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1. High-resolution LiDAR mapping of glacial landforms and ice stream lobes in Finland

Author

N. Putkinen, N. Eyles, S. Putkinen, A.E.K. Ojala, J.-P. Palmu, P. Sarala, T. Väänänen, J. Räisänen, J. Saarelainen, N. Ahtonen, H. Rönty, A. Kiiskinen, T. Rauhaniemi, and T. Tervo

Subjects
Finland, LiDAR, glacial geology mapping, paleo-ice streams, Fennoscandian Ice Sheet, Geology, QE1-996.5
Abstract

Newly available high resolution airborne LiDAR (Light Detection And Ranging) technology is generating unprecedented next-generation imagery of Earth surface features. LiDAR datasets are being employed by the Geological Survey of Finland (GTK) as part of a new national geological initiative (Glacier Dynamic database: GDdatabase) to rapidly and cost-effectively map glacial landforms and sediments left by the last (Late Weichselian) Fennoscandian Ice Sheet (FIS). There is a high demand for such data in hydrogeological, geoengineering and mineral exploration projects and also for the identification of glacial landforms that provide key information regarding ice sheet rheology, growth and decay. An important step forward with this new technique is the recognition of paleo- ice stream corridors with fast flowing ice (> 3 km yr-1 based on modern ice sheets), surrounded by stagnant or sluggish-flowing ice. Precise geomorphic criteria are now available for recognition of paleo-ice streams, based on the elongation of subglacial streamlined bedforms and the presence of megascale glacial lineations. Flow sets of drumlins and megascale glacial lineations can now be mapped in high resolution using LiDAR and are now seen as genetically related forms in a continuum that records increasing ice flow velocity and the creation of a low friction bed. This paper briefly outlines the nature of the GDdatabase and the methodology behind its construction and provides examples of principal bedform types that record the dynamic interplay of paleo-ice stream lobes in the Finnish sector of the last FIS.

Published
2017
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2. Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis

Author

Prezja, Fabi, Annala, Leevi, Kiiskinen, Sampsa, and Ojala, Timo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets. However, obtaining these datasets poses significant challenges due to patient privacy concerns and data collection restrictions. Additive data augmentation, which enhances data variability, emerges as a promising solution. Yet, it's unclear which augmentation techniques are most effective for KOA. This study explored various data augmentation methods, including adversarial augmentations, and their impact on KOA classification model performance. While some techniques improved performance, others commonly used underperformed. We identified potential confounding regions within the images using adversarial augmentation. This was evidenced by our models' ability to classify KL0 and KL4 grades accurately, with the knee joint omitted. This observation suggested a model bias, which might leverage unrelated features for classification currently present in radiographs. Interestingly, removing the knee joint also led to an unexpected improvement in KL1 classification accuracy. To better visualize these paradoxical effects, we employed Grad-CAM, highlighting the associated regions. Our study underscores the need for careful technique selection for improved model performance and identifying and managing potential confounding regions in radiographic KOA deep learning., Comment: 16 pages, 10 figures

Published
2023
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3. Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks

Author

Prezja, Fabi, Annala, Leevi, Kiiskinen, Sampsa, Lahtinen, Suvi, and Ojala, Timo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we trained a CycleGAN model to synthesize past and future stages of KOA on any genuine radiograph. The model was validated using a Convolutional Neural Network that was deceived into misclassifying disease stages in transformed images, demonstrating the CycleGAN's ability to effectively transform disease characteristics forward or backward in time. The model was particularly effective in synthesizing future disease states and showed an exceptional ability to retroactively transition late-stage radiographs to earlier stages by eliminating osteophytes and expanding knee joint space, signature characteristics of None or Doubtful KOA. The model's results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic usage in healthcare. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development., Comment: 29 pages, 10 figures

Published
2023

4. Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification

Author

Prezja, Fabi, Annala, Leevi, Kiiskinen, Sampsa, Lahtinen, Suvi, and Ojala, Timo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progression. One promising classification avenue involves applying deep learning methods; however, these techniques demand extensive, diversified datasets, which pose substantial challenges due to medical data collection restrictions. Existing practices typically resort to smaller datasets and transfer learning. However, this approach often inherits unnecessary pre-learned features that can clutter the classifier's vector space, potentially hampering performance. This study proposes a novel paradigm for improving post-training specialized classifiers by introducing adaptive variance thresholding (AVT) followed by Neural Architecture Search (NAS). This approach led to two key outcomes: an increase in the initial accuracy of the pre-trained KOA models and a 60-fold reduction in the NAS input vector space, thus facilitating faster inference speed and a more efficient hyperparameter search. We also applied this approach to an external model trained for KOA classification. Despite its initial performance, the application of our methodology improved its average accuracy, making it one of the top three KOA classification models., Comment: 26 pages, 5 figures

Published
2023

6. Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning

Author

Prezja, Fabi, Annala, Leevi, Kiiskinen, Sampsa, Lahtinen, Suvi, Ojala, Timo, Ruusuvuori, Pekka, and Kuopio, Teijo

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition
Abstract

In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) in facilitating the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of convolutional neural networks (CNNs) to classify diverse tissue types from whole slide microscope images accurately. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid Deep and ensemble machine learning model that surpassed all preceding solutions for this classification task. Our model achieved 96.74% accuracy on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in advancing the task, we have made them publicly available for further research and development., Comment: 34 pages, 9 figures

Published
2023
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7. Socio-demographic and genetic risk factors for drug adherence and persistence across 5 common medication classes

Author

Mattia Cordioli, Andrea Corbetta, Hanna Maria Kariis, Sakari Jukarainen, Pekka Vartiainen, Tuomo Kiiskinen, Matteo Ferro, FinnGen, Estonian Biobank Research Team, Markus Perola, Mikko Niemi, Samuli Ripatti, Kelli Lehto, Lili Milani, and Andrea Ganna

Subjects
Science
Abstract

Abstract Low drug adherence is a major obstacle to the benefits of pharmacotherapies and it is therefore important to identify factors associated with discontinuing or being poorly adherent to a prescribed treatment regimen. Using high-quality nationwide health registry data and genome-wide genotyping, we evaluate the impact of socio-demographic and genetic risk factors on adherence and persistence for 5 common medication classes that require long-term, regular therapy (N = 1,814,591 individuals from Finnish nationwide registries, 217,005 with genetic data from Finland and Estonia). Need for social assistance and immigration status show a notable negative effect on persistence and adherence across the examined medications (odd ratios between 0.48 and 0.82 for persistence and between 1.1% to 4.3% decrease in adherence) while demographic and health factors show comparably modest or inconsistent effects. A genome-wide scan does not identify genetic variants associated with the two phenotypes, while some pharmacogenes (i.e. CYP2C9 and SLCO1B1) are modestly associated with persistence, but not with adherence. We observe significant genetic correlations between medication adherence and participation in research studies. Overall, our findings suggest that socio-economically disadvantaged groups would benefit from targeted interventions to improve the dispensing and uptake of pharmacological treatments.

Published
2024
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9. Effect of Unbleached and Bleached Softwood Cellulose Pulp Fibers on Poly(lactic acid) Properties

Author

Faizan Asad, Kirsi Immonen, Titta Kiiskinen, and Essi Sarlin

Subjects
composite, biocomposite, cellulose fiber, pla, poly(lactic acid), Biotechnology, TP248.13-248.65
Abstract

Global regulations are guiding society towards more sustainable material solutions. This increasing awareness of the need for environmentally friendly alternatives has led to a greater emphasis on biocomposites, which combine natural fibers with bio-based polymers. This study investigates how bleached softwood pulp fibers (BSWPF) and unbleached softwood pulp fibers (UBSWPF) affect the characteristics of poly(lactic acid) (PLA)-based biocomposites. UBSWPF is a more cost-effective option because it is manufactured with less processing steps than BSWPF. However, it is largely unexplored as a reinforcement in biopolymers. Through investigating the mechanical, thermal, and morphological aspects of the biocomposites, this study showed that UBSWP increased the modulus and impact strength of the PLA biocomposites better than BSWPF. The impact strength, modulus, and tensile strength of PLA-BSWPF and PLA-UBSWPF improved as the fiber content increased. However, a decrease in tensile strength was seen at higher percentages of UBSWPF in PLA. Despite the decrease in tensile strength at higher UBSWPF concentrations, both types of fibers improved the mechanical properties of the biocomposites, demonstrating a potential sustainable reinforcing material for PLA biocomposites.

Published
2024

10. ExoClock Project III: 450 new exoplanet ephemerides from ground and space observations

Author

Kokori, A., Tsiaras, A., Edwards, B., Jones, A., Pantelidou, G., Tinetti, G., Bewersdorff, L., Iliadou, A., Jongen, Y., Lekkas, G., Nastasi, A., Poultourtzidis, E., Sidiropoulos, C., Walter, F., Wünsche, A., Abraham, R., Agnihotri, V. K., Albanesi, R., Arce-Mansego, E., Arnot, D., Audejean, M., Aumasson, C., Bachschmidt, M., Baj, G., Barroy, P. R., Belinski, A. A., Bennett, D., Benni, P., Bernacki, K., Betti, L., Biagini, A., Bosch, P., Brandebourg, P., Brát, L., Bretton, M., Brincat, S. M., Brouillard, S., Bruzas, A., Bruzzone, A., Buckland, R. A., Caló, M., Campos, F., Carreno, A., Rodrigo, J. -A. Carrion, Casali, R., Casalnuovo, G., Cataneo, M., Chang, C. -M., Changeat, L., Chowdhury, V., Ciantini, R., Cilluffo, M., Coliac, J. -F., Conzo, G., Correa, M., Coulon, G., Crouzet, N., Crow, M. V., Curtis, I., Daniel, D., Dawes, S., Dauchet, B., Deldem, M., Deligeorgopoulos, D., Dransfield, G., Dymock, R., Eenmäe, T., Evans, P., Esseiva, N., Falco, C., Farfán, R. G., Fernández-Lajús, E., Ferratfiat, S., Ferreira, S. L., Ferretti, A., Fiołka, J., Fowler, M., Futcher, S. R., Gabellini, D., Gainey, T., Gaitan, J., Gajdoš, P., García-Sánchez, A., Garlitz, J., Gillier, C., Gison, C., Horta, F. Grau, Grivas, G., Gonzales, J., Gorshanov, D., Guerra, P., Guillot, T., Haswell, C. A., Haymes, T., Hentunen, V. -P., Hills, K., Hose, K., Humbert, T., Hurter, F., Hynek, T., Irzyk, M., Jacobsen, J., Jannetta, A. L., Johnson, K., Jóźwik-Wabik, P., Kaeouach, A. E., Kang, W., Kiiskinen, H., Kim, T., Kivila, Ü., Koch, B., Kolb, U., Kučáková, H., Lai, S. -P., Laloum, D., Lasota, S., Lewis, L. A., Liakos, G. -I., Libotte, F., Lopresti, C., Lomoz, F., Majewski, R., Malcher, A., Mallonn, M., Mannucci, M., Marchini, A., Mari, J. -M., Marino, A., Marino, G., Mario, J. -C., Marquette, J. -B., Martínez-Bravo, F. A., Mašek, M., Matassa, P., Michel, P., Michelet, J., Miller, M., Miny, E., Mollier, T., Molina, D., Monteleone, B., Montigiani, N., Morales-Aimar, M., Mortari, F., Morvan, M., Mugnai, L. V., Murawski, G., Naponiello, L., Naves, R., Naudin, J. -L., Néel, D., Neito, R., Neveu, S., Noschese, A., Öğmen, Y., Ohshima, O., Orbanic, Z., Pace, E. P., Pantacchini, C., Paschalis, N. I., Pereira, C., Peretto, I., Perroud, V., Phillips, M., Pintr, P., Pioppa, J. -B., Plazas, J., Poelarends, A. J., Popowicz, A., Purcell, J., Quinn, N., Raetz, M., Rees, D., Regembal, F., Rocchetto, M., Rocci, P. -F., Rockenbauer, M., Roth, R., Rousselot, L., Rubia, X., Ruocco, N., Russo, E., Salisbury, M., Salvaggio, F., Santos, A., Savage, J., Scaggiante, F., Sedita, D., Shadick, S., Silva, A. F., Sioulas, N., Školník, V., Smith, M., Smolka, M., Solmaz, A., Stanbury, N., Stouraitis, D., Tan, T. -G., Theusner, M., Thurston, G., Tifner, F. -P., Tomacelli, A., Tomatis, A., Trnka, J., Tylšar, M., Valeau, P., Vignes, J. -P., Villa, A., Sureda, A. Vives, Vora, K., Vrašťák, M., Walliang, D., Wenzel, B., Wright, D. E., Zambelli, R., Zhang, M., and Zíbar, M.

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

The ExoClock project has been created with the aim of increasing the efficiency of the Ariel mission. It will achieve this by continuously monitoring and updating the ephemerides of Ariel candidates over an extended period, in order to produce a consistent catalogue of reliable and precise ephemerides. This work presents a homogenous catalogue of updated ephemerides for 450 planets, generated by the integration of $\sim$18000 data points from multiple sources. These sources include observations from ground-based telescopes (ExoClock network and ETD), mid-time values from the literature and light-curves from space telescopes (Kepler/K2 and TESS). With all the above, we manage to collect observations for half of the post-discovery years (median), with data that have a median uncertainty less than one minute. In comparison with literature, the ephemerides generated by the project are more precise and less biased. More than 40\% of the initial literature ephemerides had to be updated to reach the goals of the project, as they were either of low precision or drifting. Moreover, the integrated approach of the project enables both the monitoring of the majority of the Ariel candidates (95\%), and also the identification of missing data. The dedicated ExoClock network effectively supports this task by contributing additional observations when a gap in the data is identified. These results highlight the need for continuous monitoring to increase the observing coverage of the candidate planets. Finally, the extended observing coverage of planets allows us to detect trends (TTVs - Transit Timing Variations) for a sample of 19 planets. All products, data, and codes used in this work are open and accessible to the wider scientific community., Comment: Recommended for publication to ApJS (reviewer's comments implemented). Main body: 13 pages, total: 77 pages, 7 figures, 7 tables. Data available at http://doi.org/10.17605/OSF.IO/P298N

Published
2022
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11. Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning

Author

Fabi Prezja, Leevi Annala, Sampsa Kiiskinen, Suvi Lahtinen, Timo Ojala, Pekka Ruusuvuori, and Teijo Kuopio

Subjects
Deep learning, CRC, Histopathology, Biomarkers, Hybrid model, Vision Transformer, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) to facilitate the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of CNNs to accurately classify diverse tissue types from whole slide microscope images. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid deep transfer learning and ensemble machine learning model that improves upon previous approaches, including a transformer and neural architecture search baseline for this task. We employed a pairing of the EfficientNetV2 architecture with a random forest classification head. Our model achieved 96.74% accuracy (95% CI: 96.3%-97.1%) on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in the task, we have made them publicly available.

Published
2024
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12. V392 Persei: a \gamma-ray bright nova eruption from a known dwarf nova

Author

Murphy-Glaysher, F. J., Darnley, M. J., Harvey, É. J., Newsam, A. M., Page, K. L., Starrfield, S., Wagner, R. M., Woodward, C. E., Terndrup, D. M., Kafka, S., Heras, T. Arranz, Berardi, P., Bertrand, E., Biernikowicz, R., Boussin, C., Boyd, D., Buchet, Y., Bundas, M., Coulter, D., Dejean, D., Diepvens, A., Dvorak, S., Edlin, J., Eenmae, T., Eggenstein, H., Fournier, R., Garde, O., Gout, J., Janzen, D., Jordanov, P., Kiiskinen, H., Lane, D., Larochelle, R., Leadbeater, R., Mankel, D., Martineau, G., Miller, I., Modic, R., Montier, J., Aimar, M. Morales, Muyllaert, E., Nogues, R. Naves, O'Keeffe, D., Oksanen, A., Pyatnytskyy, M., Rast, R., Rodgers, B., Perez, D. Rodriguez, Schorr, F., Schwendeman, E., Shadick, S., Sharpe, S., Alfaro, F. Soldán, Sove, T., Stone, G., Tordai, T., Venne, R., Vollmann, W., Vrastak, M., and Wenzel, K.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract

V392 Persei is a known dwarf nova (DN) that underwent a classical nova eruption in 2018. Here we report ground-based optical, Swift UV and X-ray, and Fermi-LAT \gamma-ray observations following the eruption for almost three years. V392 Per is one of the fastest evolving novae yet observed, with a $t_2$ decline time of 2 days. Early spectra present evidence for multiple and interacting mass ejections, with the associated shocks driving both the \gamma-ray and early optical luminosity. V392 Per entered Sun-constraint within days of eruption. Upon exit, the nova had evolved to the nebular phase, and we saw the tail of the super-soft X-ray phase. Subsequent optical emission captured the fading ejecta alongside a persistent narrow line emission spectrum from the accretion disk. Ongoing hard X-ray emission is characteristic of a standing accretion shock in an intermediate polar. Analysis of the optical data reveals an orbital period of 3.230 \pm 0.003 days, but we see no evidence for a white dwarf (WD) spin period. The optical and X-ray data suggest a high mass WD, the pre-nova spectral energy distribution (SED) indicates an evolved donor, and the post-nova SED points to a high mass accretion rate. Following eruption, the system has remained in a nova-like high mass transfer state, rather than returning to the pre-nova DN low mass transfer configuration. We suggest that this high state is driven by irradiation of the donor by the nova eruption. In many ways, V392 Per shows similarity to the well-studied nova and DN GK Persei., Comment: Accepted by MNRAS. 20 pages including references, with 37 pages of supplementary material

Published
2022
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13. Real-world clinical profile, RET mutation testing, treatments and patient-related outcomes for medullary thyroid cancer in Europe

Author

Grace Segall, Ravinder Singh, Min-Hua Jen, Isaac Sanderson, Alex Rider, Katie Lewis, and Urpo Kiiskinen

Subjects
medullary thyroid cancer, ret mutation, real-world data, treatment patterns, patient-reported outcomes, europe, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

Objective: This study aimed to describe real-world patient and physician characteristics, rearranged during transfection (RET) mutation testing and results, treatment patterns and patient-reported outcomes (PROs) in advanced or metastatic medullary thyroid cancer (aMTC) across five populous European countries. Methods: Cross-sectional physician and patient surveys were used to collect quantitative and qualitative data in France, Germany, Italy, Spain and the UK from July to December 2020, prior to the introduction of selective RET inhibitors in Europe. Physicians completed patient record forms and a survey about their specialty and practice site. Patients were asked to provide PRO data using four validated instruments, including the EuroQol 5 Dimension (EQ-5D) questionnaire. Results: The physician-reported sample included 275 patients with aMTC, including 79 patients with RET mutation-positive disease; median age was 60 and 56 years, respectively. Overall, 75% were tested for RET mutation (35% germline only, 21% somatic only and 44% both). Common physician-cited barriers to RET mutation testing included high cost, difficulty accessing the latest tests and time delay for results. First-line systemic therapy (most commonly vandetanib or cabozantinib) was prescribed for 69% of patients overall and 82% of the RET mutation-positive subgroup. Second-line therapy was prescribed for 12% of patients who received first-line therapy; most patients remained on first-line therapy at data capture. PROs revealed a substantial disease/treatment burden. Conclusion: Patients with aMTC report a substantial disease/treatment burden. Outcomes could be improved by identifying patients eligible for treatment with selective RET inhibitors through more optimal RET mutation testing.

Published
2024
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15. TWINGEN: protocol for an observational clinical biobank recall and biomarker cohort study to identify Finnish individuals with high risk of Alzheimer’s disease

Author

Tommi Vasankari, Henri Vähä-Ypyä, Oskari Heikinheimo, Athena Matakidou, Jaakko Kaprio, Eero Vuoksimaa, Ying Wu, Kai Kaarniranta, Peeter Karihtala, Wei Zhou, Caroline Fox, Sanna-Kaisa Herukka, Sarah Smith, Apinya Lertratanakul, Katri Kaukinen, Johannes Kettunen, Minna Männikkö, Eeva Sliz, Markus Perola, Veikko Salomaa, Hilkka Soininen, Ilkka Kalliala, Mika Kähönen, Hao Chen, Andrey Loboda, Laure Morin-Papunen, Terhi Piltonen, Anders Mälarstig, Jason Miller, Jussi Hernesniemi, Henrike Heyne, Sirkku Peltonen, Daniel Gordin, Masahiro Kanai, Benjamin Challis, Juha Sinisalo, David Rice, Fredrik Åberg, Aarno Palotie, Samuli Ripatti, Oili Kaipiainen-Seppänen, Aki Havulinna, Satu Strausz, Tuomo Kiiskinen, Sanni Ruotsalainen, Jukka Koskela, Tuula Palotie, Mark Daly, Howard Jacob, Heiko Runz, Sally John, Robert Plenge, Mark McCarthy, Julie Hunkapiller, Dawn Waterworth, Petri Virolainen, Terhi Kilpi, Jukka Partanen, Anne Pitkäranta, Veli-Matti Kosma, Outi Tuovila, Raimo Pakkanen, Shameek Biswas, Xinli Hu, Johanna Schleutker, Mikko Arvas, Olli Carpen, Reetta Hinttala, Arto Mannermaa, Valtteri Julkunen, Anne Remes, Reetta Kälviäinen, Mikko Hiltunen, Jukka Peltola, Pentti Tienari, Juha Rinne, Adam Ziemann, Jeffrey Waring, Nizar Smaoui, Anne Lehtonen, Susan Eaton, Sanni Lahdenperä, Natalie Bowers, Edmond Teng, Fanli Xu, David Pulford, Martti Färkkilä, Sampsa Pikkarainen, Airi Jussila, Timo Blomster, Mikko Kiviniemi, Markku Voutilainen, Fedik Rahimov, Joseph Maranville, Tim Lu, Kirsi Kalpala, Melissa Miller, Linda McCarthy, Kari Eklund, Antti Palomäki, Pia Isomäki, Laura Pirilä, Johanna Huhtakangas, Marla Hochfeld, Nan Bing, Jorge Esparza Gordillo, Nina Mars, Tarja Laitinen, Margit Pelkonen, Paula Kauppi, Hannu Kankaanranta, Terttu Harju, Hubert Chen, Teemu Niiranen, Kaj Metsärinne, Marja-Riitta Taskinen, Tiinamaija Tuomi, Jari Laukkanen, Audrey Chu, Jaakko Parkkinen, Heikki Joensuu, Tuomo Meretoja, Lauri Aaltonen, Annika Auranen, Päivi Auvinen, Klaus Elenius, Relja Popovic, Bridget Riley-Gillis, Jennifer Schutzman, Heli Lehtonen, Stefan McDonough, Diptee Kulkarni, Terhi Ollila, Hannu Uusitalo, Erich Strauss, Kaisa Tasanen, Laura Huilaja, Katariina Hannula-Jouppi, Teea Salmi, Leena Koulu, David Choy, Anu Jalanko, Risto Kajanne, Mari Kaunisto, Chia-Yen Chen, Robert Yang, Kirsi Auro, Clement Chatelain, Mitja Kurki, Juha Karjalainen, Kimmo Palin, Priit Palta, Susanna Lemmelä, Manuel Rivas, Arto Lehisto, Andrea Ganna, Vincent Llorens, Kati Kristiansson, Kati Hyvärinen, Jarmo Ritari, Katri Pylkäs, Minna Karjalainen, Tuomo Mantere, Eeva Kangasniemi, Sami Heikkinen, Eija Laakkonen, Anu Loukola, Päivi Laiho, Tuuli Sistonen, Essi Kaiharju, Markku Laukkanen, Elina Järvensivu, Sini Lähteenmäki, Lotta Männikkö, Regis Wong, Hannele Mattsson, Tero Hiekkalinna, Kati Donner, Kalle Pärn, Elina Kilpeläinen, Hannele Laivuori, Harri Siirtola, Sirpa Soini, Teijo Kuopio, Ioanna Tachmazidou, Katja Kivinen, Pekka Nieminen, Adam Platt, Sanna Toppila-Salmi, Antti Mäkitie, Elmo Saarentaus, Kristiina Aittomäki, Elisabeth Widen, Marja Vääräsmäki, Erkki Isometsä, Laura Addis, Mika Helminen, Taneli Raivio, Mart Kals, Majd Mouded, Hanna Ollila, Vuokko Anttonen, Tarja Kokkola, Robert Graham, Amanda Elliott, Ali Abbasi, Bridget Riley-Gills, Dirk Paul, Katherine Klinger, Deepak Raipal, Antti Hakanen, Raisa Serpi, Johanna Mäkelä, Mengzhen Liu, Neha Raghavan, Adriana Huertas-Vazquez, Nicole Renaud, Roosa Kallionpää, John Eicher, Minna Raivio, Juulia Partanen, Riitta Lahesmaa, Glenda Lassi, Joanna Betts, Rajashree Mishra, Felix Vaura, Joel Rämö, Mary Pat Reeve, Johanna Mattson, Sauli Vuoti, Esa Pitkänen, Joni A Turunen, Stephanie Loomis, Pirkko Pussinen, Aino Salminen, Tuula Salo, Ulla Palotie, Maria Siponen, Liisa Suominen, Päivi Mäntylä, Ulvi Gursoy, Kirsi Sipilä, Venla Kurra, Laura Kotaniemi-Talonen, Outi Uimari, Taru Tukiainen, Niko Välimäki, Janet Kumar, Juha Mehtonen, Shabbeer Hassan, Pietro Della Briotta Parolo, Mutaamba Maasha, Javier Garcia-Tabuenca, Jiwoo Lee, Kristin Tsuo, Nina Pitkänen, Eero Punkka, Huei-Yi Shen, Mervi Aavikko, L. Elisa Lahtela, Timo P. Sipilä, Awaisa Ghazal, Sami Koskelainen, Teemu Paajanen, Shuang Luo, Javier Gracia-Tabuenca, Tiina Luukkaala, Iida Vähätalo, Marco Hautalahti, Tom Southerington, Paula Iso-Markku, Jaana Suvisaari, Zhihao Ding, Qingqin S Li, Amy Hart, Perttu Terho, Alessandro Porello, Anastasia Kytölä, Antti Aarnisalo, Aoxing Liu, Argyro Bizaki-Vallaskangas, Auli Toivola, Debby Ngo, Dermot Reilly, Ekaterina Khramtsova, Elisa Rahikkala, Eric Green, Eveliina Salminen, Fabiana Farias, George Okafo, Heidi Silven, Heli Salminen-Mankonen, Henna Palin, Iiris Hovatta, Jaakko Tyrmi, Jae-Hoon Sul, Jenni Aittokallio, Jyrki Pitkänen, Karen He, Katriina Aalto-Setälä, Maarit Niinimäki, Malla-Maria Linna, Marc Jung, Margaret G. Ehm, Marianna Niemi, Meijian Guan, Mike Mendelson, Minna Brunfeldt, Natalia Pujol, Nathan Lawless, Oluwaseun Alexander Dada, Rigbe Weldatsadik, Riikka Arffman, Rion Pendergrass, Sahar Mozaffari, Samuel Lessard, Sanna Siltanen, Shanmukha Sampath Padmanabhuni, Simonne Longerich, Susanna Savukoski, Thomas Damm Als, Timo Hiltunen, Tomi P. Mäkelä, Triin Laisk, Tytti Willberg, Varpu Jokimaa, Aija Kyttälä, Toni T Saari, Aino Aaltonen, Sari Aaltonen, Sari Kärkkäinen, Hilkka Liedes, Miina Ollikainen, Teemu Palviainen, Ilona Ruotsalainen, Mia Urjansson, Markus M Forsberg, Jaakko Lähteenmäki, Pia Nyberg, Jani Tikkanen, Mari E. Niemi, and Rodos Rodosthenous

Subjects
Medicine
Abstract

Introduction A better understanding of the earliest stages of Alzheimer’s disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among THL Biobank donors according to TWINGEN study criteria.Methods and analysis A multi-centre study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behaviour and sleep. A subcohort is being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. The data collected in TWINGEN will be returned to THL Biobank from where it can later be requested for other biobank studies such as FinnGen that supported TWINGEN.Ethics and dissemination This recall study consists of FTC/THL Biobank/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

Published
2024
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16. ExoClock project II: A large-scale integrated study with 180 updated exoplanet ephemerides

Author

Kokori, A., Tsiaras, A., Edwards, B., Rocchetto, M., Tinetti, G., Bewersdorff, L., Jongen, Y., Lekkas, G., Pantelidou, G., Poultourtzidis, E., Wünsche, A., Aggelis, C., Agnihotri, V. K., Arena, C., Bachschmidt, M., Bennett, D., Benni, P., Bernacki, K., Besson, E., Betti, L., Biagini, A., Brandebourg, P., Bretton, M., Brincat, S. M., Caló, M., Campos, F., Casali, R., Ciantini, R., Crow, M. V., Dauchet, B., Dawes, S., Deldem, M., Deligeorgopoulos, D., Dymock, R., Eenmäe, T., Evans, P., Esseiva, N., Falco, C., Ferratfiat, S., Fowler, M., Futcher, S. R., Gaitan, J., Horta, F. Grau, Guerra, P., Hurter, F., Jones, A., Kang, W., Kiiskinen, H., Kim, T., Laloum, D., Lee, R., Lomoz, F., Lopresti, C., Mallonn, M., Mannucci, M., Marino, A., Mario, J. -C., Marquette, J. -B., Michelet, J., Miller, M., Mollier, T., Molina, D., Montigiani, N., Mortari, F., Morvan, M., Mugnai, L. V., Naponiello, L., Nastasi, A., Neito, R., Pace, E., Papadeas, P., Paschalis, N., Pereira, C., Perroud, V., Phillips, M., Pintr, P., Pioppa, J. -B., Popowicz, A., Raetz, M., Regembal, F., Rickard, K., Roberts, M., Rousselot, L., Rubia, X., Savage, J., Sedita, D., Shave-Wall, D., Sioulas, N., Školník, V., Smith, M., St-Gelais, D., Stouraitis, D., Strikis, I., Thurston, G., Tomacelli, A., Tomatis, A., Trevan, B., Valeau, P., Vignes, J. -P., Vora, K., Vrašťák, M., Walter, F., Wenzel, B., Wright, D. E., and Zíbar, M.

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

The ExoClock project is an inclusive, integrated, and interactive platform that was developed to monitor the ephemerides of the Ariel targets to increase the mission efficiency. The project makes the best use of all available resources, i.e., observations from ground telescopes, mid-time values from the literature and finally, observations from space instruments. Currently, the ExoClock network includes 280 participants with telescopes capable of observing 85\% of the currently known Ariel candidate targets. This work includes the results of $\sim$1600 observations obtained up to the 31st of December 2020 from the ExoClock network. These data in combination with $\sim$2350 mid-time values collected from the literature are used to update the ephemerides of 180 planets. The analysis shows that 40\% of the updated ephemerides will have an impact on future scheduling as either they have a significantly improved precision, or they have revealed biases in the old ephemerides. With the new observations, the observing coverage and rate for half of the planets in the sample has been doubled or more. Finally, from a population perspective, we identify that the differences in the 2028 predictions between the old and the new ephemerides have an STD that is double what is expected from gaussian uncertainties. These findings have implications for planning future observations, where we will need to account for drifts potentially greater than the prediction uncertainties. The updated ephemerides are open and accessible to the wider exoplanet community both from our Open Science Framework (OSF) repository and our website., Comment: 9 pages (47 with appendices and references), 8 figures, 2 tables. Submitted to ApJS. Revised based on the reviewer's comments

Published
2021
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17. Thrombosis risk in single- and double-heterozygous carriers of factor V Leiden and prothrombin G20210A in FinnGen and the UK Biobank

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Viollet, Coralie, Petrovski, Slavé, Chen, Chia-Yen, John, Sally, Okafo, George, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Pendergrass, Rion, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Vlahiotis, Anna, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Waterworth, Dawn, O´Donnell, Chris, Renaud, Nicole, Mäkelä, Tomi P., Kaprio, Jaakko, Ruddock, Minna, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Kristiansson, Kati, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Ding, Zhihao, Jung, Marc, Tuoken, Hanati, Biswas, Shameek, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Obeidat, Ma´en, Chung, Jonathan, Zierer, Jonas, Niemi, Mari, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Runz, Heiko, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Laitinen, Tarja, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Paul, Dirk, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Rivas, Manuel, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Cooper, Helen, Öller, Denise, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Ryu, Justine, Rämö, Joel T., Jurgens, Sean J., Sanna-Cherchi, Simone, Bauer, Kenneth A., Haj, Amelia, Choi, Seung Hoan, Ellinor, Patrick T., and Bendapudi, Pavan K.

Published
2024
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18. High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Petrovski, Slavé, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Yang, Robert, ODonnell, Chris, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Rivas, Manuel, Waterworth, Dawn, Renaud, Nicole, Obeidat, Maen, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Viollet, Coralie, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Als, Thomas Damm, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Kristiansson, Kati, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Cooper, Helen, Öller, Denise, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Pärn, Kalle, Kals, Mart, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Youssef, Omar, Zidi-Mouaffak, Yossra H.S., and Tamlander, Max

Published
2024
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19. Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus

Author

Mathieson, Iain, Day, Felix R., Barban, Nicola, Tropf, Felix C., Brazel, David M., Vaez, Ahmad, van Zuydam, Natalie, Bitarello, Bárbara D., Gardner, Eugene J., Akimova, Evelina T., Azad, Ajuna, Bergmann, Sven, Bielak, Lawrence F., Boomsma, Dorret I., Bosak, Kristina, Brumat, Marco, Buring, Julie E., Cesarini, David, Chasman, Daniel I., Chavarro, Jorge E., Cocca, Massimiliano, Concas, Maria Pina, Davey Smith, George, Davies, Gail, Deary, Ian J., Esko, Tõnu, Faul, Jessica D., Franco, Oscar, Ganna, Andrea, Gaskins, Audrey J., Gelemanovic, Andrea, de Geus, Eco J. C., Gieger, Christian, Girotto, Giorgia, Gopinath, Bamini, Grabe, Hans Jörgen, Gunderson, Erica P., Hayward, Caroline, He, Chunyan, van Heemst, Diana, Hill, W. David, Hoffmann, Eva R., Homuth, Georg, Hottenga, Jouke Jan, Huang, Hongyang, Hyppӧnen, Elina, Ikram, M. Arfan, Jansen, Rick, Johannesson, Magnus, Kamali, Zoha, Kardia, Sharon L. R., Kavousi, Maryam, Kifley, Annette, Kiiskinen, Tuomo, Kraft, Peter, Kühnel, Brigitte, Langenberg, Claudia, Liew, Gerald, Lind, Penelope A., Luan, Jian’an, Mägi, Reedik, Magnusson, Patrik K. E., Mahajan, Anubha, Martin, Nicholas G., Mbarek, Hamdi, McCarthy, Mark I., McMahon, George, Medland, Sarah E., Meitinger, Thomas, Metspalu, Andres, Mihailov, Evelin, Milani, Lili, Missmer, Stacey A., Mitchell, Paul, Møllegaard, Stine, Mook-Kanamori, Dennis O., Morgan, Anna, van der Most, Peter J., de Mutsert, Renée, Nauck, Matthias, Nolte, Ilja M., Noordam, Raymond, Penninx, Brenda W. J. H., Peters, Annette, Peyser, Patricia A., Polašek, Ozren, Power, Chris, Pribisalic, Ajka, Redmond, Paul, Rich-Edwards, Janet W., Ridker, Paul M., Rietveld, Cornelius A., Ring, Susan M., Rose, Lynda M., Rueedi, Rico, Shukla, Vallari, Smith, Jennifer A., Stankovic, Stasa, Stefánsson, Kári, Stöckl, Doris, Strauch, Konstantin, Swertz, Morris A., Teumer, Alexander, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, Thurik, A. Roy, Timpson, Nicholas J., Turman, Constance, Uitterlinden, André G., Waldenberger, Melanie, Wareham, Nicholas J., Weir, David R., Willemsen, Gonneke, Zhao, Jing Hau, Zhao, Wei, Zhao, Yajie, Snieder, Harold, den Hoed, Marcel, Ong, Ken K., Mills, Melinda C., and Perry, John R. B.

Published
2023
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20. Use of electronic health record data mining for heart failure subtyping

Author

Matti A. Vuori, Tuomo Kiiskinen, Niina Pitkänen, Samu Kurki, Hannele Laivuori, Tarja Laitinen, Sampo Mäntylahti, Aarno Palotie, FinnGen, and Teemu J. Niiranen

Subjects
Heart failure, Ejection fraction, Data mining, Text mining, Electronic health records, HFrEF, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract

Abstract Objective To assess whether electronic health record (EHR) data text mining can be used to improve register-based heart failure (HF) subtyping. EHR data of 43,405 individuals from two Finnish hospital biobanks were mined for unstructured text mentions of ejection fraction (EF) and validated against clinical assessment in two sets of 100 randomly selected individuals. Structured laboratory data was then incorporated for a categorization by HF subtype (HF with mildly reduced EF, HFmrEF; HF with preserved EF, HFpEF; HF with reduced EF, HFrEF; and no HF). Results In 86% of the cases, the algorithm-identified EF belonged to the correct HF subtype range. Sensitivity, specificity, PPV and NPV of the algorithm were 94–100% for HFrEF, 85–100% for HFmrEF, and 96%, 67%, 53% and 98% for HFpEF. Survival analyses using the traditional diagnosis of HF were in concordance with the algorithm-based ones. Compared to healthy individuals, mortality increased from HFmrEF (hazard ratio [HR], 1.91; 95% confidence interval [CI], 1.24–2.95) to HFpEF (2.28; 1.80–2.88) to HFrEF group (2.63; 1.97–3.50) over a follow-up of 1.5 years. We conclude that quantitative EF data can be efficiently extracted from EHRs and used with laboratory data to subtype HF with reasonable accuracy, especially for HFrEF.

Published
2023
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23. Genome-wide screen of otosclerosis in population biobanks: 27 loci and shared associations with skeletal structure

Author

Rämö, Joel T., Kiiskinen, Tuomo, Seist, Richard, Krebs, Kristi, Kanai, Masahiro, Karjalainen, Juha, Kurki, Mitja, Hämäläinen, Eija, Häppölä, Paavo, Havulinna, Aki S., Hautakangas, Heidi, Mägi, Reedik, Palta, Priit, Esko, Tõnu, Metspalu, Andres, Pirinen, Matti, Karczewski, Konrad J., Ripatti, Samuli, Milani, Lili, Stankovic, Konstantina M., Mäkitie, Antti, Daly, Mark J., and Palotie, Aarno

Published
2023
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26. Risk of Midlife Stroke After Adverse Pregnancy Outcomes: The FinnGen Study

Author

Miller, Eliza C., Kauko, Anni, Tom, Sarah E., Laivuori, Hannele, Niiranen, Teemu, Bello, Natalie A., Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Matakidou, Athena, Platt, Adam, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Hunkapiller, Julie, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Fox, Caroline, Mälarstig, Anders, Klinger, Katherine, Raipal, Deepak, Green, Eric, Graham, Robert, Yang, Robert, Donnell, Chris O´, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Runz, Heiko, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Hunkapiller, Julie, Ehm, Margaret G., Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Mälarstig, Anders, Hu, Xinli, Klinger, Katherine, Graham, Robert, Green, Eric, Mozaffari, Sahar, Waterworth, Dawn, Renaud, Nicole, Obeidat, Ma´en, Ripatti, Samuli, Schleutker, Johanna, Perola, Markus, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Laukkanen, Jari, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Runz, Heiko, Lahdenperä, Sanni, Biswas, Shameek, Hunkapiller, Julie, Bowers, Natalie, Teng, Edmond, Pendergrass, Rion, Xu, Fanli, Pulford, David, Auro, Kirsi, Addis, Laura, Eicher, John, Li, Qingqin S, He, Karen, Khramtsova, Ekaterina, Raghavan, Neha, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Daly, Mark, Abbasi, Ali, Waring, Jeffrey, Smaoui, Nizar, Rahimov, Fedik, Lehtonen, Anne, Lu, Tim, Bowers, Natalie, Pendergrass, Rion, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Hu, Xinli, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Abbasi, Ali, Waring, Jeffrey, Rahimov, Fedik, Lertratanakul, Apinya, Smaoui, Nizar, Lehtonen, Anne, Hochfeld, Marla, Bowers, Natalie, Pendergrass, Rion, Esparza Gordillo, Jorge, Auro, Kirsi, Waterworth, Dawn, Farias, Fabiana, Kalpala, Kirsi, Bing, Nan, Hu, Xinli, Laitinen, Tarja, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Smaoui, Nizar, Lassi, Glenda, Eaton, Susan, Chen, Hubert, Pendergrass, Rion, Bowers, Natalie, Betts, Joanna, Auro, Kirsi, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Kähönen, Mika, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Laukkanen, Jari, Elliott, Amanda, Pat Reeve, Mary, Ruotsalainen, Sanni, Challis, Benjamin, Paul, Dirk, Hunkapiller, Julie, Bowers, Natalie, Pendergrass, Rion, Chu, Audrey, Auro, Kirsi, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Miller, Melissa, Meretoja, Tuomo, Joensuu, Heikki, Carpén, Olli, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Elenius, Päivi Auvinen Klaus, Schleutker, Johanna, Pitkänen, Esa, Mars, Nina, Daly, Mark, Popovic, Relja, Waring, Jeffrey, Riley-Gillis, Bridget, Lehtonen, Anne, Schutzman, Jennifer, Hunkapiller, Julie, Bowers, Natalie, Pendergrass, Rion, Kulkarni, Diptee, Auro, Kirsi, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A, Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Pitkänen, Esa, Liu, Mengzhen, Runz, Heiko, Loomis, Stephanie, Strauss, Erich, Bowers, Natalie, Chen, Hao, Pendergrass, Rion, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Smaoui, Nizar, Rahimov, Fedik, Lehtonen, Anne, Choy, David, Pendergrass, Rion, Waterworth, Dawn, Kalpala, Kirsi, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Pendergrass, Rion, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Kettunen, Johannes, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Pat Reeve, Mary, Daly, Mark, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Liu, Mengzhen, Riley-Gillis, Bridget, Pendergrass, Rion, Kumar, Janet, Auro, Kirsi, Hovatta, Iiris, Chen, Chia-Yen, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Damm Als, Thomas, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Salminen, Eveliina, Rahikkala, Elisa, Kettunen, Johannes, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Ripatti, Samuli, Daly, Mark, Karjalainen, Juha, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della, Pietro, Parolo, Briotta, Zhou, Wei, Maasha, Mutaamba, Hassan, Shabbeer, Lemmelä, Susanna, Rivas, Manuel, Niemi, Mari E., Palotie, Aarno, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Tukiainen, Taru, Pat Reeve, Mary, Heyne, Henrike, Mars, Nina, Rämö, Joel, Saarentaus, Elmo, Ollila, Hanna, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Elliott, Amanda, Kristiansson, Kati, Arvas, Mikko, Hyvärinen, Kati, Ritari, Jarmo, Carpén, Olli, Kettunen, Johannes, Pylkäs, Katri, Sliz, Eeva, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Mannermaa, Arto, Laakkonen, Eija, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Terho, Perttu, Soini, Sirpa, Partanen, Jukka, Punkka, Eero, Serpi, Raisa, Siltanen, Sanna, Kosma, Veli-Matti, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Palin, Henna, Linna, Malla-Maria, Kurki, Mitja, Karjalainen, Juha, Della, Pietro, Parolo, Briotta, Lehisto, Arto, Mehtonen, Juha, Zhou, Wei, Kanai, Masahiro, Maasha, Mutaamba, Havulinna, Aki, Lemmelä, Susanna, Kiiskinen, Tuomo, Elisa Lahtela, L., Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Alexander Dada, Oluwaseun, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Ruotsalainen, Sanni, Donner, Kati, Sipilä, Timo P., Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Kristiansson, Kati, Lemmelä, Susanna, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Palta, Priit, Pärn, Kalle, Kals, Mart, Luo, Shuang, Laitinen, Tarja, Pat Reeve, Mary, Sampath Padmanabhuni, Shanmukha, Niemi, Marianna, Siirtola, Harri, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Pitkänen, Jyrki, Hautalahti, Marco, Mäkelä, Johanna, Smith, Sarah, and Southerington, Tom

Published
2023
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27. FinnGen provides genetic insights from a well-phenotyped isolated population

Author

Kurki, Mitja I., Karjalainen, Juha, Palta, Priit, Sipilä, Timo P., Kristiansson, Kati, Donner, Kati M., Reeve, Mary P., Laivuori, Hannele, Aavikko, Mervi, Kaunisto, Mari A., Loukola, Anu, Lahtela, Elisa, Mattsson, Hannele, Laiho, Päivi, Della Briotta Parolo, Pietro, Lehisto, Arto A., Kanai, Masahiro, Mars, Nina, Rämö, Joel, Kiiskinen, Tuomo, Heyne, Henrike O., Veerapen, Kumar, Rüeger, Sina, Lemmelä, Susanna, Zhou, Wei, Ruotsalainen, Sanni, Pärn, Kalle, Hiekkalinna, Tero, Koskelainen, Sami, Paajanen, Teemu, Llorens, Vincent, Gracia-Tabuenca, Javier, Siirtola, Harri, Reis, Kadri, Elnahas, Abdelrahman G., Sun, Benjamin, Foley, Christopher N., Aalto-Setälä, Katriina, Alasoo, Kaur, Arvas, Mikko, Auro, Kirsi, Biswas, Shameek, Bizaki-Vallaskangas, Argyro, Carpen, Olli, Chen, Chia-Yen, Dada, Oluwaseun A., Ding, Zhihao, Ehm, Margaret G., Eklund, Kari, Färkkilä, Martti, Finucane, Hilary, Ganna, Andrea, Ghazal, Awaisa, Graham, Robert R., Green, Eric M., Hakanen, Antti, Hautalahti, Marco, Hedman, Åsa K., Hiltunen, Mikko, Hinttala, Reetta, Hovatta, Iiris, Hu, Xinli, Huertas-Vazquez, Adriana, Huilaja, Laura, Hunkapiller, Julie, Jacob, Howard, Jensen, Jan-Nygaard, Joensuu, Heikki, John, Sally, Julkunen, Valtteri, Jung, Marc, Junttila, Juhani, Kaarniranta, Kai, Kähönen, Mika, Kajanne, Risto, Kallio, Lila, Kälviäinen, Reetta, Kaprio, Jaakko, Kerimov, Nurlan, Kettunen, Johannes, Kilpeläinen, Elina, Kilpi, Terhi, Klinger, Katherine, Kosma, Veli-Matti, Kuopio, Teijo, Kurra, Venla, Laisk, Triin, Laukkanen, Jari, Lawless, Nathan, Liu, Aoxing, Longerich, Simonne, Mägi, Reedik, Mäkelä, Johanna, Mäkitie, Antti, Malarstig, Anders, Mannermaa, Arto, Maranville, Joseph, Matakidou, Athena, Meretoja, Tuomo, Mozaffari, Sahar V., Niemi, Mari E. K., Niemi, Marianna, Niiranen, Teemu, O´Donnell, Christopher J., Obeidat, Ma´en, Okafo, George, Ollila, Hanna M., Palomäki, Antti, Palotie, Tuula, Partanen, Jukka, Paul, Dirk S., Pelkonen, Margit, Pendergrass, Rion K., Petrovski, Slavé, Pitkäranta, Anne, Platt, Adam, Pulford, David, Punkka, Eero, Pussinen, Pirkko, Raghavan, Neha, Rahimov, Fedik, Rajpal, Deepak, Renaud, Nicole A., Riley-Gillis, Bridget, Rodosthenous, Rodosthenis, Saarentaus, Elmo, Salminen, Aino, Salminen, Eveliina, Salomaa, Veikko, Schleutker, Johanna, Serpi, Raisa, Shen, Huei-yi, Siegel, Richard, Silander, Kaisa, Siltanen, Sanna, Soini, Sirpa, Soininen, Hilkka, Sul, Jae Hoon, Tachmazidou, Ioanna, Tasanen, Kaisa, Tienari, Pentti, Toppila-Salmi, Sanna, Tukiainen, Taru, Tuomi, Tiinamaija, Turunen, Joni A., Ulirsch, Jacob C., Vaura, Felix, Virolainen, Petri, Waring, Jeffrey, Waterworth, Dawn, Yang, Robert, Nelis, Mari, Reigo, Anu, Metspalu, Andres, Milani, Lili, Esko, Tõnu, Fox, Caroline, Havulinna, Aki S., Perola, Markus, Ripatti, Samuli, Jalanko, Anu, Laitinen, Tarja, Mäkelä, Tomi P., Plenge, Robert, McCarthy, Mark, Runz, Heiko, Daly, Mark J., and Palotie, Aarno

Published
2023
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28. Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases

Author

Kiiskinen, Tuomo, Helkkula, Pyry, Krebs, Kristi, Karjalainen, Juha, Saarentaus, Elmo, Mars, Nina, Lehisto, Arto, Zhou, Wei, Cordioli, Mattia, Jukarainen, Sakari, Rämö, Joel T., Mehtonen, Juha, Veerapen, Kumar, Räsänen, Markus, Ruotsalainen, Sanni, Maasha, Mutaamba, Niiranen, Teemu, Tuomi, Tiinamaija, Salomaa, Veikko, Kurki, Mitja, Pirinen, Matti, Palotie, Aarno, Daly, Mark, Ganna, Andrea, Havulinna, Aki S., Milani, Lili, and Ripatti, Samuli

Published
2023
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29. The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Petrovski, Slavé, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Yang, Robert, O´Donnell, Chris, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Rivas, Manuel, Waterworth, Dawn, Renaud, Nicole, Obeidat, Ma´en, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Viollet, Coralie, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Als, Thomas Damm, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Kristiansson, Kati, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Pärn, Kalle, Kals, Mart, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Jones, Samuel E., Maisha, Fahrisa I., Strausz, Satu J., Lammi, Vilma, Cade, Brian E., Tervi, Anniina, Helaakoski, Viola, Broberg, Martin E., Lane, Jacqueline M., Redline, Susan, Saxena, Richa, and Ollila, Hanna M.

Published
2023
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34. Genetic architecture of human plasma lipidome and its link to cardiovascular disease.

Author

Tabassum, Rubina, Rämö, Joel T, Ripatti, Pietari, Koskela, Jukka T, Kurki, Mitja, Karjalainen, Juha, Palta, Priit, Hassan, Shabbeer, Nunez-Fontarnau, Javier, Kiiskinen, Tuomo TJ, Söderlund, Sanni, Matikainen, Niina, Gerl, Mathias J, Surma, Michal A, Klose, Christian, Stitziel, Nathan O, Laivuori, Hannele, Havulinna, Aki S, Service, Susan K, Salomaa, Veikko, Pirinen, Matti, FinnGen Project, Jauhiainen, Matti, Daly, Mark J, Freimer, Nelson B, Palotie, Aarno, Taskinen, Marja-Riitta, Simons, Kai, and Ripatti, Samuli

Subjects
FinnGen Project, Plasma, Humans, Cardiovascular Diseases, Lipids, Genome-Wide Association Study, Lipidomics
Abstract

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P

Published
2019

37. The First Post-Kepler Brightness Dips of KIC 8462852

Author

Boyajian, Tabetha S., Alonso, Roi, Ammerman, Alex, Armstrong, David, Ramos, A. Asensio, Barkaoui, K., Beatty, Thomas G., Benkhaldoun, Z., Benni, Paul, Bentley, Rory, Berdyugin, Andrei, Berdyugina, Svetlana, Bergeron, Serge, Bieryla, Allyson, Blain, Michaela G., Blanco, Alicia Capetillo, Bodman, Eva H. L., Boucher, Anne, Bradley, Mark, Brincat, Stephen M., Brink, Thomas G., Briol, John, Brown, David J. A., Budaj, J., Burdanov, A., Cale, B., Carbo, Miguel Aznar, Garcia, R. Castillo, Clark, Wendy J, Clayton, Geoffrey C., Clem, James L., Coker, Phillip H, Cook, Evan M., Copperwheat, Chris M., Curtis, J., Cutri, R. M., Cseh, B., Cynamon, C. H., Daniels, Alex J., Davenport, James R. A., Deeg, Hans J., De Lorenzo, Roberto, De Jaeger, Thomas, Desrosiers, Jean-Bruno, Dolan, John, Dowhos, D. J., Dubois, Franky, Durkee, R., Dvorak, Shawn, Easley, Lynn, Edwards, N., Ellis, Tyler G., Erdelyi, Emery, Ertel, Steve, Farfán, Rafael. G., Farihi, J., Filippenko, Alexei V., Foxell, Emma, Gandolfi, Davide, Garcia, Faustino, Giddens, F., Gillon, M., González-Carballo, Juan-Luis, González-Fernández, C., Hernández, J. I. González, Graham, Keith A., Greene, Kenton A., Gregorio, J., Hallakoun, Na'ama, Hanyecz, Ottó, Harp, G. R., Henry, Gregory W., Herrero, E., Hildbold, Caleb F., Hinzel, D., Holgado, G., Ignácz, Bernadett, Ivanov, Valentin D., Jehin, E., Jermak, Helen E., Johnston, Steve, Kafka, S., Kalup, Csilla, Kardasis, Emmanuel, Kaspi, Shai, Kennedy, Grant M., Kiefer, F., Kielty, C. L., Kessler, Dennis, Kiiskinen, H., Killestein, T. L., King, Ronald A., Kollar, V., Korhonen, H., Kotnik, C., Könyves-Tóth, Réka, Kriskovics, Levente, Krumm, Nathan, Krushinsky, Vadim, Kundra, E., Lachapelle, Francois-Rene, Lacourse, D., Lake, P., Lam, Kristine, Lamb, Gavin P., Lane, Dave, Lau, Marie Wingyee, Lewin, Pablo, Lintott, Chris, Lisse, Carey, Logie, Ludwig, Longeard, Nicolas, Villanueva, M. Lopez, Ludington, E. Whit, Mainzer, A., Malo, Lison, Maloney, Chris, Mann, A., Mantero, A., Marengo, Massimo, Marchant, Jon, González, M. J. Martinez, Masiero, Joseph R., Mauerhan, Jon C., Mccormac, James, Mcneely, Aaron, Meng, Huan Y. A., Miller, Mike, Molnar, Lawrence A., Morales, J. C., Morris, Brett M., Muterspaugh, Matthew W., Nespral, David, Nugent, C. R., Nugent, Katherine M., Odasso, A., O'keeffe, Derek, Oksanen, A., O'meara, John M., Ordasi, András, Osborn, Hugh, Ott, John J., Parks, J. R., Perez, Diego Rodriguez, Petriew, Vance, Pickard, R., Pál, András, Plavchan, P., Plaza, C. Westendorp, Pollacco, Don, Nuñez, F. Pozo, Pozuelos, F. J., Rau, Steve, Redfield, Seth, Relles, Howard, Ribas, I., Richards, Jon, Saario, Joonas L. O., Safron, Emily J., Sallai, J. Martin, Sárneczky, Krisztián, Schaefer, Bradley E., Schumer, Clea F., Schwartzendruber, Madison, Siegel, Michael H., Siemion, Andrew P. V., Simmons, Brooke D., Simon, Joshua D., Simón-Diaz, S., Sitko, Michael L., Socas-Navarro, Hector, Sódor, Á., Starkey, Donn, Steele, Iain A., Stone, Geoff, Street, R. A., Sullivan, Tricia, Suomela, J., Swift, J. J., Szabó, Gyula M., Szabó, Róbert, Szakáts, Róbert, Szalai, Tamás, Tanner, Angelle M., Toledo-Padrón, B., Tordai, Tamás, Triaud, Amaury H. M. J., Turner, Jake D., Ulowetz, Joseph H., Urbanik, Marian, Vanaverbeke, Siegfried, Vanderburg, Andrew, Vida, Krisztián, Vietje, Brad P., Vinkó, József, Von Braun, K., Waagen, Elizabeth O., Walsh, Dan, Watson, Christopher A., Weir, R. C., Wenzel, Klaus, Williamson, Michael W., Wright, Jason T., Wyatt, M. C., Zheng, Weikang, and Zsidi, Gabriella

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics
Abstract

We present a photometric detection of the first brightness dips of the unique variable star KIC 8462852 since the end of the Kepler space mission in 2013 May. Our regular photometric surveillance started in October 2015, and a sequence of dipping began in 2017 May continuing on through the end of 2017, when the star was no longer visible from Earth. We distinguish four main 1-2.5% dips, named "Elsie," "Celeste," "Skara Brae," and "Angkor", which persist on timescales from several days to weeks. Our main results so far are: (i) there are no apparent changes of the stellar spectrum or polarization during the dips; (ii) the multiband photometry of the dips shows differential reddening favoring non-grey extinction. Therefore, our data are inconsistent with dip models that invoke optically thick material, but rather they are in-line with predictions for an occulter consisting primarily of ordinary dust, where much of the material must be optically thin with a size scale <<1um, and may also be consistent with models invoking variations intrinsic to the stellar photosphere. Notably, our data do not place constraints on the color of the longer-term "secular" dimming, which may be caused by independent processes, or probe different regimes of a single process., Comment: 19 pages, 8 figures, accepted for publication in ApJL

Published
2018
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38. Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis

Author

Fabi Prezja, Leevi Annala, Sampsa Kiiskinen, and Timo Ojala

Subjects
knee joint osteoarthritis (KOA), global disability, data augmentation, technique selection, data variability, deep learning, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95
Abstract

Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets. However, obtaining these datasets poses significant challenges due to patient privacy and data collection restrictions. Additive data augmentation, which enhances data variability, emerges as a promising solution. Yet, it’s unclear which augmentation techniques are most effective for KOA. Our study explored data augmentation methods, including adversarial techniques. We used strategies like horizontal cropping and region of interest (ROI) extraction, alongside adversarial methods such as noise injection and ROI removal. Interestingly, rotations improved performance, while methods like horizontal split were less effective. We discovered potential confounding regions using adversarial augmentation, shown in our models’ accurate classification of extreme KOA grades, even without the knee joint. This indicated a potential model bias towards irrelevant radiographic features. Removing the knee joint paradoxically increased accuracy in classifying early-stage KOA. Grad-CAM visualizations helped elucidate these effects. Our study contributed to the field by pinpointing augmentation techniques that either improve or impede model performance, in addition to recognizing potential confounding regions within radiographic images of knee osteoarthritis.

Published
2023
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43. Systematisation of Systems Solving Physics Boundary Value Problems

Author

Rossi, Tuomo, Räbinä, Jukka, Mönkölä, Sanna, Kiiskinen, Sampsa, Lohi, Jonni, Kettunen, Lauri, Barth, Timothy J., Series Editor, Griebel, Michael, Series Editor, Keyes, David E., Series Editor, Nieminen, Risto M., Series Editor, Roose, Dirk, Series Editor, Schlick, Tamar, Series Editor, Vermolen, Fred J., editor, and Vuik, Cornelis, editor

Published
2021
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44. Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women

Author

Mars, Nina, primary, Kerminen, Sini, additional, Tamlander, Max, additional, Pirinen, Matti, additional, Jakkula, Eveliina, additional, Aaltonen, Kirsimari, additional, Meretoja, Tuomo, additional, Heinävaara, Sirpa, additional, Widén, Elisabeth, additional, Ripatti, Samuli, additional, Palotie, Aarno, additional, Daly, Mark, additional, Riley-Gills, Bridget, additional, Jacob, Howard, additional, Paul, Dirk, additional, Matakidou, Athena, additional, Platt, Adam, additional, Runz, Heiko, additional, John, Sally, additional, Okafo, George, additional, Lawless, Nathan, additional, Plenge, Robert, additional, Maranville, Joseph, additional, McCarthy, Mark, additional, Hunkapiller, Julie, additional, Ehm, Margaret G., additional, Auro, Kirsi, additional, Longerich, Simonne, additional, Fox, Caroline, additional, Mälarstig, Anders, additional, Klinger, Katherine, additional, Raipal, Deepak, additional, Green, Eric, additional, Graham, Robert, additional, Yang, Robert, additional, O'Donnell, Chris, additional, Mäkelä, Tomi P., additional, Kaprio, Jaakko, additional, Virolainen, Petri, additional, Hakanen, Antti, additional, Kilpi, Terhi, additional, Perola, Markus, additional, Partanen, Jukka, additional, Pitkäranta, Anne, additional, Junttila, Juhani, additional, Serpi, Raisa, additional, Laitinen, Tarja, additional, Kosma, Veli-Matti, additional, Laukkanen, Jari, additional, Hautalahti, Marco, additional, Tuovila, Outi, additional, Pakkanen, Raimo, additional, Waring, Jeffrey, additional, Riley-Gillis, Bridget, additional, Rahimov, Fedik, additional, Tachmazidou, Ioanna, additional, Chen, Chia-Yen, additional, Ding, Zhihao, additional, Jung, Marc, additional, Biswas, Shameek, additional, Pendergrass, Rion, additional, Pulford, David, additional, Raghavan, Neha, additional, Huertas-Vazquez, Adriana, additional, Sul, Jae-Hoon, additional, Hu, Xinli, additional, Mozaffari, Sahar, additional, Waterworth, Dawn, additional, Renaud, Nicole, additional, Obeidat, Ma'en, additional, Schleutker, Johanna, additional, Arvas, Mikko, additional, Carpén, Olli, additional, Hinttala, Reetta, additional, Kettunen, Johannes, additional, Mannermaa, Arto, additional, Aalto-Setälä, Katriina, additional, Kähönen, Mika, additional, Mäkelä, Johanna, additional, Kälviäinen, Reetta, additional, Julkunen, Valtteri, additional, Soininen, Hilkka, additional, Remes, Anne, additional, Hiltunen, Mikko, additional, Peltola, Jukka, additional, Raivio, Minna, additional, Tienari, Pentti, additional, Rinne, Juha, additional, Kallionpää, Roosa, additional, Partanen, Juulia, additional, Abbasi, Ali, additional, Ziemann, Adam, additional, Smaoui, Nizar, additional, Lehtonen, Anne, additional, Eaton, Susan, additional, Lahdenperä, Sanni, additional, Bowers, Natalie, additional, Teng, Edmond, additional, Xu, Fanli, additional, Addis, Laura, additional, Eicher, John, additional, Li, Qingqin S, additional, He, Karen, additional, Khramtsova, Ekaterina, additional, Färkkilä, Martti, additional, Koskela, Jukka, additional, Pikkarainen, Sampsa, additional, Jussila, Airi, additional, Kaukinen, Katri, additional, Blomster, Timo, additional, Kiviniemi, Mikko, additional, Voutilainen, Markku, additional, Lu, Tim, additional, McCarthy, Linda, additional, Hart, Amy, additional, Guan, Meijian, additional, Miller, Jason, additional, Kalpala, Kirsi, additional, Miller, Melissa, additional, Eklund, Kari, additional, Palomäki, Antti, additional, Isomäki, Pia, additional, Pirilä, Laura, additional, Kaipiainen-Seppänen, Oili, additional, Huhtakangas, Johanna, additional, Mars, Nina, additional, Lertratanakul, Apinya, additional, Close, David, additional, Hochfeld, Marla, additional, Gordillo, Jorge Esparza, additional, Farias, Fabiana, additional, Bing, Nan, additional, Pelkonen, Margit, additional, Kauppi, Paula, additional, Kankaanranta, Hannu, additional, Harju, Terttu, additional, Lahesmaa, Riitta, additional, Mackay, Alex, additional, Lassi, Glenda, additional, Chen, Hubert, additional, Betts, Joanna, additional, Mishra, Rajashree, additional, Mouded, Majd, additional, Ngo, Debby, additional, Niiranen, Teemu, additional, Vaura, Felix, additional, Salomaa, Veikko, additional, Metsärinne, Kaj, additional, Aittokallio, Jenni, additional, Hernesniemi, Jussi, additional, Gordin, Daniel, additional, Sinisalo, Juha, additional, Taskinen, Marja-Riitta, additional, Tuomi, Tiinamaija, additional, Hiltunen, Timo, additional, Elliott, Amanda, additional, Reeve, Mary Pat, additional, Ruotsalainen, Sanni, additional, Challis, Benjamin, additional, Chu, Audrey, additional, Reilly, Dermot, additional, Mendelson, Mike, additional, Parkkinen, Jaakko, additional, Joensuu, Heikki, additional, Mattson, Johanna, additional, Salminen, Eveliina, additional, Auranen, Annika, additional, Karihtala, Peeter, additional, Auvinen, Päivi, additional, Elenius, Klaus, additional, Pitkänen, Esa, additional, Popovic, Relja, additional, Schutzman, Jennifer, additional, Kulkarni, Diptee, additional, Porello, Alessandro, additional, Loboda, Andrey, additional, Lehtonen, Heli, additional, McDonough, Stefan, additional, Vuoti, Sauli, additional, Kaarniranta, Kai, additional, Turunen, Joni A, additional, Ollila, Terhi, additional, Uusitalo, Hannu, additional, Karjalainen, Juha, additional, Liu, Mengzhen, additional, Loomis, Stephanie, additional, Strauss, Erich, additional, Chen, Hao, additional, Tasanen, Kaisa, additional, Huilaja, Laura, additional, Hannula-Jouppi, Katariina, additional, Salmi, Teea, additional, Peltonen, Sirkku, additional, Koulu, Leena, additional, Choy, David, additional, Wu, Ying, additional, Pussinen, Pirkko, additional, Salminen, Aino, additional, Salo, Tuula, additional, Rice, David, additional, Nieminen, Pekka, additional, Palotie, Ulla, additional, Siponen, Maria, additional, Suominen, Liisa, additional, Mäntylä, Päivi, additional, Gursoy, Ulvi, additional, Anttonen, Vuokko, additional, Sipilä, Kirsi, additional, Laivuori, Hannele, additional, Kurra, Venla, additional, Kotaniemi-Talonen, Laura, additional, Heikinheimo, Oskari, additional, Kalliala, Ilkka, additional, Aaltonen, Lauri, additional, Jokimaa, Varpu, additional, Vääräsmäki, Marja, additional, Uimari, Outi, additional, Morin-Papunen, Laure, additional, Niinimäki, Maarit, additional, Piltonen, Terhi, additional, Kivinen, Katja, additional, Widen, Elisabeth, additional, Tukiainen, Taru, additional, Välimäki, Niko, additional, Laakkonen, Eija, additional, Tyrmi, Jaakko, additional, Silven, Heidi, additional, Sliz, Eeva, additional, Arffman, Riikka, additional, Savukoski, Susanna, additional, Laisk, Triin, additional, Pujol, Natalia, additional, Kumar, Janet, additional, Hovatta, Iiris, additional, Isometsä, Erkki, additional, Veerapen, Kumar, additional, Ollila, Hanna, additional, Suvisaari, Jaana, additional, Als, Thomas Damm, additional, Mäkitie, Antti, additional, Bizaki-Vallaskangas, Argyro, additional, Toppila-Salmi, Sanna, additional, Willberg, Tytti, additional, Saarentaus, Elmo, additional, Aarnisalo, Antti, additional, Rahikkala, Elisa, additional, Aittomäki, Kristiina, additional, Åberg, Fredrik, additional, Kurki, Mitja, additional, Havulinna, Aki, additional, Mehtonen, Juha, additional, Palta, Priit, additional, Hassan, Shabbeer, additional, Della Briotta Parolo, Pietro, additional, Zhou, Wei, additional, Maasha, Mutaamba, additional, Lemmelä, Susanna, additional, Rivas, Manuel, additional, Niemi, Mari E., additional, Liu, Aoxing, additional, Lehisto, Arto, additional, Ganna, Andrea, additional, Llorens, Vincent, additional, Heyne, Henrike, additional, Rämö, Joel, additional, Rodosthenous, Rodos, additional, Strausz, Satu, additional, Palotie, Tuula, additional, Palin, Kimmo, additional, Garcia-Tabuenca, Javier, additional, Siirtola, Harri, additional, Kiiskinen, Tuomo, additional, Lee, Jiwoo, additional, Tsuo, Kristin, additional, Kristiansson, Kati, additional, Hyvärinen, Kati, additional, Ritari, Jarmo, additional, Pylkäs, Katri, additional, Karjalainen, Minna, additional, Mantere, Tuomo, additional, Kangasniemi, Eeva, additional, Heikkinen, Sami, additional, Pitkänen, Nina, additional, Lessard, Samuel, additional, Chatelain, Clément, additional, Terho, Perttu, additional, Soini, Sirpa, additional, Punkka, Eero, additional, Siltanen, Sanna, additional, Kuopio, Teijo, additional, Jalanko, Anu, additional, Shen, Huei-Yi, additional, Kajanne, Risto, additional, Aavikko, Mervi, additional, Kanai, Masahiro, additional, Lahtela, L. Elisa, additional, Kaunisto, Mari, additional, Kilpeläinen, Elina, additional, Sipilä, Timo P., additional, Dada, Oluwaseun Alexander, additional, Ghazal, Awaisa, additional, Kytölä, Anastasia, additional, Weldatsadik, Rigbe, additional, Donner, Kati, additional, Loukola, Anu, additional, Laiho, Päivi, additional, Sistonen, Tuuli, additional, Kaiharju, Essi, additional, Laukkanen, Markku, additional, Järvensivu, Elina, additional, Lähteenmäki, Sini, additional, Männikkö, Lotta, additional, Wong, Regis, additional, Toivola, Auli, additional, Brunfeldt, Minna, additional, Mattsson, Hannele, additional, Koskelainen, Sami, additional, Hiekkalinna, Tero, additional, Paajanen, Teemu, additional, Pärn, Kalle, additional, Kals, Mart, additional, Luo, Shuang, additional, Sinha, Vishal, additional, Niemi, Marianna, additional, Gracia-Tabuenca, Javier, additional, Helminen, Mika, additional, Luukkaala, Tiina, additional, Vähätalo, Iida, additional, Pitkänen, Jyrki, additional, Smith, Sarah, additional, and Southerington, Tom, additional

Published
2024
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46. Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants

Author

Saarentaus, Elmo Christian, Havulinna, Aki Samuli, Mars, Nina, Ahola-Olli, Ari, Kiiskinen, Tuomo Tapio Johannes, Partanen, Juulia, Ruotsalainen, Sanni, Kurki, Mitja, Urpa, Lea Martta, Chen, Lei, Perola, Markus, Salomaa, Veikko, Veijola, Juha, Männikkö, Minna, Hall, Ira M., Pietiläinen, Olli, Kaprio, Jaakko, Ripatti, Samuli, Daly, Mark, and Palotie, Aarno

Published
2021
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47. Author Correction: FinnGen provides genetic insights from a well-phenotyped isolated population

Author

Kurki, Mitja I., Karjalainen, Juha, Palta, Priit, Sipilä, Timo P., Kristiansson, Kati, Donner, Kati M., Reeve, Mary P., Laivuori, Hannele, Aavikko, Mervi, Kaunisto, Mari A., Loukola, Anu, Lahtela, Elisa, Mattsson, Hannele, Laiho, Päivi, Della Briotta Parolo, Pietro, Lehisto, Arto A., Kanai, Masahiro, Mars, Nina, Rämö, Joel, Kiiskinen, Tuomo, Heyne, Henrike O., Veerapen, Kumar, Rüeger, Sina, Lemmelä, Susanna, Zhou, Wei, Ruotsalainen, Sanni, Pärn, Kalle, Hiekkalinna, Tero, Koskelainen, Sami, Paajanen, Teemu, Llorens, Vincent, Gracia-Tabuenca, Javier, Siirtola, Harri, Reis, Kadri, Elnahas, Abdelrahman G., Sun, Benjamin, Foley, Christopher N., Aalto-Setälä, Katriina, Alasoo, Kaur, Arvas, Mikko, Auro, Kirsi, Biswas, Shameek, Bizaki-Vallaskangas, Argyro, Carpen, Olli, Chen, Chia-Yen, Dada, Oluwaseun A., Ding, Zhihao, Ehm, Margaret G., Eklund, Kari, Färkkilä, Martti, Finucane, Hilary, Ganna, Andrea, Ghazal, Awaisa, Graham, Robert R., Green, Eric M., Hakanen, Antti, Hautalahti, Marco, Hedman, Åsa K., Hiltunen, Mikko, Hinttala, Reetta, Hovatta, Iiris, Hu, Xinli, Huertas-Vazquez, Adriana, Huilaja, Laura, Hunkapiller, Julie, Jacob, Howard, Jensen, Jan-Nygaard, Joensuu, Heikki, John, Sally, Julkunen, Valtteri, Jung, Marc, Junttila, Juhani, Kaarniranta, Kai, Kähönen, Mika, Kajanne, Risto, Kallio, Lila, Kälviäinen, Reetta, Kaprio, Jaakko, Kerimov, Nurlan, Kettunen, Johannes, Kilpeläinen, Elina, Kilpi, Terhi, Klinger, Katherine, Kosma, Veli-Matti, Kuopio, Teijo, Kurra, Venla, Laisk, Triin, Laukkanen, Jari, Lawless, Nathan, Liu, Aoxing, Longerich, Simonne, Mägi, Reedik, Mäkelä, Johanna, Mäkitie, Antti, Malarstig, Anders, Mannermaa, Arto, Maranville, Joseph, Matakidou, Athena, Meretoja, Tuomo, Mozaffari, Sahar V., Niemi, Mari E. K., Niemi, Marianna, Niiranen, Teemu, O´Donnell, Christopher J., Obeidat, Ma´en, Okafo, George, Ollila, Hanna M., Palomäki, Antti, Palotie, Tuula, Partanen, Jukka, Paul, Dirk S., Pelkonen, Margit, Pendergrass, Rion K., Petrovski, Slavé, Pitkäranta, Anne, Platt, Adam, Pulford, David, Punkka, Eero, Pussinen, Pirkko, Raghavan, Neha, Rahimov, Fedik, Rajpal, Deepak, Renaud, Nicole A., Riley-Gillis, Bridget, Rodosthenous, Rodosthenis, Saarentaus, Elmo, Salminen, Aino, Salminen, Eveliina, Salomaa, Veikko, Schleutker, Johanna, Serpi, Raisa, Shen, Huei-yi, Siegel, Richard, Silander, Kaisa, Siltanen, Sanna, Soini, Sirpa, Soininen, Hilkka, Sul, Jae Hoon, Tachmazidou, Ioanna, Tasanen, Kaisa, Tienari, Pentti, Toppila-Salmi, Sanna, Tukiainen, Taru, Tuomi, Tiinamaija, Turunen, Joni A., Ulirsch, Jacob C., Vaura, Felix, Virolainen, Petri, Waring, Jeffrey, Waterworth, Dawn, Yang, Robert, Nelis, Mari, Reigo, Anu, Metspalu, Andres, Milani, Lili, Esko, Tõnu, Fox, Caroline, Havulinna, Aki S., Perola, Markus, Ripatti, Samuli, Jalanko, Anu, Laitinen, Tarja, Mäkelä, Tomi P., Plenge, Robert, McCarthy, Mark, Runz, Heiko, Daly, Mark J., and Palotie, Aarno

Published
2023
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48. Genetics of 35 blood and urine biomarkers in the UK Biobank

Author

Sinnott-Armstrong, Nasa, Tanigawa, Yosuke, Amar, David, Mars, Nina, Benner, Christian, Aguirre, Matthew, Venkataraman, Guhan Ram, Wainberg, Michael, Ollila, Hanna M., Kiiskinen, Tuomo, Havulinna, Aki S., Pirruccello, James P., Qian, Junyang, Shcherbina, Anna, Rodriguez, Fatima, Assimes, Themistocles L., Agarwala, Vineeta, Tibshirani, Robert, Hastie, Trevor, Ripatti, Samuli, Pritchard, Jonathan K., Daly, Mark J., and Rivas, Manuel A.

Published
2021
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49. The effects of a 2-year physical activity and dietary intervention on plasma lipid concentrations in children: the PANIC Study

Author

Eloranta, Aino-Maija, Sallinen, Taisa, Viitasalo, Anna, Lintu, Niina, Väistö, Juuso, Jalkanen, Henna, Tompuri, Tuomo T., Soininen, Sonja, Haapala, Eero A., Kiiskinen, Sanna, Schnurr, Theresia M., Kilpeläinen, Tuomas O., Mikkonen, Santtu, Savonen, Kai, Atalay, Mustafa, Brage, Soren, Laaksonen, David E., Lindi, Virpi, Ågren, Jyrki, Schwab, Ursula, Jääskeläinen, Jarmo, and Lakka, Timo A.

Published
2021
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50. The framework of the Päijät-Häme Sport, Experiences, and Well-being Road Map 2030

Author

Ilkka Väänänen, Kirsi Kiiskinen, and Kati Peltonen

Subjects
smart specialisation, regional innovative capacity, competitiveness, business development, digitalization, cooperation, sustainable solutions, internationalization, Regional economics. Space in economics, HT388, Regional planning, HT390-395
Abstract

An inductive, qualitative content approach and participating co-creation were used to establish the Road Map for Sport, Experiences, and Well-being 2030 framework for the Päijät-Häme region of Finland. The themes of development are: With the development of conditions and infrastructure towards versatile and accessible actions; With digitalization towards effective accessibility; With the shared brand message towards an excellent customer experience; and With strong expertise towards seamless cooperation. The framework illustrates business development, sustainable solutions, dissemination and internationalization as cross-cutting perspectives. Furthermore, it provides regional guidelines for long-term collaborative development work; the identification of new synergies; the development of the sport, tourism and well-being business; and the strengthening of systematic and experimental innovation. It is a comprehensive regional plan for social interaction, and political–administrative decision-making and practice in Päijät-Häme. The framework can be used in other regions in a cross-administrative way to identify the specific characteristics of a region, highlight competitive advantages and outline the excellence of stakeholders.

Published
2021
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