A general framework for integrative analysis of incomplete multiomics data
Open accessAbstract
There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative omics measurements, such as metabolite levels and protein expressions, are subject to detection limits in that the measurements below (or above) certain thresholds are not detectable. In this article, we propose a rigorous and powerful approach to handle missing values and detection limits in integrative analysis of multiomics data. We relate quantitative omics variables to genetic variants and other variables through linear regression models and relate phenotypes to quantitative omics variables and other variables through generalized linear models. We derive the joint-likelihood for the two sets of models by allowing arbitrary patterns of missing values and detection limits for quantitative omics variables. We carry out maximum-likelihood estimation through computationally fast and stable algorithms. The resulting estimators are approximately unbiased and statistically efficient. An application to a major study on chronic obstructive lung disease yielded new biological insights.
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iMODA's power to detect effects of interest varies with the proportion of missing data, and power decreases as missingness increases.
“The power of the iMODA framework to detect the effects of interest has been reported to vary according to the proportion of data that are missing. The authors considered cases where 50% and 30% of data were missing, finding that power decreases with increased missingness.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Lin et al., 2020
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Cites
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,384 · see citing works
Browning, S. R., & Browning, B. L. (2007) · The American Journal of Human Genetics · doi:10.1086/521987 · Cited by 3,138 · see citing works
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609 · see citing works
Statistical Analysis with Missing Data.
Chatfield, C., Little, R. J. A., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982783 · Cited by 2,203 · see citing works
MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes
Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20533 · Cited by 1,843 · see citing works
Genetic epidemiology of copd (copdgene) study design
Regan, E. A., Hokanson, J. E., Murphy, J. R., Make, B., Lynch, D. A., Beaty, T. H., Curran-Everett, D., Silverman, E. K., & Crapo, J. D. (2011) · COPD: Journal of Chronic Obstructive Pulmonary Disease · doi:10.3109/15412550903499522 · Cited by 1,313 · see citing works
An integrative genomics approach to infer causal associations between gene expression and disease
Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., GuhaThakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., et al. (2005) · Nature Genetics · doi:10.1038/ng1589 · Cited by 1,043 · see citing works
Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq182 · Cited by 786 · see citing works
Helsel, D. R. (2006) · Chemosphere · doi:10.1016/j.chemosphere.2006.04.051 · Cited by 505 · see citing works
Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS): Table 1
Couper, D. J., LaVange, L. M., Han, M. K., Barr, R. G., Bleecker, E. R., Hoffman, E. A., Kanner, R. E., Kleerup, E. C., Martínez, F. J., Woodruff, P. G., et al. (2013) · Thorax · doi:10.1136/thoraxjnl-2013-203897 · Cited by 396 · see citing works
iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data
Wang, W., Baladandayuthapani, V., Morris, J. S., Broom, B. M., Manyam, G. C., & Do, K.-A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts655 · Cited by 154 · see citing works
Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies
Lin, D., & Zeng, D. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000000808 · Cited by 146 · see citing works
Data Integration in Genetics and Genomics: Methods and Challenges
Hamid, J. S., Hu, P., Roslin, N. M., Ling, V., Greenwood, C. M. T., & Beyene, J. (2009) · Human Genomics and Proteomics · doi:10.4061/2009/869093 · Cited by 144 · see citing works
Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD
Sun, W., Kechris, K. J., Jacobson, S. R., Drummond, M., Hawkins, G. A., Yang, J., Chen, T., Quibrera, P. M., Anderson, W. H., Barr, R. G., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006011 · Cited by 139 · see citing works
Estimating the odds ratio when exposure has a limit of detection
Cole, S. R., Chu, H., Nie, L., & Schisterman, E. F. (2009) · International Journal of Epidemiology · doi:10.1093/ije/dyp269 · Cited by 125 · see citing works
Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets
Xiong, Q., Ancona, N., Hauser, E. R., Mukherjee, S., & Furey, T. S. (2012) · Genome Research · doi:10.1101/gr.124370.111 · Cited by 103 · see citing works
Genetic Determinants Influencing Human Serum Metabolome among African Americans
Yu, B., Zheng, Y., Alexander, D. C., Morrison, A. C., Coresh, J., & Boerwinkle, E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004212 · Cited by 102 · see citing works
Linear Regression With an Independent Variable Subject to a Detection Limit
Nie, L., Chu, H., Liu, C., Cole, S. R., Vexler, A., & Schisterman, E. F. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181ce97d8 · Cited by 97 · see citing works
Simple and Efficient Analysis of Disease Association with Missing Genotype Data
Lin, D. Y., Hu, Y., & Huang, B. E. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.11.004 · Cited by 90 · see citing works
Joint analysis of SNP and gene expression data in genetic association studies of complex diseases
Huang, Y.-T., VanderWeele, T. J., & Lin, X. (2014) · The Annals of Applied Statistics · doi:10.1214/13-aoas690 · Cited by 88 · see citing works
JOINT ANALYSIS OF SNP AND GENE EXPRESSION DATA IN GENETIC ASSOCIATION STUDIES OF COMPLEX DISEASES1Preprint
Huang, Y., Tyler, J., V, & Lin, X. (2013) · Cited by 88 · see citing works
Structured Matrix Completion with Applications to Genomic Data Integration
Cai, T., Cai, T., & Zhang, A. R. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1021005 · Cited by 67 · see citing works
Voillet, V., Besse, P. C., Liaubet, L., Cristobal, M. S., & González, I. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1273-5 · Cited by 66 · see citing works
O’Neal, W. K., Anderson, W. H., Basta, P. V., Carretta, E. E., Doerschuk, C. M., Barr, R. G., Bleecker, E. R., Christenson, S. A., Curtis, J. L., Han, M. K., et al. (2014) · Journal of Translational Medicine · doi:10.1186/1479-5876-12-9 · Cited by 52 · see citing works
A Tobit Variance-Component Method for Linkage Analysis of Censored Trait Data
Epstein, M. P., Lin, X., & Boehnke, M. (2003) · The American Journal of Human Genetics · doi:10.1086/367924 · Cited by 49 · see citing works
An integrative imputation method based on multi-omics datasets
Lin, D., Zhang, J., Li, J., Xu, C., Deng, H., & Wang, Y. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1122-6 · Cited by 39 · see citing works
More powerful genetic association testing via a new statistical framework for integrative genomics
Zhao, S. D., Cai, T. T., & Li, H. (2014) · Biometrics · doi:10.1111/biom.12206 · Cited by 31 · see citing works
Semiparametric variance‐component models for linkage and association analyses of censored trait data
Diao, G., & Lin, D. Y. (2006) · Genetic Epidemiology · doi:10.1002/gepi.20168 · Cited by 12 · see citing works
Integrative genomic testing of cancer survival using semiparametric linear transformation models
Huang, Y., Cai, T., & Kim, E. (2016) · Statistics in Medicine · doi:10.1002/sim.6900 · Cited by 2 · see citing works
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Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181 · see citing works
“Emphasizing analyses more consistent with a causal framework, iMODA (Lin et al., 2020) proposes a hierarchical regression framework for quantifying (i) the effects of genotypes on quantitative ‘omics, (ii) the effects of quantitative ‘omics on response phenotypes of interest, and (iii) the direct and indirect effects…”
MethodMulti-omics clustering for cancer subtyping based on latent subspace learning
Ye, X., Shang, Y., Shi, T., Zhang, W., & Sakurai, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107223 · Cited by 26 · see citing works
Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods
Avery, C. L., Howard, A. G., Ballou, A. F., Buchanan, V. L., Collins, J. M., Downie, C. G., Engel, S. M., Graff, M., Highland, H. M., Lee, M. P., et al. (2022) · Environmental Health Perspectives · doi:10.1289/ehp9098 · Cited by 18 · see citing works
Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection
Seok, E., Ghassabian, A., Wang, Y., & Liu, M. (2023) · Statistics in Biosciences · doi:10.1007/s12561-023-09408-3 · Cited by 10 · see citing works
Mediation analysis of multiple mediators with incomplete omics data
Kidd, J., Raulerson, C. K., Mohlke, K. L., & Lin, D. Y. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22504 · Cited by 3 · see citing works
Improving the Power to Detect Indirect Effects in Mediation Analysis
Kidd, J., & Lin, D. Y. (2023) · Statistics in Biosciences · doi:10.1007/s12561-023-09386-6 · Cited by 2 · see citing works
iSubGen generates integrative disease subtypes by pairwise similarity assessment
Fox, N. S., Tian, M., Markowitz, A. L., Haider, S. A., Li, C. H., & Boutros, P. C. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100884 · Cited by 1 · see citing works
Zhu, M., Li, R., Wu, S., Chen, J., & Sun, K. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1831823 · Cited by 1 · see citing works
Jiang, X.-Z., Guo, L.-J., Zhang, M.-T., Ji, G.-L., Liu, H.-T., Zheng, Y., & Zhou, J. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1846105 · Cited by 2 · see citing works
Alyabs, N., & Chiou, S. H. (2022) · Stats · doi:10.3390/stats5020029 · Cited by 1 · see citing works
Li, Y., Wong, K. Y., Howard, A. G., Gordon‐Larsen, P., Highland, H. M., Graff, M., North, K. E., Downie, C. G., Avery, C. L., Yu, B., et al. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100245 · Cited by 1 · see citing works
Chen, B. D., Lee, C., Tapia, A. L., Reiner, A. P., Tang, H., Kooperberg, C. L., Manson, J. E., Li, Y., & Raffield, L. M. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22578 · Cited by 1 · see citing works
iSubGen: Integrative Subtype Generation by Pairwise Similarity AssessmentPreprint
Fox, N. S., Haider, S. A., Li, C. H., & Boutros, P. C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.13.444087
Li, Y., Wong, K. Y., Howard, A. G., Gordon‐Larsen, P., Highland, H. M., Graff, M., North, K. E., Downie, C. G., Avery, C. L., Yu, B., et al. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100338
Statistical Methods in Genetic Epidemiology
Scherag, A., Thomas, D., & Bickeböller, H. (2024) · Springer eBooks · doi:10.1007/978-1-4614-6625-3_62-1
Hypothesis tests of indirect effects for multiple mediators
Kidd, J., Howard, A. G., Highland, H. M., Gordon‐Larsen, P., Bancks, M. P., Carnethon, M. R., & Lin, D. Y. (2025) · Statistical Methods & Applications · doi:10.1007/s10260-024-00777-7
- Authors
- Lin, D., Zeng, D., & Couper, D.
- Affiliations
- Department of Biostatistics University of North Carolina Chapel Hill North Carolina
- University of North Carolina at Chapel Hill (US)
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2020-07-21
- Volume
- 44
- Issue
- 7
- Pages
- 646-664
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC7951090
- OpenAlex
- W3044532026
- Cited by
- 16
- References
- 29
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 5 · 2022: 3 · 2021: 1
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Human Genome Research Institute (R01 HG009974)
- National Institutes of Health (R01HG009974, P01CA142538)
- COPD Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multi-omics integration, missing data, detection limit, joint likelihood, maximum likelihood estimation, linear regression, generalized linear models, genetic variants, chronic obstructive pulmonary disease, metabolomics, proteomics, transcriptomics, DNA methylation
- MeSH terms
- Data Analysis, Algorithms, Genotype, Humans, Models, Genetic, Phenotype, Linear Models, Sequence Analysis, DNA, Sequence Analysis, RNA, Genomics, Proteomics
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