J., & Park, T. (2022) · Genomics & Informatics · doi:10.5808/gi.22074 · Cited by 6
Wang, Z. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20555 · Cited by 7
A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci
Qian, J., Núñez, S., Reed, E. R., Reilly, M. P., & Foulkes, A. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148218 · Cited by 8
Guatelli‐Steinberg, D., Pampush, J. D., O’Hara, M. C., Xing, S., McGraw, W. S., & Ferrell, R. J. (2017) · The Anatomical Record · doi:10.1002/ar.23703 · Cited by 7
Watson, A., Guitton, B., Soriano, A., Rivallan, R., Vignes, H., Farrera, I., Hüettel, B., POZO, C. A. D., Falavigna, V. D. S., Coupel‐Ledru, A., et al. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1352757 · Cited by 6
Malzahn, D., Friedrichs, S., & Bickeböller, H. (2016) · BMC Proceedings · doi:10.1186/s12919-016-0042-9 · Cited by 6
Hughes, D. A., Li‐Gao, R., Bull, C. J., de Mutsert, R., Rosendaal, F. R., Mook-Kanamori, D. O., van Dijk, K. W., & Timpson, N. J. (2024) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2024.03.009 · Cited by 7
Rochat, R. H., Fuentes, L. D. L., Stormo, G. D., Dávila‐Román, V. G., & Gu, C. C. (2007) · Human Heredity · doi:10.1159/000104227 · Cited by 6
Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package
Emily, M., Sounac, N., Kroell, F., & Houée-Bigot, M. (2020) · Journal of Statistical Software · doi:10.18637/jss.v095.i12 · Cited by 6
GWAS significance thresholds in large cohorts of European ancestry
Cheruiyot, E. K., Yang, T., & McRae, A. F. (2025) · Genetics · doi:10.1093/genetics/iyaf056 · Cited by 7
Yamamoto, Y., Kawai, W., Hayashi, T., Uga, M., Kyutoku, Y., & Dan, I. (2024) · Neurophotonics · doi:10.1117/1.nph.11.3.035004 · Cited by 4
Human genetic variation determines 24-hour rhythmic gene expression and disease risk
Chen, Y., Liu, P., Sabo, A., & Guan, D. (2025) · Nature Communications · doi:10.1038/s41467-025-59524-5 · Cited by 7
Sample Size Analysis for Pharmacogenetic Studies
Tseng, C., & Shao, Y. (2010) · Statistics in Biopharmaceutical Research · doi:10.1198/sbr.2009.08076 · Cited by 5
Using BayesModelS to provide Bayesian- and phylogenetically-informed primate body mass predictions
Pampush, J. D., Fuselier, E. J., & Yapuncich, G. (2021) · Journal of Human Evolution · doi:10.1016/j.jhevol.2021.103077 · Cited by 5
Gutierrez-Barragan, F., Ithapu, V. K., Chris Hinrichs, A., Maumet, C., Johnson, S. C., Nichols, T. E., & Singh, V. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.025 · Cited by 5
Practical Implications of Coalescent Theory
Marjoram, P., & Joyce, P. (2010) · Springer eBooks · doi:10.1007/978-0-387-09760-2_4 · Cited by 5
Generation of Gene Ontology benchmark datasets with various types of positive signal
Törönen, P., Pehkonen, P., & Holm, L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-319 · Cited by 5
Byrne, E. M., McRae, A. F., Duffy, D. L., Zhao, Z., Martin, N. G., Whitfield, J. B., Visscher, P. M., & Montgomery, G. W. (2009) · Diabetologia · doi:10.1007/s00125-009-1510-9 · Cited by 5
Power in network neurosciencePreprint
Helwegen, K., Libedinsky, I., & van den Heuvel, M. P. (2022) · doi:10.31234/osf.io/8bu2j · Cited by 5
Bishop, D. (2021) · F1000Research · doi:10.12688/f1000research.73520.1 · Cited by 4
Dependence aliasing and the control of family-wise error rate in multiple hypothesis testing
Machado, A. M. C. (2014) · Statistics and Computing · doi:10.1007/s11222-014-9459-z · Cited by 4
The Genetic Architecture of a Congenital Heart Defect Is Related to Its Fitness Cost
Akhirome, E., Regmi, S. D., Magnan, R. A., Ugwu, N., Qin, Y., Schulkey, C. E., Cheverud, J. M., & Jay, P. Y. (2021) · Genes · doi:10.3390/genes12091368 · Cited by 5
Self-citationLimardi, P., Oktavianthi, S., Priliani, L., Lestari, R., Saraswati, M. R., Suastika, K., & Malik, S. G. (2022) · PeerJ · doi:10.7717/peerj.13149 · Cited by 4
No association between CTNNBL1 and episodic memory performance
Liu, T., Li, S., Papenberg, G., Schröder, J., Roehr, J. T., Nietfeld, W., Lindenberger, U., & Bertram, L. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.93 · Cited by 4
A prediction‐based test for multiple endpoints
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Metadata
- Authors
- Cheverud, J. M.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Cheverud, James M
- ORCID
- Cheverud, James M
- Venue
- Heredity
- Published
- 2001
- Volume
- 87
- Issue
- 1
- Pages
- 52-58
- ISSN
- 0018-067X (ISSN-L)
- OpenAlex
- W2054881132
- Cited by
- 421
- References
- 21
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 13 · 2023: 10 · 2022: 21 · 2021: 21 · 2020: 18 · 2019: 9 · 2018: 17 · 2017: 10 · 2016: 30 · 2015: 21 · 2014: 14 · 2013: 24 · 2012: 25
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Heredity (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (9726433)
- National Institutes of Health (5r01dk052514-06)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK52514)
- Division of Environmental Biology
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- multiple testing correction, interval mapping, genome scan, Bonferroni correction, significance threshold, marker density, chromosome length, QTL mapping
- MeSH terms
- Analysis of Variance, Biometry, Chromosome Mapping, Computer Simulation, Genetic Markers, Genotype, Genetic Linkage, Models, Genetic, Statistics as Topic, Models, Statistical, Quantitative Trait, Heritable
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