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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Genetic association studies of complex traits: design and analysis issues
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Key concepts in genetic epidemiology
Burton, P. R., Tobin, M. D., & Hopper, J. L. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67322-9 · Cited by 294
Genome-wide association scan for five major dimensions of personality
Terracciano, A., Sanna, S., Uda, M., Deiana, B., Usala, G., Busonero, F., Maschio, A., Scally, M., Patriciu, N. S., Chen, W.-M., et al. (2008) · Molecular Psychiatry · doi:10.1038/mp.2008.113 · Cited by 295
An Icelandic example of the impact of population structure on association studies
Helgason, A., Yngvadóttir, B., Hrafnkelsson, B., Gulcher, J. R., & Stefánsson, K. (2004) · Nature Genetics · doi:10.1038/ng1492 · Cited by 285
Takata, R., Akamatsu, S., Kubo, M., Takahashi, A., Hosono, N., Kawaguchi, T., Tsunoda, T., Inazawa, J., Kamatani, N., Ogawa, O., et al. (2010) · Nature Genetics · doi:10.1038/ng.635 · Cited by 284
“To adjust for an unknown genetic heterogeneity of our results, we also calculated λ-corrected P values ( P GC”
ResultHuyghe, J. R., Jackson, A., Fogarty, M. P., Buchkovich, M. L., Stančáková, A., Stringham, H. M., Sim, X., Yang, L., Fuchsberger, C., Cederberg, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2507 · Cited by 280
Genetic mapping of quantitative trait loci in crops
XU, Y. C., Li, P., Yang, Z., & Xu, C. (2016) · The Crop Journal · doi:10.1016/j.cj.2016.06.003 · Cited by 303
“The Q matrix can be obtained by any of the following methods: genomic control (GC) [35], structured association (SA) analysis [36], or principal component analysis (PCA) [37].”
MethodMägi, R., Horikoshi, M., Sofer, T., Mahajan, A., Kitajima, H., Franceschini, N., McCarthy, M. I., & Morris, A. P. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx280 · Cited by 283
“Association summary statistics from the meta-analysis were then corrected for a second round of genomic control (43) (kGC1⁄41.”
MethodDense fine-mapping study identifies new susceptibility loci for primary biliary cirrhosis
The UK Primary Biliary Cirrhosis (PBC) Consortium, Liu, J. Z., The Wellcome Trust Case Control Consortium 3, Almarri, M. A., Gaffney, D. J., Mells, G. F., Jostins, L., Cordell, H. J., Ducker, S. J., Day, D. B., et al. (2012) · Nature Genetics · doi:10.1038/ng.2395 · Cited by 274
The genetic architecture of economic and political preferences
Benjamin, D. J., Cesarini, D. A., van der Loos, M., Dawes, C. T., Koellinger, P., Magnusson, P. K. E., Chabris, C. F., Conley, D., Laibson, D., Johannesson, M., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120666109 · Cited by 283
“42); there is no evidence of inflated test statistics across the traits, with estimated λs (43) in the range of 0.”
BackgroundRichards, J. B., Kavvoura, F. K., Rivadeneira, F., Styrkársdóttir, U., Estrada, K., Halldórsson, B. V., Hsu, Y., Zillikens, M. C., Wilson, S. G., Mullin, B. H., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-8-200910200-00006 · Cited by 281
“Population stratification had little effect after removal of such individuals (44) (Appendix Table 2), and the associations between SNPs and BMD were corrected for the genomic inflation factors in each study.”
BackgroundAminkeng, F., Bhavsar, A. P., Visscher, H., Rassekh, S. R., Li, Y., Lee, J. W., Brunham, L. R., Caron, H. N., van Dalen, E. C., Kremer, L. C. M., et al. (2015) · Nature Genetics · doi:10.1038/ng.3374 · Cited by 280
Asano, K., Matsushita, T., Umeno, J., Hosono, N., Takahashi, A., Kawaguchi, T., Matsumoto, T., Matsui, T., Kakuta, Y., Kinouchi, Y., et al. (2009) · Nature Genetics · doi:10.1038/ng.482 · Cited by 272
“We therefore used λ GC -corrected P values ( P GC ) to adjust for the unknown genetic heterogeneity of the GWAS result”
MethodGenome-wide association analysis of plasma lipidome identifies 495 genetic associations
Ottensmann, L., Tabassum, R., Ruotsalainen, S., Gerl, M. J., Klose, C. D., Widén, E., FinnGen, Simons, K., Ripatti, S., & Pirinen, M. (2023) · Nature Communications · doi:10.1038/s41467-023-42532-8 · Cited by 275
Family-based designs for genome-wide association studies
Ott, J., Kamatani, Y., & Lathrop, M. G. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2989 · Cited by 278
“Thus, other sources of allele frequencies can greatly increase the rate of false positive results, which is why statisticians have developed analytical methods to handle such heterogeneit”
BackgroundGenome-wide association study of recurrent early-onset major depressive disorder
Shi, J., Potash, J. B., Knowles, J. A., Weissman, M. M., Coryell, W. H., Scheftner, W. A., Lawson, W. B., DePaulo, J. R., Gejman, P. V., Sanders, A. R., et al. (2010) · Molecular Psychiatry · doi:10.1038/mp.2009.124 · Cited by 270
Hinds, D. A., Barnholt, K. E., Mesa, R. A., Kiefer, A. K., Do, C. B., Eriksson, N., Mountain, J. L., Francke, U., Tung, J. Y., Nguyen, H. (., et al. (2016) · Blood · doi:10.1182/blood-2015-06-652941 · Cited by 267
“Results were adjusted for residual inflation using genomic control.(36) In regions with genome-wide significant associations, we performed stepwise conditional association tests to identify secondary signals.”
MethodSherlock: Detecting Gene-Disease Associations by Matching Patterns of Expression QTL and GWAS
He, X., Fuller, C. K., Song, Y., Meng, Q., Zhang, B., Yang, X., & Li, H. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.03.022 · Cited by 241
“17), so we applied the genomic control procedure before further analysis.(31) With only cis eSNPs, we identified a single SNP, close to ORMDL3 (MIM 610075), that passes the respective thresholds in both GWAS and eQTL, consistent with the previous findings.”
ResultSchumann, G., Coin, L. J., Lourdusamy, A., Charoen, P., Berger, K. H., Stacey, D., Desrivières, S., Aliev, F. A., Khan, A. A., Amin, N., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1017288108 · Cited by 264
Muglia, P., Tozzi, F., Galwey, N. W., Francks, C., Upmanyu, R., Kong, X., Antoniades, A., Domenici, E., Perry, J., Rothen, S., et al. (2008) · Molecular Psychiatry · doi:10.1038/mp.2008.131 · Cited by 267
Kawaguchi, T., Sumida, Y., Umemura, A., Matsuo, K., Takahashi, M., Takamura, T., Yasui, K., Saibara, T., Hashimoto, E., Kawanaka, M., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038322 · Cited by 261
“Assessment of population stratification of inflation of p-value was carried out by the genomic control method for asymptotic trend test [25].”
MethodDávila, S., Hibberd, M. L., Dass, R. H., Wong, H. E. E., Sahiratmadja, E., Bonnard, C., Alisjahbana, B., Szeszko, J. S., Balabanova, Y., Drobniewski, F. A., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000218 · Cited by 263
“The correction factor was calculated according to the method of Devlin and Roeder [33].”
MethodPopulation structure in genetic studies: Confounding factors and mixed models
Sul, J. H., Martin, L. S., & Eskin, E. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007309 · Cited by 286
“A general purpose approach to correct for population structure, or any type of confounding in association studies, is genomic control [25, 26].”
BackgroundJoubert, B. R., den Dekker, H. T., Felix, J. F., Bohlin, J., Ligthart, S., Beckett, E., Tiemeier, H. W., van Meurs, J. B. J., Uitterlinden, A. G., Albert Hofman, D., et al. (2016) · Nature Communications · doi:10.1038/ncomms10577 · Cited by 268
Population stratification confounds genetic association studies among Latinos
Choudhry, S., Coyle, N. E., Tang, H., Salari, K., Lind, D. L., Clark, S., Tsai, H., Naqvi, M., Phong, A., Ung, N., et al. (2005) · Human Genetics · doi:10.1007/s00439-005-0071-3 · Cited by 259
A Genomic Background Based Method for Association Analysis in Related Individuals
Amin, N., van Duijn, C. M., & Aulchenko, Y. S. (2007) · PLoS ONE · doi:10.1371/journal.pone.0001274 · Cited by 262
“Following Devlin and Roeder [13,14] we suggest estimation of the test statistic inflation factor l by regressing the trait on N loci.”
MethodKayser, M., Liu, F., Janssens, A. C. J. W., Rivadeneira, F., Lao, O., van Duijn, K., Vermeulen, M., Arp, P., Jhamai, M. M., van IJcken, W. F. J., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.10.003 · Cited by 260
Pereira, R., Phillips, C., Pinto, N., Santos, C., dos Santos, S. E. B., Amorim, A., Carracedo, Á., & Gusmão, L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029684 · Cited by 263
“stratification and correct for such effects using methods that infer genetic ancestry, each with particular pros and cons [14–16].”
MethodSumHer better estimates the SNP heritability of complex traits from summary statistics
Speed, D., & Balding, D. J. (2019) · Nature Genetics · doi:10.1038/s41588-018-0279-5 · Cited by 254
Genome‐wide association scan of attention deficit hyperactivity disorder
Neale, B. M., Lasky-Su, J. A., Anney, R., Franke, B., Zhou, K., Maller, J., Vásquez, A. A., Asherson, P. J., Chen, W., Banaschewski, T., et al. (2008) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30866 · Cited by 262
“Then, the distribution of association test statistics was inspected for each of these slices, with a particular focus on the l (defined as the observed median w(2) divided by the expected median w(2)) [Devlin and Roeder, 1999].”
MethodWacholder, S., Rothman, N., & Caporaso, N. E. (2002) · PubMed · Cited by 255
Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight
Freathy, R. M., Mook‐Kanamori, D. O., Sovio, U., Prokopenko, I., Timpson, N. J., Berry, D. J., Warrington, N. M., Widén, E., Hottenga, J., Kaakinen, M., et al. (2010) · Nature Genetics · doi:10.1038/ng.567 · Cited by 252
Famoso, A. N., Clark, R. T., Shaff, J. E., Craft, E. J., McCouch, S. R., & Kochian, L. V. (2010) · PLANT PHYSIOLOGY · doi:10.1104/pp.110.156794 · Cited by 254
“This can lead to confusion because trait or performance differences may be con founded with subpopulation structure, leading to false positives (type 1 error; Devlin and Roeder, 1999; Pritchard and Donnelly, 2001; Yu et al., 2006; Zhao et al., 2007).”
BackgroundDiscovery of shared genomic loci using the conditional false discovery rate approach
Smeland, O. B., Frei, O., Shadrin, A. A., O’Connell, K. S., Fan, C. C., Bahrami, S., Holland, D., Djurovic, S., Thompson, W. K., Dale, A. M., et al. (2019) · Human Genetics · doi:10.1007/s00439-019-02060-2 · Cited by 261
Syndrome-causing mutations of theBLMgene in persons in the Bloom's Syndrome Registry
GERMAN, J. L., Sanz, M. M., Ciocci, S., Ye, T. Z., & Ellis, N. A. (2007) · Human Mutation · doi:10.1002/humu.20501 · Cited by 252
“The presence of alleles that are inherited identical-by-descent in persons who do not know themselves to be related has been referred to as ‘‘cryptic relatedness’’ [Devlin and Roeder, 1999].”
BackgroundAccounting for Population Stratification in DNA Methylation Studies
Barfield, R., Almli, L. M., Kilaru, V., Smith, A. K., Mercer, K. B., Duncan, R., Klengel, T., Mehta, D., Binder, E. B., Epstein, M. P., et al. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21789 · Cited by 248
“…to account for population stratification in GWAS include the use of genomic control (GC) to correct inflated test statistics [Bacanu et al., 2000; Devlin and Roeder, 1999; Devlin et al., 2001a] and inclusion of the top principal C© 2014 WILEY PERIODICALS, INC. components (PCs) of genome-wide…”
MethodInfluentialVariants in the fetal genome near FLT1 are associated with risk of preeclampsia
McGinnis, R. E., Steinthórsdóttir, V., Williams, N., Þorleifsson, G., Shooter, S., Hjartardottir, S., Bumpstead, S., Stefánsdóttir, L., Hildyard, L., Sigurdsson, J. K., et al. (2017) · Nature Genetics · doi:10.1038/ng.3895 · Cited by 259
Satten, G., Flanders, W., & Yang, Q.-H. (2001) · American Journal of Human Genetics · doi:10.1086/318195 · Cited by 249
“Fortunately, if population substructure affects allele frequencies of the candidate gene, then it should also affect allele frequencies of other genes as well (Devlin and Roeder 1999; Pritchard and Rosenberg 1999).”
BackgroundA genome-wide association study of COPD identifies a susceptibility locus on chromosome 19q13
Cho, M. H., Castaldi, P. J., Wan, E. S., Siedliński, M., Hersh, C. P., DeMeo, D. L., Himes, B. E., Sylvia, J. S., Klanderman, B. J., Ziniti, J. P., et al. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddr524 · Cited by 253
“Genomic inflation factors (74) were calculated using GenABEL (75).”
MethodGenetic signatures of high-altitude adaptation in Tibetans
Yang, J., Jin, Z., Chen, J., Huang, X., Li, X., Liang, Y., Mao, J.-Y., Chen, X., Zheng, Z., Bakshi, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617042114 · Cited by 258
The genomic distribution of population substructure in four populations using 8,525 autosomal SNPs
Shriver, M., Kennedy, G., Parra, E., Lawson, H. A., Sonpar, V. A., Huang, J., Akey, J., & Jones, K. (2004) · Human Genomics · doi:10.1186/1479-7364-1-4-274 · Cited by 253
BMES GWAS Group, Hysi, P. G., Cheng, C., Springelkamp, H., MacGregor, S., Bailey, J. N. C., Wojciechowski, R., Vitart, V., Nag, A., Hewitt, A. W., et al. (2014) · Nature Genetics · doi:10.1038/ng.3087 · Cited by 251
“Within-study genomic inflation factors 10 ranged between 0.992 and 1.043 (Supplementary Table 2 and Supplementary Figure 1), indicating a lack of major population stratification bias within each study.”
InfluentialAnalysis and application of European genetic substructure using 300 K SNP information
Tian, C., Plenge, R. M., Ransom, M., Lee, A., Villoslada, P., Selmi, C., Klareskog, L., Pulver, A. E., Qi, L., Gregersen, P. K., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.0040004 · Cited by 247
“False discovery rate statistics [14] were determined using HelixTree 5.”
BackgroundMethodControlling false discoveries in genome scans for selection
François, O., Martins, H., Caye, K., & Schoville, S. D. (2015) · Molecular Ecology · doi:10.1111/mec.13513 · Cited by 259
“…approaches to test calibration: (i) empirical null-hypothesis testing where test corrections are usually carried out on the basis of inflation factors and (ii) the inclusion of random effects to account for confounding errors in statistical models (Devlin & Roeder 1999; Efron 2004; Yu et al. 2006).”
BackgroundMethodInfluentialA Unified Framework for Association Analysis with Multiple Related Phenotypes
Stephens, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065245 · Cited by 240
“These tables include a combined Z statistic (computed from all available individuals contributing to the meta-analysis at that SNP, and corrected for population stratification by Genomic Control [58]), Zj , and a sample size nj for each SNP j.”
MethodZhu, Z., Lee, P. H., Chaffin, M. D., Chung, W., Loh, P., Lu, Q., Christiani, D. C., & Liang, L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0121-0 · Cited by 239
Whole-genome sequencing of a sporadic primary immunodeficiency cohort
Thaventhiran, J. E. D., Allen, H. L., Burren, O. S., Rae, W. M., Greene, D. J., Staples, E., Zhang, Z., Farmery, J. H. R., Simeoni, I., Rivers, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2265-1 · Cited by 242
“After genomic control correction 44 the only genome-wide significant (p 5x10 -8 ) signal was at the MHC locus, with several suggestive (p 1x10 -5 ) signals (Extended Data Fig.”
Holland, D., Frei, O., Desikan, R. S., Fan, C. C., Shadrin, A. A., Smeland, O. B., Sundar, V. S., Thompson, P. M., Andreassen, O. A., & Dale, A. M. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008612 · Cited by 239
“The model”
InfluentialLimou, S., Clerc, S. L., Coulonges, C., Carpentier, W., Dina, C., Delaneau, O., Labib, T., Taing, L., SLADEK, R. M., ANRS Genomic Group, et al. (2008) · The Journal of Infectious Diseases · doi:10.1086/596067 · Cited by 245
“We also computed the genomic inflation factor [12].”
MethodCommon variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations
GIANT consortium, Okada, Y., Kubo, M., Ohmiya, H., Takahashi, A., Kumasaka, N., Hosono, N., Maeda, S., Wen, W., Dorajoo, R., et al. (2012) · Nature Genetics · doi:10.1038/ng.1086 · Cited by 256
“0) using mach2qtl software, and a genomic-control correction was applie”
MethodNg, M. C. Y., Shriner, D., Chen, B. H., Li, J., Chen, W., Guo, X., Liu, J., Bielinski, S. J., Yanek, L. R., Nalls, M. A., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004517 · Cited by 243
“Genomic control corrections [11] were applied to each study (l= 1.”
MethodGenome-Wide Association Study of Major Recurrent Depression in the U.K. Population
Lewis, C. M., Ng, M., Butler, A. W., Cohen‐Woods, S., Uher, R., Pirlo, K. J., Weale, M. E., Schosser, A., Paredes, U. M., Rivera, M., et al. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091380 · Cited by 243
Takeuchi, F., Serizawa, M., Yamamoto, K., Fujisawa, T., Nakashima, E., Ohnaka, K., Ikegami, H., Sugiyama, T., Katsuya, T., Miyagishi, M., et al. (2009) · Diabetes · doi:10.2337/db08-1494 · Cited by 235
“The subjects were enrolled from regions of Japan with no strong population stratification (38), and although some variance inflation partly attributable to the subtle subpopulation structure was apparent, such confounding influences could be sufficiently removed using Eigenstrat (30) and genomiccontrol adjustment (31).”
BackgroundMethodThe meta-analysis of genome-wide association studies
Thompson, J. R., Attia, J., & Minelli, C. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr020 · Cited by 251
“It is now common practice to divide all test statistics by l, the ratio of the empirical to the null median test statistic [73].”
BackgroundEuropean Population Substructure: Clustering of Northern and Southern Populations
Seldin, M. F., Shigeta, R., Villoslada, P., Selmi, C. F., Tuomilehto, J., SILVA, G. H. D. O., Belmont, J. W., Klareskog, L., & Gregersen, P. K. (2005) · PLoS Genetics · doi:10.1371/journal.pgen.0020143 · Cited by 234
“The use of either structured association tests or genomic control strategies has been suggested by several investigators [8,10,40–42].”
Malosetti, M., van der Linden, C. G., Vosman, B., & van Eeuwijk, F. A. (2006) · Genetics · doi:10.1534/genetics.105.054932 · Cited by 232
Genome-Wide Association of Lipid-Lowering Response to Statins in Combined Study Populations
Barber, M., Mangravite, L. M., Hyde, C. L., Chasman, D. I., Smith, J. D., McCarty, C. A., Li, X., Wilke, R. A., Rieder, M. J., Williams, P. T., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009763 · Cited by 231
“03 [29].”
Mangino, M., Hwang, S., Spector, T. D., Hunt, S. C., Kimura, M., Fitzpatrick, A. L., Christiansen, L., Petersen, I., Elbers, C. C., Harris, T. B., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds382 · Cited by 228
“Individual cohort results were corrected for residual inflation of the test statistics using lambda of genomic control (GC) estimates (66).”
MethodLu, Y., Zhang, S., Shah, T. M., Xie, C., Hao, Z., Li, X., Farkhari, M., Ribaut, J., Cao, M., Rong, T., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006105107 · Cited by 228
“Excluding these effects from the LD analysis (6, 7) can be commonly achieved using the STRUCTURE program (8, 9), principal component analysis (10), or random molecular markers to estimate pairwise relatedness among all individuals via a mixed linear model (5).”
MethodWijsman, E. M., Pankratz, N., Choi, Y., Rothstein, J. H., Faber, K. M., Cheng, R., Lee, J. H., Bird, T. D., Bennett, D. A., Diaz-Arrastia, R. R., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001308 · Cited by 229
“We therefore corrected the cross-strata tests by dividing by l as a genome control correction factor [74].”
MethodGenetic predictors of response to antidepressants in the GENDEP project
Uher, R., Huezo-Diaz, P., Perroud, N., Smith, R., Rietschel, M., Mors, O., Hauser, J., Maier, W., Kozel, D., Henigsberg, N., et al. (2009) · The Pharmacogenomics Journal · doi:10.1038/tpj.2009.12 · Cited by 226
Schork, A. J., Won, H., Appadurai, V., Nudel, R., Gandal, M., Delaneau, O., Revsbech Christiansen, M., Hougaard, D. M., Bækved-Hansen, M., Bybjerg-Grauholm, J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0320-0 · Cited by 223
PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations
Paschou, P., Ziv, E., Burchard, E. G., Choudhry, S., Rodriguez-Cintron, W., Mahoney, M. W., & Drineas, P. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030160 · Cited by 302
“]. Detection of and correction for stratification in a given dataset is a problem that has been discussed at length in recent literature [”
Huan, T., Joehanes, R., Song, C., Peng, F., Guo, Y., Mendelson, M., Yao, C., Liu, C., Ma, J., Richard, M. A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12228-z · Cited by 224
Testing for Population Subdivision and Association in Four Case-Control Studies
Ardlie, K., Lunetta, K. L., & Seielstad, M. T. (2002) · The American Journal of Human Genetics · doi:10.1086/341719 · Cited by 222
“…statistical power lost to family-based study designs, a recent trend has focused on recognizing when structure may affect a case-control study and correcting for it when it is present (Devlin and Roeder 1999; Pritchard and Rosenberg 1999; Pritchard et al. 2000a, 2000b; Reich and Goldstein 2001).”
BackgroundMethodFerreira, M. A. R., Matheson, M. C., Tang, C. S., Granell, R., Ang, W., Hui, J., Kiefer, A. K., Duffy, D. L., Baltic, S., Danoy, P., et al. (2014) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2013.10.030 · Cited by 216
Variant in the sequence of the LINGO1 gene confers risk of essential tremor
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A genome‐wide association study identifies an osteoarthritis susceptibility locus on chromosome 7q22
Kerkhof, H. J. M., Lories, R. J. U., Meulenbelt, I., Jónsdóttir, I., Valdes, A. M., Arp, P., Ingvarsson, Þ., Jhamai, M. M., Jónsson, H., Stolk, L., et al. (2010) · Arthritis & Rheumatism · doi:10.1002/art.27184 · Cited by 226
Implications of correlations between skin color and genetic ancestry for biomedical research
Parra, E. J., Kittles, R. A., & Shriver, M. D. (2004) · Nature Genetics · doi:10.1038/ng1440 · Cited by 220
“Fortunately, there are several statistical methods available to control for the confounding effects of population structure in association studie”
MethodThe genetics of variation in gene expression
Cheung, V. G., & Spielman, R. S. (2002) · Nature Genetics · doi:10.1038/ng1036 · Cited by 220
“This potential power of association studies for locating genes has led to several associated-based statistical method”
BackgroundAssociation studies for asthma and atopic diseases: a comprehensive review of the literature
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“[16,17], but these require genotyping the case and control”
BackgroundSequence Variants in the RNF212 Gene Associate with Genome-Wide Recombination Rate
Kong, A., Þorleifsson, G., Stefánsson, H., Másson, G., Helgason, A., Gudbjartsson, D. F., Jonsdottir, G. M., Gudjonsson, S. A., Sverrisson, S. Þ., Thorlacius, T., et al. (2008) · Science · doi:10.1126/science.1152422 · Cited by 219
Estimation of allele frequency and association mapping using next-generation sequencing data
Kim, S. Y., Lohmueller, K. E., Albrechtsen, A., Li, Y., Korneliussen, T. S., Tian, G., Grarup, N., Jiang, T., Andersen, G., Witte, D. R., et al. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-231 · Cited by 220
“The “Inflation” factor [44] is shown in the upper left corner of each figure.”
BackgroundTraylor, M., Persyn, E., Tomppo, L., Klasson, S., Abedi, V., Bakker, M. K., Torres, N., Li, L., Bell, S., Rutten‐Jacobs, L. C. A., et al. (2021) · The Lancet Neurology · doi:10.1016/s1474-4422(21)00031-4 · Cited by 216
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Genome-wide Copy-Number-Variation Study Identified a Susceptibility Gene, UGT2B17, for Osteoporosis
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Rapid variance components–based method for whole-genome association analysis
Svishcheva, G. R., Axenovich, T. I., Belonogova, N. M., van Duijn, C. M., & Aulchenko, Y. S. (2012) · Nature Genetics · doi:10.1038/ng.2410 · Cited by 213
“The analyses were run on data from 500 to 3,000 individuals at 456,516 SNP markers (Online Methods) using a single-core Intel Xeon X5550 at 2.67 GHz with 36 GB of random-access memory (RAM).”
MethodInfluentialEstimating Kinship in Admixed Populations
Thornton, T. A., Tang, H., Hoffmann, T. J., Ochs‐Balcom, H. M., Caan, B. J., & RISCH, N. J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.05.024 · Cited by 210
Confounding from Cryptic Relatedness in Case-Control Association Studies
Voight, B. F., & Pritchard, J. K. (2005) · PLoS Genetics · doi:10.1371/journal.pgen.0010032 · Cited by 216
“Yet a widely cited 1999 paper by Devlin and Roeder (that introduced the genomic control concept) argued that, in fact, ‘‘cryptic relatedness’’ (referring to the idea that some members of a case-control sample might actually be close relatives, unbeknownst to the investigator) is likely to be a far more important confo…”
BackgroundMethodInfluentialPopulation Stratification in Genetic Association Studies
Hellwege, J. N., Keaton, J. M., Giri, A., Gao, X., Edwards, D. R. V., & Edwards, T. L. (2017) · Current Protocols in Human Genetics · doi:10.1002/cphg.48 · Cited by 232
“It was developed for dichotomous traits (Devlin & Roeder, 1999; Devlin, Bacanu, & Population Stratification in Genetic Association Studies 1.22.8 Supplement 95 Current Protocols in Human Genetics Genetic Mapping 1.22.9 Current Protocols in Human Genetics Supplement 95 Roeder, 2004; Devlin, Roeder,…”
InfluentialGenetic Determinants of Circulating Sphingolipid Concentrations in European Populations
Hicks, A. A., Pramstaller, P. P., Johansson, Å., Vitart, V., Rudan, I., Ugocsai, P., Aulchenko, Y. S., Franklin, C. S., Liebisch, G., Erdmann, J., et al. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000672 · Cited by 209
“The Genomic Control procedure [35] was used to account for under-estimation of the standard errors of effects, which occurs because of pedigree structure present in the data [36].”
MethodEpigenome-wide association studies without the need for cell-type composition
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“95 (n=38); (2) duplicate genotypes (n=20); (3) cryptic relatedness (n=84); (4) mismatch between genotypic and self-reported sex and race (n=29); and (5) case misclassification (n=14).”
BackgroundGrétarsdóttir, S., Baas, A. F., Þorleifsson, G., Hólm, H., den Heijer, M., de Vries, J. P. P. M., Kranendonk, S. E., Zeebregts, C. J., van Sterkenburg, S. M., Geelkerken, R. H., et al. (2010) · Nature Genetics · doi:10.1038/ng.622 · Cited by 211
Hsu, Y., Zillikens, M. C., Wilson, S. G., Farber, C. R., Demissie, S., Soranzo, N., Bianchi, E. N., Grundberg, E., Liang, L., Richards, J. B., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000977 · Cited by 210
Common variants at VRK2 and TCF4 conferring risk of schizophrenia
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“W e estimated the inflation factor for the genome-wide association by calculating the average of the 302,379 chi-square statistics, which was a method of genomic control [40] to adjust for both relatedness and potential population stratification.”
The nature of confounding in genome-wide association studies
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Introduction to Genetic Association StudiesPreprint
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“Genomic control (GC) assumes that population strati fi cation in fl ates the association test statistics by a constant factor λ , which can be estimated from the median or mean test statistic from a series of unlinked SNPs genotyped in both cases and controls (Devlin and Roeder 1999).”
GWAF: an R package for genome-wide association analyses with family data
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Novel loci and pathways significantly associated with longevity
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“Population stratification was low (genomic inflation (GC) factor (Devlin and Roeder, 1999) for stage 1 l: 1.”
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Population Substructure and Control Selection in Genome-Wide Association Studies
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“The intent in our study is to use the over-dispersion factor for the purpose of evaluating the effectiveness in the correction of PS by adjusting for a given set of PCs, but not for the adjustment for association testing results as originally proposed by others [27,28].”
BackgroundMethodInfluentialImproved ancestry inference using weights from external reference panels
Chen, C., Pollack, S. J., Hunter, D. J., Hirschhorn, J. N., Kraft, P. L., & Price, A. L. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt144 · Cited by 187
“We computed adjusted association statistics using genomic control (Devlin and Roeder, 1999), predicted PCs based on genome-wide SNPs, PCs obtained by direct PCA and predicted PCs based on AIMs and compared the (cid:5) 2 statistics and P -values from these adjusted analyses.”
Method
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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