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Statistical power and significance testing in large-scale genetic studies

Sham, Pak C., Purcell, Shaun M.·2014·Nature Reviews Genetics

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Abstract

Significance testing was developed as an objective method for summarizing statistical evidence for a hypothesis. It has been widely adopted in genetic studies, including genome-wide association studies and, more recently, exome sequencing studies. However, significance testing in both genome-wide and exome-wide studies must adopt stringent significance thresholds to allow multiple testing, and it is useful only when studies have adequate statistical power, which depends on the characteristics of the phenotype and the putative genetic variant, as well as the study design. Here, we review the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
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
Self-citation
A method and server for predicting damaging missense mutations.
Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194
Statistical Methods for Research Workers
Fisher, R. A. (1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,883
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
Bootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350
A haplotype map of the human genome
The International HapMap Consortium (2005) · Nature · doi:10.1038/nature04226 · Cited by 5,858
Bootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & HINKLEY, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,478
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
Rectangular Confidence Regions for the Means of Multivariate Normal Distributions
Šidák, Z. (1967) · Journal of the American Statistical Association · doi:10.1080/01621459.1967.10482935 · Cited by 2,904
Genome-wide association studies for complex traits: consensus, uncertainty and challenges
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., & Hirschhorn, J. N. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2344 · Cited by 2,891
Five Years of GWAS Discovery
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425
Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test
Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.05.029 · Cited by 2,373
IX. On the problem of the most efficient tests of statistical hypotheses
Neyman, J., & Pearson, E. S. (1933) · Philosophical Transactions of the Royal Society of London Series A Containing Papers of a Mathematical or Physical Character · doi:10.1098/rsta.1933.0009 · Cited by 2,370
Statistical Methods for Research Workers.
I., L., & Fisher, R. A. (1926) · Journal Of The Royal Statistical Society · doi:10.2307/2341488 · Cited by 2,171
Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits
Purcell, S., Cherny, S. S., & Sham, P. C. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.1.149 · Cited by 2,057
Self-citation
Functional impact of global rare copy number variation in autism spectrum disorders
Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., Regan, R., Conroy, J., Magalhaes, T. R., Correia, C., Abrahams, B. S., et al. (2010) · Nature · doi:10.1038/nature09146 · Cited by 2,013
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies
Wacholder, S., Chanock, S. J., García‐Closas, M., ghormli, L. K. E., & Rothman, N. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh075 · Cited by 1,753
Exome sequencing as a tool for Mendelian disease gene discovery
Bamshad, M. J., Ng, S. B. H., Bigham, A. W., Tabor, H. K., Emond, M. J., Nickerson, D. A., & Shendure, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3031 · Cited by 1,744
A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other
Nyholt, D. R. (2004) · The American Journal of Human Genetics · doi:10.1086/383251 · Cited by 1,699
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
ON THE PROBLEM OF THE MOST EFFICIENT TESTS OF STATISTICAL HYPOTHESES
Neyman, J., & Pearson, E. S. (1967) · University of California Press eBooks · doi:10.1525/9780520339897-008 · Cited by 1,545
Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence Data
Li, B., & Leal, S. M. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.06.024 · Cited by 1,527
Why Most Discovered True Associations Are Inflated
Ioannidis, J. P. A. (2008) · Epidemiology · doi:10.1097/ede.0b013e31818131e7 · Cited by 1,495
Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix
Li, J., & Ji, L. (2005) · Heredity · doi:10.1038/sj.hdy.6800717 · Cited by 1,458
De Novo Gene Disruptions in Children on the Autistic Spectrum
Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y.-H., Narzisi, G., Leotta, A., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.009 · Cited by 1,446
A tutorial on statistical methods for population association studies
Balding, D. J. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1916 · Cited by 1,380
Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome
Ng, S. B. H., Bigham, A. W., Buckingham, K. J., Hannibal, M., McMillin, M. J., Gildersleeve, H. I. S., Beck, A. E., Tabor, H. K., Cooper, G. M., Mefford, H. C., et al. (2010) · Nature Genetics · doi:10.1038/ng.646 · Cited by 1,349
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
A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic
Madsen, B. E., & Browning, S. R. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000384 · Cited by 1,113
Null hypothesis significance testing: a review of an old and continuing controversy
Nickerson, R. S. (2000) · Psychological Methods · doi:10.1037/1082-989x.5.2.241 · Cited by 1,090
Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants
Fu, W., O’Connor, T. D., Jun, G., Kang, H. M., Abecasis, G. R., Leal, S. M., Gabriel, S., Rieder, M. J., Altshuler, D., Shendure, J., et al. (2012) · Nature · doi:10.1038/nature11690 · Cited by 969
Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets
Li, M.-X., Yeung, J. M. Y., Cherny, S. S., & Sham, P. C. (2012) · Human Genetics · doi:10.1007/s00439-011-1118-2 · Cited by 962
Self-citation
Rare Variants of IFIH1 , a Gene Implicated in Antiviral Responses, Protect Against Type 1 Diabetes
Nejentsev, S., Walker, N., Riches, D., Egholm, M., & Todd, J. A. (2009) · Science · doi:10.1126/science.1167728 · Cited by 958
Estimation of the multiple testing burden for genomewide association studies of nearly all common variants
Pe'er, I., Yelensky, R., Altshuler, D., & Daly, M. J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20303 · Cited by 783
Pooled Association Tests for Rare Variants in Exon-Resequencing Studies
Price, A. L., Kryukov, G. V., de Bakker, P. I. W., Purcell, S. M., Staples, J., Wei, L.-J., & Sunyaev, S. R. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.04.005 · Cited by 755
Self-citation
Optimal tests for rare variant effects in sequencing association studies
Lee, S., Wu, M. C., & Lin, X. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs014 · Cited by 705
Estimation of significance thresholds for genomewide association scans
Dudbridge, F., & Gusnanto, A. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20297 · Cited by 703
An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people
Nelson, M. R., Wegmann, D., Ehm, M. G., Kessner, D., St. Jean, P., Verzilli, C., Shen, J., Tang, Z., Bacanu, S.-A., Fraser, D., et al. (2012) · Science · doi:10.1126/science.1217876 · Cited by 647
Sample size requirements for matched case‐control studies of gene–environment interaction
Gauderman, W. J. (2001) · Statistics in Medicine · doi:10.1002/sim.973 · Cited by 643
Searching for missing heritability: Designing rare variant association studies
Zuk, O., Schaffner, S. F., Samocha, K., Do, R., Hechter, E., Kathiresan, S., Daly, M. J., Neale, B. M., Sunyaev, S. R., & Lander, E. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322563111 · Cited by 635
Sample Size Requirements for Association Studies of Gene-Gene Interaction
Gauderman, W. J. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.5.478 · Cited by 615
Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies
Kryukov, G. V., Pennacchio, L. A., & Sunyaev, S. R. (2007) · The American Journal of Human Genetics · doi:10.1086/513473 · Cited by 598
The Non-Central χ 2 - and F-Distribution and their Applications
PATNAIK, P. B. (1949) · Biometrika · doi:10.2307/2332542 · Cited by 598
Testing for an Unusual Distribution of Rare Variants
Neale, B. M., Rivas, M. A., Voight, B. F., Altshuler, D., Devlin, B., Orho‐Melander, M., Kathiresan, S., Purcell, S., Roeder, K., & Daly, M. J. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001322 · Cited by 592
Self-citation
The non-central chi2- and F-distributions and their applications.
PATNAIK, P. B. (1949) · PubMed · Cited by 556
Bayesian statistical methods for genetic association studies
Stephens, M., & Balding, D. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2615 · Cited by 480
The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits
Voight, B. F., Kang, H. M., Ding, J., Palmer, C. D., Sidore, C., Chines, P. S., Burtt, N. P., Fuchsberger, C., Li, Y., Erdmann, J., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002793 · Cited by 475
Statistical analysis strategies for association studies involving rare variants
Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2867 · Cited by 455
Combined Linkage and Association Sib-Pair Analysis for Quantitative Traits
Fulker, D. W., Cherny, S. S., Sham, P. C., & Hewitt, J. K. (1999) · The American Journal of Human Genetics · doi:10.1086/302193 · Cited by 443
Self-citation
Analysis of copy number variations at 15 schizophrenia-associated loci
Rees, E., Walters, J. T. R., Georgieva, L., Isles, A. R., Chambert, K. D., Richards, A. L., Mahoney-Davies, G., Legge, S. E., Moran, J. L., McCarroll, S. A., et al. (2014) · British Journal of Psychiatry · doi:10.1192/bjp.bp.113.131052 · Cited by 443
A Note on the Calculation of Empirical P Values from Monte Carlo Procedures
North, B. V., Curtis, D., & Sham, P. C. (2002) · The American Journal of Human Genetics · doi:10.1086/341527 · Cited by 426
Self-citation
Bayes factors for genome‐wide association studies: comparison with P ‐values
Wakefield, J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20359 · Cited by 404
Differential confounding of rare and common variants in spatially structured populations
Mathieson, I., & McVean, G. (2012) · Nature Genetics · doi:10.1038/ng.1074 · Cited by 385
Overcoming the Winner’s Curse: Estimating Penetrance Parameters from Case-Control Data
Zöllner, S. K., & Pritchard, J. K. (2007) · The American Journal of Human Genetics · doi:10.1086/512821 · Cited by 383
Exome sequencing and the genetic basis of complex traits
Kiezun, A., Garimella, K., Do, R., Stitziel, N. O., Neale, B. M., McLaren, P. J., Gupta, N., Sklar, P., Sullivan, P. F., Moran, J. L., et al. (2012) · Nature Genetics · doi:10.1038/ng.2303 · Cited by 372
Self-citation
Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies
Duggal, P., Gillanders, E. M., Holmes, T. N., & Wilson, J. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-516 · Cited by 333
Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes
He, X., Sanders, S. J., Liu, L., De Rubeis, S., Lim, E. T., Sutcliffe, J. S., Schellenberg, G. D., Gibbs, R. A., Daly, M. J., Buxbaum, J. D., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003671 · Cited by 304
Genetic associations: false or true?
Ioannidis, J. P. A. (2003) · Trends in Molecular Medicine · doi:10.1016/s1471-4914(03)00030-3 · Cited by 289
What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations
Panagiotou, O. A., Ioannidis, J. P. A., & for the Genome-Wide Significance Project (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr178 · Cited by 286
On multiple‐testing correction in genome‐wide association studies
Moskvina, V., & Schmidt, K. M. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20331 · Cited by 275
A General Framework for Detecting Disease Associations with Rare Variants in Sequencing Studies
Lin, D.-Y., & Tang, Z.-Z. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.07.015 · Cited by 275
Rare Complete Knockouts in Humans: Population Distribution and Significant Role in Autism Spectrum Disorders
Lim, E. T., Raychaudhuri, S., Sanders, S. J., Stevens, C., Sabo, A., MacArthur, D. G., Neale, B. M., Kirby, A., Ruderfer, D. M., Fromer, M., et al. (2013) · Neuron · doi:10.1016/j.neuron.2012.12.029 · Cited by 266
Self-citation
Power of Linkage versus Association Analysis of Quantitative Traits, by Use of Variance-Components Models, for Sibship Data
Sham, P. C., Cherny, S. S., Purcell, S., & Hewitt, J. K. (2000) · The American Journal of Human Genetics · doi:10.1086/302891 · Cited by 260
Self-citation
A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases
Li, M., Gui, H., Kwan, J. S. H., Bao, S., & Sham, P. C. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1257 · Cited by 248
Self-citation
General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies
Lee, S., Teslovich, T. M., Boehnke, M., & Lin, X. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.05.010 · Cited by 222
Comparison of statistical tests for disease association with rare variants
Basu, S., & Pan, W. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20609 · Cited by 215
Genome‐wide significance for dense SNP and resequencing data
Hoggart, C. J., Clark, T. G., Iorio, M. D., Whittaker, J. C., & Balding, D. J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20292 · Cited by 213
A new measure of the effective number of tests, a practical tool for comparing families of non‐independent significance tests
Galwey, N. W. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20408 · Cited by 189
Efficient Computation of Significance Levels for Multiple Associations in Large Studies of Correlated Data, Including Genomewide Association Studies
Dudbridge, F., & Koeleman, B. P. C. (2004) · The American Journal of Human Genetics · doi:10.1086/423738 · Cited by 166
Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies
Zhong, H., & Prentice, R. L. (2008) · Biostatistics · doi:10.1093/biostatistics/kxn001 · Cited by 158
Immunochip analyses identify a novel risk locus for primary biliary cirrhosis at 13q14, multiple independent associations at four established risk loci and epistasis between 1p31 and 7q32 risk variants
Juran, B. D., Hirschfield, G. M., Invernizzi, P., Atkinson, E. J., Li, Y., Xie, G., Kosoy, R., Ransom, M., Sun, Y., Bianchi, I., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds359 · Cited by 150
Computational and statistical approaches to analyzing variants identified by exome sequencing
Stitziel, N. O., Kieżun, A., & Sunyaev, S. R. (2011) · Genome biology · doi:10.1186/gb-2011-12-9-227 · Cited by 147
Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies
Li, D., Lewinger, J. P., Gauderman, W. J., Murcray, C. E., & Conti, D. V. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20628 · Cited by 144
Discovery of Rare Variants via Sequencing: Implications for the Design of Complex Trait Association Studies
Li, B., & Leal, S. M. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000481 · Cited by 142
Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls
Liu, L., Sabo, A., Neale, B. M., Nagaswamy, U., Stevens, C., Lim, E., Bodea, C. A., Muzny, D., Reid, J. G., Banks, E., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003443 · Cited by 135
Sample size determination for studies of gene-environment interaction
Luan, J., Wong, M., Day, N., & Wareham, N. (2001) · International Journal of Epidemiology · doi:10.1093/ije/30.5.1035 · Cited by 123
Maximizing association statistics over genetic models
González, J. R., Carrasco, J. L., Dudbridge, F., Armengol, L., Estivill, X., & Moreno, V. R. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20299 · Cited by 122
Including known covariates can reduce power to detect genetic effects in case-control studies
Pirinen, M., Donnelly, P., & Spencer, C. C. A. (2012) · Nature Genetics · doi:10.1038/ng.2346 · Cited by 117
Linkage Analysis in the Next-Generation Sequencing Era
Bailey-Wilson, J., & Wilson, A. F. (2011) · Human Heredity · doi:10.1159/000334381 · Cited by 112
The Empirical Power of Rare Variant Association Methods: Results from Sanger Sequencing in 1,998 Individuals
Ladouceur, M., Dastani, Z., Aulchenko, Y. S., Greenwood, C. M. T., & Richards, J. B. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002496 · Cited by 109
Rapid Simulation of P Values for Product Methods and Multiple-Testing Adjustment in Association Studies
Seaman, S. R., & Müller‐Myhsok, B. (2005) · The American Journal of Human Genetics · doi:10.1086/428140 · Cited by 102
Family-based association tests for sequence data, and comparisons with population-based association tests
Ionita‐Laza, I., Lee, S., Makarov, V., Buxbaum, J. D., & Lin, X. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.308 · Cited by 87
A Note on the Calculation of Empirical P Values from Monte Carlo Procedures
North, B. V., Curtis, D., & Sham, P. C. (2003) · The American Journal of Human Genetics · doi:10.1086/346173 · Cited by 75
Self-citation
Efficient Approximation of P‐value of the Maximum of Correlated Tests, with Applications to Genome‐Wide Association Studies
Li, Q., Zheng, G., Li, Z., & Yu, K. F. (2008) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2008.00437.x · Cited by 74
Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates
So, H., & Sham, P. C. (2011) · Behavior Genetics · doi:10.1007/s10519-011-9450-9 · Cited by 73
Self-citation
Estimating Odds Ratios in Genome Scans: An Approximate Conditional Likelihood Approach
Ghosh, A., Zou, F., & Wright, F. A. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.03.002 · Cited by 66
Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics
Hu, Y., Berndt, S. I., Gustafsson, S., Ganna, A., Berndt, S. I., Gustafsson, S., Mägi, R., Ganna, A., Wheeler, E., Feitosa, M. F., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.06.011 · Cited by 65
Replication Strategies for Rare Variant Complex Trait Association Studies via Next-Generation Sequencing
Liu, D. J., & Leal, S. M. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.10.025 · Cited by 59
How Many Genetic Variants Remain to Be Discovered?
Yudi Pawitan, Y., Seng, K. C., & Magnusson, P. K. E. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007969 · Cited by 47
Pooled Association Tests for Rare Variants in Exon-Resequencing Studies
Price, A. L., Kryukov, G. V., de Bakker, P. I. W., Purcell, S., Staples, J., Wei, L., & Sunyaev, S. R. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.05.014 · Cited by 43
Self-citation
THE POWER FUNCTION OF THE TEST FOR THE DIFFERENCE BETWEEN TWO PROPORTIONS IN A 2×2 TABLE
PATNAIK, P. B. (1948) · Biometrika · doi:10.1093/biomet/35.1-2.157 · Cited by 39
Power Comparisons of the Transmission/Disequilibrium Test and Sib–Transmission/Disequilibrium-Test Statistics
Whittaker, J. C., & Lewis, C. M. (1999) · The American Journal of Human Genetics · doi:10.1086/302485 · Cited by 32
Statistical Guidance for Experimental Design and Data Analysis of Mutation Detection in Rare Monogenic Mendelian Diseases by Exome Sequencing
Zhi, D., & Chen, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031358 · Cited by 29
SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits
Li, B., Wang, G., & Leal, S. M. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts499 · Cited by 21
Design Considerations for Massively Parallel Sequencing Studies of Complex Human Disease
Feng, B., Tavtigian, S. V., Southey, M. C., & Goldgar, D. E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023221 · Cited by 20
Empirical power of very rare variants for common traits and disease: results from sanger sequencing 1998 individuals
Ladouceur, M., Zheng, H., Greenwood, C. M. T., & Richards, J. B. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.284 · Cited by 12
Three Ways of Combining Genotyping and Resequencing in Case-Control Association Studies
Longmate, J., Larson, G. P., Krontiris, T. G., & Sommer, S. S. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014318 · Cited by 9
Combining effects from rare and common genetic variants in an exome-wide association study of sequence data
Aschard, H., Qiu, W., Paşaniuc, B., Zaitlen, N., Cho, M. H., & Carey, V. J. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s44 · Cited by 9
Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data
Saad, M., Pierre, A. S., Bohossian, N., Macé, M., & Martinez, M. M. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s33 · Cited by 5
Novel Sib Pair Selection Strategy Increases Power in Quantitative Association Analysis
Kwan, J. S. H., Cherny, S. S., Kung, A. W. C., & Sham, P. C. (2009) · Behavior Genetics · doi:10.1007/s10519-009-9284-x · Cited by 4
Self-citation
Detecting multiple causal rare variants in exome sequence data
Ye, K. Q., & Engelman, C. D. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20644 · Cited by 4

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The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection
GERAD1 Consortium, Pardiñas, A. F., CRESTAR Consortium, Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S. E., Bishop, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0059-2 · Cited by 1,552
All for One and One for All: Mental Disorders in One Dimension
Caspi, A., & Moffitt, T. E. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17121383 · Cited by 918
Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions
Hoban, S., Kelley, J. L., Lotterhos, K. E., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016) · The American Naturalist · doi:10.1086/688018 · Cited by 876
“The result is a distribution of P values or test statistics for that data set, and many of the identi ﬁ ed loci will be false positives if multiple testing is not corrected for (Sham and Purcell 2014).”
Advancing the understanding of autism disease mechanisms through genetics
de la Torre-Ubieta, L., Won, H., Stein, J. L., & Geschwind, D. H. (2016) · Nature Medicine · doi:10.1038/nm.4071 · Cited by 827

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Authors: Sham, P. C., & Purcell, S. M.
Affiliations: University of Hong Kong (HK)
Massachusetts General Hospital (US)
Corresponding author: Sham, Pak C.
ORCID: Sham, Pak C.
Purcell, Shaun M.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2014-04-17
Volume: 15
Issue: 5
Pages: 335-346
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/nrg3706
OpenAlex: W2116514399
Cited by: 573
References: 102
Citations by year: 2026: 23 · 2025: 26 · 2024: 22 · 2023: 50 · 2022: 28 · 2021: 40 · 2020: 54 · 2019: 58 · 2018: 73 · 2017: 77 · 2016: 65 · 2015: 45 · 2014: 11
FWCI: 8.75
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute of Mental Health (R01MH099126)
National Human Genome Research Institute (R01HG005827)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords: statistical power, significance testing, multiple testing correction, rare variants, genome-wide association study, exome sequencing, significance threshold, research design
MeSH terms: Data Interpretation, Statistical, Gene Frequency, Genetic Testing, Genotype, Humans, Case-Control Studies, Sequence Analysis, DNA, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study

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Statistical power and significance testing in large-scale genetic studies

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