Genomics has already begun to enter clinical practice, diagnosing patients with rare disease and informing cancer treatments. As other high-throughput technologies are developing, multiple omics technologies will be used in a similar fashion. Here, we describe the use of integrative omics for rare and common germline diseases, as well as cancers, and describe the challenges therein.
Exome and genome sequencing have already been successful in aiding the diagnosis of patients with Mendelian diseases. Recently, RNA sequencing (RNA-seq) has been used to supplement such analyses by identifying transcriptomic aberrations that led to the identification of previously missed causal variants.
Identifying risk factors for common germline diseases remains a considerable challenge: omics technologies have been instrumental in elucidating molecular mechanisms of disease. Recently, the first examples of longitudinal integrative omics profiles in a single individual have been performed, which have suggested personalized therapies.
Integrative omics has been a useful process for identifying driver genes, as well as molecular signatures of cancers. In particular, such analyses have indicated prognoses, as well as targeted therapies.
Multiple analytical, technical and interpretational challenges remain in the path to clinical adoption of integrative omics. Large reference panels of such data sets, as well as clinical guidelines, will be necessary to complete this transition.
Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.
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Worthey, E. A. et al. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet. Med. 13, 255–262 (2011). This is the first paper describing the treatment-changing diagnosis in an individual patient using exome sequencing, paving the way for clinical applications of genomics.
Ng, S. B. et al. Exome sequencing identifies the cause of a mendelian disorder. Nat. Genet. 42, 30–35 (2010).
Taylor, J. C. et al. Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nat. Genet. 47, 717–726 (2015).
Ashley, E. A. et al. Clinical assessment incorporating a personal genome. Lancet 375, 1525–1535 (2010).
Dewey, F. E. et al. Phased whole-genome genetic risk in a family quartet using a major allele reference sequence. PLoS Genet. 7, e1002280 (2011).
Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012).
Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).
Scriver, C. R., Neal, J. L., Saginur, R. & Clow, A. The frequency of genetic disease and congenital malformation among patients in a pediatric hospital. Can. Med. Assoc. J. 108, 1111–1115 (1973).
Buehler, J. W., Strauss, L. T., Hogue, C. J. & Smith, J. C. Birth weight-specific causes of infant mortality, United States, 1980. Public Health Rep. 102, 162–171 (1987).
Kochanek, K. D., Kirmeyer, S. E., Martin, J. A., Strobino, D. M. & Guyer, B. Annual summary of vital statistics: 2009. Pediatrics 129, 338–348 (2012).
Yang, Y. et al. Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. N. Engl. J. Med. 369, 1502–1511 (2013).
Jacob, H. J. et al. Genomics in clinical practice: lessons from the front lines. Sci Transl Med. 5, 194cm5 (2013).
Lee, H. et al. Clinical exome sequencing for genetic identification of rare Mendelian disorders. JAMA 312, 1880–1887 (2014).
Chandrasekharappa, S. C. et al. Massively parallel sequencing, aCGH, and RNA-Seq technologies provide a comprehensive molecular diagnosis of Fanconi anemia. Blood 121, e138–e148 (2013).
Kremer, L. S. et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat. Commun. 8, 15824 (2017).
Cummings, B. B. et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci Transl Med. 9, eaal5209 (2017). References 15 and 16 use transcriptome sequencing to provide molecular diagnoses that were missed by exome sequencing for patients with rare disease.
Flannick, J. & Florez, J. C. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat. Rev. Genet. 17, 535–549 (2016).
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Grove, J. et al. Common risk variants identified in autism spectrum disorder. bioRxiv https://doi.org/10.1101/224774 (2017).
Manolio, T. A. Bringing genome-wide association findings into clinical use. Nat. Rev. Genet. 14, 549–558 (2013).
Moreau, Y. & Tranchevent, L.-C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat. Rev. Genet. 13, 523–536 (2012).
Ryan, C. J. et al. High-resolution network biology: connecting sequence with function. Nat. Rev. Genet. 14, 865–879 (2013).
Mitra, K., Carvunis, A.-R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013).
Cowen, L., Ideker, T., Raphael, B. J. & Sharan, R. Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18, 551–562 (2017).
Ogura, Y. et al. A frameshift mutation in NOD2 associated with susceptibility to Crohn's disease. Nature 411, 603–606 (2001).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Liu, J. Z. et al. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).
Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012).
Li, J. et al. Identification of human neuronal protein complexes reveals biochemical activities and convergent mechanisms of action in autism spectrum disorders. Cell Systems 1, 361–374 (2015).
Li, J. et al. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol. Syst. Biol. 10, 774–774 (2014).
Replication, T. D. G. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).
Lage, K. et al. Genetic and environmental risk factors in congenital heart disease functionally converge in protein networks driving heart development. Proc. Natl Acad. Sci. USA 109, 14035–14040 (2012).
Lage, K. Protein-protein interactions and genetic diseases: the interactome. Biochim. Biophys. Acta 1842, 1971–1980 (2014).
Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).
Albert, F. W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).
Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).
Schaub, M. A., Boyle, A. P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).
Karczewski, K. J. et al. Systematic functional regulatory assessment of disease-associated variants. Proc. Natl Acad. Sci. USA 110, 9607–9612 (2013).
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015). References 42 and 43 elucidate the relative contribution of regulatory (non-protein-coding) variation to human diseases and traits.
Spain, S. L. & Barrett, J. C. Strategies for fine-mapping complex traits. Hum. Mol. Genet. 24, R111–R119 (2015).
Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc. Natl Acad. Sci. USA 111, 13127–13132 (2014).
ENCODE Project Consortium et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Adrianto, I. et al. Association of a functional variant downstream of TNFAIP3 with systemic lupus erythematosus. Nat. Genet. 43, 253–258 (2011).
Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature 518, 365–369 (2015).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015). This paper provides a mechanistic explanation for the influence of the FTO locus on human obesity, using multiple omics technologies to bridge GWAS results to physiology.
Poldrack, R. A. et al. Long-term neural and physiological phenotyping of a single human. Nat. Commun. 6, 8885 (2015).
Piening, B. D. et al. Integrative personal omics profiles during periods of weight gain and loss. Cell Syst. https://doi.org/10.1016/j.cels.2017.12.013 (2018).
Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project: dynamic analysis of microbiome–host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289 (2014).
Price, N. D. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 57, 289–756 (2017). References 6 and 54 report on the use of multiple omics assays longitudinally within the same individual or individuals to influence health outcomes.
Guo, L. et al. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc. Natl Acad. Sci. USA 112, E4901–E4910 (2015).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
The GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
eGTEx Project. Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. Nat. Genet. 49, 1664–1670 (2017).
Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).
Frattini, V. et al. The integrated landscape of driver genomic alterations in glioblastoma. Nat. Genet. 45, 1141–1149 (2013).
Kumar, A. et al. Substantial interindividual and limited intraindividual genomic diversity among tumors from men with metastatic prostate cancer. Nat. Med. 22, 369–378 (2016).
Sato, Y. et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat. Genet. 45, 860–867 (2013). References 59 and 62 characterize molecular signatures of cancers using genome, transcriptome and methylome sequencing to identify driver genes and subtypes of cancers.
Wang, J. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).
Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55–62 (2016).
Liu, T. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016).
Yu, K.-H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016).
Yu, K.-H. & Snyder, M. Omics profiling in precision oncology. Mol. Cell. Proteom. 15, 2525–2536 (2016).
Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015).
Yao, L., Tak, Y. G., Berman, B. P. & Farnham, P. J. Functional annotation of colon cancer risk SNPs. Nat. Commun. 5, 5114 (2014).
Melton, C., Reuter, J. A., Spacek, D. V. & Snyder, M. Recurrent somatic mutations in regulatory regions of human cancer genomes. Nat. Genet. 47, 710–716 (2015).
Araya, C. L. et al. Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations. Nat. Genet. 48, 117–125 (2015).
Chong, J. X. et al. The genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities. Am. J. Hum. Genet. 97, 199–215 (2015).
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A. & Kim, D. Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 85–97 (2015).
Fridley, B. L., Lund, S., Jenkins, G. D. & Wang, L. A. Bayesian integrative genomic model for pathway analysis of complex traits. Genet. Epidemiol. 36, 352–359 (2012).
Holzinger, E. R., Dudek, S. M., Frase, A. T., Pendergrass, S. A. & Ritchie, M. D. ATHENA: the analysis tool for heritable and environmental network associations. Bioinformatics 30, 698–705 (2014).
Argelaguet, R. et al. Multi-omics factor analysis disentangles heterogeneity in blood cancer. bioRxiv https://doi.org/10.1101/217554 (2017).
Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).
Wray, N. R. et al. Research review: polygenic methods and their application to psychiatric traits. J. Child Psychol. Psychiatry 55, 1068–1087 (2014).
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
Manrai, A. K. et al. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375, 655–665 (2016). References 79 and 80 highlight the challenges of using polygenic risk scores on under-studied populations.
Stein, L. D. The case for cloud computing in genome informatics. Genome Biol. 11, 207 (2010).
Dudley, J. T. & Butte, A. J. In silico research in the era of cloud computing. Nat. Biotechnol. 28, 1181–1185 (2010).
Wall, J. D. et al. Estimating genotype error rates from high-coverage next-generation sequence data. Genome Res. 24, 1734–1739 (2014).
Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).
Ding, L., Wendl, M. C., McMichael, J. F. & Raphael, B. J. Expanding the computational toolbox for mining cancer genomes. Nat. Rev. Genet. 15, 556–570 (2014).
Gerlinger, M. et al. Ultra-deep T cell receptor sequencing reveals the complexity and intratumour heterogeneity of T cell clones in renal cell carcinomas. J. Pathol. 231, 424–432 (2013).
Bianchi, D. W. et al. Noninvasive prenatal testing and incidental detection of occult maternal malignancies. JAMA 314, 162–169 (2015).
Cheon, J. Y., Mozersky, J. & Cook-Deegan, R. Variants of uncertain significance in BRCA: a harbinger of ethical and policy issues to come? Genome Med. 6, 121 (2014).
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–551 (2013).
Minikel, E. V. et al. Quantifying prion disease penetrance using large population control cohorts. Sci. Transl Med. 8, 322ra9 (2016).
Whiffin, N. et al. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet. Med. 19, 1151–1158 (2017).
Green, R. C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013).
Knoppers, B. M., Zawati, M. H. & Sénécal, K. Return of genetic testing results in the era of whole-genome sequencing. Nat. Rev. Genet. 16, 553–559 (2015).
Kasowski, M. et al. Variation in transcription factor binding among humans. Science 328, 232–235 (2010).
Mele, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).
Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).
Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
Marbach, D. et al. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat. Methods 13, 366–370 (2016).
Osborn, M. J. et al. CRISPR/Cas9 targeted gene editing and cellular engineering in Fanconi anemia. Stem Cells Dev. 25, 1591–1603 (2016).
Carmichael, N., Tsipis, J., Windmueller, G., Mandel, L. & Estrella, E. 'Is it going to hurt?': the impact of the diagnostic Odyssey on children and their families. J. Genet. Counsel 24, 325–335 (2014).
Burke, W., Zimmern, R. L. & Kroese, M. Defining purpose: a key step in genetic test evaluation. Genet. Med. 9, 675–681 (2007).
Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).
Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation https://doi.org/10.1161/CIRCULATIONAHA.116.024436 (2017). References 77, 103 and 104 discuss using polygenic risk scores to stratify patients with heart disease into risk groups that respond differently to statin treatment.
Maier, R. M., Visscher, P. M., Robinson, M. R. & Wray, N. R. Embracing polygenicity: a review of methods and tools for psychiatric genetics research. Psychol. Med. https://doi.org/10.1017/S0033291717002318 (2017).
Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).
Li, X. et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15, e2001402 (2017).
Craig, D. W. et al. Genome and transcriptome sequencing in prospective metastatic triple-negative breast cancer uncovers therapeutic vulnerabilities. Mol. Cancer Ther. 12, 104–116 (2013).
Borad, M. J. et al. Integrated genomic characterization reveals novel, therapeutically relevant drug targets in FGFR and EGFR pathways in sporadic intrahepatic cholangiocarcinoma. PLoS Genet. 10, e1004135 (2014).
Kostic, A. D., Xavier, R. J. & Gevers, D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology 146, 1489–1499 (2014).
Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012).
Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 5, e9085 (2010).
Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).
Levy, M., Kolodziejczyk, A. A., Thaiss, C. A. & Elinav, E. Dysbiosis and the immune system. Nat. Rev. Immunol. 17, 219–232 (2017).
Benson, A. K. et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl Acad. Sci. USA 107, 18933–18938 (2010).
Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).
Knights, D. et al. Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med. 6, 119 (2014).
Goodrich, J. K., Davenport, E. R., Clark, A. G. & Ley, R. E. The relationship between the human genome and microbiome comes into view. Annu. Rev. Genet. 51, 413–433 (2017).
Hall, A. B., Tolonen, A. C. & Xavier, R. J. Human genetic variation and the gut microbiome in disease. Nat. Rev. Genet. 18, 690–699 (2017).
Chu, H. et al. Gene-microbiota interactions contribute to the pathogenesis of inflammatory bowel disease. Science 352, 1116–1120 (2016).
Holmes, E., Li, J. V., Athanasiou, T., Ashrafian, H. & Nicholson, J. K. Understanding the role of gut microbiome–host metabolic signal disruption in health and disease. Trends Microbiol. 19, 349–359 (2011).
Wang, C. et al. High-throughput, high-fidelity HLA genotyping with deep sequencing. Proc. Natl Acad. Sci. USA 109, 8676–8681 (2012).
Wittig, M. et al. Development of a high-resolution NGS-based HLA-typing and analysis pipeline. Nucleic Acids Res. 43, e70 (2015).
Zhang, M.-J. et al. Comparison of outcomes after HLA-matched sibling and unrelated donor transplantation for children with high-risk acute lymphoblastic leukemia. Biol. Blood Marrow Transplant. 18, 1204–1210 (2012).
McCarroll, S. A. et al. Donor-recipient mismatch for common gene deletion polymorphisms in graft-versus-host disease. Nat. Genet. 41, 1341–1344 (2009).
Li, Y. R., Levine, J. E., Hakonarson, H. & Keating, B. J. Making the genomic leap in HCT: application of second-generation sequencing to clinical advances in hematopoietic cell transplantation. Eur. J. Hum. Genet. 22, 715–723 (2014).
Snyder, T. M., Khush, K. K., Valantine, H. A. & Quake, S. R. Universal noninvasive detection of solid organ transplant rejection. Proc. Natl Acad. Sci. USA 108, 6229–6234 (2011).
De Vlaminck, I. et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl Med. 6, 241ra77 (2014).
De Vlaminck, I. et al. Noninvasive monitoring of infection and rejection after lung transplantation. Proc. Natl Acad. Sci. USA 112, 13336–13341 (2015).
Yang, J. Y. C. & Sarwal, M. M. Transplant genetics and genomics. Nat. Rev. Genet. 2003, 449 (2017).
K.J.K. is supported by the US National Institute of General Medical Sciences (NIGMS) Fellowship F32GM115208. M.P.S. is supported by grants from the National Institutes of Health (NIH).
M.P.S. is a cofounder of Personalis, SensOmics and Qbio and is on the scientific advisory board of Personalis, SensOmics, Qbio, Epinomics and Genapsys. K.J.K. declares no competing interests.
The property of a molecular finding that would result in a specific medical recommendation that is expected to improve a disease outcome.
- Mendelian diseases
Diseases caused by a single locus or gene and that follow Mendelian patterns of inheritance (for example, dominant or recessive).
- Genetic aetiology
The genetic factors that cause a particular disease.
- Expression quantitative trait loci
(eQTLs). Genetic variants that are statistically associated with gene expression.
The fraction of phenotypic variability of a trait that can be attributed to additive genetic variation.
- DNase hypersensitivity
A measure of openness of chromatin, as measured by its sensitivity to cleavage by DNase I.
- Structural variants
A class of genetic variation that is typically 1 kb or larger, which includes copy number duplications, insertions or deletions, as well as translocations and inversions.
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Karczewski, K., Snyder, M. Integrative omics for health and disease. Nat Rev Genet 19, 299–310 (2018). https://doi.org/10.1038/nrg.2018.4
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