The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Genomic Control for Association Studies
Abstract
Summary. A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. To achieve this goal, we propose a statistical method that has several optimal properties: It can be used with case‐control data and yet, like family‐based designs, controls for population heterogeneity; it is insensitive to the usual violations of model assumptions, such as cases failing to be strictly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better performance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.
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In the citing paper
“the conventional genomic inflation factor (lambda or λ used interchangeably below) is dependent on the expected number of true associations”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [31]
In the citing paper
“Because in a typical epigenome-wide association study (EWAS), it is expected that small effects from many CpGs might be associated with the phenotype, the genomic inflation factor would overestimate actual test-statistic inflation”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [31]
In the citing paper
“The genomic control inflation factor (λ) 11 was 1.037, suggesting little evidence for residual stratification.”
From Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease (2009) · cites this paper as [11]
In the citing paper
“The most popular are genomic control (GC)21,22, principal component analysis (PCA)23 and linear mixed models24.”
From Differential confounding of rare and common variants in spatially structured populations (2012) · cites this paper as [22]
In the citing paper
“The overall inflation of the results due to population stratification is negligible: a modified [7] genomic control factor [10] (see Text S1).”
From Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes (2013) · cites this paper as [10]
In the citing paper
“Prior to the gene-based analyses all individual SNP P-values were corrected for the genomic inflation factor (λ; to normalise for unaccounted variation, due to factors such as population stratification; Devlin and Roeder, 1999).”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Devlin and Roeder, 1999
In the citing paper
“Residual stratification was further corrected at the meta-analytic level with study-specific genomic controls56”
From Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use (2019) · cites this paper as [56]
In the citing paper
“For the discovery, we applied two rounds of genomic control corrections to each individual GWAS and the combined meta results, respectively 51.”
From Seven new loci associated with age-related macular degeneration (2013) · cites this paper as [51]
In the citing paper
“Methods were developed to detect and adjust for such biases (67–69) as well as unexpected relatedness between subjects.”
From Genetic Mapping in Human Disease (2008) · cites this paper as [67]
Citations
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Genetics of 38 blood and urine biomarkers in the UK BiobankPreprint
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“The genomic control approach was used to account for spurious association due to population stratification [30] and because the breeding line is a composite line derived from three different breeds.”
MethodCarty, C. L., Johnson, N. A., Hutter, C. M., Reiner, A. P., Peters, U., Tang, H., & Kooperberg, C. L. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddr489 · Cited by 100
“Using l, we investigated correction of P-values using genomic control to account for potential residual confounding by genetic ancestry (34,35).”
Romagnoni, A., Jégou, S., Steen, K. V., Wainrib, G., Hugot, J., Peyrin‐Biroulet, L., Chamaillard, M., Colombel, J.-F., Cottone, M., D’Amato, M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-46649-z · Cited by 104
Gratacòs, M., Costas, J., de Cid, R., Bayés, M., González, J. R., Baca‐García, E., de Diego‐Otero, Y., Fernández‐Aranda, F., Fernández‐Piqueras, J., Guitart, M., et al. (2008) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30902 · Cited by 101
“Putative stratification was corrected by the genomic control method [Devlin and Roeder, 1999].”
MethodDudaniec, R. Y., Yong, C., Lancaster, L. T., SVENSSON, E. I., & Hansson, B. (2018) · Molecular Ecology · doi:10.1111/mec.14709 · Cited by 102
A direct approach to estimating false discovery rates conditional on covariates
Boca, S. M., & Leek, J. T. (2018) · PeerJ · doi:10.7717/peerj.6035 · Cited by 97
Ionita‐Laza, I., McQueen, M. B., Laird, N. M., & Lange, C. (2007) · The American Journal of Human Genetics · doi:10.1086/519748 · Cited by 98
Potkin, S. G., Turner, J. A., Guffanti, G., Lakatos, A., Torri, F., Keator, D. B., & Macciardi, F. M. (2009) · Cognitive Neuropsychiatry · doi:10.1080/13546800903059829 · Cited by 99
“A method called Genomic Control (GC) (Bacanu, Devlin, & Roeder, 2000; Devlin & Roeder, 1999) is typically used to correct association tests for the estimated inflation score.”
MethodTalkowski, M. E., Seltman, H. J., Bassett, A. S., Brzustowicz, L. M., Chen, X., Chowdari, K. V., Collier, D. A., Cordeiro, Q., Corvin, A. P., Deshpande, S. N., et al. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.02.015 · Cited by 97
“Fst ranges from 0 to 1, and the estimate for these RGS4 SNPs are consistent with the frequent observation of little heterogeneity among samples of European ancestry (Morton 1992; Devlin and Roeder 1999; Devlin et al 2001; Chakraborty 1993).”
ResultSelf-citationGenomics in Sudden Cardiac Death
Arking, D. E., Chugh, S. S., Chakravarti, A., & Spooner, P. M. (2004) · Circulation Research · doi:10.1161/01.res.0000123861.16082.95 · Cited by 99
Traffic-Related Air Pollution, Oxidative Stress Genes, and Asthma (ECHRS)
Castro-Giner, F., Künzli, N., Jacquemin, B., Forsberg, B., de Cid, R., Sunyer, J., Jarvis, D., Briggs, D., Vienneau, D., Norbäck, D., et al. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0900589 · Cited by 99
“First, the genomic control approach (Devlin and Roeder 1999) showed a minimal effect [inflation factor (λ) = 1.”
BackgroundInfluentialZeng, B., Bendl, J. M., Kosoy, R., Fullard, J. F., Hoffman, G. E., & Roussos, P. (2022) · Nature Genetics · doi:10.1038/s41588-021-00987-9 · Cited by 92
“Results from 50 simulations were aggregated, and we used genomic inflation factor 76 and quantile–quantile (Q–”
Genome-Wide Association Studies in an Isolated Founder Population from the Pacific Island of Kosrae
Lowe, J. K., Maller, J., Pe’er, I., Neale, B. M., Salit, J., Kenny, E. E., Shea, J., Burkhardt, R., Smith, J. G., Ji, W., et al. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000365 · Cited by 97
“In all subsequent analyses, we applied genomic control to adjust the association scores for excess relatedness [19].”
Zhang, S., Zhu, X., & Zhao, H. (2002) · Genetic Epidemiology · doi:10.1002/gepi.10196 · Cited by 98
“Several statistical methods were recently proposed to utilize genomic markers to control for population stratification that may be present in a sample of unrelated individuals, both in the analysis of qualitative traits [Devlin and Roeder, 1999; Bacanu et al., 2000; Devlin et al., 2001; Pritchard et al., 2000b; Reich…”
BackgroundMethodRobinson, N. A., Gopikrishna, G., Baranski, M., Katneni, V. K., Shekhar, M. S., Shanmugakarthik, J., Jothivel, S., Gopal, C., Ravichandran, P., Gitterle, T., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-731 · Cited by 98
“Genomic control is believed by some authors to circumvent the need for Bonferroni correction for multiple testing [89].”
BackgroundClauw, P., Coppens, F., Korte, A., Herman, D., Slabbinck, B., Dhondt, S., Daele, T. V., Milde, L. D., Vermeersch, M., Maleux, K., et al. (2016) · The Plant Cell · doi:10.1105/tpc.16.00483 · Cited by 98
“To create a highconfidence data set, a selection was conducted against inflated models by calculating the genomic inflation factor (l) (Devlin and Roeder, 1999) and performing the D’Agostino test for normality (D’Agostino, 1986), as non-normal distributed data potentially lead to model inflation.”
MethodCollaborative Cross and Diversity Outbred data resources in the Mouse Phenome Database
Bogue, M. A., Churchill, G. A., & Chesler, E. J. (2015) · Mammalian Genome · doi:10.1007/s00335-015-9595-6 · Cited by 94
“Exciting developments in genetic analysis and computation have greatly increased the rigor with which investigators can combine studies across populations through the integration of population structure into mapping analysis (Devlin and Roeder 1999; Yu et al. 2006).”
BackgroundGenome-wide association analysis of feed intake and residual feed intake in Nellore cattle
Santana, M. H. D. A., Utsunomiya, Y. T., Neves, H. H. D. R., Gomes, R. D. C., Garcia, J. F., Fukumasu, H., Silva, S. D. L. E., Júnior, G. A. O., Alexandre, P. A., Leme, P. R., et al. (2014) · BMC Genetics · doi:10.1186/1471-2156-15-21 · Cited by 97
Polygenic Risk for Externalizing Disorders
Salvatore, J. E., Alıev, F., Bucholz, K. K., Arpana Agrawal, A., HESSELBROCK, V. M., Hesselbrock, M. N., Bauer, L. O., Kuperman, S., Schuckit, M. A., Kramer, J., et al. (2014) · Clinical Psychological Science · doi:10.1177/2167702614534211 · Cited by 104
“The gene-by-environment effects continued to be significant—bParents × Polygene = –1.60, t(154) = –1.88, p = .02; bPeers × Polygene = 1.97, t(155) = 2.84, p < .01—and again indicated that genetic variance increased at low levels of parental monitoring and high levels of peer substance use.”
BackgroundInfluentialChromosome Painting In Silico in a Bacterial Species Reveals Fine Population Structure
Yahara, K., Furuta, Y., Oshima, K., Yoshida, M., Azuma, T., Hattori, M., Uchiyama, I., & Kobayashi, I. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst055 · Cited by 98
“They are, for example, an important basis for association studies (Devlin and Roeder 1999; Nakamura et al. 2005).”
BackgroundGenome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke
Traylor, M., Zhang, C. R., Adib‐Samii, P., Devan, W. J., Parsons, O., Lanfranconi, S., Gregory, S., Cloonan, L. K., Falcone, G. J., Radmanesh, F., et al. (2015) · Neurology · doi:10.1212/wnl.0000000000002263 · Cited by 99
“To control for any excess signal that might result from study-wise inflation of p values, we performed genomic control correction, multiplying the standard errors from each study by the square root of the genomic inflation factor.(21) Heterogeneity was assessed using Cochran q statistic.”
Transethnic meta-analysis identifies GSDMA and PRDM1 as susceptibility genes to systemic sclerosis
Terao, C., Kawaguchi, T., Dieudé, P., Varga, J., Kuwana, M., Hudson, M., Kawaguchi, Y., Matucci‐Cerinic, M., Ohmura, K., Riemekasten, G., et al. (2017) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2016-210645 · Cited by 99
Barcelona, V., Huang, Y., Crusto, C. A., Sun, Y. V., & Taylor, J. Y. (2017) · Biological Research For Nursing · doi:10.1177/1099800417748759 · Cited by 96
“We applied the genomic control approach (Devlin & Roeder, 1999) to address residual unmeasured confounding and inflation of Type I error and used false discovery rate (FDR) to correct for multiple comparisons.”
MethodHardcastle, A. J., Lišková, P., Bykhovskaya, Y., McComish, B. J., Davidson, A. E., Inglehearn, C. F., Li, X., Choquet, H., Habeeb, M., Lucas, S. E. M., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01784-0 · Cited by 100
Knight, J. A., Skol, A. D., Shinde, A., Hastings, D., Walgren, R. A., Shao, J., Tennant, T. R., Banerjee, M., Allan, J. M., Beau, M. M. L., et al. (2009) · Blood · doi:10.1182/blood-2008-10-183244 · Cited by 97
“Age at first cancer, y†† 50 (7-85) 51 (12-78)”
Song, K. S., & Elston, R. C. (2005) · Statistics in Medicine · doi:10.1002/sim.2350 · Cited by 97
“To investigate how well the HWD and CA trend test statistics account for correlations due to population structure or cryptic relatedness, we performed simulations similar to those performed by Devlin and Roeder [23] for the CA trend test statistic.”
BackgroundMethodInfluentialGeographical structure and differential natural selection among North European populationsPreprint
McEvoy, B. P., Montgomery, G., McRae, A. F., Ripatti, S., Perola, M., Spector, T. D., Cherkas, L., Ahmadi, K. R., Boomsma, D., Willemsen, G., et al. (2009) · Genome Research · doi:10.1101/gr.083394.108 · Cited by 95
Liu, X., Helenius, D., Skotte, L., Beaumont, R. N., Wielscher, M., Geller, F., Juodakis, J., Mahajan, A., Bradfield, J. P., Lin, F. T. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11881-8 · Cited by 96
Sillanpää, M. J. (2010) · Heredity · doi:10.1038/hdy.2010.91 · Cited by 94
“…this assumption does not hold for individuals showing more complex relationship structures, and the potential existence of such discrepancy in the data is generally known as a cryptic relatedness problem (for example, Devlin and Roeder, 1999; Voight and Pritchard, 2005; Zhang and Deng, 2010).”
BackgroundInfluentialGenetic utility of broadly defined bipolar schizoaffective disorder as a diagnostic concept
Hamshere, M. L., Green, E., Jones, I. R., Jones, L. A., Moskvina, V., Kirov, G., Grozeva, D., Nikolov, I., Vukcevic, D., Caesar, S., et al. (2009) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.108.061424 · Cited by 96
Complexity and Power in Case-Control Association Studies
Longmate, J. (2001) · The American Journal of Human Genetics · doi:10.1086/320106 · Cited by 96
“…by the continuing practical importance of the case-control design, as well as by recent methodological work permitting recognition and adjustment for potentially confounding population structure (Devlin and Roeder 1999; Pritchard and Rosenberg 1999; Bacanu et al. 2000; Schork et al. 2001).”
BackgroundShen, X., Klarić, L., Sharapov, S., Mangino, M., Zheng, N., Wu, D., Trbojević‐Akmačić, I., Pučić‐Baković, M., Rudan, I., Polašek, O., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00453-3 · Cited by 93
CXCR4 involvement in neurodegenerative diseases
Bonham, L. W., Karch, C. M. M., Fan, C. C., Tan, C. H., Geier, E. G., Wang, Y., Wen, N., Broce, I., Li, Y., Barkovich, M. J., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-017-0049-7 · Cited by 94
Genetic variation in PLEKHG1 is associated with white matter hyperintensities (n = 11,226)
Traylor, M., Tozer, D. J., Croall, I. D., Ford, D., Olorunda, A. O., Boncoraglio, G. B., Dichgans, M., Lemmens, R., Rosand, J. M., Rost, N. S., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000006952 · Cited by 98
“01).(17,19) QQ plots data are available from Open Science Framework (figures e-1 to e-4; osf.”
MethodGu, D., Su, S., Ge, D., Chen, S., Huang, J., Li, B., Chen, R., & Qiang, B. (2006) · Hypertension · doi:10.1161/01.hyp.0000219041.66702.45 · Cited by 95
Association Study of Nonsynonymous Single Nucleotide Polymorphisms in Schizophrenia
Carrera, N., Arrojo, M., Sanjuán, J., Ramos‐Ríos, R., Paz, E., Suárez‐Rama, J. J., Páramo, M., Agra, S., Brenlla, J., Martínez, S., et al. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.09.032 · Cited by 93
BDNF Val66Met is Associated with Introversion and Interacts with 5-HTTLPR to Influence Neuroticism
Terracciano, A., Tanaka, T., Sutin, A. R., Deiana, B., Balaci, L., Sanna, S., Olla, N., Maschio, A., Uda, M., Ferrucci, L., et al. (2009) · Neuropsychopharmacology · doi:10.1038/npp.2009.213 · Cited by 96
“In the SardiNIA sample, we checked the genomic control value for our GWA analyses (Devlin and Roeder, 1999), and carried out a principal component analysis of genome-wide SNP data in a subset of unrelated individuals (Price et al, 2006).”
MethodNatural Variation in Arabidopsis. How Do We Find the Causal Genes?
Weigel, D., & Nordborg, M. (2005) · PLANT PHYSIOLOGY · doi:10.1104/pp.104.900157 · Cited by 94
“Statistical methods that help to correct for this problem have been proposed (e.g. Devlin and Roeder, 1999; Pritchard et al., 2000), but they will not bypass the need for experimental studies.”
MethodMeasuring and using admixture to study the genetics of complex diseases
Halder, I., & Shriver, M. D. (2003) · Human Genomics · doi:10.1186/1479-7364-1-1-52 · Cited by 93
“Methodological advancements have been made to offset the potential problems arising from association between unlinked loci by conditioning on parental admixture,(13,29) and to detect and correct for population stratification.(59,60) Use of Bayesian AM(13,29,59) can take into consideration various uncertainties, includ…”
BackgroundHeck, A., Fastenrath, M., Ackermann, S., Auschra, B., Bickel, H., Coynel, D., Gschwind, L., Jessen, F., Kaduszkiewicz, H., Maier, W., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.01.010 · Cited by 97
“Lambda is defined as themedian c(2) association test statistic divided by the theoretical distribution under the null distribution (Devlin and Roeder, 1999).”
BackgroundInfluentialIdentification of Susceptibility Loci for Cutaneous Squamous Cell Carcinoma
Asgari, M. M., Wang, W., Ioannidis, N. M., Itnyre, J., Hoffmann, T. J., Jorgenson, E., & Whittemore, A. S. (2016) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2016.01.013 · Cited by 96
“We checked for residual population stratification by examining the inflation factor lGC (Devlin and Roeder 1999), and Q-Q plots of percentiles of the observed distribution of test statistics versus those expected under the global null hypothesis (see Supplementary Materials).”
MethodLiu, Z., Sun, C., Yan, Y., Li, G., Shi, F., Wu, G., Liu, A., & Yang, N. (2018) · Scientific Reports · doi:10.1038/s41598-018-29162-7 · Cited by 93
Association mapping of partitioning loci in barley
Cockram, J., White, J., Leigh, F. J., Lea, V. J., Chiapparino, E., Laurie, D. A., Mackay, I., Powell, W., & O’Sullivan, D. M. (2008) · BMC Genetics · doi:10.1186/1471-2156-9-16 · Cited by 93
“Here, we apply a combination of Structured Association (SA) [18] to correct for population sub-structure and Genomic Control (GC) [19] to correct for residual inflation of the test statistic due to unidentified population structure effects and residual confounding arising, for example, from close pedigree relationship…”
InfluentialOkada, Y., Takahashi, A., Ohmiya, H., Kumasaka, N., Kamatani, Y., Hosono, N., Tsunoda, T., Matsuda, K., Tanaka, T., Kubo, M., et al. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq551 · Cited by 93
Li, M., Kwan, J. S. H., & Sham, P. C. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.08.004 · Cited by 93
NCAM1 and Neurocognition in Schizophrenia
Sullivan, P. F., Keefe, R. S. E., Lange, L. A., Lange, E. M., Stroup, T. S., Lieberman, J. A., & Maness, P. F. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.07.036 · Cited by 92
Pillas, D., Hoggart, C., Evans, D. M., O’Reilly, P. F., Sipilä, K., Lähdesmäki, R. E., Millwood, I. Y., Kaakinen, M., Netuveli, G., Blane, D., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000856 · Cited by 91
“Genomic control [32] was then used to correct the residual population stratification.”
MethodTeruel, B. M., Parra, E. J., Fuentes-Smith, E., Salas, A., Buttenschøn, H. N., Demontis, D., Torres, M. L., Padrón, L. C. M., Gómez-Cabezas, E. J., Álvarez‐Iglesias, V., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004488 · Cited by 93
“Therefore, we implemented genome control (GC) [38] methods to correct for type I error inflation.”
MethodAssociation study of 182 candidate genes in anorexia nervosa
Pinheiro, A. P., Bulik, C. M., Thornton, L. M., Sullivan, P. F., Root, T. L., Bloss, C. S., Berrettini, W. H., Schork, N. J., Kaye, W. H., Bergen, A. W., et al. (2010) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.31082 · Cited by 97
“Finally, population stratification was assessed through genomic control [Devlin and Roeder, 1999]: lambda (l)¼ 1.035 is consistent with an acceptably small amount of population stratification.”
MethodInfluentialEstrada, K., Krawczak, M., Schreiber, S., van Duijn, K., Stolk, L., van Meurs, J. B. J., Liu, F., Penninx, B. W. J. H., Smit, J. H., Vogelzangs, N., et al. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp296 · Cited by 93
“In agreement with previous recommendations (16), results were deemed significant at a genome-wide level if the locus-specific unadjusted P-value was smaller than 5 10(28) (17).”
MethodResultRzehak, P., Saffery, R., Reischl, E., Covic, M., Wahl, S., Grote, V., Xhonneux, A., Langhendries, J., Ferré, N., Closa‐Monasterolo, R., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155554 · Cited by 93
“Model adjustment for child’s sex, age at blood draw, study centre, maternal education and postnatal smoke exposure was also conducted because lambda (λ) [67] was substantially improved from a model adjusted for cell mixture only (λ = 1.”
MethodGenome-Wide Association Studies in Nephrology Research
Köttgen, A. (2010) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2010.05.018 · Cited by 92
Martins, K., Gugger, P. F., Llanderal‐Mendoza, J., González‐Rodríguez, A., Fitz‐Gibbon, S. T., Zhao, J., Rodríguez-Correa, H. A., Oyama, K., & Sork, V. L. (2018) · Evolutionary Applications · doi:10.1111/eva.12684 · Cited by 92
An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis
Liu, J., Carnero‐Montoro, E., van Dongen, J., Lent, S., Nedeljkovic, I., Ligthart, S., Tsai, P.-C., Martin, T. C., Mandaviya, P. R., Jansen, R., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10487-4 · Cited by 88
Zee, R. Y. L. (2003) · Human Molecular Genetics · doi:10.1093/hmg/ddh039 · Cited by 91
Lo, K. S., Wilson, J. G., Lange, L. A., Folsom, A. R., Galarneau, G., Ganesh, S. K., Grant, S. F. A., Keating, B. J., McCarroll, S. A., Mohler, E. R., et al. (2010) · Human Genetics · doi:10.1007/s00439-010-0925-1 · Cited by 90
“Individual study results, as well as meta-analytic results, were scaled using genomic control (Devlin and Roeder 1999).”
Bhatnagar, P., Purvis, S. H., Barron‐Casella, E. A., DeBaun, M. R., Casella, J. F., Arking, D. E., & Keefer, J. R. (2011) · Journal of Human Genetics · doi:10.1038/jhg.2011.12 · Cited by 89
van den Berg, S., Vandenplas, J., van Eeuwijk, F. A., Bouwman, A. C., Lopes, M. S., & Veerkamp, R. F. (2019) · Genetics Selection Evolution · doi:10.1186/s12711-019-0445-y · Cited by 92
Nakatochi, M., Kanai, M., Nakayama, A., Hishida, A., Kawamura, Y., Ichihara, S., Akiyama, M., Ikezaki, H., Furusyo, N., Shimizu, S., et al. (2019) · Communications Biology · doi:10.1038/s42003-019-0339-0 · Cited by 91
Osteopontin and Systemic Lupus Erythematosus Association: A Probable Gene-Gender Interaction
Han, S., Guthridge, J. M., Harley, I. T. W., Sestak, A. L., Kim-Howard, X., Kaufman, K. M., Namjou, B., Deshmukh, H., Bruner, G. R., Espinoza, L. R., et al. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001757 · Cited by 90
“Additionally, genomic control (GC) [38], structured association (STRAT) [39], and principal components analysis (PCA) [40] were applied to control for population stratification in race specific analysis.”
MethodThe Importance of Genealogy in Determining Genetic Associations with Complex Traits
Newman, D. L., Abney, M., McPeek, M. S., Ober, C., & Cox, N. J. (2001) · The American Journal of Human Genetics · doi:10.1086/323659 · Cited by 90
Hancock, D. B., Reginsson, G. W., Gaddis, N. C., Chen, X., Saccone, N. L., Lutz, S. M., Qaiser, B., Sherva, R., Steinberg, S., Zink, F., et al. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.149 · Cited by 85
Genome-wide association of anthropometric traits in African- and African-derived populations
Kang, S. J., Chiang, C. W. K., Palmer, C. D., Tayo, B. O., Lettre, G., Butler, J. L., Hackett, R., Adeyemo, A., Guiducci, C., Berzins, I., et al. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq154 · Cited by 90
“Each genome-wide scan was individually corrected for genomic inflation (50) prior to meta-analysis, but not corrected again after meta-analysis.”
Sigurðsson, S., Alexandersson, K. F., Sulem, P., Feenstra, B., Guðmundsdóttir, S., Halldorsson, G. H., Ólafsson, S., Sigurðsson, Á., Rafnar, Þ., Thorgeirsson, T. E., et al. (2017) · Nature Communications · doi:10.1038/ncomms15789 · Cited by 91
“To correct for the relatedness of the Icelandic individuals included in this study, we applied the method of genomic control 47 where the inflation in the w 2 values was estimated on the basis of a subset of about 300,000 common variants, and P v alues were adjusted by dividing the corresponding w 2…”
Polymorphisms in predicted miRNA binding sites and osteoporosis
Lei, S., Papasian, C. J., & Deng, H. (2010) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.186 · Cited by 89
“The inflation factor l, calculated by Genomic Control,(36) was 1.007, indicating that potential population stratification in this homogeneous US white population is very minimal.”
MethodInfluentialOtt, J. (2004) · Neurology · doi:10.1212/wnl.63.6.955 · Cited by 89
Keen‐Kim, D., Mathews, C. A., Reus, V. I., Lowe, T. L., Herrera, L. D., Budman, C. L., Gross‐Tsur, V., Pulver, A. E., BRUUN, R. D., Erenberg, G., et al. (2006) · Human Molecular Genetics · doi:10.1093/hmg/ddl408 · Cited by 90
“Genomic control approaches (12) are becoming widely used, but may not be sensitive to even moderate levels of stratification (13).”
BackgroundCommon and rare variants associating with serum levels of creatine kinase and lactate dehydrogenase
Kristjansson, R. P., Oddsson, Á., Helgason, H., Sveinbjörnsson, G., Arnadottir, G. A., Jensson, B. Ö., Jónasdóttir, Á., Jónasdóttir, A., Walters, G. B., Sulem, G., et al. (2016) · Nature Communications · doi:10.1038/ncomms10572 · Cited by 90
Genetic variation at 16q24.2 is associated with small vessel stroke
Traylor, M., Malik, R., Nalls, M. A., Cotlarciuc, I., Radmanesh, F., Þorleifsson, G., Hanscombe, K. B., Langefeld, C. D., Saleheen, D., Rost, N. S., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24840 · Cited by 90
“inflation was accounted for by adjusting results by the genomic inflation factor, k.(21) In all analyses, SNPs with imputation qual-”
MethodInkster, B., Nichols, T. E., Saemann, P. G., Auer, D. P., Holsboer, F., Muglia, P., & Matthews, P. M. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.065 · Cited by 90
“The whole-genome association analysis of the full sample of cases and controls produced a genomic control (Devlin and Roeder 1999) of λ=1.002 (Muglia et al., 2008), consistent with a lack of major population structure; cases and controls therefore were accepted as being genetically homogenous.”
BackgroundMethodInfluentialSha, B., Yang, T., Zhao, L., Chen, X., Guo, Y., Chen, Y., Pan, F., Zhang, Z., Dong, S., Xu, X., et al. (2009) · Journal of Human Genetics · doi:10.1038/jhg.2009.10 · Cited by 88
Cipriani, V., Leung, H.-T., Plagnol, V., Bunce, C., Khan, J. C., Shahid, H., Moore, A. T., Harding, S. P., Bishop, P. N., Hayward, C., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds225 · Cited by 90
Common variation near ROBO2 is associated with expressive vocabulary in infancy
Pourcain, B. S., Cents, R. A. M., Whitehouse, A., Haworth, C. M. A., Davis, O. S. P., O’Reilly, P. F., Roulstone, S., Wren, Y. E., Ang, Q. W., Velders, F. P., et al. (2014) · Nature Communications · doi:10.1038/ncomms5831 · Cited by 88
Traylor, M., Mäkelä, K.-M., Kilarski, L. L., Holliday, E. G., Devan, W. J., Nalls, M. A., Wiggins, K. L., Zhao, W., Cheng, Y.-C., Achterberg, S., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004469 · Cited by 88
“Genomic control was used to correct for any residual inflation due to population stratification [45].”
MethodZindler, T., Frieling, H., Neyazi, A., Bleich, S., & Friedel, E. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03559-6 · Cited by 92
“This therefore quantifies the magnitude of the bulk inflation and of the excess false positive rate [27].”
BackgroundNon-replication of association studies: “pseudo-failures” to replicate?
Gorroochurn, P., Hodge, S. E., Heiman, G. A., Durner, M., & Greenberg, D. A. (2007) · Genetics in Medicine · doi:10.1097/gim.0b013e3180676d79 · Cited by 88
Case‐control association testing in the presence of unknown relationships
Choi, Y., Wijsman, E. M., & Weir, B. S. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20418 · Cited by 82
“In the proposed method, the idea of correcting the variance using genomic data is similar to the idea behind genomic control (GC) [Devlin and Roeder, 1999].”
BackgroundMethodInfluentialRobust Genomic Control for Association Studies
Zheng, G., Freidlin, B., & Gastwirth, J. L. (2006) · The American Journal of Human Genetics · doi:10.1086/500054 · Cited by 87
“Devlin and Roeder (1999) studied2 2Z (1), … ,Z (1)1 c both Bayesian and frequentist approaches for estimating .”
BackgroundMethodInfluentialRitchie, M. D., & Motsinger‐Reif, A. A. (2005) · Pharmacogenomics · doi:10.2217/14622416.6.8.823 · Cited by 89
“Testing for population stratification can be performed in a variety of ways, including genomic control [39] and using software such as structure and STRAT [40], as well as Bayesian methods [41].”
MethodA genome-wide association study of serum uric acid in African Americans
Charles, B. A., Shriner, D., Doumatey, A. P., Chen, G., Zhou, J., Huang, H., Herbert, A. G., Gerry, N. P., Christman, M. F., Adeyemo, A., et al. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-17 · Cited by 88
“Possible inflation of the type I error rate due to population stratification was investigated using genomic control [31].”
MethodA fully adjusted two‐stage procedure for rank‐normalization in genetic association studies
Sofer, T., Zheng, X., Gogarten, S. M., Laurie, C. A., Grinde, K. E., Shaffer, J. R., Shungin, D., O’Connell, J. R., Durazo‐Arvizo, R. A., Raffield, L. M., et al. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22188 · Cited by 87
Genome-wide association study of antipsychotic-induced QTc interval prolongation
Åberg, K. A., Adkins, D. E., Liu, Y., McClay, J. L., Bukszár, J., Jia, P., Zhao, Z., Perkins, D. O., Stroup, T. S., Lieberman, J. A., et al. (2010) · The Pharmacogenomics Journal · doi:10.1038/tpj.2010.76 · Cited by 87
Genome‐wide association study in German patients with attention deficit/hyperactivity disorder
Hinney, A., Scherag, A., Jarick, I., Albayrak, Ö., Pütter, C., Pechlivanis, S., Dauvermann, M. R., Beck, S., Weber, H., Scherag, S., et al. (2011) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.31246 · Cited by 92
“The ratio of the median of the empirically observed distribution of the test statistic to the expected median is defined as the genomic inflation factor l which can be used to address possibly population stratification effects [Devlin and Roeder, 1999].”
MethodClinical reviews in allergy and immunology
Leung, D. Y. M., Ledford, D. K., Wechsler, M. E., Fulkerson, P. C., Bochner, B. S., Gauvreau, G. M., Gleich, G. J., Henkel, T., Kolbeck, R., Mathur, S. K., et al. (2012) · Cited by 87
A Comparison of Association Methods Correcting for Population Stratification in Case–Control Studies
Wu, C., DeWan, A. T., Hoh, J. J., & Wang, Z. (2011) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2010.00639.x · Cited by 93
“One can directly estimate a correction factor for the variance such as genomic control (GC) (Devlin and Roeder, 1999), or estimate dependence structure and use it to correct the test statistic (Rakovski and Stram, 2009; Thornton and McPeek, 2010; Kang et al., 2010; Zhang et al., 2010).”
BackgroundMethodInfluentialAssimes, T. L., Hólm, H., Kathiresan, S., Reilly, M. P., Þorleifsson, G., Voight, B. F., Erdmann, J., Willenborg, C., Vaidya, D. M., Xie, C., et al. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.06.022 · Cited by 85
“21 using a method of genomic control (36) by calculating the average of the observed chi-square statistics for the genome-wide SNP set, which accounts for relatedness and for potential population stratification.”
Wang, J.-C., Foroud, T., Hinrichs, A. L., Le, N., Bertelsen, S. E., Budde, J., Harari, O., Koller, D. L., Wetherill, L. F., Arpana Agrawal, A., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2012.143 · Cited by 85
Cattaert, T., Calle, M. L., DUDEK, S. M., John, J. M., Lishout, F. V., Urrea, V., Ritchie, M. D., & Steen, K. V. (2010) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2010.00604.x · Cited by 85
“Because MB-MDR allows for covariate adjustment, population substructure characteristics can in principle be accounted for during an MB-MDR screen (Devlin & Roeder, 1999).”
BackgroundKoch, I. J., Clark, M. M., Thompson, M. J., Deere, K. A., Wang, J., Duarte, L., Gnanadesikan, G. E., McCoy, E. L., Rubbi, L., Stahler, D. R., et al. (2015) · Molecular Ecology · doi:10.1111/mec.13480 · Cited by 88
“If the genomic control factor k was still inflated (i.e. genomewide type 1 error rate; Devlin & Roeder 1999), EWASher computed the PCs across all samples, included the top PC as a covariate and then repeated the process.”
MethodMechanisms of Disease: the genetic basis of coronary heart disease
Kullo, I. J., & Ding, K. (2007) · Nature Clinical Practice Cardiovascular Medicine · doi:10.1038/ncpcardio0982 · Cited by 86
“The replication of SNP disease associations in independent samples is crucial for validating results, and by genotyping 'neutral' markers throughout the genome, potentially confounding population substructures can be excluded”
BackgroundFinding Genes Influencing Susceptibility to Complex Diseases in the Post-Genome Era
Rannala, B. H. (2001) · American Journal of PharmacoGenomics · doi:10.2165/00129785-200101030-00005 · Cited by 84
Genome wide association mapping of agro-morphological and disease resistance traits in sugarcane
Gouy, M., Rousselle, Y., Chane, A. T., Anglade, A., Royaert, S., Nibouche, S., & Costet, L. (2014) · Euphytica · doi:10.1007/s10681-014-1294-y · Cited by 88
“To measure the inflation of the test statistic, we computed the inflation factor k (Devlin and Roeder 1999) for each statistical model.”
MethodAssociation of HLA–C3 and smoking with vasculitis in patients with rheumatoid arthritis
Turesson, C., Schaid, D. J., Weyand, C. M., Jacobsson, L., Goronzy, J. J., Petersson, I. F., Dechant, S. A., Nyhäll-Wåhlin, B.-M., Truedsson, L., Sturfelt, G. K., et al. (2006) · Arthritis & Rheumatism · doi:10.1002/art.22057 · Cited by 85
Tracing Sub-Structure in the European American Population with PCA-Informative Markers
Paschou, P., Petros Drineas, B., Lewis, J., Nievergelt, C. M., Nickerson, D. A., Smith, J. D., Ridker, P. M., Chasman, D. I., Krauss, R. M., & Ziv, E. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000114 · Cited by 86
“The uniform adjustment proposed by the method of genomic control could be too conservative [16,17], while structured association testing is computationally impractical for very large datasets [18].”
MethodDetecting adaptive evolution based on association with ecological gradients: Orientation matters!
Frichot, É., Schoville, S. D., de Villemereuil, P., Gaggiotti, O. E., & François, O. (2015) · Heredity · doi:10.1038/hdy.2015.7 · Cited by 82
“Following Devlin and Roeder (1999), we obtained a genomic in fl ation factor after computing the median of the squared (combined) z -scores for each K , divided by the median of the chi-square distribution with one degree of freedom.”
MethodMa, L., Yang, J., Runesha, H. B., Tanaka, T., Ferrucci, L., Bandinelli, S., & Da, Y. (2010) · BMC Medical Genetics · doi:10.1186/1471-2350-11-55 · Cited by 85
“In addition to the GLS method to account for sib correlation, the genomic control (GC) method [32] was used to account for potential sub-population structures in the three generation cohort of the FHS data set.”
Metadata
- Authors
- Devlin, B., & Roeder, K.
- Affiliations
- University of Pittsburgh (US)
- Carnegie Mellon University (US)
- Corresponding author
- Devlin, B.
- Roeder, Kathryn
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-12
- Volume
- 55
- Issue
- 4
- Pages
- 997-1004
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2008047653
- Cited by
- 3,162
- References
- 34
- Citations by year
- 2026: 86 · 2025: 79 · 2024: 87 · 2023: 84 · 2022: 115 · 2021: 102 · 2020: 94 · 2019: 118 · 2018: 128 · 2017: 124 · 2016: 137 · 2015: 170 · 2014: 143 · 2013: 175 · 2012: 172
- FWCI
- 14.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- University of Pittsburgh
- National Institutes of Health
- National Institute of Mental Health (NIMH 57881, P01MH056193, R01MH057881, NIMH 56193)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- 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 diversity and population structure
- Keywords
- genomic control, association studies, single nucleotide polymorphisms, case-control data, population heterogeneity, multiple testing correction, false positives, complex diseases, model assumption violation
- MeSH terms
- Bayes Theorem, Biometry, Genetic Techniques, Humans, Models, Genetic, Models, Statistical, Genome, Human, Case-Control Studies
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