Cited by 421Genetic Mapping and Diversity in Plants and AnimalsGenetic Associations and EpidemiologyGenetic and phenotypic traits in livestock

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

A simple correction for multiple comparisons in interval mapping genome scans

Open access

Cheverud, James M·2001·Heredity

DOI 10.1046/j.1365-2540.2001.00901.x

Open this paper in CoChat — free account Open PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1046/j.1365-2540.2001.00901.x

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Genetic Data Analysis II.
Weir, B. S. (1997) · Biometrics · doi:10.2307/2533134 · Cited by 2,263
A simple regression method for mapping quantitative trait loci in line crosses using flanking markers
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
Genetic Data Analysis II. By Bruce S. Weir, Sunderland, Massachusetts. Sinauer Associates, Inc.445 pages. ISBN 0-87893-902-4.
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

Show all 21 references

Sorted by citations

Items 101–200 of 421

High pressure induced changes in beef muscle proteome: Correlation with quality parameters
Marcos, B., & Mullen, A. M. (2013) · Meat Science · doi:10.1016/j.meatsci.2013.12.008 · Cited by 50
Random genetic drift, natural selection, and noise in human cranial evolution
Roseman, C. C. (2016) · American Journal of Physical Anthropology · doi:10.1002/ajpa.22918 · Cited by 53
Aberrant inflammatory profile in acute but not recovered anorexia nervosa
Nilsson, I. A. K., Millischer, V., Göteson, A., Hübel, C., Thornton, L. M., Bulik, C. M., Schalling, M., & Landén, M. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.05.024 · Cited by 52
An Epistatic Genetic Basis for Physical Activity Traits in Mice
Leamy, L. J., Pomp, D., & Lightfoot, J. T. (2008) · Journal of Heredity · doi:10.1093/jhered/esn045 · Cited by 50
GWAS significance thresholds for deep phenotyping studies can depend upon minor allele frequencies and sample size
Asif, H., Alliey‐Rodriguez, N., Keedy, S., Tamminga, C. A., Sweeney, J. A., Pearlson, G. D., Clementz, B. A., Keshavan, M. S., Buckley, P. F., Liu, C., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0670-3 · Cited by 48
PERMORY: an LD-exploiting permutation test algorithm for powerful genome-wide association testing
Pahl, R., & Schäfer, H. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq399 · Cited by 49
Tumor necrosis factor‐α promoter polymorphism is associated with the risk of Parkinson's disease
Wu, Y., Feng, I., Lyu, R., Chang, K., Lin, Y., Chan, H., Hu, F.-J., Lee‐Chen, G., & Chen, C. (2006) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30435 · Cited by 50
Correction for multiple testing in a gene region
Hendricks, A. E., Dupuis, J., Logue, M. W., Myers, R. H., & Lunetta, K. L. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2013.144 · Cited by 51
Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle
Sahana, G., Cai, Z., Sanchez, M.-P., Bouwman, A. C., & Boichard, D. (2023) · Journal of Dairy Science · doi:10.3168/jds.2022-22694 · Cited by 64
Gene-Based Analysis of Regionally Enriched Cortical Genes in GWAS Data Sets of Cognitive Traits and Psychiatric Disorders
Ersland, K. M., Christoforou, A., Stansberg, C. T., Espeseth, T., Mattheisen, M., Mattingsdal, M., Hardarson, G. A., Hansen, T. F., Fernandes, C. P. D., Giddaluru, S., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031687 · Cited by 49
Biotyping in psychosis: using multiple computational approaches with one data set
Tamminga, C. A., Clementz, B. A., Pearlson, G. D., Keshavan, M. S., Gershon, E. S., Ivleva, E. I., McDowell, J. E., Meda, S. A., Keedy, S., Calhoun, V. D., et al. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00849-8 · Cited by 49
How to analyze many contingency tables simultaneously in genetic association studies
Dickhaus, T., Straßburger, K., Schunk, D., Morcillo-Suárez, C., Illig, T., & Navarro, A. (2012) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/1544-6115.1776 · Cited by 45
Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq
Chen, M., Fan, W., Ji, F., Hua, H., Liu, J., Yan, M., Ma, Q., Fan, J., Wang, Q., Zhang, S., et al. (2021) · Molecular Plant · doi:10.1016/j.molp.2021.01.003 · Cited by 48
Wear and its effects on dental topography measures in howling monkeys (Alouatta palliata)
Pampush, J. D., Spradley, J. P., Morse, P. E., Harrington, A. R., Allen, K. L., Boyer, D., & Kay, R. F. (2016) · American Journal of Physical Anthropology · doi:10.1002/ajpa.23077 · Cited by 46
29 False positives and false negatives in genome scans
Rao, D. C., & Gu, C. C. (2001) · Advances in genetics · doi:10.1016/s0065-2660(01)42038-4 · Cited by 46
Prospective evaluation of multiplicative hybrid earthquake forecasting models in California
Bayona, J. A., Savran, W. H., Rhoades, D. A., & Werner, M. J. (2022) · Geophysical Journal International · doi:10.1093/gji/ggac018 · Cited by 45
Allelic variations in superoxide dismutase-1 (SOD1) gene and renal and cardiovascular morbidity and mortality in type 2 diabetic subjects
Neves, A. L., Mohammedi, K., Emery, N., Roussel, R., Fumeron, F., Marre, M., & Velho, G. (2012) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2012.04.023 · Cited by 45
Genetic variation in neuronal glutamate transport genes and associations with posttraumatic seizure
Ritter, A. C., Kammerer, C. M., Brooks, M. M., Conley, Y. P., & Wagner, A. K. (2016) · Epilepsia · doi:10.1111/epi.13397 · Cited by 45
Association Mapping of Flowering and Height Traits in Germplasm Enhancement of Maize Doubled Haploid (GEM‐DH) Lines
Vanous, A. E., Gardner, C. A. C., Blanco, M. H., Martin-Schwarze, A. M., Lipka, A. E., Flint-Garcia, S. A., Bohn, M., Edwards, J. W., & Lübberstedt, T. (2018) · The Plant Genome · doi:10.3835/plantgenome2017.09.0083 · Cited by 47
Genetic and environmental contributions to variation in baboon cranial morphology
Roseman, C. C., Willmore, K. E., Rogers, J., Hildebolt, C. F., Sadler, B. E., Richtsmeier, J. T., & Cheverud, J. M. (2010) · American Journal of Physical Anthropology · doi:10.1002/ajpa.21341 · Cited by 45
Self-citation
The FMR1 Premutation and Attention-Deficit Hyperactivity Disorder (ADHD): Evidence for a Complex Inheritance
Hunter, J. E., Epstein, M. P., Tinker, S. W., Abramowitz, A. J., & Sherman, S. L. (2011) · Behavior Genetics · doi:10.1007/s10519-011-9520-z · Cited by 43
Characterizing the metabolic phenotype of intestinal villus blunting in Zambian children with severe acute malnutrition and persistent diarrhea
Farràs, M., Chandwe, K., Mayneris‐Perxachs, J., Amadi, B., Louis-Auguste, J., Besa, E. C., Zyambo, K., Guerrant, R. L., Kelly, P. M., & Swann, J. R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192092 · Cited by 44
Genome-Wide Meta-Analysis of Myopia and Hyperopia Provides Evidence for Replication of 11 Loci
Simpson, C. L., Wojciechowski, R., Oexle, K., Murgia, F., Portas, L., Li, X., Verhoeven, V. J. M., Vitart, V., Schäche, M., Hosseini, S. M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107110 · Cited by 43
Quantitative trait loci with additive effects on palatability and fatty acid composition of meat in a Wagyu–Limousin F2 population
Alexander, L. J., MacNeil, M. D., Geary, T. W., Snelling, W. M., Rule, D. C., & Scanga, J. A. (2007) · Animal Genetics · doi:10.1111/j.1365-2052.2007.01643.x · Cited by 43
Adaptation to local climate in multi-trait space: evidence from silver fir (Abies alba Mill.) populations across a heterogeneous environment
Csilléry, K., Ovaskainen, O., Sperisen, C., Buchmann, N., Widmer, A., & Gugerli, F. (2019) · Heredity · doi:10.1038/s41437-019-0240-0 · Cited by 41
Detecting multiple associations in genome-wide studies
Dudbridge, F., Gusnanto, A., & Koeleman, B. P. C. (2006) · Human Genomics · doi:10.1186/1479-7364-2-5-310 · Cited by 42
Association analysis of the chromosome 4p15–p16 candidate region for bipolar disorder and schizophrenia
Christoforou, A., Hellard, S. L., Thomson, P. A., Morris, S. W., Tenesa, A., Pickard, B. S., Wray, N. R., Muir, W. J., Blackwood, D., Porteous, D. J., et al. (2007) · Molecular Psychiatry · doi:10.1038/sj.mp.4002003 · Cited by 40
Selecting fish‐based metrics responding to human pressures in French natural lakes and reservoirs: towards the development of a fish‐based index (FBI) for French lakes
Launois, L., Veslot, J., Irz, P., & Argillier, C. (2010) · Ecology Of Freshwater Fish · doi:10.1111/j.1600-0633.2010.00467.x · Cited by 41
The F7 Gene and Clotting Factor VII Levels: Dissection of a Human Quantitative Trait Locus
Soria, J. M., Almasy, L. A., Souto, J. C., Sabater‐Lleal, M., Fontcuberta, J., & Blangero, J. (2009) · Human Biology · doi:10.3378/027.081.0627 · Cited by 40
Genome Wide Association Study of Beef Traits in Local Alpine Breed Reveals the Diversity of the Pathways Involved and the Role of Time Stratification
Mancin, E., Tuliozi, B., Pegolo, S., Sartori, C., & Mantovani, R. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.746665 · Cited by 41
An epistatic genetic basis for fluctuating asymmetry of tooth size and shape in mice
Leamy, L. J., Workman, M. S., Routman, E. J., & Cheverud, J. M. (2005) · Heredity · doi:10.1038/sj.hdy.6800637 · Cited by 39
Self-citation
Genome-wide significant loci for addiction and anxiety
Hodgson, K., Almasy, L. A., Knowles, E. E. M., Kent, J. W., Curran, J. E., Dyer, T. D., Göring, H. H. H., Olvera, R. L., Fox, P. T., Pearlson, G. D., et al. (2016) · European Psychiatry · doi:10.1016/j.eurpsy.2016.03.004 · Cited by 38
Genetic Control of Susceptibility to Infection with Candida albicans in Mice
Radovanovic, I., Mullick, A., & Gros, P. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018957 · Cited by 38
Depression and Anxiety Symptoms Among Women Who Carry the FMR1 Premutation: Impact of Raising a Child With Fragile X Syndrome Is Moderated by CRHR1 Polymorphisms
Hunter, J. E., Leslie, M., Novak, G., Hamilton, D. R., Shubeck, L., Charen, K. H., Abramowitz, A. J., Epstein, M. P., Lori, A., Binder, E., et al. (2012) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32061 · Cited by 42
Habitat‐specific larval dispersal and marine connectivity: implications for spatial conservation planning
Anadón, J. D., Mancha-Cisneros, M. D. M., Best, B. D., & Gerber, L. R. (2013) · Ecosphere · doi:10.1890/es13-00119.1 · Cited by 37
Allelic variations in superoxide dismutase-1 (SOD1) gene are associated with increased risk of diabetic nephropathy in type 1 diabetic subjects
Mohammedi, K., Maimaitiming, S., Emery, N., Bellili-Muñoz, N., Roussel, R., Fumeron, F., Hadjadj, S., Marre, M., & Velho, G. (2011) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2011.08.033 · Cited by 37
Copy Number Variation
Macé, A., Kutalik, Z., & Valsesia, A. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7868-7_14 · Cited by 40
Genetic Variants of LRRK2 in Taiwanese Parkinson’s Disease
Wu, Y., Chang, K., Chang, W.-T., Hsiao, Y.-C., Hsu, H.-C., Jiang, P.-R., Chen, Y.-C., Chao, C., Chang, Y.-C., Lee, B.-H., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082001 · Cited by 36
Genetic Architecture of Adiposity and Organ Weight Using Combined Generation QTL Analysis
Fawcett, G. L., Roseman, C. C., Jarvis, J. P., Wang, B., Wolf, J. B., & Cheverud, J. M. (2008) · Obesity · doi:10.1038/oby.2008.300 · Cited by 36
Self-citation
Genome‐wide significant localization for working and spatial memory: Identifying genes for psychosis using models of cognition
Knowles, E. E. M., Carless, M. A., Almeida, M., Curran, J. E., McKay, D., Sprooten, E., Dyer, T. D., Göring, H. H. H., Olvera, R. L., Fox, P. T., et al. (2013) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32211 · Cited by 35
Extraordinary Claims in the Literature on High-Intensity Interval Training (HIIT): I. Bonafide Scientific Revolution or a Looming Crisis of Replication and Credibility?
Panteleimon Ekkekakis, A., Swinton, P. A., & Tiller, N. B. (2023) · Sports Medicine · doi:10.1007/s40279-023-01880-7 · Cited by 39
Pharmacogenetic association of hypertension candidate genes with fasting glucose in the GenHAT Study
Irvin, M. R., Lynch, A. I., Kabagambe, E. K., Tiwari, H. K., Barzilay, J. I., Eckfeldt, J. H., Boerwinkle, E., Davis, B. R., Ford, C. E., & Arnett, D. K. (2010) · Journal of Hypertension · doi:10.1097/hjh.0b013e32833c7a4d · Cited by 34
Promoter polymorphisms modulating HSPA5 expression may increase susceptibility to Taiwanese Alzheimer’s disease
Hsu, W.-W., Wang, H., Lee, L.-C., Fung, H., Lin, J., Hsu, H.-P., Wu, Y.-Y., Ro, L. ‐., Hu, F.-J., Chang, Y., et al. (2008) · Journal of Neural Transmission · doi:10.1007/s00702-008-0117-5 · Cited by 34
The Genetic Architecture of Fluctuating Asymmetry of Mandible Size and Shape in a Population of Mice: Another Look
Leamy, L. J., Klingenberg, C. P., Sherratt, E., Wolf, J. B., & Cheverud, J. M. (2015) · Symmetry · doi:10.3390/sym7010146 · Cited by 33
Self-citation
Intravenous administration of LPS activates the kynurenine pathway in healthy male human subjects: a prospective placebo-controlled cross-over trial
Millischer, V., Heinzl, M. W., Faka, A., Resl, M., Trepci, A., Klammer, C., Egger, M., Dieplinger, B., Clodi, M., & Schwieler, L. (2021) · Journal of Neuroinflammation · doi:10.1186/s12974-021-02196-x · Cited by 36
Genetic Influences on Maternal Care
Peripato, A. C., & Cheverud, J. M. (2002) · The American Naturalist · doi:10.1086/342900 · Cited by 33
Self-citation
Employing biomarkers of healthy ageing for leveraging genetic studies into human longevity
Deelen, J., van den Akker, E. B., Trompet, S., van Heemst, D., Mooijaart, S. P., Slagboom, P. E., & Beekman, M. (2016) · Experimental Gerontology · doi:10.1016/j.exger.2016.06.013 · Cited by 31
Disentangling Prenatal and Postnatal Maternal Genetic Effects Reveals Persistent Prenatal Effects on Offspring Growth in Mice
Wolf, J. B., Leamy, L. J., Roseman, C. C., & Cheverud, J. M. (2011) · Genetics · doi:10.1534/genetics.111.130591 · Cited by 33
Self-citation
Genetic analysis of developmental and adaptive traits in three doubled haploid populations of barley (Hordeum vulgare L.)
Obsa, B. T., Eglinton, J. K., Coventry, S., March, T. J., Langridge, P., & Fleury, D. (2016) · Theoretical and Applied Genetics · doi:10.1007/s00122-016-2689-z · Cited by 34
Multiple-testing correction in metabolome-wide association studies
Peluso, A., Glen, R. C., & Ebbels, T. M. D. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-03975-2 · Cited by 36
Phenotypic Integration Without Modularity: Testing Hypotheses About the Distribution of Pleiotropic Quantitative Trait Loci in a Continuous Space
Roseman, C. C., Kenney‐Hunt, J. P., & Cheverud, J. M. (2009) · Evolutionary Biology · doi:10.1007/s11692-009-9067-6 · Cited by 32
Self-citation
The CHRNA5/A3/B4 Gene Cluster and Tobacco, Alcohol, Cannabis, Inhalants and Other Substance Use Initiation: Replication and New Findings Using Mixture Analyses
Lubke, G. H., Stephens, S. H., Lessem, J. M., Hewitt, J. K., & Ehringer, M. A. (2012) · Behavior Genetics · doi:10.1007/s10519-012-9529-y · Cited by 31
Brain-Enriched Coding and Long Non-coding RNA Genes Are Overrepresented in Recurrent Neurodevelopmental Disorder CNVs
Alinejad‐Rokny, H., Heng, J. I., & Forrest, A. R. R. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108307 · Cited by 30
The cost of large numbers of hypothesis tests on power, effect size and sample size
Lazzeroni, L. C., & Ray, A. (2010) · Molecular Psychiatry · doi:10.1038/mp.2010.117 · Cited by 33
Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia
de Jong, S., Vidler, L. R., Mokrab, Y., Collier, D. A., & Breen, G. D. (2016) · Journal of Psychopharmacology · doi:10.1177/0269881116653109 · Cited by 26
Fine‐mapping of Obesity‐related Quantitative Trait Loci in an F9/10 Advanced Intercross Line
Fawcett, G. L., Jarvis, J. P., Roseman, C. C., Wang, B., Wolf, J. B., & Cheverud, J. M. (2009) · Obesity · doi:10.1038/oby.2009.411 · Cited by 30
Self-citation
Genetic control of susceptibility to infection with Plasmodium chabaudi chabaudi AS in inbred mouse strains
Laroque, A., Min‐Oo, G., Tam, M., Radovanovic, I., Stevenson, M. M., & Gros, P. (2011) · Genes and Immunity · doi:10.1038/gene.2011.67 · Cited by 30
Genetic analysis of NFE2L2 promoter variation in Taiwanese Parkinson's disease
Chen, Y., Wu, Y., Wu, Y.-C., Lee‐Chen, G., & Chen, C. (2012) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2012.10.018 · Cited by 32
Evidence for Nucleolus Organizer Regions as the Units of Regulation in Nucleolar Dominance in Arabidopsis thaliana Interecotype Hybrids
Lewis, M. S., Cheverud, J. M., & Pikaard, C. S. (2004) · Genetics · doi:10.1534/genetics.103.026203 · Cited by 29
Self-citation
Repetitive Concussive and Subconcussive Injury in a Human Tau Mouse Model Results in Chronic Cognitive Dysfunction and Disruption of White Matter Tracts, But Not Tau Pathology
Gangolli, M., Benetatos, J., Esparza, T. J., Fountain, E. M., Seneviratne, S., & Brody, D. L. (2018) · Journal of Neurotrauma · doi:10.1089/neu.2018.5700 · Cited by 28
Inequitable Flow of Animals in and Out of Shelters: Comparison of Community-Level Vulnerability for Owner-Surrendered and Subsequently Adopted Animals
Ly, L. H., Gordon, E. W., & Protopopova, A. (2021) · Frontiers in Veterinary Science · doi:10.3389/fvets.2021.784389 · Cited by 33
Serum phosphatidylinositol as a biomarker for bipolar disorder liability
Knowles, E. E. M., Meikle, P. J., Huynh, K., Göring, H. H. H., Olvera, R. L., Mathias, S. R., Duggirala, R., Almasy, L. A., Blangero, J., Curran, J. E., et al. (2017) · Bipolar Disorders · doi:10.1111/bdi.12468 · Cited by 30
Regional Structural Characterization of the Brain of Schizophrenia Patients1
Yushkevich, P. A., Dubb, A., Xie, Z., Gur, R. C., Gur, R. E., & Gee, J. C. (2005) · Academic Radiology · doi:10.1016/j.acra.2005.06.014 · Cited by 27
Genetic variation in FAAH is associated with cannabis use disorders in a young adult sample of Mexican Americans.
Melroy-Greif, W. E., Wilhelmsen, K. C., & Ehlers, C. L. (2016) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2016.06.021 · Cited by 26
Grow with the flow: a latitudinal cline in physiology is associated with more variable precipitation in Erythranthe cardinalis
Muir, C. D., & Angert, A. L. (2017) · Journal of Evolutionary Biology · doi:10.1111/jeb.13184 · Cited by 22
Properties of the Test Statistic under Null Hypothesis and the Calculation of LOD Threshold in Quantitative Trait Loci (QTL) Mapping
Zi-Qi, S. U. N., Hui-Hui, L. I., Zhang, L., & Jian-Kang, W. A. N. G. (2013) · ACTA AGRONOMICA SINICA · doi:10.3724/sp.j.1006.2013.00001 · Cited by 23
Genome‐wide association study of genetic variants related to anthracycline‐induced cardiotoxicity in early breast cancer
Park, B., Sim, S. H., Lee, K. S., Kim, H. J., & Park, I. H. (2020) · Cancer Science · doi:10.1111/cas.14446 · Cited by 25
High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population
Montes, C. M., Fox, C. M., Sanz‐Sáez, Á., Serbin, S. P., Kumagai, E., Krause, M. D., Xavier, A., Specht, J. E., Beavis, W. D., Bernacchi, C. J., et al. (2022) · Genetics · doi:10.1093/genetics/iyac065 · Cited by 25
Polymorphisms in the LPL and CETP Genes and Haplotype in the ESR1 Gene Are Associated with Metabolic Syndrome in Women from Southwestern Mexico
Cahua‐Pablo, J. Á., Cruz, M., Méndez‐Palacios, A., Antúnez-Ortiz, D. L., Vences-Velázquez, A., Alarcón‐Romero, L. D. C., Parra, E. J., Tello‐Flores, V. A., Leyva‐Vázquez, M. A., Valladares‐Salgado, A., et al. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms160921539 · Cited by 26
Multivariate mixed inheritance models for QTL detection on porcine chromosome 6
Szyda, J., Grindflek, E. H., Liu, Z., & Lien, S. (2003) · Genetics Research · doi:10.1017/s0016672302006043 · Cited by 25
Genetic architecture of Arabidopsis thaliana response to infection by Pseudomonas syringae
Kover, P. X., Wolf, J. B., Kunkel, B. N., & Cheverud, J. M. (2005) · Heredity · doi:10.1038/sj.hdy.6800651 · Cited by 25
Self-citation
Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005. Accurate inference and estimation in population genomics.
Hahn, M. (2006) · Molecular biology and evolution · doi:10.1093/molbev/msj094 · Cited by 33
Improving the experience of machine learning in compressive strength prediction of industrial concrete considering mixing proportions, engineered ratios and atmospheric features
Akber, M. Z. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.137884 · Cited by 26
A Genome-Wide Scan for Breast Cancer Risk Haplotypes among African American Women
Song, C., Chen, G. K., Millikan, R. C., Ambrosone, C. B., John, E. M., Bernstein, L., Zheng, W., Hu, J. J., Ziegler, R. G., Nyante, S. J., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057298 · Cited by 26
Norepinephrine Genes Predict Response Time Variability and Methylphenidate-Induced Changes in Neuropsychological Function in Attention Deficit Hyperactivity Disorder
Kim, B.-N., Kim, J.-W., Cummins, T. D. R., Bellgrove, M. A., Hawi, Z., Hong, S.-B., Yang, Y.-H., Kim, H.-J., Shin, M., Cho, S.-C., et al. (2013) · Journal of Clinical Psychopharmacology · doi:10.1097/jcp.0b013e31828f9fc3 · Cited by 24
Patterning of geographic variation in Middle Pleistocene Homo frontal bone morphology
Athreya, S. G. (2006) · Journal of Human Evolution · doi:10.1016/j.jhevol.2005.11.005 · Cited by 24
Generalization of Associations of Kidney-Related Genetic Loci to American Indians
Franceschini, N., Haack, K., Almasy, L. A., Laston, S., Lee, E. T., Best, L. G., Fabsitz, R. R., MacCluer, J. W., Howard, B. V., Umans, J. G., et al. (2013) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.02300213 · Cited by 24
Effective Sample Size: Quick Estimation of the Effect of Related Samples in Genetic Case-Control Association Analyses
Yang, Y., Remmers, E. F., Ogunwole, C. B., Kastner, D. L., Gregersen, P. K., & Li, W. (2007) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2010.12.006 · Cited by 21
Polymorphisms and haplotypes across the osteoprotegerin gene associated with bone mineral density and osteoporotic fractures
Jurado, S., Nogués, X., Águeda, L., García‐Giralt, N., Urreizti, R., Yoskovitz, G., Pérez-Edo, L., Saló, G., Carreras, R., Mellibovsky, L., et al. (2009) · Osteoporosis International · doi:10.1007/s00198-009-0956-4 · Cited by 24
Genetic variation in SH3-domain GRB2-like (endophilin)-interacting protein 1 has a major impact on fat mass
Cummings, N., Shields, K., Curran, J. E., Bozaoglu, K., Trevaskis, J. L., Gluschenko, K., Cai, G., Comuzzie, A. G., Dyer, T. D., Walder, K. R., et al. (2011) · International Journal of Obesity · doi:10.1038/ijo.2011.67 · Cited by 26
Role of class II human leucocyte antigens in the progression from early to definite systemic sclerosis
Vigone, B., Santaniello, A., Marchini, M., Montanelli, G., Caronni, M., Severino, A., & Beretta, L. (2014) · Lara D. Veeken · doi:10.1093/rheumatology/keu381 · Cited by 23
Healing quantitative trait loci in a combined cross analysis using related mouse strain crosses
Cheverud, J. M., Lawson, H. A., Funk, R., Zhou, J., Blankenhorn, E. P., & Heber‐Katz, E. (2011) · Heredity · doi:10.1038/hdy.2011.94 · Cited by 21
Self-citation
COMTVal158Met polymorphism influences the susceptibility to framing in decision‐making: OFC‐amygdala functional connectivity as a mediator
Gao, X., Gong, P., Liu, J., Hu, J., Li, Y., Yu, H., Gong, X., Xiang, Y., Jiang, C., & Zhou, X. (2016) · Human Brain Mapping · doi:10.1002/hbm.23142 · Cited by 23
Strong Evidence for a Novel Schizophrenia Risk Locus on Chromosome 1p31.1 in Homogeneous Pedigrees From Tamil Nadu, India
Holliday, E. G., Nyholt, D. R., Tirupati, S., John, S., Ramachandran, P., Mangala, R., Ramadoss, A. J., Jeyagurunathan, A., Kottiswaran, S., Smith, H. J., et al. (2008) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2008.08030442 · Cited by 22
Haplotypes in the CRP Gene Associated with Increased BMI and Levels of CRP in Subjects with Type 2 Diabetes or Obesity from Southwestern Mexico
Martínez-Calleja, A., Quiróz-Vargas, I., Parra‐Rojas, I., Muñóz-Valle, J. F., Leyva‐Vázquez, M. A., Fernández‐Tilapa, G., Vences-Velázquez, A., Cruz, M., Salazar-Martı́nez, E., & Flores‐Alfaro, E. (2012) · Experimental Diabetes Research · doi:10.1155/2012/982683 · Cited by 22
Maternal attachment insecurity, maltreatment history, and depressive symptoms are associated with broad DNA methylation signatures in infants
Robakis, T. K., Roth, M. C., King, L. S., Humphreys, K. L., Ho, M., Zhang, X., Chen, Y., Li, T., Rasgon, N., Watson, K. T., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01592-w · Cited by 21
Combined linkage and association analyses identify a novel locus for obesity near PROX1 in Asians
Kim, H.-J., Yoo, Y. J., Ju, Y. S., Lee, S., Cho, S., Sung, J., Kim, J., & Seo, J. (2013) · Obesity · doi:10.1002/oby.20153 · Cited by 21
Significance Levels in Genome-Wide Interaction Analysis (GWIA)
Becker, T., Herold, C., Meesters, C., Mattheisen, M., & Baur, M. P. (2010) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2010.00610.x · Cited by 21
Diagnosis-independent loss of T-cell costimulatory molecules in individuals with cytomegalovirus infection
Ford, B. N., Teague, T. K., Bayouth, M., Yolken, R. H., Bodurka, J., Irwin, M. R., Paulus, M. P., & Savitz, J. B. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.03.013 · Cited by 21
Data-Mining Methods as Useful Tools for Predicting Individual Drug Response: Application to CYP2D6 Data
Sabbagh, A., & Darlu, P. (2006) · Human Heredity · doi:10.1159/000096416 · Cited by 21
Quantitative trait loci with additive effects on growth and carcass traits in a Wagyu–Limousin F2 population
Alexander, L. J., Geary, T. W., Snelling, W. M., & MacNeil, M. D. (2007) · Animal Genetics · doi:10.1111/j.1365-2052.2007.01616.x · Cited by 21
Stability Analysis of Kernel Quality Traits in Exotic‐Derived Doubled Haploid Maize Lines
Vanous, A. E., Gardner, C. A. C., Blanco, M. H., Martin-Schwarze, A. M., Wang, J., Li, X., Lipka, A. E., Flint-Garcia, S. A., Bohn, M., Edwards, J. W., et al. (2018) · The Plant Genome · doi:10.3835/plantgenome2017.12.0114 · Cited by 22
Testing for association in case-control genome-wide association studies with shared controls
Chen, Z., Huang, H., & Ng, H. K. T. (2013) · Statistical Methods in Medical Research · doi:10.1177/0962280212474061 · Cited by 20
A New Approach to Account for the Correlations among Single Nucleotide Polymorphisms in Genome-Wide Association Studies
Chen, Z., & Liu, Q. (2011) · Human Heredity · doi:10.1159/000330135 · Cited by 22
Weighted multiple testing correction for correlated tests
Xie, C. (2011) · Statistics in Medicine · doi:10.1002/sim.4434 · Cited by 20
The genetic basis of quantitative variation in susceptibility of Arabidopsis thaliana to Pseudomonas syringae (Pst DC3000): evidence for a new genetic factor of large effect
Kover, P. X., & Cheverud, J. M. (2007) · New Phytologist · doi:10.1111/j.1469-8137.2007.01985.x · Cited by 20
Self-citation
Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
Caballo, M., Pangallo, D. R., Sanderink, W. B. G., Hernandez, A. M., Lyu, S. H., Molinari, F., Boone, J. M., Mann, R. M., & Sechopoulos, I. (2020) · Medical Physics · doi:10.1002/mp.14610 · Cited by 19
Variability in Associations of Phosphatidylcholine Molecular Species with Metabolic Syndrome in Mexican–American Families
Kulkarni, H. R., Meikle, P. J., Mamtani, M., Weir, J. M., Barlow, C. K., Jowett, J. B. M., Bellis, C., Dyer, T. D., Johnson, M. P., Rainwater, D. L., et al. (2013) · Lipids · doi:10.1007/s11745-013-3781-7 · Cited by 18
Genome-wide scan identifies a quantitative trait locus at 4p15.3 for serum urate
Cummings, N., Dyer, T. D., Kotea, N., Kowlessur, S., Chitson, P., Zimmet, P., Blangero, J., & Jowett, J. B. M. (2010) · European Journal of Human Genetics · doi:10.1038/ejhg.2010.97 · Cited by 17
Estimation and inference in generalized additive coefficient models for nonlinear interactions with high-dimensional covariates
Ma, S., Carroll, R. J., Liang, H., & Xu, S. (2015) · The Annals of Statistics · doi:10.1214/15-aos1344 · Cited by 18

Authors: Cheverud, J. M.
Affiliations: Washington University in St. Louis (US)
Corresponding author: Cheverud, James M
ORCID: Cheverud, James M
Venue: Heredity
Publisher: Springer Science and Business Media LLC
Published: 2001
Volume: 87
Issue: 1
Pages: 52-58
ISSN: 0018-067X (ISSN-L)
DOI: 10.1046/j.1365-2540.2001.00901.x
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A simple correction for multiple comparisons in interval mapping genome scans

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks