Phenomics: the next challenge

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

Model selection and multimodel inference: a practical information-theoretic approach.

Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495

Finding the missing heritability of complex diseases

Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336

‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*

Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882

The Endophenotype Concept in Psychiatry: Etymology and Strategic Intentions

Gottesman, I. I., & Gould, T. D. (2003) · American Journal of Psychiatry · doi:10.1176/appi.ajp.160.4.636 · Cited by 5,615

The measurement of selection on correlated characters

Lande, R., & Arnold, S. J. (1983) · Evolution · doi:10.1111/j.1558-5646.1983.tb00236.x · Cited by 5,437

XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.

Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387

Biological, clinical and population relevance of 95 loci for blood lipids

Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487

Major lipids, apolipoproteins, and risk of vascular disease

The Emerging Risk Factors Collaboration* (2009) · JAMA · doi:10.1001/jama.2009.1619 · Cited by 2,731

Arterial and Cardiac Aging: Major Shareholders in Cardiovascular Disease Enterprises: Part I: Aging Arteries: A “Set Up” for Vascular Disease

Lakatta, E. G., & Levy, D. (2003) · Circulation · doi:10.1161/01.cir.0000048892.83521.58 · Cited by 2,244

Cis-regulatory elements: Molecular mechanisms and evolutionary processes underlying divergence

Wittkopp, P. J., & Kalay, G. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3095 · Cited by 1,231

Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women

Hoyles, L., Fernández-Real, J.-M., Federici, M., Serino, M., Abbott, J., Charpentier, J., Heymes, C., Luque, J. L., Anthony, E., Barton, R. H., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0061-3 · Cited by 720

Systems genetics approaches to understand complex traits

Civelek, M., & Lusis, A. J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3575 · Cited by 621

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

Mungall, C. J., McMurry, J. A., Köhler, S., Balhoff, J. P., Borromeo, C., Brush, M., Carbon, S., Conlin, T., Dunn, N., Engelstad, M., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1128 · Cited by 441

QST–FST comparisons: evolutionary and ecological insights from genomic heterogeneity

Leinonen, T., McCairns, R. J. S., O'Hara, R. B., & Merilä, J. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3395 · Cited by 422

Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy

Fabris, M., Abbriano, R. M., Pernice, M., Sutherland, D. L., Commault, A. S., Hall, C. C., Labeeuw, L., McCauley, J. I., Kuzhiuparambil, U., Ray, P., et al. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00279 · Cited by 382

Livestock metabolomics and the livestock metabolome: A systematic review

Goldansaz, S. A., Guo, A. C., Sajed, T., Steele, M. A., Plastow, G. S., & Wishart, D. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177675 · Cited by 379

Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP)

Messina, C. D., Technow, F., Tang, T., Totir, R., Gho, C., & Cooper, M. (2018) · European Journal of Agronomy · doi:10.1016/j.eja.2018.01.007 · Cited by 225

Animal biometrics: quantifying and detecting phenotypic appearance

Kühl, H. S., & Burghardt, T. (2013) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2013.02.013 · Cited by 189

Omics and Drug Response

Meyer, U. A., Zanger, U. M., & Schwab, M. (2013) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010510-100502 · Cited by 157

Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology

Haas, R., Zelezniak, A., Iacovacci, J., Kamrad, S., Townsend, S., & Ralser, M. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.08.009 · Cited by 131

Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants

Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., & Kersting, K. (2016) · Scientific Reports · doi:10.1038/srep22482 · Cited by 118

Phenomic studies on diseases: potential and challenges

Ying, W. (2023) · Phenomics · doi:10.1007/s43657-022-00089-4 · Cited by 96

Genetics of human longevity within an eco-evolutionary nature-nurture framework

Giuliani, C., Garagnani, P., & Franceschi, C. (2018) · Circulation Research · doi:10.1161/circresaha.118.312562 · Cited by 92

Review: application of artificial intelligence in phenomics

Nabwire, S., Suh, H.-K., Kim, M. S., Baek, I., & Cho, B.-K. (2021) · Sensors · doi:10.3390/s21134363 · Cited by 91

Beyond Endometriosis Genome-Wide Association Study: From Genomics to Phenomics to the Patient

Rahmioglu, N., Morris, A., Nyholt, D., Montgomery, G., Becker, C., Missmer, S., & Zondervan, K. (2016) · Seminars in Reproductive Medicine · doi:10.1055/s-0036-1585408 · Cited by 84

Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

Johnson, K. W., Shameer, K., Glicksberg, B. S., Readhead, B., Sengupta, P. P., Björkegren, J. L. M., Kovacic, J. C., & Dudley, J. T. (2017) · JACC: Basic to Translational Science · doi:10.1016/j.jacbts.2016.11.010 · Cited by 83

Glycomics meets genomics, epigenomics and other high throughput omics for system biology studies

Zoldoš, V., Horvat, T., & Lauc, G. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2012.12.007 · Cited by 56

Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies: Integrating Breeder, Agronomist, and Farmer Perspectives

Cooper, M., Messina, C. D., Tang, T., Gho, C., Powell, O. M., Podlich, D. W., Technow, F., & Hammer, G. L. (2022) · Plant Breeding Reviews · doi:10.1002/9781119874157.ch8 · Cited by 56

Epigenetic control of variation and stochasticity in metabolic disease

Panzeri, I., & Pospisilik, J. A. (2018) · Molecular Metabolism · doi:10.1016/j.molmet.2018.05.010 · Cited by 50

Prediction of Genetic Interactions Using Machine Learning and Network Properties

Madhukar, N. S., Elemento, O., & Pandey, G. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00172 · Cited by 39

Widespread genotype-phenotype correlations in intellectual disability

Casanova, E. L., Gerstner, Z., Sharp, J. L., Casanova, M. F., & Feltus, F. A. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00535 · Cited by 31