Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants
Open accessAbstract
Recent developments in sequencing technologies have made it possible to uncover both rare and common genetic variants. Genome-wide association studies (GWASs) can test for the effect of common variants, whereas sequence-based association studies can evaluate the cumulative effect of both rare and common variants on disease risk. Many groupwise association tests, including burden tests and variance-component tests, have been proposed for this purpose. Although such tests do not exclude common variants from their evaluation, they focus mostly on testing the effect of rare variants by upweighting rare-variant effects and downweighting common-variant effects and can therefore lose substantial power when both rare and common genetic variants in a region influence trait susceptibility. There is increasing evidence that the allelic spectrum of risk variants at a given locus might include novel, rare, low-frequency, and common genetic variants. Here, we introduce several sequence kernel association tests to evaluate the cumulative effect of rare and common variants. The proposed tests are computationally efficient and are applicable to both binary and continuous traits. Furthermore, they can readily combine GWAS and whole-exome-sequencing data on the same individuals, when available, and are also applicable to deep-resequencing data of GWAS loci. We evaluate these tests on data simulated under comprehensive scenarios and show that compared with the most commonly used tests, including the burden and variance-component tests, they can achieve substantial increases in power. We next show applications to sequencing studies for Crohn disease and autism spectrum disorders. The proposed tests have been incorporated into the software package SKAT.
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The authors performed a burden test using SKAT adjusting for previously defined covariates to find individual genes with more rare variants in cases or controls.
“we performed a burden test using SKAT14 adjusting for previously defined covariates (Online Methods).”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [14]
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
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 · see citing works
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895 · see citing works
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063 · see citing works
Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., Land, S. J., Lu, X., & Ruden, D. M. (2012) · Fly · doi:10.4161/fly.19695 · Cited by 11,816 · see citing works
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,216 · see citing works
Hapeman, D. E., & Agresti, A. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,406 · see citing works
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446 · see citing works
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 · see citing works
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 2,317 · see citing works
Self-citationSporadic autism exomes reveal a highly interconnected protein network of de novo mutations
O’Roak, B. J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B. P., Levy, R., Ko, A., Lee, C., Smith, J. D., et al. (2012) · Nature · doi:10.1038/nature10989 · Cited by 2,165 · see citing works
Hypothesis testing when a nuisance parameter is present only under the alternative
Davies, R. B. (1987) · Biometrika · doi:10.1093/biomet/74.1.33 · Cited by 2,085 · see citing works
(2003) · Technometrics · doi:10.1198/tech.2003.s28 · Cited by 1,580 · see citing works
Diagnostic exome sequencing in persons with severe intellectual disability
de Ligt, J., Willemsen, M. H., van Bon, B. W. M., Kleefstra, T., Yntema, H. G., Kroes, T., Vulto-van Silfhout, A. T., Koolen, D. A., de Vries, P., Gilissen, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1206524 · Cited by 1,546 · see citing works
Li, B., & Leal, S. M. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.06.024 · Cited by 1,527 · see citing works
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 · see citing works
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 · see citing works
Lee, S., Emond, M. J., Bamshad, M. J., Barnes, K. C., Rieder, M. J., Nickerson, D. A., Christiani, D. C., Wurfel, M. M., & Lin, X. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.06.007 · Cited by 1,040 · see citing works
Self-citationLesage, S., Zouali, H., Cézard, J., Colombel, J., Bélaïche, J., Almer, S. H., Tysk, C., O'Morain, C. A., Gassull, M. A., Binder, V., et al. (2002) · The American Journal of Human Genetics · doi:10.1086/339432 · Cited by 992 · see citing works
Gao, X., Starmer, J., & Martin, E. R. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20310 · Cited by 849 · see citing works
National Institute of Diabetes and Digestive Kidney Diseases Inflammatory Bowel Disease Genetics Consortium (NIDDK IBDGC), Rivas, M. A., United Kingdom Inflammatory Bowel Disease Genetics Consortium, International Inflammatory Bowel Disease Genetics Consortium, Beaudoin, M., Gardet, A., Stevens, C., Sharma, Y., Zhang, C. K., Boucher, G., et al. (2011) · Nature Genetics · doi:10.1038/ng.952 · Cited by 771 · see citing works
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 · see citing works
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 683 · see citing works
Self-citationCalibrating a coalescent simulation of human genome sequence variation
Schaffner, S. F., Foo, C., Gabriel, S., Reich, D., Daly, M. J., & Altshuler, D. (2005) · Genome Research · doi:10.1101/gr.3709305 · Cited by 616 · see citing works
Categorical Data Analysis, Second Edition
Chimka, J. R. (2003) · IIE Transactions · doi:10.1080/07408170390206977-4700 · Cited by 604 · see citing works
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 583 · see citing works
Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs
The Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ), Lee, S. H., The International Schizophrenia Consortium (ISC), The Molecular Genetics of Schizophrenia Collaboration (MGS), DeCandia, T. R., Ripke, S., Yang, J., Sullivan, P. F., Goddard, M. E., Keller, M. C., et al. (2012) · Nature Genetics · doi:10.1038/ng.1108 · Cited by 579 · see citing works
Self-citationHypothesis testing when a nuisance parameter is present only under the alternative
Davies, R. B. (1977) · Biometrika · doi:10.1093/biomet/64.2.247 · Cited by 488 · see citing works
400: A Method for Combining Non-Independent, One-Sided Tests of Significance
Brown, M. B. (1975) · Biometrics · doi:10.2307/2529826 · Cited by 473 · see citing works
Annotating non-coding regions of the genome.
Alexander, R. P., Fang, G., Rozowsky, J., Snyder, M., & Gerstein, M. B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2814 · Cited by 464 · see citing works
Common genetic variants, acting additively, are a major source of risk for autism
Klei, L., Sanders, S. J., Murtha, M. T., Hus, V., Lowe, J. K., Willsey, A. J., Moreno-De-Luca, D., Yu, T. W., Fombonne, E., Geschwind, D., et al. (2012) · Molecular Autism · doi:10.1186/2040-2392-3-9 · Cited by 418 · see citing works
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 · see citing works
A Data-Adaptive Sum Test for Disease Association with Multiple Common or Rare Variants
Han, F., & Pan, W. (2010) · Human Heredity · doi:10.1159/000288704 · Cited by 306 · see citing works
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 274 · see citing works
Large-Scale Gene-Centric Meta-analysis across 32 Studies Identifies Multiple Lipid Loci
Asselbergs, F. W., Guo, Y., van Iperen, E. P. A., Sivapalaratnam, S., Tragante, V., Lanktree, M. B., Lange, L. A., Almoguera, B., Appelman, Y., Barnard, J., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.08.032 · Cited by 247 · see citing works
Liu, D. J., & Leal, S. M. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001156 · Cited by 236 · see citing works
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 · see citing works
A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease
Ionita‐Laza, I., Buxbaum, J. D., Laird, N. M., & Lange, C. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001289 · Cited by 146 · see citing works
Self-citationHypothesis testing in semiparametric additive mixed models
Zhang, D. (2003) · Biostatistics · doi:10.1093/biostatistics/4.1.57 · Cited by 136 · see citing works
Optimal Detection of Heterogeneous and Heteroscedastic Mixtures
Cai, T., Jeng, X. J., & Jin, J. (2011) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2011.00778.x · Cited by 128 · see citing works
Zheng, G., & Chen, Z. (2005) · Biometrics · doi:10.1111/j.0006-341x.2005.030531.x · Cited by 103 · see citing works
Diogo, D., Kurreeman, F. A. S., Stahl, E. A., Liao, K. P., Gupta, N., Greenberg, J. D., Rivas, M. A., Hickey, B., Flannick, J. A., Thomson, B. P., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.11.012 · Cited by 93 · see citing works
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 · see citing works
Self-citationTzeng, J., Zhang, D., Pongpanich, M., Smith, C., McCarthy, M. I., Sale, M. M., Worrall, B. B., Hsu, F., Thomas, D. C., & Sullivan, P. F. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.07.007 · Cited by 79 · see citing works
Ionita-Laza, I., Makarov, V., & Buxbaum, J. D. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.04.010 · Cited by 57 · see citing works
Self-citationAn Exponential Combination Procedure for Set-Based Association Tests in Sequencing Studies
Chen, L., Hsu, L., Gamazon, E. R., Cox, N. J., & Nicolae, D. L. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.09.017 · Cited by 42 · see citing works
Joint Association Testing of Common and Rare Genetic Variants Using Hierarchical Modeling
Cardin, N. J., Mefford, J., & Witte, J. S. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21659 · Cited by 7 · see citing works
Assessment of Pooled Association Tests for Rare Genetic Variants within a Unified FrameworkPreprint
Derkach, A., Lawless, J. F., & Sun, L. (2012) · arXiv (Cornell University) · Cited by 2 · see citing works
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Wightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00921-z · Cited by 1,011 · see citing works
Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia
Genovese, G., Fromer, M., Stahl, E. A., Ruderfer, D. M., Chambert, K., Landén, M., Moran, J. L., Purcell, S. M., Sklar, P., Sullivan, P. F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4402 · Cited by 444 · see citing works
Mirabello, L., Zhu, B., Koster, R., Karlins, E., Dean, M., Yeager, M., Gianferante, M., Spector, L. G., Morton, L. M., Karyadi, D., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.0197 · Cited by 243 · see citing works
Ramanan, V. K., Risacher, S. L., Nho, K., Kim, S., Shen, L., McDonald, B. C., Yoder, K. K., Hutchins, G. D., West, J. D., Tallman, E. F., et al. (2015) · Brain · doi:10.1093/brain/awv231 · Cited by 133 · see citing works
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Gulsuner, S., Stein, D. J., Susser, E. S., Sibeko, G., Pretorius, A., Walsh, T., Majara, L., Mndini, M. M., Mqulwana, S. G., Ntola, O. A., et al. (2020) · Science · doi:10.1126/science.aay8833 · Cited by 118 · see citing works
Fabbri, C., Kasper, S., Kautzky, A., Bartova, L., Dold, M., Zohar, J., Souery, D., Montgomery, S., Albani, D., Raimondi, I., et al. (2019) · British Journal of Psychiatry · doi:10.1192/bjp.2018.256 · Cited by 61 · see citing works
Kierczak, M., Rafati, N., Höglund, J., Gourlé, H., Lo Faro, V., Schmitz, D., Ek, W. E., Gyllensten, U., Enroth, S., Ekman, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30208-8 · Cited by 22 · see citing works
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- Authors
- Ionita-Laza, I., Lee, S., Makarov, V., Buxbaum, J. D., & Lin, X.
- Affiliations
- Harvard University (US)
- Columbia University (US)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Lin, Xihong
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 92
- Issue
- 6
- Pages
- 841-853
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC3675243
- OpenAlex
- W2087936498
- Cited by
- 433
- References
- 50
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 29 · 2023: 20 · 2022: 26 · 2021: 50 · 2020: 48 · 2019: 35 · 2018: 50 · 2017: 45 · 2016: 47 · 2015: 30 · 2014: 20 · 2013: 6
- FWCI
- 22.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Foundation for the National Institutes of Health
- National Heart, Lung, and Blood Institute (K99HL113164)
- National Institute of Mental Health (R01MH089025, MH100233, MH089025, R01MH095797, U01MH100233)
- National Human Genome Research Institute (R03HG005908)
- National Cancer Institute (R37CA076404, P01CA134294, R35CA197449)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- SKAT, rare variants, common genetic variants, burden test, statistical power, Crohn's disease, autism spectrum disorder
- MeSH terms
- Algorithms, Child Development Disorders, Pervasive, Computer Simulation, Crohn Disease, Data Interpretation, Statistical, Gene Frequency, Genetic Testing, Humans, Models, Genetic, Risk, Software, Logistic Models, Genetic Predisposition to Disease, Low Density Lipoprotein Receptor-Related Protein-2, Nod2 Signaling Adaptor Protein, Genetic Association Studies
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