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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Cited by
Sorted by citations
Items 1,001–1,023 of 1,023
Sex-specific DNA methylation differences in Alzheimer’s disease pathology
Zhang, L., Young, J. I., Gomez, L., Silva, T. C., Schmidt, M. A., Cai, J., Chen, X., Martin, E. R., & Wang, L. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01177-8 · Cited by 58
“Because systematic inflation of test statistics can lead to an increase in the number of false-positive results, traditionally genomic inflation factor [31] is typically used to quantify the amount of inflation in genome-wide association studies (GWAS) of genetic variants.”
BackgroundMethodGenome‐wide analyses of aggressiveness in attention‐deficit hyperactivity disorder
Brevik, E. J., van Donkelaar, M. M. J., Weber, H., Sánchez‐Mora, C., Jacob, C., Rivero, O., Kittel‐Schneider, S., Garcia‐Martínez, I., Aebi, M., van Hulzen, K., et al. (2016) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32434 · Cited by 57
“Genomic control, QQ plotting, and regional plotting of top loci were applied to check the integrity of test statistics [Devlin and Roeder, 1999; Cuellar-Partida et al., 2015].”
Xu, Z., Shen, X., Pan, W., & Initiative, F. T. A. D. N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102312 · Cited by 51
“Furthermore, we also calculated the genomic inflation factor l [35] in longitudinal data analysis.”
MethodMoskvina, V., O'Dushlaine, C., Purcell, S., Craddock, N., Holmans, P., & O'Donovan, M. C. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20636 · Cited by 49
“Single SNP association P -values were generated using the Armitage trend (1 df) test and then genomic control [Devlin and Roeder, 1999] adjusted based on the observed l GC for the whole data set of 1.08.”
MethodInfluentialChung, R.-H., Ma, D., Wang, K., Hedges, D. J., Jaworski, J. M., Gilbert, J. R., Cuccaro, M. L., Wright, H. H., Abramson, R. K., Konidari, I., et al. (2011) · Molecular Autism · doi:10.1186/2040-2392-2-18 · Cited by 49
“The analysis of the ACC data set had an inflation factor (that is, l value [15]) of 1.”
Meta-Analysis of Genome-Wide Association Studies
de Bakker, P. I. W., Neale, B. M., & Daly, M. J. (2010) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top81 · Cited by 48
Genome-wide genetic links between amyotrophic lateral sclerosis and autoimmune diseases
Li, C. Y., Yang, T. M., Ou, R. W., Wei, Q. Q., & Shang, H. F. (2021) · BMC Medicine · doi:10.1186/s12916-021-01903-y · Cited by 48
Sung, Y. J., de las Fuentes, L., Winkler, T. W., Chasman, D. I., Bentley, A. R., Kraja, A. T., Ntalla, I., Warren, H. R., Guo, X., Schwander, K., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz070 · Cited by 43
“In stage 1, genomic control correction (104) was applied twice, first for cohort-specific GWAS results if their genomic control lambda value was greater than 1 and again after the metaanalysis.”
MethodQuantitative Trait Loci Mapping in Plants: Concepts and Approaches
Sehgal, D., Singh, R., & Rajpal, V. R. (2016) · Sustainable Development and Biodiversity · doi:10.1007/978-3-319-27090-6_2 · Cited by 50
“Among many methods developed to deal with this problem, ‘genomic control’ (GC) method (Devlin and Roeder 1999) estimates association using a large number of putative neutral markers or markers not thought to be involved in controlling the trait of interest.”
MethodVélez, J. I., Chandrasekharappa, S. C., Henao, E., Martinez, A. F., Harper, U., Jones, M., Solomon, B. D., Lopez, L., Garcia, G., Aguirre-Acevedo, D. C., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.81 · Cited by 44
The DNA methylome in panic disorder: A case-control and longitudinal psychotherapy-epigenetic study
Ziegler, C., Grundner-Culemann, F., Schiele, M. A., Schlosser, P., Kollert, L., Mahr, M., Gajewska, A., Lesch, K.-P., Deckert, J., Köttgen, A., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0648-6 · Cited by 47
Wang, H., Haiman, C. A., Kolonel, L. N., Henderson, B. E., Wilkens, L. R., Le Marchand, L., & Stram, D. O. (2010) · Human Genetics · doi:10.1007/s00439-010-0841-4 · Cited by 45
“2006), or by adjusting for a single factor that captures the average effect of confounding as in the genomic control (GC) method (Devlin and Roeder 1999).”
Candidate gene studies of bipolar disorder
Jones, I., & Craddock, N. (2001) · Annals of Medicine · doi:10.3109/07853890108998753 · Cited by 44
Distinct sex-specific DNA methylation differences in Alzheimer’s disease
C. Silva, T., Zhang, W., Young, J. I., Gomez, L., Schmidt, M. A., Varma, A., Chen, X. S., Martin, E. R., & Wang, L. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-01070-z · Cited by 33
“Inflation assessment and correction We estimated genomic inflation factors (lambda values) using both the conventional approach [44] and the bacon method [45], which is specifically proposed for a more accurate assessment of inflations in EWAS.”
MethodGenome-Wide Scan of Wool Production Traits in Akkaraman Sheep
Arzik, Y., Kizilaslan, M., Behrem, S., White, S. N., Piel, L. M. W., & Cinar, M. U. (2023) · Genes · doi:10.3390/genes14030713 · Cited by 31
“Genomic controls were applied to the p-values of all SNPs in the analysis of each trait to avoid any potential inflation after the use of the mixed model analysis approach [35].”
MethodRNA-Seq optimization with eQTL gold standards
Ellis, S. E., Gupta, S., Ashar, F. N., Bader, J. S., West, A. B., & Arking, D. E. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-892 · Cited by 30
“Using a cut-off of three SD from the mean, 20.2% [7,027/34,738] of genes tested for differential expression between cases and controls had at least one sample flagged as an outlier for gene expression level.”
BackgroundInfluentialMeisner, J., Albrechtsen, A., & Hanghøj, K. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04375-2 · Cited by 30
Backes, C., Rühle, F., Stoll, M., Haas, J., Frese, K., Franke, A., Lieb, W., Wichmann, H.-E., Weis, T., Kloos, W., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-622 · Cited by 26
“The genomic inflation factor was calculated as median of all SNPs divided by the median of a chi square distribution with 1 degree of freedom and was used to correct p-values of the association analyses for genomic control (GC) in order to effectively adjust for population stratification [50].”
MethodCrossett, A., Kent, B. P., Klei, L., Ringquist, S., Trucco, M., Roeder, K., & Devlin, B. (2010) · Statistics in Medicine · doi:10.1002/sim.4057 · Cited by 15
“If not modeled correctly, even subtle correlations between individuals of common ancestry begin to affect the distribution of tests of association causing a greater number of spurious associations [7; 8; 9].”
BackgroundSelf-citationLatendresse, S. J., Musci, R., & Maher, B. S. (2018) · Prevention Science · doi:10.1007/s11121-017-0785-1 · Cited by 12
“Several methods, including genomic control (Devlin and Roeder 1999) and STRUCTURE (Pritchard and Rosenberg 1999; Pritchard and Donnelly 2001) are available to deal with this issue by either correcting the test statistic for the average level of stratification or a priori grouping of population…”
Schiele, M. A., Crespo Salvador, O., Lipovsek, J., Schwarte, K., Schlosser, P., Zwanzger, P., Arolt, V., Baune, B. T., Köttgen, A., & Domschke, K. (2024) · International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyae045 · Cited by 13
Novel estimators for family-based genome-wide association studies increase power and robustness
Guan, J., Nehzati, S. M., Benjamin, D. J., & Young, A. I. (2022) · doi:10.1101/2022.10.24.513611 · Cited by 2
“While a mean Z(2) statistic greater than 1 is a common measure of inflation in GWAS[16, 17], this statistic is not a completely fair way to compare the biases due to stratification across estimators that have different sampling variances: for example, for estimators with the same bias but different sampling variances,…”
Background
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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