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
Cites
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Statistical Decision Theory and Bayesian Analysis
Berger, J. O. (1985) · Springer Series in Statistics · doi:10.1007/978-1-4757-4286-2 · Cited by 7,722
The Future of Genetic Studies of Complex Human Diseases
Risch, N., & Merikangas, K. (1996) · Science · doi:10.1126/science.273.5281.1516 · Cited by 5,355
An introduction to population genetics theory
Crow, J. F., & Kimura, M. (1970) · Medical Entomology and Zoology · Cited by 4,223
Spielman, R. S., McGinnis, R. E., & Ewens, W. J. (1993) · PubMed · Cited by 3,679
The History and Geography of Human Genes
Cavalli-Sforza, L. L., Menozzi, P., & Piazza, A. (2018) · Princeton University Press eBooks · doi:10.2307/j.ctv301gjp · Cited by 3,175
BARNETT, V. D., Lewis, T., & Abeles, F. F. (1979) · Physics Today · doi:10.1063/1.2995737 · Cited by 2,834
Wang, D. G., Fan, J., Siao, C.-J., Berno, A. J., Young, P. R., Sapolsky, R., Ghandour, G., Perkins, N., Winchester, E., Spencer, J. B., et al. (1998) · Science · doi:10.1126/science.280.5366.1077 · Cited by 2,162
Bayes and Empirical Bayes Methods for Data Analysis
Carlin, B. P., & Louis, T. A. (2001) · Technometrics · doi:10.1198/tech.2001.s608 · Cited by 2,110
Tests for Linear Trends in Proportions and Frequencies
Armitage, P. (1955) · Biometrics · doi:10.2307/3001775 · Cited by 2,092
Cited by
Sorted by citations
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities
Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,633
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205
“METAL has an option to estimate a genomic control parameter (Devlin and Roeder 1999) for each input file and apply an appropriate genomic control correction to input statistics prior to performing meta-analysis.”
MethodLD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
Population Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280
“If e([1]) is the principal eigenvector of the matrix X, this means that the sum of squares”
BackgroundGenetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534
Yu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M., Doebley, J., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2005) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,155
Inference of population structure using multilocus genotype data: dominant markers and null alleles
Falush, D., Stephens, M., & Pritchard, J. K. (2007) · Molecular Ecology Notes · doi:10.1111/j.1471-8286.2007.01758.x · Cited by 3,468
“First, ancestral population frequencies were generated at 500 biallelic loci, with gene frequencies generated from a beta distribution (the bi-allelic version of a Dirichlet distribution) with parameter λ = 1 (see, e.g. Devlin & Roeder 1999; Falush et al .”
MethodHarold, D., Abraham, R., Hollingworth, P., Sims, R., Gerrish, A., Hamshere, M. L., Pahwa, J. S., Moskvina, V., Dowzell, K., Williams, A., et al. (2009) · Nature Genetics · doi:10.1038/ng.440 · Cited by 3,326
“The t(10;11)(p13;q14) in the U937 cell line results in the fusion of the AF10 gene and CALM, encoding a new member of the AP-3 clathrin assembly protein family.”
BackgroundWiller (2013) · doi:10.1038/ng.2797 · Cited by 2,983
“Explicit adjustments for population structure using principal component 41 or mixed model approaches 42 were carried out in 24 studies (35% of individuals); all studies were adjusted using genomic control prior to meta-analysis 11 .”
BackgroundMethodA genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Mapping genomic loci implicates genes and synaptic biology in schizophrenia
Trubetskoy, V., Pardiñas, A. F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T. B., Bryois, J., Chen, C.-Y., Dennison, C. A., Hall, L. S., et al. (2022) · Nature · doi:10.1038/s41586-022-04434-5 · Cited by 2,964
Genome-wide association studies for common diseases and complex traits
Hirschhorn, J. N., & Daly, M. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1521 · Cited by 2,923
Environment dominates over host genetics in shaping human gut microbiota
Rothschild, D., Weissbrod, O., Barkan, E., Kurilshikov, A., Korem, T., Zeevi, D., Costea, P. I., Godneva, A., Kalka, I. N., Bar, N., et al. (2018) · Nature · doi:10.1038/nature25973 · Cited by 2,859
Variance component model to account for sample structure in genome-wide association studies
Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S.-Y., Freimer, N. B., Sabatti, C., & Eskin, E. (2010) · Nature Genetics · doi:10.1038/ng.548 · Cited by 2,817
“(7) © 2 01 0 N at u re A m er ic a, In c.”
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis
The International Multiple Sclerosis Genetics Consortium & The Wellcome Trust Case Control Consortium 2 (2011) · Nature · doi:10.1038/nature10251 · Cited by 2,697
“However, even use of all seven top principal components that captured genome-wide effects in our data resulted in an unacceptably high genomic inflation: for example, the genomic control facto”
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609
“Approaches for dealing with population structure for genotyped SNPs (such as conditioning on inferred covariates that code for population stratification 24 or applying Genomic Control 25 ) can also be applied to imputed SNPs and should work just as well.”
MethodSatterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.036 · Cited by 2,321
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447
“Overdispersion of association statistics was assessed by the genomic control method 62 ( Supplementary Table 15 ), and adjusted values were submitted for meta-analysis.”
Genome-wide association study identifies novel breast cancer susceptibility loci
Easton, D. F., Pooley, K. A., Dunning, A. M., Pharoah, P. D. P., Thompson, D., Ballinger, D. G., Struewing, J. P., Morrison, J., Field, H., Luben, R., et al. (2007) · Nature · doi:10.1038/nature05887 · Cited by 2,319
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
DIAGRAM Consortium, Dupuis, J., GIANT Consortium, Global BPgen Consortium, Anders Hamsten on behalf of Procardis Consortium, the MAGIC investigators, Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., et al. (2010) · Nature Genetics · doi:10.1038/ng.520 · Cited by 2,166
Mixed linear model approach adapted for genome-wide association studies
Zhang, Z., Ersoz, E. S., Lai, C., Todhunter, R. J., Tiwari, H. K., Gore, M. A., Bradbury, P. J., Yu, J., Arnett, D. K., Ordovás, J. M., et al. (2010) · Nature Genetics · doi:10.1038/ng.546 · Cited by 2,132
Association mapping in structured populations.
Pritchard, J. K., Stephens, M., Rosenberg, N., & Donnelly, P. (2000) · American Journal of Human Genetics · doi:10.1086/302959 · Cited by 2,066
“In recent work, Devlin and Roeder (1999) describe an alternative method for association mapping in structured populations using genome screen data of biallelic markers.”
the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segrè, A. V., Steinthorsdottir, V., Strawbridge, R. J., Khan, H., Grallert, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2383 · Cited by 1,912
“The results of each GWAS were corrected for residual population structure using the genomic control inflation factor 50 and were combined via fixed-effects inverse-variance weighted meta-analysis.”
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The Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (2011) · Nature Genetics · doi:10.1038/ng.940 · Cited by 1,919
“Therefore, our primary analysis used unadjusted P values (nevertheless, see Table 2 for stage 1 P values adjusted for λ (ref”
Mahajan, A., Taliun, D., Thurner, M., Robertson, N. R., Torres, J. M., Rayner, N. W., Payne, A. J., Steinthorsdottir, V., Scott, R. A., Grarup, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0241-6 · Cited by 1,744
Tutorial: a guide to performing polygenic risk score analyses
Choi, S. W., Mak, T. S. H., & O’Reilly, P. F. (2020) · Nature Protocols · doi:10.1038/s41596-020-0353-1 · Cited by 2,058
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945
23andMe Research Team, Liu, M., Psychiatry, H. A.-I., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., Datta, G., Davila-Velderrain, J., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0307-5 · Cited by 1,896
Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease
Cardiogenics, Schunkert, H., the CARDIoGRAM Consortium, König, I. R., Kathiresan, S., Reilly, M. P., Assimes, T. L., Holm, H., Preuss, M., Stewart, A. F. R., et al. (2011) · Nature Genetics · doi:10.1038/ng.784 · Cited by 1,866
Searching for genetic determinants in the new millennium
Risch, N. J. (2000) · Nature · doi:10.1038/35015718 · Cited by 1,839
“In fact, the same markers can also be used to assess the significance of any putative associatio”
Efficient Control of Population Structure in Model Organism Association Mapping
Kang, H. M., Zaitlen, N., Wade, C. M., Kirby, A. W., Heckerman, D. E., Daly, M. J., & Eskin, E. (2008) · Genetics · doi:10.1534/genetics.107.080101 · Cited by 1,869
“The most widely used methods to reduce such bias in human association mapping are genomic control (D evlin and R oeder 1999), structured association (P ritchard et al. 2000), and principal component analysis (P atterson et al. 2006; P rice et al. 2006).”
MethodIL28B is associated with response to chronic hepatitis C interferon-α and ribavirin therapy
Study, F. T. H. C., Suppiah, V., Moldovan, M. V., Ahlenstiel, G., Berg, T., Weltman, M., Abate, M. L., Bassendine, M. F., Spengler, U., Dore, G. J., et al. (2009) · Nature Genetics · doi:10.1038/ng.447 · Cited by 1,829
“The genomic inflation factor was calculated based on the sample median value of this statisti”
MethodTwelve type 2 diabetes susceptibility loci identified through large-scale association analysis
The MAGIC investigators, Voight, B. F., The GIANT Consortium, Scott, L. J., Steinthorsdottir, V., Morris, A. P., Dina, C., Welch, R. P., Zeggini, E., Huth, C., et al. (2010) · Nature Genetics · doi:10.1038/ng.609 · Cited by 1,761
Score Tests for Association between Traits and Haplotypes when Linkage Phase Is Ambiguous
Schaid, D. J., Rowland, C. M., Tines, D. E., Jacobson, R. M., & Poland, G. A. (2002) · The American Journal of Human Genetics · doi:10.1086/338688 · Cited by 1,777
“…b b single covariate representing the allele dosage (e.g.,Xg , 1, 2), the score statistic times is theX p 0 (N 1)/Ng same as Armitage’s test for trend in proportions, which is a popular statistic used to compare genotype frequencies between cases and controls (Sasieni 1997; Devlin and Roeder 1999).”
MethodGenome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
Atwell, S., Huang, Y. S., Vilhjálmsson, B. J., Willems, G., Horton, M., Li, Y., Meng, D., Platt, A., Tarone, A. M., Hu, T. T., et al. (2010) · Nature · doi:10.1038/nature08800 · Cited by 1,780
“The fact that population structure can cause confounding and lead to an elevated false-positive rate is well known and the relative advantages of alternative statistical methods to correct for this have been much debated 10 , 11 , 12 , 27 .”
Scuteri, A., Sanna, S., Chen, W.-M., Uda, M., Albai, G., Strait, J., Najjar, S., Nagaraja, R., Orrú, M., Usala, G., et al. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030115 · Cited by 1,724
“The genomic control parameter [29] for our initial analysis of each trait ranged from 1.”
MethodInfluentialGenetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712
“(20) As in a joint analysis, if the individual-level genotype data of the discovery sample are unavailable, we can estimate the LD correlations from the reference sample and approximate a conditional analysis as”
LEA: An R package for landscape and ecological association studies
Frichot, É., & François, O. (2015) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12382 · Cited by 1,761
Genetic Variants Associated with Lp(a) Lipoprotein Level and Coronary Disease
Clarke, R., Peden, J. F., Hopewell, J. C., Kyriakou, T., Goel, A., Heath, S. C., Parish, S., Barlera, S., Franzosi, M. G., Rust, S., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0902604 · Cited by 1,671
Common variants conferring risk of schizophrenia
†Genetic Risk and Outcome in Psychosis (GROUP), Stefansson, H., Ophoff, R. A., Steinberg, S., Andreassen, O. A., Cichon, S., Rujescu, D., Werge, T., Pietiläinen, O. P. H., Mors, O., et al. (2009) · Nature · doi:10.1038/nature08186 · Cited by 1,677
A comprehensive review of genetic association studies
Hirschhorn, J. N., Lohmueller, K., Byrne, E., & Hirschhorn, K. (2002) · Genetics in Medicine · doi:10.1097/00125817-200203000-00002 · Cited by 1,674
Fritsche, L. G., Igl, W., Bailey, J. N. C., Grassmann, F., Sengupta, S., Bragg-Gresham, J. L., Burdon, K. P., Hebbring, S. J., Wen, C., Gorski, M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3448 · Cited by 1,544
A Common Variant on Chromosome 9p21 Affects the Risk of Myocardial Infarction
Helgadottir, A., Thorleifsson, G., Manolescu, A., Gretarsdottir, S., Blondal, T., Jonasdottir, A., Jonasdottir, A., Sigurdsson, A., Baker, A., Palsson, A., et al. (2007) · Science · doi:10.1126/science.1142842 · Cited by 1,594
Newly identified loci that influence lipid concentrations and risk of coronary artery disease
Willer, C. J., Sanna, S., Jackson, A. U., Scuteri, A., Bonnycastle, L. L., Clarke, R., Heath, S. C., Timpson, N. J., Najjar, S. S., Stringham, H. M., et al. (2008) · Nature Genetics · doi:10.1038/ng.76 · Cited by 1,585
New genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580
Efficient Bayesian mixed-model analysis increases association power in large cohorts
Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507
“…is a recent step toward addressing this issue, proposing a z-statistic heuristically calibrated by assuming that the vast majority of variants are unassociated (as in genomic control 33 ), but such a technique is prone to deflation when large sample sizes cause inflation due to polygenicity 13,24 .”
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Mathieson, I., Lazaridis, I., Rohland, N., Mallick, S., Patterson, N., Roodenberg, S. A., Harney, E., Stewardson, K., Fernandes, D., Novak, M., et al. (2015) · Nature · doi:10.1038/nature16152 · Cited by 1,515
Genome-wide association analysis identifies 13 new risk loci for schizophrenia
Multicenter Genetic Studies of Schizophrenia Consortium, Ripke, S., Psychosis Endophenotypes International Consortium, Wellcome Trust Case Control Consortium 2, O'Dushlaine, C., Chambert, K., Moran, J. L., Kähler, A. K., Akterin, S., Bergen, S. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2742 · Cited by 1,496
Genetic Mapping in Human Disease
Altshuler, D., Daly, M. J., & Lander, E. S. (2008) · Science · doi:10.1126/science.1156409 · Cited by 1,452
Genetics in geographically structured populations: defining, estimating and interpreting FST
Holsinger, K. E., & Weir, B. S. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2611 · Cited by 1,480
Psychiatric GWAS Consortium Bipolar Disorder Working Group, Sklar, P., Ripke, S., Scott, L. J., Andreassen, O. A., Cichon, S., Craddock, N., Edenberg, H. J., Nurnberger, J. I., Rietschel, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.943 · Cited by 1,344
Association study designs for complex diseases
Cardon, L. R., & Bell, J. I. (2001) · Nature Reviews Genetics · doi:10.1038/35052543 · Cited by 1,395
Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants
Wellcome Trust Case Control Consortium, & The Australo-Anglo-American Spondylitis Consortium (TASC) (2007) · Nature Genetics · doi:10.1038/ng.2007.17 · Cited by 1,374
Status and Prospects of Association Mapping in Plants
Zhu, C., Gore, M. A., Buckler, E. S., & Yu, J. (2008) · The Plant Genome · doi:10.3835/plantgenome2008.02.0089 · Cited by 1,395
“With GC, a set of random markers is used to estimate the degree that test statistics are inl ated by population structure, assuming such structure has a similar ef ect on all loci (Devlin and Roeder, 1999).”
BackgroundMethodA tutorial on statistical methods for population association studies
Balding, D. J. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1916 · Cited by 1,380
Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci
BIRAC Consortium, Stahl, E. A., YEAR Consortium, Raychaudhuri, S., Remmers, E. F., Xie, G., Eyre, S., Thomson, B. P., Li, Y., Kurreeman, F. A. S., et al. (2010) · Nature Genetics · doi:10.1038/ng.582 · Cited by 1,253
“56 million SNPs in each collection, corrected for residual inflation using genomic contro”
MethodFaST linear mixed models for genome-wide association studies
Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209
“The value λ is defined as the ratio of the median observed to median theoretical p-value, after conversion from p-value space to log-likelihood space by way of an inverse chi-square mapping [1, 16].”
MethodGenome-wide association study identifies eight loci associated with blood pressure
Wellcome Trust Case Control Consortium, Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M. D., Bochud, M., Coin, L., Najjar, S. S., Zhao, J. H., Heath, S. C., et al. (2009) · Nature Genetics · doi:10.1038/ng.361 · Cited by 1,215
“Test statistics from association analysis of SBP and DBP from each cohort were adjusted using genomic contro”
New approaches to population stratification in genome-wide association studies
Price, A. L., Zaitlen, N., Reich, D., & Patterson, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2813 · Cited by 1,226
“Detecting stratification A widely used approach to evaluate whether confounding due to population stratification exists is to compute the genomic control λ (λ GC ), which is defined as the median χ 2 (1 degree of freedom) association statistic across SNPs divided by its theoretical median under the null distributio”
MethodPopulation stratification and spurious allelic association
Cardon, L. R., & Palmer, L. J. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12520-2 · Cited by 1,189
A mega-analysis of genome-wide association studies for major depressive disorder
Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., Breen, G., Byrne, E. M., Blackwood, D. H. R., Boomsma, D. I., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.21 · Cited by 1,120
Genome-wide association studies: theoretical and practical concerns
Wang, W. Y. S., Barratt, B. J., Clayton, D. G., & Todd, J. A. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1522 · Cited by 1,183
“These more general biases can be addressed, as described earlier, by the use of AIMs and by GENOMIC CONTROL”
MethodRare-Variant Association Analysis: Study Designs and Statistical Tests
Lee, S., Abecasis, G. R., Boehnke, M., & Lin, X. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.06.009 · Cited by 1,042
“91,92 In GWASs, principal-component analysis (PCA) and linear mixed models are commonly used to adjust for population stratification.”
MethodGene–environment interactions in human diseases
Hunter, D. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1578 · Cited by 1,132
“Methods to assess the population substructure of cases and controls by genotyping non-causal gene variants ('genomic control') have been proposed, and can be used to correct for this phenomeno”
MethodVariants in ELL2 influencing immunoglobulin levels associate with multiple myeloma
Swaminathan, B., Þorleifsson, G., Jöud, M., Ali, M., Johnsson, E., Ajore, R., Sulem, P., Halvarsson, B.-M., Eyjolfsson, G. I., Haraldsdóttir, V., et al. (2015) · Nature Communications · doi:10.1038/ncomms8213 · Cited by 1,213
ARUK Consortium, Sims, R., GERAD/PERADES, C., van der Lee, S. J., Naj, A. C., Bellenguez, C., Badarinarayan, N., Jakobsdottir, J., Kunkle, B. W., Boland, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3916 · Cited by 1,012
The NCBI dbGaP database of genotypes and phenotypes
Mailman, M. D., Feolo, M., Jin, Y., Kimura, M., Tryka, K. A., Bagoutdinov, R., Hao, L., Kiang, A., Paschall, J. E., Phan, L. D., et al. (2007) · Nature Genetics · doi:10.1038/ng1007-1181 · Cited by 1,100
“quantile-quantile plots of Cochran-Armitage test statistics (5) and genomic control lambda values ( 6 ), for consideration by the study investigators and advisory committee as they optimize data cleaning thresholds.”
BackgroundGenome-wide meta-analyses identify multiple loci associated with smoking behavior
The Tobacco and Genetics Consortium (2010) · Nature Genetics · doi:10.1038/ng.571 · Cited by 1,098
Cargill, M., Schrodi, S. J., Chang, M., Garcia, V. E., Brandon, R., Callis, K. P., Matsunami, N., Ardlie, K. G., Civello, D., Catanese, J. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/511051 · Cited by 1,086
False discovery rates: a new deal
Stephens, M. (2016) · Biostatistics · doi:10.1093/biostatistics/kxw041 · Cited by 1,132
“Approaches to deal with unmeasured confounders can be largely divided into two types: those that simply attempt to correct for the resulting inflation of test statistics (Devlin and Roeder, 1999; Efron, 2004), and those that attempt to infer confounders using clustering, principal components analysis, or factor models…”
BackgroundSegura, V., Vilhjálmsson, B. J., Platt, A., Korte, A., Seren, Ü., Long, Q., & Nordborg, M. (2012) · Nature Genetics · doi:10.1038/ng.2314 · Cited by 1,129
“Several methods have been proposed to deal with this issue, including genomic control 3 , structured association 4 , principal components analysis 5 , and mixed linear models 6 .”
Valsesia, A., Wang, Q., Gheldof, N., Carayol, J., Ruffieux, H., Clark, T., Shenton, V., Oyston, L. J., Lefebvre, G. C., Métairon, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08492-8 · Cited by 1,084
A variant in CDKAL1 influences insulin response and risk of type 2 diabetes
Steinthorsdottir, V., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Jonsdottir, T., Walters, G. B., Styrkarsdottir, U., Gretarsdottir, S., Emilsson, V., Ghosh, S., et al. (2007) · Nature Genetics · doi:10.1038/ng2043 · Cited by 1,072
“The results were adjusted for relatedness between individuals and potential population stratification by genomic contro”
BackgroundIdentification of loci associated with schizophrenia by genome-wide association and follow-up
Molecular Genetics of Schizophrenia Collaboration, O'Donovan, M. C., Craddock, N., Norton, N., Williams, H., Peirce, T., Moskvina, V., Nikolov, I., Hamshere, M., Carroll, L., et al. (2008) · Nature Genetics · doi:10.1038/ng.201 · Cited by 1,070
Large-scale genotyping identifies 41 new loci associated with breast cancer risk
Michailidou, K., kConFab Investigators, Australian Ovarian Cancer Study Group, The GENICA (Gene Environment Interaction and Breast Cancer in Germany) Network, Hall, P. N., González‐Neira, A., Ghoussaini, M., Dennis, J., Milne, R. L., Schmidt, M. K., et al. (2013) · Nature Genetics · doi:10.1038/ng.2563 · Cited by 1,080
Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity
Collaborative Association Study of Psoriasis (CASP), Tsoi, L. C., Genetic Analysis of Psoriasis Consortium, Psoriasis Association Genetics Extension, Wellcome Trust Case Control Consortium 2, Spain, S. L., Knight, J., Ellinghaus, E., Stuart, P. E., Capon, F., et al. (2012) · Nature Genetics · doi:10.1038/ng.2467 · Cited by 1,024
The genetic architecture of type 2 diabetes
Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature · doi:10.1038/nature18642 · Cited by 1,051
Strange, A., Spencer, C. C. A., Band, G., Bellenguez, C., Freeman, C., Pirinen, M., Rautanen, A., Su, Z., Donnelly, P., Capon, F., et al. (2010) · Nature Genetics · doi:10.1038/ng.694 · Cited by 1,056
“After removal of known and replicated psoriasis association loci, the overdispersion facto”
BackgroundDIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Mahajan, A., Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Consortium, Go, M. J., Zhang, W., Below, J. E., Gaulton, K. J., et al. (2014) · Nature Genetics · doi:10.1038/ng.2897 · Cited by 1,034
Bentham, J., Morris, D. L., Graham, D. S. C., Pinder, C. L., Tombleson, P., Behrens, T. W., Martin, J. J., Fairfax, B. P., Knight, J. C., Chen, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3434 · Cited by 1,004
Genetic association mapping and genome organization of maize
Yu, J., & Buckler, E. S. (2006) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2006.02.003 · Cited by 974
“With GC, a set of random markers is used to estimate the degree of inflation of the test statistics generated by population structure, assuming such structure has a similar effect on all loci [37].”
Li, Y., Willer, C., Sanna, S., & Abecasis, G. (2009) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev.genom.9.081307.164242 · Cited by 990
Advantages and pitfalls in the application of mixed-model association methods
Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L. (2014) · Nature Genetics · doi:10.1038/ng.2876 · Cited by 981
Binder, E. B., Salyakina, D., Lichtner, P., Wochnik, G. M., Ising, M., Pütz, B., Papiol, S., Seaman, S., Lucae, S., Kohli, M. A., et al. (2004) · Nature Genetics · doi:10.1038/ng1479 · Cited by 977
“To control for stratification, we used the genomic control approach of ref”
Methodpcadapt : an R package to perform genome scans for selection based on principal component analysis
Luu, K., Bazin, É., & Blum, M. G. B. (2016) · Molecular Ecology Resources · doi:10.1111/1755-0998.12592 · Cited by 1,019
“149 This constant is estimated by the genomic inflation factor defined here as the median of 150 the Mahalanobis distances divided by the median of the chi-square distribution with K 151 degrees of freedom (Devlin and Roeder, 1999).”
MethodHow to Interpret a Genome-wide Association Study
Pearson, T. A. (2008) · JAMA · doi:10.1001/jama.299.11.1335 · Cited by 1,005
The nature of nurture: Effects of parental genotypes
Kong, A., Thorleifsson, G., Frigge, M. L., Vilhjalmsson, B. J., Young, A. I., Thorgeirsson, T. E., Benonisdottir, S., Oddsson, A., Halldorsson, B. V., Masson, G., et al. (2018) · Science · doi:10.1126/science.aan6877 · Cited by 949
Epigenetic Signatures of Cigarette Smoking
Joehanes, R., Just, A. C., Marioni, R. E., Pilling, L. C., Reynolds, L. M., Mandaviya, P. R., Guan, W., Xu, T., Elks, C. E., Aslibekyan, S. W., et al. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001506 · Cited by 934
MAGIC, Heid, I. M., Jackson, A. U., Randall, J. C., Winkler, T. W., Qi, L., Steinthorsdottir, V., Thorleifsson, G., Zillikens, M. C., Speliotes, E. K., et al. (2010) · Nature Genetics · doi:10.1038/ng.685 · Cited by 931
The effects of human population structure on large genetic association studies
Marchini, J., Cardon, L. R., Phillips, M. S., & Donnelly, P. (2004) · Nature Genetics · doi:10.1038/ng1337 · Cited by 869
Common genetic variants influence human subcortical brain structures
Initiative, T. A. D. N., Hibar, D. P., The CHARGE Consortium, EPIGEN, IMAGEN, SYS, Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivières, S., et al. (2015) · Nature · doi:10.1038/nature14101 · Cited by 858
Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24
Guðmundsson, J., Sulem, P., Manolescu, A. R., Amundadottir, L. T., Gudbjartsson, D. F., Helgason, A., Rafnar, Þ., Bergþorsson, J. Þ., Agnarsson, B. A., Baker, A., et al. (2007) · Nature Genetics · doi:10.1038/ng1999 · Cited by 856
Genome-wide association analysis identifies 20 loci that influence adult height
Initiative, D. G., Weedon, M. N., The Wellcome Trust Case Control Consortium, Cambridge GEM Consortium, Lango, H., Lindgren, C. M., Wallace, C., Evans, D. M., Mangino, M., Freathy, R. M., et al. (2008) · Nature Genetics · doi:10.1038/ng.121 · Cited by 826
Shah, S. H., Henry, A., Roselli, C., Lin, H., Sveinbjörnsson, G., Fatemifar, G., Hedman, Å. K., Wilk, J. B., Morley, M. P., Chaffin, M. D., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13690-5 · Cited by 836
GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., Westra, H.-J., Shakhbazov, K., Abdellaoui, A., Agrawal, A., et al. (2013) · Science · doi:10.1126/science.1235488 · Cited by 827
TRAF1–C5 as a Risk Locus for Rheumatoid Arthritis — A Genomewide Study
Plenge, R. M., Seielstad, M., Padyukov, L., Lee, A. T., Remmers, E. F., Ding, B., Liew, A., Khalili, H., Chandrasekaran, A., Davies, L. R. L., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa073491 · Cited by 823
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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