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Human Complex Trait Genetics: Lifting the Lid of the Genomics Toolbox - from Pathways to Prediction

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Prioritization and comprehensive analysis of genes related to major depressive disorder

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Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer

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Circulating, Imaging, and Genetic Biomarkers in Cardiovascular Risk Prediction

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Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

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Response to ‘Predicting the diagnosis of autism spectrum disorder using gene pathway analysis’

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Global epidemiology of spondyloarthritis

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Prediction of Ankylosing Spondylitis in the HUNT Study by a Genetic Risk Score Combining 110 Single-nucleotide Polymorphisms of Genome-wide Significance

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Statistical models and computational tools for predicting complex traits and diseases

Chung, W. (2021) · Genomics & Informatics · doi:10.5808/gi.21053 · Cited by 16

Addressing the ethical and societal challenges posed by genome-wide association studies of behavioral and brain-related traits

de Hemptinne, M., & Posthuma, D. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01333-4 · Cited by 14

Common variants explain a large fraction of the variability in the liability to psoriasis in a Han Chinese population

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Determination of Persons at a High Risk of Falling in a Population of Healthy Community-dwelling Elderly Japanese

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DNA Methylation in Ovarian Cancer Susceptibility

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Using whole genome scores to compare three clinical phenotyping methods in complex diseases

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Deciphering the Genetic Architecture of Parkinson’s Disease in IndiaPreprint

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Genomic prediction of survival against Vibrio harveyi in leopard coral grouper (Plectropomus leopardus) using GBLUP, weighted GBLUP, and BayesCπ

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Multivariate genome-wide association study models to improve prediction of Crohn’s disease risk and identification of potential novel variants

Garza-Hernandez, D., Estrada, K., & Treviño, V. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105398 · Cited by 13

Genomic risk prediction

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Assessment of familial risk in patients with hidradenitis suppurativa

Bruinsma, R. L., Fajgenbaum, K., Yang, Y., Melendez-Gonzalez, M. D. M., Mohlke, K. L., Li, Y., & Sayed, C. J. (2020) · British Journal of Dermatology · doi:10.1111/bjd.19664 · Cited by 12

Common genetic variation contributes significantly to the risk of developing chronic lymphocytic leukemia

Bernardo, M. C. D., Broderick, P., Catovsky, D., & Houlston, R. S. (2012) · Haematologica · doi:10.3324/haematol.2012.072140 · Cited by 12

A scoring strategy combining statistics and functional genomics supports a possible role for common polygenic variation in autism

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The burden of multiple sclerosis variants in continental Italians and Sardinians

Barizzone, N., Zara, I., Sorosina, M., Lupoli, S., Porcu, E., Pitzalis, M., Żołędziewska, M., Esposito, F., Leone, M. A., Mulas, A., et al. (2015) · Multiple Sclerosis Journal · doi:10.1177/1352458515596599 · Cited by 11

Criteria for evaluating risk prediction of multiple outcomes

Dudbridge, F. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220929039 · Cited by 10

Integrating genomics into evolutionary medicine

Rodríguez, J. A., Marigorta, U. M., & Navarro, A. (2014) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2014.08.009 · Cited by 12

Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

McInnes, G., Daneshjou, R., Katsonis, P., Lichtarge, O., Srinivasan, R., Rana, S., Predrag Radivojac, A., Mooney, S. D., Pagel, K. A., Stamboulian, M. H., et al. (2019) · Human Mutation · doi:10.1002/humu.23825 · Cited by 12

Whole Genome Prediction of Bladder Cancer Risk With the Bayesian LASSO

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Promoting Health Literacy for People With Disabilities and Clinicians Through a Teamwork Model

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Wellness and Health Omics Linked to the Environment: The WHOLE Approach to Personalized Medicine

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Unidentified Genetic Variants Influence Pancreatic Cancer Risk: An Analysis of Polygenic Susceptibility in the P an S can Study

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Heritability estimates on Hodgkin’s lymphoma: a genomic- versus population-based approach

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Genetics and the Individualized Prediction of Fracture

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Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option

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Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response

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Establishment of a post‐race biomarkers database and application of pathway analysis to identify potential biomarkers in post‐race equine plasma

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Development and psychometric testing of the dyadic communication observational coding scheme in DEmentia care (DCODE): family dyadic communication in dementia

Kim, S., Liu, W., Daack‐Hirsch, S., & Williams, K. N. (2022) · Aging & Mental Health · doi:10.1080/13607863.2022.2126819 · Cited by 9

Genomic profiling and diagnostic biomarkers in Alzheimer's disease

Escott‐Price, V., & Jones, L. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30202-8 · Cited by 8

Genetic risk scores in complex eye disorders

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R2ROC: an efficient method of comparing two or more correlated AUC from out-of-sample prediction using polygenic scores

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Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a steprank scheme

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GenNet framework: interpretable neural networks for phenotype predictionPreprint

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Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models

Liu, D., Kababji, S. E., Mitsakakis, N., Pilgram, L., Walters, T. D., Clemons, M. J., Pond, G. R., El-Hussuna, A., & Emam, K. E. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03266-3 · Cited by 14

Polygenic risk score prediction accuracy convergence

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A comparison of genomic profiles of complex diseases under different models

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Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks

Hopper, J. L., Dowty, J. G., Nguyen, T. L., Li, S., Dite, G. S., MacInnis, R. J., Makalic, E., Schmidt, D. F., Bui, M., Stone, J., et al. (2023) · International Journal of Epidemiology · doi:10.1093/ije/dyad086 · Cited by 6

Estimating disorder probability based on polygenic prediction using the BPC approach

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Optimizing genomic prediction using low‐density marker panels for streptococcosis resistance in red tilapia (Oreochromis spp.)

Sukhavachana, S., Tongyoo, P., Luengnaruemitchai, A., & Poompuang, S. (2021) · Animal Genetics · doi:10.1111/age.13114 · Cited by 6

Genomic Prediction of Complex Disease RiskPreprint

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Breast and bowel cancers diagnosed in people ‘too young to have cancer’: A blueprint for research using family and twin studies

Hopper, J. L., Li, S., MacInnis, R. J., Dowty, J. G., Nguyen, T. L., Bui, M., Dite, G. S., Esser, V. F. C., Ye, Z., Makalic, E., et al. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22555 · Cited by 5

Polygenic risk scores

Brown, M. A. (2023) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2023.152330 · Cited by 5

Genetic Architecture of Complex Human Traits: What Have We Learned from Genome-Wide Association Studies?

Zhang, G. (2015) · Current Genetic Medicine Reports · doi:10.1007/s40142-015-0083-9 · Cited by 5

Validity of using ad hoc methods to analyze secondary traits in case‐control association studies

Yung, G., & Lin, X. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21994 · Cited by 5

Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores From Genetically Correlated Phenotypes

Barnett, E. J., Zhang‐James, Y., & Faraone, S. V. (2025) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.33043 · Cited by 6

Evaluating the impact of family history and polygenic risk scores on cardiometabolic disease risk

Onyenobi, E., Oyibo, K., Zhong, M., & Adebamowo, S. N. (2026) · BMC Medicine · doi:10.1186/s12916-026-04666-6 · Cited by 4

Genomic selection for survival under naturally occurring Saprolegnia oomycete infection in farmed European whitefish Coregonus lavaretus

Calboli, F. C. F., Iso‐Touru, T., Bitz, O., Fischer, D., Nousiainen, A., Koskinen, H., Tapio, M., Tapio, I., & Kause, A. (2023) · Journal of Animal Science · doi:10.1093/jas/skad333 · Cited by 4

Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures

Shrestha, M., Bai, Z., Gholipourshahraki, T., Hjelholt, A. J., Hu, S., Kjølby, M., Rohde, P. D., & Sørensen, P. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011783 · Cited by 4

Analytical and simulation methods for estimating the potential predictive ability of genetic profiling: a comparison of methods and results

Kundu, S., Karssen, L. C., & Janssens, A. C. J. W. (2012) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.89 · Cited by 4

Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity

Franco, M., & Vivo, J.-M. (2021) · Mathematics · doi:10.3390/math9212826 · Cited by 4

Indication Criteria of Hysteroscopic Surgery for Secondary Infertility due to Symptomatic Cesarean Scar Defect Based on Clinical Outcomes: A Retrospective Cohort Study

Tsuji, S., Nobuta, Y., Yoneoka, Y., Nakamura, A., Amano, T., Takebayashi, A., Hanada, T., & Murakami, T. (2023) · Journal of Minimally Invasive Gynecology · doi:10.1016/j.jmig.2023.03.012 · Cited by 4

Variable Selection in ROC Regression

Wang, B. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/436493 · Cited by 4

Assessment of polygenic risk of hypertension

Limonova, A. S., Ershova, A. I., Kiseleva, A. V., Ramensky, V. E., Vyatkin, Y. V., Kutsenko, V. A., Meshkov, A. N., & Drapkina, O. M. (2023) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2022-3464 · Cited by 6

Using Genetics to Increase Specificity of Outcome Prediction in Psychiatric Disorders: Prospects for Progression

Holmans, P. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20081181 · Cited by 4

Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning

Bracher‐Smith, M., & Escott‐Price, V. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03330-4 · Cited by 5

Polygenic risk scores for glaucoma: Impact on diagnosis and disease course

Kolovos, A., Aung, T., Khawaja, A. P., Wiggs, J. L., & Craig, J. E. (2026) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2026.101469 · Cited by 3

What if we had whole-genome sequence data for millions of individuals?

Visscher, P. M., & Gibson, G. C. (2013) · Genome Medicine · doi:10.1186/gm484 · Cited by 3

Direct-to-Consumer Genetic Testing

Wright, C. F., & MacArthur, D. G. (2011) · Springer eBooks · doi:10.1007/978-1-61779-530-5_10 · Cited by 5

WARE: Wet AMD Risk-Evaluation Tool as a Clinical Decision-Support System Integrating Genetic and Non-Genetic Factors

Fabrizio, C., Termine, A., Caputo, V., Megalizzi, D., Zampatti, S., Falsini, B., Cusumano, A., Eandi, C. M., Ricci, F., Giardina, E., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12071034 · Cited by 3

Future Directions in Genetics of Psychiatric Disorders

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A non- APOE Polygenic score for Alzheimer’s disease and APOE-ε4 have independent associations with dementia in the Health and Retirement StudyPreprint

Bakulski, K. M., Vadari, H. S., Faul, J. D., Heeringa, S. G., Kardia, S. L., Langa, K. M., Smith, J. A., Manly, J. J., Mitchell, C. M., Benke, K. S., et al. (2020) · medRxiv · doi:10.1101/2020.02.10.20021667 · Cited by 3

Evaluation of Bayesian Linear Regression Models as a Fine Mapping toolPreprint

Shrestha, M., Bai, Z., Gholipourshahraki, T., Hjelholt, A. J., Hu, S., Kjølby, M., Rohde, P. D., & Sørensen, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.01.555889 · Cited by 3

Using genomic context informed genotype data and within-model ancestry adjustment to classify type 2 diabetes

Barnett, E. J., Zhang‐James, Y., Hess, J., Glatt, S. J., & Faraone, S. V. (2025) · Communications Medicine · doi:10.1038/s43856-025-01189-8 · Cited by 2

Thai Twin Registry: Description of the Initial Stage

Nanakorn, S., Chusilp, K., & Kutanan, W. (2012) · Twin Research and Human Genetics · doi:10.1017/thg.2012.72 · Cited by 2

Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

Anguita‐Ruiz, A., González‐Gil, E. M., Rupérez, A. I., Llorente‐Cantarero, F. J., Pastor‐Villaescusa, B., Alcalá‐Fdez, J., Moreno, L. A., Gil, Á., Gil‐Campos, M., Bueno, G., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9061705 · Cited by 2

Genome Sequencing in an Individual Presenting with 22q11.2 Deletion Syndrome and Juvenile Idiopathic Arthritis

Sobrinho, R. P. D. O., Appenzeller, S., Holanda, I. P., Heleno, J. L., Jorente, J., Vieira, T. P., & Steiner, C. E. (2024) · Genes · doi:10.3390/genes15040513 · Cited by 2

MicroRNA binding site variants–new potential markers of primary osteoporosis in men and women

Yalaev, B. I., Deev, R. V., Tyurin, A. V., Ринатович, С. Р., Smirnov, K., Константиновна, Е. А., Mokrysheva, N. G., Minniakhmetov, I. R., & Khusainova, R. I. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1470310 · Cited by 2

HPA-axis multilocus genetic interaction with stress life events in predicting changes in adolescent suicidal ideation

Zhao, X., Wang, D., Chang, M., He, Z. J., Zeng, Z., Ren, M., Hu, Y., & Li, Z. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.03.138 · Cited by 2

Methods to Assess Genetic Risk Prediction

Schulz, C., & Padmanabhan, S. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6625-7_2 · Cited by 2

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Huang, L., Hu, X., Wang, Y., & Fu, Y. (2022) · Entropy · doi:10.3390/e24070873 · Cited by 2

Correction: Novel Genetic Analysis for Case-Control Genome-Wide Association Studies: Quantification of Power and Genomic Prediction Accuracy

Lee, S., & Wray, N. R. (2013) · PLoS ONE · doi:10.1371/annotation/0b29c9c7-a86d-4e0f-bbb8-5c29b16e2884 · Cited by 2

Evaluating genetic-based disease prediction approaches through simulation

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GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy

Wang, X., Jiang, J., Zhang, Z., & Zhang, Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag133 · Cited by 1

Genetic determinants of lipid homeostasis

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Commentary

Witte, J. S., & Visscher, P. M. (2012) · Epidemiology · doi:10.1097/ede.0b013e31826cc118 · Cited by 1

A clustered optimal ROC curve method for family-based genetic risk prediction

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1 Polygenic methods and their application to psychiatric traits

Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A. E., Dudbridge, F., & Middeldorp, M. (2016) · Cited by 1

Discriminating head trauma outcomes using machine learning and genomics

Ibrahim, O., Sutherland, H. G. E., Lea, R. A., Nasrallah, F. A., Maksemous, N., Smith, R. A., Haupt, L. M., & Griffiths, L. R. (2021) · Journal of Molecular Medicine · doi:10.1007/s00109-021-02158-z · Cited by 1

Polygenic risk score: a useful clinical instrument for disease prediction and risk categorization

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Relative contributions of the host genome, microbiome, and environment to the metabolic profile

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