Association screening of common and rare genetic variants by penalized regression
Open accessAbstract
Motivation: This article extends our recent research on penalized estimation methods in genome-wide association studies to the realm of rare variants. Results: The new strategy is tested on both simulated and real data. Our findings on breast cancer data replicate previous results and shed light on variant effects within genes. Availability: Rare variant discovery by group penalized regression is now implemented in the free program Mendel at http://www.genetics.ucla.edu/software/ Contact: huazhou@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Newer regression techniques using regularization and shrinkage can overcome collinearity and overfitting problems.
“Newer regression techniques that make use of regularization and shrinkage parameters to control for collinearity and overfitting can be used to overcome these problems.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [76]
Ridge regression and LASSO have been considered in genetic association analysis contexts, and other methods have been proposed.
“Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [76]
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
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 · see citing works
Ideal Spatial Adaptation by Wavelet Shrinkage
Donoho, D. L., & Johnstone, I. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.425 · Cited by 8,730 · see citing works
Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261 · see citing works
The Structure of Haplotype Blocks in the Human Genome
Gabriel, S. B., Schaffner, S. F., Nguyen, H., Moore, J. M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., et al. (2002) · Science · doi:10.1126/science.1069424 · Cited by 5,863 · see citing works
Identification of the breast cancer susceptibility gene BRCA2
Wooster, R., Bignell, G. R., Lancaster, J., Swift, S., Seal, S., Mangion, J., Collins, N., Gregory, S. G., Gumbs, C. E., Micklem, G., et al. (1995) · Nature · doi:10.1038/378789a0 · Cited by 3,645 · see citing works
The Wellcome Trust Case Control Consortium, U.K.
Mossman, K. (2008) · Scientific American · doi:10.1038/scientificamerican0108-42a · Cited by 2,714 · see citing works
Pathwise coordinate optimization
Friedman, J. H., Hastie, T. J., Höfling, H., & Tibshirani, R. J. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas131 · Cited by 1,906 · see citing works
Risks of cancer in BRCA1-mutation carriers
FORD, D. (1994) · The Lancet · doi:10.1016/s0140-6736(94)91578-4 · Cited by 1,850 · see citing works
Rare Structural Variants Disrupt Multiple Genes in Neurodevelopmental Pathways in Schizophrenia
Walsh, T., McClellan, J. M., McCarthy, S. E., Addington, A. M., Pierce, S. B., Cooper, G. M., Nord, A. S., Kusenda, M., Malhotra, D., Bhandari, A., et al. (2008) · Science · doi:10.1126/science.1155174 · Cited by 1,800 · see citing works
The Group Lasso for Logistic Regression
Meier, L., van de Geer, S., & Bühlmann, P. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00627.x · Cited by 1,681 · 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
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
Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol
Cohen, J. C., Kiss, R. S., Pertsemlidis, A., Marcel, Y. L., McPherson, R., & Hobbs, H. H. (2004) · Science · doi:10.1126/science.1099870 · Cited by 1,108 · see citing works
Penalized Regressions: The Bridge versus the Lasso
Fu, W. J. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474784 · Cited by 1,096 · see citing works
The Genetic Basis of Human Cancer
Vogelstein, B., & Kinzler, K. W. (1997) · Cited by 1,082 · see citing works
Human genetic variation and its contribution to complex traits
Frazer, K. A., Murray, S. S., Schork, N. J., & Topol, E. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2554 · Cited by 1,073 · see citing works
L 1-Regularization Path Algorithm for Generalized Linear Models
Park, M. Y., & Hastie, T. J. (2007) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00607.x · Cited by 859 · see citing works
Genome-wide association analysis by lasso penalized logistic regression
Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., & Lange, K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp041 · Cited by 785 · see citing works
Self-citationCoordinate descent algorithms for lasso penalized regressionPreprint
Wu, T. T., & Lange, K. L. (2008) · Cited by 740 · see citing works
Self-citationGenome-wide in situ exon capture for selective resequencing
Hodges, E. C., Xuan, Z., Balija, V. S., Kramer, M., Molla, M. N., Smith, S. W., Middle, C. M., Rodesch, M. J., Albert, T. J., Hannon, G. J., et al. (2007) · Nature Genetics · doi:10.1038/ng.2007.42 · Cited by 677 · see citing works
A note on the group lasso and a sparse group lassoPreprint
Friedman, J. H., Hastie, T., & Tibshirani, R. J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.0736 · Cited by 645 · see citing works
Coordinate descent algorithms for lasso penalized regression
Wu, T. T., & Lange, K. (2008) · The Annals of Applied Statistics · doi:10.1214/07-aoas147 · Cited by 563 · see citing works
Self-citationNumerical Analysis for Statisticians
Lange, K. L. (2010) · Statisctics and computing/Statistics and computing · doi:10.1007/978-1-4419-5945-4 · Cited by 517 · see citing works
Self-citationRecent and ongoing selection in the human genome
Nielsen, R., Hellmann, I., Hubisz, M. J., Bustamante, C. D., & Clark, A. G. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2187 · Cited by 501 · see citing works
The Genetic Basis of Human Cancer
Rubin, C. M. (1998) · Annals of Internal Medicine · doi:10.7326/0003-4819-129-9-199811010-00045 · Cited by 415 · see citing works
Autoimmune diseases: insights from genome-wide association studies
Lettre, G., & Rioux, J. D. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn246 · Cited by 317 · see citing works
Genome-wide association studies in cancer
Easton, D. F., & Eeles, R. A. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn287 · Cited by 244 · see citing works
Darwinian and demographic forces affecting human protein coding genesPreprint
Nielsen, R., Hubisz, M. J., Hellmann, I., Torgerson, D. G., Andrés, A. M., Albrechtsen, A., Gutenkunst, R. N., Adams, M. D., Cargill, M., Boyko, A. R., et al. (2009) · Genome Research · doi:10.1101/gr.088336.108 · Cited by 149 · see citing works
Johnson, N., Fletcher, O., Palles, C., Rudd, M. F., Webb, E. L., Sellick, G. S., dos‐Santos‐Silva, I., McCormack, V., Gibson, L., Fraser, A., et al. (2007) · Human Molecular Genetics · doi:10.1093/hmg/ddm050 · Cited by 124 · see citing works
Wöhrle, D., Salat, U., Hameister, H., Vogel, W., & Steinbach, P. J. (2001) · The American Journal of Human Genetics · doi:10.1086/322739 · Cited by 123 · see citing works
Heterogeneity in the severity of cystic fibrosis and the role of CFTR gene mutations
Dean, M. C., & Santis, G. (1994) · Human Genetics · doi:10.1007/bf00201659 · Cited by 99 · see citing works
Azzopardi, D., Dallosso, A. R., Eliason, K., Hendrickson, B. C., Jones, N., Rawstorne, E., Colley, J. P., Moskvina, V., FRYE, C. A., Sampson, J. R., et al. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-5733 · Cited by 85 · see citing works
Radiation therapy tolerance limits: For one or for all?-Janeway lecture
Peters, L. J. (1996) · Cancer · doi:10.1002/(sici)1097-0142(19960601)77:11 · Cited by 66 · see citing works
Sehl, M. E., Langer, L. R., Papp, J. C., et al. (2009) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-08-1417 · Cited by 41 · see citing works
Self-citationCroiseau, P., & Cordell, H. J. (2009) · BMC Proceedings · doi:10.1186/1753-6561-3-s7-s61 · Cited by 15 · see citing works
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Gatti, 1998, Ataxia-telangiectasia, 275
- Lange, 2001, Mendel version 4.0: a complete package for the exact genetic analysis of discrete traits in pedigree and population data sets, Am. J. Hum. Genet., 69, 504
Cited by
Sorted by citations
Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2013) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2012.681250 · Cited by 1,380 · see citing works
“This criterion was also discussed in the article by Zhou et al. (2010).”
BackgroundAnalysing biological pathways in genome-wide association studies
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2884 · Cited by 757 · see citing works
“In fact, hierarchical models using LASSO have been successfully applied in simultaneous multivariate analyses of all GWA study SNP”
MethodStatistical 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 · see citing works
The impact of rare and low-frequency genetic variants in common disease
Bomba, L., Walter, K., & Soranzo, N. (2017) · Genome biology · doi:10.1186/s13059-017-1212-4 · Cited by 342 · see citing works
“Other tests have been developed to account for signal sparsity across the tested region and include least absolute shrinkage and selection operator (LASSO) and the exponential combination (EC) test [92, 93].”
MethodA selective review of group selection in high-dimensional models
Huang, J., Breheny, P. J., & Ma, S. (2012) · Cited by 350 · see citing works
A Selective Review of Group Selection in High-Dimensional Models
Huang, J., Breheny, P. J., & Ma, S. (2012) · Statistical Science · doi:10.1214/12-sts392 · Cited by 300 · see citing works
“Applications of group selection methods to genetic association analysis are discussed in Breheny and Huang (2009) and Zhou et al. (2010).”
BackgroundMethodComparison 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
“Although we acknowledge the limitation of our current simulation set-ups, they do illustrate some useful properties of various tests, such as how they perform in the presence of opposite association directions, of non-causal RVs and/or CVs, and of correlated SNVs.”
InfluentialA Powerful and Adaptive Association Test for Rare Variants
Pan, W., Kim, J., Zhang, Y., Shen, X., & Wei, P. (2014) · Genetics · doi:10.1534/genetics.114.165035 · Cited by 172 · see citing works
Regularized Machine Learning in the Genetic Prediction of Complex Traits
Okser, S., Pahikkala, T., Airola, A., Salakoski, T., Ripatti, S., & Aittokallio, T. A. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004754 · Cited by 156 · see citing works
“Regularization methods based on sparse group Lasso, for instance, can be extended to rare variants and pathway-driven variant selection [22,43].”
MethodLi, Y., Nan, B., & Zhu, J. (2015) · Biometrics · doi:10.1111/biom.12292 · Cited by 131 · see citing works
“…et al. (2009) and Zhou and Zhu (2010) adopted a Lγ , 0 < γ < 1, penalty to select important groups while removing unimportant variables within them; Zhou et al. (2010) used a penalized logistic regression with a mixed L1/L2 penalty to select both common and rare variants in a genome-wide…”
MethodControlling the Rate of GWAS False Discoveries
Brzyski, D., Peterson, C. B., Sobczyk, P., Candès, E. J., Bogdan, M., & Sabatti, C. (2016) · Genetics · doi:10.1534/genetics.116.193987 · Cited by 126 · see citing works
“The variant index j is taken to correspond to the order of the position of each SNP in the genome.”
BackgroundMendel: the Swiss army knife of genetic analysis programs
Lange, K., Papp, J. C., Sinsheimer, J. S., Sripracha, R., Zhou, H., & Sobel, E. M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt187 · Cited by 109 · see citing works
Self-citationGroup sparse canonical correlation analysis for genomic data integration
Lin, D., Zhang, J., Li, J., Calhoun, V. D., Deng, H., & Wang, Y. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-245 · Cited by 109 · see citing works
“[27] applied it to genomic feature identification.”
Penalized logistic regression for high-dimensional DNA methylation data with case-control studies
Sun, H., & Wang, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts145 · Cited by 96 · see citing works
In search of low-frequency and rare variants affecting complex traits
Panoutsopoulou, K., Tachmazidou, I., & Zeggini, E. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt376 · Cited by 87 · see citing works
“A plethora of such locus-specific statistical approaches have been developed and fall broadly into a few categories: collapsing methods based on summary statistics (Cohort Allelic Sum Test (41); Combined Multivariate and Collapsing Test (42); Weighted Sum Test (43); Variable-Threshold Approach (44)); methods based on…”
MethodMieth, B., Kloft, M., Rodríguez, J. A., Sonnenburg, S., Vobruba, R., Morcillo-Suárez, C., Farré, X., Marigorta, U. M., Fehr, E., Dickhaus, T., et al. (2016) · Scientific Reports · doi:10.1038/srep36671 · Cited by 80 · see citing works
Group and sparse group partial least square approaches applied in genomics context
Liquet, B., de Micheaux, P. L., Hejblum, B. P., & Thiébaut, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv535 · Cited by 64 · see citing works
Mieth, B., Rozier, A., Rodríguez, J. A., Höhne, M., Görnitz, N., & Müller, K. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab065 · Cited by 64 · see citing works
Pineda, S., Real, F. X., Kogevinas, M., Carrato, A., Chanock, S. J., Malats, N., & Steen, K. V. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005689 · Cited by 61 · see citing works
Next generation analytic tools for large scale genetic epidemiology studies of complex diseases
Mechanic, L. E., Chen, H., Amos, C. I., Chatterjee, N., Cox, N. J., Divi, R. L., Fan, R., Harris, E. L., Jacobs, K. B., Kraft, P. L., et al. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20652 · Cited by 56 · see citing works
“by grouping SNPs into genes, genes into pathways, or environmental exposures) with the goal of replicating the model [Zhou et al., 2010].”
MethodIonita‐Laza, I., Capanu, M., Rubeis, S. D., McCallum, K., & Buxbaum, J. D. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004729 · Cited by 56 · see citing works
“Such methods have recently been applied to sequencing data [61,62].”
Association Tests for Rare Variants
Nicolae, D. L. (2016) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022609 · Cited by 50 · see citing works
Multilayer knockoff filter: Controlled variable selection at multiple resolutions
Katsevich, E., & Sabatti, C. (2019) · The Annals of Applied Statistics · doi:10.1214/18-aoas1185 · Cited by 41 · see citing works
“While a number of these have been implemented in the context of genetic association studies [18, 41], their application has been hampered by the lack of inferential guarantees on the selection.”
Group Sparse Optimization for Images Recovery Using Capped Folded Concave Functions
Pan, L., & Chen, X. (2021) · SIAM Journal on Imaging Sciences · doi:10.1137/19m1304799 · Cited by 47 · see citing works
Bayesian Variable Selection Regression of Multivariate Responses for Group Data
Liquet, B., Mengersen, K. L., Pettitt, A. N., & Sutton, M. (2017) · Bayesian Analysis · doi:10.1214/17-ba1081 · Cited by 44 · see citing works
“Zhou (2010) applied this approach to genomic feature identification.”
MethodSilver, M. J., Montana, G., & Initiative, A. D. N. (2012) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1755 · Cited by 43 · see citing works
“Interestingly, P-GLAW is relatively insensitive to variation in the number and distribution of SNPs within causal pathways, as might be expected from the smoothing properties of the GL penalty, which ensures that all SNPs within a selected pathway are retained in the model (Zhou et al., 2010).”
Lange, K. L., Papp, J. C., Sinsheimer, J. S., & Sobel, E. M. (2013) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115638 · Cited by 44 · see citing works
“Furthermore t , when the number of predictors exceeds the number of the desirable properties of simplicity, speed, and stability (Alexander & Lange 2011b, Friedman et al. 2007, Wu & Lange 2008, Wu et al. 2009, Zhou et al. 2010).”
BackgroundMethodInfluentialSelf-citationMallick, H., & Yi, N. (2014) · Statistics and Its Interface · doi:10.4310/sii.2014.v7.n4.a12 · Cited by 44 · see citing works
PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data
Hoffman, G. E., Logsdon, B. A., & Mezey, J. G. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003101 · Cited by 39 · see citing works
“The trend of increased sample sizes and genome marker coverage in GWAS also opens the opportunity to identify genetic interactions that are currently difficult to detect, including epistasis and gene|environment interactions, which could be identified by incorporating group penalty approaches [30,66,67] within our fra…”
BackgroundMethodSilver, M. J., Chen, P., Li, R.-Y., Cheng, C.-Y., Wong, T., Tai, E., Teo, Y. Y., & Montana, G. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003939 · Cited by 44 · see citing works
“SGL has been used for the identification of rare variants in a casecontrol study by grouping SNPs into genes [22]; for the identification of genomic regions whose copy number variations have an impact on RNA expression levels [23]; and to model geographical factors driving climate change [24].”
Gupta, A., Labus, J. S., Kilpatrick, L. A., Bonyadi, M., Ashe-McNalley, C., Heendeniya, N., Bradesi, S., Chang, L., & Mayer, E. A. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-0996-9 · Cited by 35 · see citing works
“To determine the potential gene predictors of sgACC thickness, a penalized ordinary least squares linear regression analysis controlling for TGMV, race and age was performed using Mendel software, version 13 (Lange et al. 2013; Zhou et al. 2010).”
MethodStatistical analysis of genetic interactions
Yi, N. (2010) · Genetics Research · doi:10.1017/s0016672310000595 · Cited by 35 · see citing works
“Recently, penalized likelihood approach and hierarchical models have been applied to rare variants analysis (Zhou et al., 2010; Yi & Zhi, 2010).”
MethodAslibekyan, S. W., Brown, E. E., Reynolds, R. J., et al. (2013) · The Pharmacogenomics Journal · doi:10.1038/tpj.2013.11 · Cited by 34 · see citing works
Sparsity with sign-coherent groups of variables via the cooperative-Lasso
Chiquet, J., Grandvalet, Y., & Charbonnier, C. (2012) · The Annals of Applied Statistics · doi:10.1214/11-aoas520 · Cited by 33 · see citing works
Using LASSO regression to detect predictive aggregate effects in genetic studies
Fontanarosa, J., & Dai, Y. D. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s69 · Cited by 33 · see citing works
“To improve accuracy, some studies have imposed an arbitrary p-value cutoff to limit the number of genetic variants in the LASSO model [9], whereas others have applied the model across all variants using the LASSO penalty and a group penalty for the gene or pathway [4].”
BackgroundMethodInfluentialDenis, M., Enquobahrie, D. A., Tadesse, M. G., Gelaye, B., Sánchez, S. E., Salazar, M., Ananth, C. V., & Williams, M. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0116346 · Cited by 32 · see citing works
“For SNPs in the candidate gene analyses, a group penalty approach was used to account for the membership in a gene [40].”
MethodMat, A. M., Klopp, C., Payton, L., Jeziorski, C., Chalopin, M., Amzil, Z., Tran, D., Wikfors, G. H., Hégaret, H., Soudant, P., et al. (2018) · Aquatic Toxicology · doi:10.1016/j.aquatox.2018.03.030 · Cited by 32 · see citing works
A fast algorithm for detecting gene–gene interactions in genome-wide association studies
Li, J., Zhong, W., Li, R., & Wu, R. (2014) · The Annals of Applied Statistics · doi:10.1214/14-aoas771 · Cited by 30 · see citing works
“Zhu et al. (2011) proposed a soft-thresholding rule by adding auxiliary variables in their Sure Independent Ranking and Screening (SIRS) procedure for multi-index models with ultrahigh-dimensional covariates.”
InfluentialGroup-Sparse Model Selection: Hardness and Relaxations
Baldassarre, L., Bhan, N., Cevher, V., Kyrillidis, A., & Satpathi, S. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2602222 · Cited by 31 · see citing works
“These structures naturally arise in applications such as neuroimaging [13], [14], gene expression data [11], [15], bioinformatics [16], [17] and computer vision [7], [18].”
BackgroundConfounded by sequencing depth in association studies of rare alleles
Garner, C. P. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20574 · Cited by 29 · see citing works
“Modern regression methods that use regularization and shrinkage parameters to deal with the overfitting and colinearity issues that arise from fitting models to large, complex and spare data have recently found a new application in rare variant analysis, including ridge regression [Malo et al., 2008] and LASSO [Hoggar…”
BackgroundOpenMendel: a cooperative programming project for statistical genetics
Zhou, H., Sinsheimer, J. S., Bates, D. M., Chu, B. B., German, C., Ji, S. S., Keys, K. L., Kim, J., Ko, S., Mosher, G., et al. (2019) · Human Genetics · doi:10.1007/s00439-019-02001-z · Cited by 26 · see citing works
“edictors pthan sample individuals n[12], GWAS has traditionally focused on the marginal effects of single SNPs. Previously we introduced lasso penalized regression to GWAS to perform subset selection [81, 91]. Our recent paper [38] implements a better heuristic, iterative hard thresholding (IHT), to solve this inheren…”
MethodSelf-citationReprioritizing Genetic Associations in Hit Regions Using LASSO‐Based Resample Model Averaging
Valdar, W. S. J., Sabourin, J., Nobel, A. B., & Holmes, C. C. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21639 · Cited by 25 · see citing works
“This is typically approached through criteria-based evaluation methods [Wu et al., 2009; Zhou et al., 2010], such as AIC and BIC, empirical measures of predictive accuracy (such as cross-validation [Friedman et al., 2010]), or criteria aiming to control type I error (such as permutation [Ayers and…”
Variable Selection in Seemingly Unrelated Regressions with Random Predictors
Puelz, D., Hahn, P. R., & Carvalho, C. M. (2016) · Bayesian Analysis · doi:10.1214/17-ba1053 · Cited by 25 · see citing works
Zou, K., Kim, K.-S., Kim, K., Kang, D.-W., Park, Y.-H., Sun, H., Ha, B., Ha, J., & Jun, T. (2020) · Genes · doi:10.3390/genes12010002 · Cited by 25 · see citing works
Genetic Variation in the TP53 Pathway and Bladder Cancer Risk. A Comprehensive Analysis
Pineda, S., Milne, R. L., Calle, M. L., Rothman, N., de Maturana, E. L., Herránz, J., Kogevinas, M., Chanock, S. J., Tardón, A., Márquez, M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089952 · Cited by 21 · see citing works
“Wu et al used penalized logistic regression in a genome-wide association study applied to coeliac disease data and Zhou et al extended this work to the assessment of association for common and rare variants applied to family cancer registry data [34] [35].”
MethodMultiple Regression Methods Show Great Potential for Rare Variant Association Tests
Xu, C., Ladouceur, M., Dastani, Z., Richards, J. B., Ciampi, A., & Greenwood, C. M. T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041694 · Cited by 20 · see citing works
“Some groups first collapsed the rare variants, and then implemented a LASSO or PLS model using the common variants and the collapsed rare variants [14,16,21].”
MethodSabourin, J., Nobel, A. B., & Valdar, W. S. J. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21869 · Cited by 18 · see citing works
Reconstructing DNA copy number by joint segmentation of multiple sequences
Zhang, Z., Lange, K. L., & Sabatti, C. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-205 · Cited by 18 · see citing works
“As clearly explained in [34], “the local penalty around 0 for each member of a group relaxes as soon as the | β ij − β i , j − 1 | for one member i of the group moves off 0.””
Self-citationByrnes, A., Wu, M. C., Wright, F. A., Li, M., & Li, Y. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21747 · Cited by 18 · see citing works
“Similar strategies were recently proposed in the context of rare variant association testing [Turkmen and Lin, 2012; Zhou et al., 2010].”
MethodYuan, X., Zeng, S., & Zhang, J. (2020) · The HKU Scholars Hub (University of Hong Kong) · Cited by 18 · see citing works
Structured Sparsity: Discrete and Convex Approaches
Kyrillidis, A., Baldassarre, L., Halabi, M. E., Tran-Dinh, Q., & Cevher, V. (2015) · Applied and numerical harmonic analysis · doi:10.1007/978-3-319-16042-9_12 · Cited by 18 · see citing works
“This conclusion has lead to several new theoretical and algorithmic developments in different communities, including theoretical computer science [52], neuronal imaging [57, 74], gene expression inference [108, 121], bioinformatics [112, 141] and X-ray crystalography [23,70,94].”
BackgroundCircadian clock-related genetic risk scores and risk of placental abruption
Qiu, C., Gelaye, B., Denis, M., Tadesse, M. G., Luque-Fernández, M. Á., Enquobahrie, D. A., Ananth, C. V., Sánchez, S. E., & Williams, M. A. (2015) · Placenta · doi:10.1016/j.placenta.2015.10.005 · Cited by 16 · see citing works
“A group penalty approach was also used to account for the membership in a gene [21].”
MethodMultilocus association testing with penalized regression
Basu, S., Pan, W., Shen, X., & Oetting, W. S. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20625 · Cited by 16 · see citing works
Prioritizing individual genetic variants after kernel machine testing using variable selection
He, Q., Cai, T., Liu, Y., et al. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21993 · Cited by 16 · see citing works
“With an eye towards fine mapping, other penalized approaches have also been developed within the context of genetic association studies to identify genetic variants related to complex traits (Ayers and Cordell, 2010; Zhou et al., 2010; He and Lin, 2011).”
BackgroundGenomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation
Ritari, J., Hyvärinen, K., Koskela, S., et al. (2018) · Leukemia · doi:10.1038/s41375-018-0229-3 · Cited by 16 · see citing works
Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization
Bui, K., Park, F., Zhang, S., Qi, Y., & Xin, J. X. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.529564 · Cited by 16 · see citing works
Luo, S., & Chen, Z. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1609972 · Cited by 15 · see citing works
Identification of Grouped Rare and Common Variants via Penalized Logistic Regression
Ayers, K. L., & Cordell, H. J. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21746 · Cited by 14 · see citing works
“The sparse group lasso encourages sparsity between and within groups, and has been previously applied to GWAS for variants with frequencies > 1% [Zhou et al., 2010].”
MethodA new distance measure for non-identical data with application to image classification
Swaminathan, M., Yadav, P. K., Piloto, O., Sjöblom, T., & Cheong, I. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.018 · Cited by 14 · see citing works
Prospective Association Between Plasma Amino Acids and Multimorbidity in Older Adults
Caballero, F. F., Lana, A., Struijk, E. A., Arias‐Fernández, L., Yévenes‐Briones, H., Cárdenas‐Valladolid, J., Salinero-Fort, M. Á., Banegas, J. R., Rodríguez‐Artalejo, F., & López‐García, E. (2022) · The Journals of Gerontology Series A · doi:10.1093/gerona/glac144 · Cited by 12 · see citing works
Mallick, H., & Tiwari, H. K. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00032 · Cited by 12 · see citing works
“Recently, a number of penalized regression methods have been proposed to handle rare variants for linear and generalized linear models, most of which follow various collapsing strategies and implement various grouped penalized regression methods (Zhou et al., 2010; Ayers and Cordell, 2013).”
Chu, B. B., Keys, K. L., German, C., Zhou, H., Zhou, J. J., Sobel, E. M., Sinsheimer, J. S., & Lange, K. L. (2020) · GigaScience · doi:10.1093/gigascience/giaa044 · Cited by 12 · see citing works
“[32] treat prior weights in penalized GWAS.”
BackgroundSelf-citationFast Genome‐Wide QTL Association Mapping on Pedigree and Population Data
Zhou, H., Blangero, J., Dyer, T. D., Chan, K. H. K., Lange, K. L., & Sobel, E. M. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21988 · Cited by 12 · see citing works
“Penalized regression, such as lasso, is available to handle model selection for case-control and random sample data [Wu and Lange, 2008, Wu et al., 2009, Zhou et al., 2010, 2011] and can be generalized to variance component models.”
MethodSelf-citationFan, X., Wang, H., Sun, L., Zheng, X., Yin, X., Zuo, X., Peng, Q., Standish, K. A., Cheng, H., Zhang, Y., et al. (2019) · Epigenomics · doi:10.2217/epi-2018-0131 · Cited by 11 · see citing works
“Multiple testing issues are also accommodated [33].”
Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis
Fouladi, R., Bessonov, K., Lishout, F. V., & Steen, K. V. (2015) · Human Heredity · doi:10.1159/000381286 · Cited by 11 · see citing works
New variable selection strategy for analysis of high-dimensional DNA methylation data
Choi, J.-Y., Kim, K., & Sun, H. (2018) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720018500105 · Cited by 10 · see citing works
Regularized Rare Variant Enrichment Analysis for Case‐Control Exome Sequencing Data
Larson, N. B., & Schaid, D. J. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21783 · Cited by 9 · see citing works
A Unified Sparse Representation for Sequence Variant Identification for Complex Traits
Cao, S., Qin, H., Deng, H., & Wang, Y. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21849 · Cited by 9 · see citing works
Detecting Rare Variants for Quantitative Traits Using Nuclear Families
Guo, W., & Shugart, Y. Y. (2012) · Human Heredity · doi:10.1159/000338439 · Cited by 9 · see citing works
Kim, K., & Sun, H. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3040-x · Cited by 9 · see citing works
Sun, H., & Wang, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu207 · Cited by 8 · see citing works
Projection quantile correlation and its use in high-dimensional grouped variable screening
Liu, J., Si, Y., Niu, Y., et al. (2021) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2021.107369 · Cited by 8 · see citing works
Pseudosibship methods in the case‐parents design
Yu, Z., & Deng, L. (2011) · Statistics in Medicine · doi:10.1002/sim.4397 · Cited by 8 · see citing works
Statistical Models to Explore the Exposome: From OMICs Profiling to ‘Mechanome’ Characterization
Chadeau‐Hyam, M., & Vermeulen, R. C. H. (2018) · Springer eBooks · doi:10.1007/978-3-319-89321-1_11 · Cited by 7 · see citing works
Boutry, S., Helaers, R., Lenaerts, T., & Vikkula, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad412 · Cited by 8 · see citing works
Chen, G. K. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts015 · Cited by 7 · see citing works
“Our C++ framework, named gpu-lasso, implements the mixed L1 and L2 penalized regression model of Zhou et al. (2010) on datasets with any arbitrary number of variables.”
MethodAn Interactive Greedy Approach to Group Sparsity in High Dimensions
Qian, W., Li, W., Sogawa, Y., et al. (2018) · Technometrics · doi:10.1080/00401706.2018.1537897 · Cited by 7 · see citing works
“…(Yuan and Lin, 2006), group sparsity learning has seen its use in many practical applications including genome wide association studies (e.g., Zhou et al., 2010), neuroimaging analysis (e.g., Jenatton et al., 2012, Jiao et al., 2017), multi-task learning (e.g., Lounici et al., 2011),…”
Wang, K., Liu, Y., Lu, G., et al. (2021) · Cancer Medicine · doi:10.1002/cam4.4431 · Cited by 7 · see citing works
Incorporating biological information into association studies of sequencing data
Chen, G. K., Wei, P., & DeStefano, A. L. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20646 · Cited by 6 · see citing works
“Zhou et al. [2010] uses the L1 norm to encourage sparse models so that most coefficients stay parked at 0.”
BackgroundEvaluation of a LASSO regression approach on the unrelated samples of Genetic Analysis Workshop 17
Guo, W., Elston, R. C., & Zhu, X. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s12 · Cited by 6 · see citing works
A 2-step penalized regression method for family-based next-generation sequencing association studies
Ding, X., Su, S., Nandakumar, K., et al. (2014) · BMC Proceedings · doi:10.1186/1753-6561-8-s1-s25 · Cited by 6 · see citing works
“Fried-man et al [7] introduced the mixture of group and lasso penalties, and this approach has been explored using, for example, breast cancer GWAS data for testing common and rare variants [8].”
MethodInfluentialCaballero, F. F., Lana, A., Struijk, E. A., Arias‐Fernández, L., Yévenes‐Briones, H., Cárdenas‐Valladolid, J., Salinero-Fort, M. Á., Banegas, J. R., Rodríguez‐Artalejo, F., & López‐García, E. (2023) · The Journals of Gerontology Series A · doi:10.1093/gerona/glad122 · Cited by 5 · see citing works
Larson, N. B., McDonnell, S. K., Cannon‐Albright, L., Teerlink, C. C., Stanford, J. L., Ostrander, E. A., Isaacs, W. B., Xu, J., Cooney, K. A., Lange, E. M., et al. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21983 · Cited by 4 · see citing works
The Graph-Guided Group Lasso for Genome-Wide Association Studies
Wang, Z., & Montana, G. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17558-9 · Cited by 5 · see citing works
“[3]).”
Network-Guided Biomarker Discovery
Azencott, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50478-0_16 · Cited by 5 · see citing works
“Multi-task regularized linear regression Many multi-task variants of the lasso have been proposed [64,33], and can be extended in spirit to various structural regularizers, such as Grace [65].”
BackgroundMethodA Single-Phase, Proximal Path-Following Framework
Tran-Dinh, Q., Kyrillidis, A., & Cevher, V. (2018) · Mathematics of Operations Research · doi:10.1287/moor.2017.0907 · Cited by 5 · see citing works
“, g(x) = ‖x‖1, with applications in high-dimensional statistics, compressive sensing, scientific and medical imaging [12, 18, 20, 23, 29, 41, 47, 55], among others.”
BackgroundNetwork-guided sparse regression modeling for detection of gene-by-gene interactions
Lu, C., Latourelle, J. C., O’Connor, G. T., et al. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt139 · Cited by 4 · see citing works
“…however, computationally efficient multiple regression strategies for GWAS have begun to emerge that use various methods of high-dimensional variable selection (e.g. Ayers and Cordell, 2010; Logsdon et al. , 2010; Ma et al. , 2010; Szymczak et al. , 2009; Wu et al. , 2009, 2010; Zhou et al. 2010).”
MethodIterative hard thresholding for model selection in genome‐wide association studies
Keys, K. L., Chen, G. K., & Lange, K. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22068 · Cited by 4 · see citing works
“r tuning the regularization path using heritability estimates from GCTA Yang et al. (2010, 2011). For MCP, we used the GWAS module in Mendel Lange et al. (2013); Wu et al. (2009); Wu and Lange (2008);Zhou et al. (2010). SinceMendelrequirestheusertospecifythedesiredmodelsizek,wesimply used the best cross-validatedk fro…”
MethodSelf-citationMean squared error of ridge estimators in logistic regression
Blagus, R., & Goeman, J. J. (2019) · Statistica Neerlandica · doi:10.1111/stan.12201 · Cited by 4 · see citing works
“Shrinkage estimators are also popular in logistic regression especially with collinear and/or high-dimensional predictors (Sun & Wang, 2012; Zhou et al., 2010), where they are often used for estimating the event probability (Walter et al., 2011).”
BackgroundRare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering
Tachmazidou, I., Morris, A. P., & Zeggini, E. (2012) · Human Heredity · doi:10.1159/000346022 · Cited by 4 · see citing works
A Novel Support Vector Machine-Based Approach for Rare Variant Detection
Fang, Y., & Chiu, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071114 · Cited by 4 · see citing works
“Because rare alleles are present in only a small number of patients, the traditional variant-by-variant approach is doomed to low power [1].”
BackgroundPenalized-regression-based multimarker genotype analysis of Genetic Analysis Workshop 17 data
Ayers, K. L., Mamasoula, C., & Cordell, H. J. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s92 · Cited by 4 · see citing works
“[5] previously applied the sparse group LASSO to SNPs grouped within genes.”
MethodCao, S., Qin, H., Deng, H., et al. (2013) · doi:10.1145/2506583.2506616 · Cited by 3 · see citing works
“A couple of sparse regression methods have been developed to select a sparse set of susceptible variants from a large set of genetic variants[7;8].”
MethodBayesian nonparametric clustering and association studies for candidate SNP observations
Wang, C. I., Ruggeri, F., Hsiao, C. K., & Argiento, R. (2016) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2016.07.014 · Cited by 3 · see citing works
“Many statistical methods have been proposed based on those considerations, such as regularized regression models like lasso or ridge regression [1, 2, 3, 4, 5], gene-set enrichment analysis [4, 6, 7, 8], pathway [9], and network analysis [10].”
MethodEnhancing the discovery of rare disease variants through hierarchical modeling
Chen, G. K. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s16 · Cited by 3 · see citing works
Cao, H., & Schwarz, E. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813176-3.00011-0 · Cited by 3 · see citing works
Multivariate genome-wide association analysis by iterative hard thresholding
Chu, B. B., Ko, S., Zhou, J. J., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad193 · Cited by 2 · see citing works
“Model fitting is typically accomplished via regression with penalties such as the least absolute 24 shrinkage and selection operator (LASSO) [2, 31, 38, 44, 42], minimax concave penalty (MCP) [6, 41], 25 iterative hard thresholding (IHT) [7, 16], or Bayesian analogues [13].”
InfluentialSelf-citationKim, K., Jun, T., Ha, B., Wang, S., & Sun, H. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05505-8 · Cited by 2 · see citing works
“Regularization methods based on penalized regression have been popularly applied to analysis of high-dimensional genomic data [26–33].”
MethodHierarchical Models for Genetic Association Studies
Mallick, H. (2013) · Journal of Biometrics & Biostatistics · doi:10.4172/2155-6180.1000e124 · Cited by 2 · see citing works
“The dramatic increase in genetic discoveries involving complex diseases has provided great opportunities for statisticians to contribute critical concepts and methods to the field [2,3].”
BackgroundIdentifying rare and common variants with Bayesian variable selection
Oh, C. (2016) · BMC Proceedings · doi:10.1186/s12919-016-0059-0 · Cited by 2 · see citing works
“They first collapse rare variants and then implement a LASSO (least absolute shrinkage and selection operator) [6 – 8], partial least squares regression (PLS) model [9], or other supporting statistical methods using the common variants and the collapsed rare variants [10].”
Method
- Authors
- Zhou, H., Sehl, M. E., Sinsheimer, J. S., & Lange, K.
- Affiliations
- North Carolina State University (US)
- Corresponding author
- Zhou, Hua
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-08-06
- Volume
- 26
- Issue
- 19
- Pages
- 2375-2382
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3025646
- OpenAlex
- W2104111843
- Semantic Scholar
- 4d1ce1c69fdf6fd2ba4ec81ce7f0a6df49c6eba9
- Cited by
- 137
- References
- 37
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 1 · 2023: 9 · 2022: 3 · 2021: 7 · 2020: 5 · 2019: 13 · 2018: 6 · 2017: 5 · 2016: 14 · 2015: 11 · 2014: 15 · 2013: 17 · 2012: 14
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (R37MH059490, MH59490, R01MH059490)
- National Cancer Institute (CA-16 042, R25CA087949, CA87949, P30CA016042)
- National Institute of General Medical Sciences (R01GM053275, GM 53275)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- rare variants, common genetic variants, genome-wide association study, breast cancer, gene-level association
- MeSH terms
- Breast Neoplasms, Female, Humans, Regression Analysis, Genetic Variation, Databases, Factual, Genetic Predisposition to Disease, Genome-Wide Association Study
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