Samuli Ripatti

99.1k total citations · 4 hit papers
168 papers, 8.5k citations indexed

About

Samuli Ripatti is a scholar working on Genetics, Molecular Biology and Surgery. According to data from OpenAlex, Samuli Ripatti has authored 168 papers receiving a total of 8.5k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Genetics, 43 papers in Molecular Biology and 28 papers in Surgery. Recurrent topics in Samuli Ripatti's work include Genetic Associations and Epidemiology (77 papers), Genetic and phenotypic traits in livestock (18 papers) and Lipoproteins and Cardiovascular Health (17 papers). Samuli Ripatti is often cited by papers focused on Genetic Associations and Epidemiology (77 papers), Genetic and phenotypic traits in livestock (18 papers) and Lipoproteins and Cardiovascular Health (17 papers). Samuli Ripatti collaborates with scholars based in Finland, United States and United Kingdom. Samuli Ripatti's co-authors include Veikko Salomaa, Aarno Palotie, Aki S. Havulinna, Juni Palmgren, Matti Pirinen, Jarmo Virtamo, Philip R. Taylor, Demetrius Albanes, Olli P. Heinonen and Jussi K. Huttunen and has published in prestigious journals such as The Lancet, Nature Medicine and Nature Communications.

In The Last Decade

Samuli Ripatti

160 papers receiving 8.3k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Prostate Cancer and Supplementation With α-Tocopherol and β-Carotene: Incidence and Mortality in a Controlled Trial

1998 665 citations Samuli Ripatti et al. profile →
FINEMAP: efficient variable selection using summary data from genome-wide association studies

2016 350 citations Christian Benner, Chris C. A. Spencer et al. Bioinformatics profile →
Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers

2020 245 citations Nina Mars, Jukka Koskela et al. profile →
Genome-wide association analysis of plasma lipidome identifies 495 genetic associations

2023 103 citations Rubina Tabassum, Sanni Ruotsalainen et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Samuli Ripatti	2.7k	2.4k	1.3k	1.0k	959	168	8.5k
Susan E. Hankinson	2.8k 1.0×	2.5k 1.0×	1.9k 1.4×	679 0.7×	706 0.7×	134	13.2k
Paola Muti	1.8k 0.7×	3.9k 1.6×	1.2k 0.9×	802 0.8×	717 0.7×	253	12.2k
Nilanjan Chatterjee	4.9k 1.8×	4.0k 1.6×	1.1k 0.8×	671 0.7×	307 0.3×	254	12.9k
Emily White	2.0k 0.7×	1.9k 0.8×	1.7k 1.3×	982 1.0×	303 0.3×	232	13.5k
A. Heather Eliassen	2.1k 0.8×	2.1k 0.9×	1.6k 1.3×	417 0.4×	367 0.4×	320	10.8k
Kathy J. Helzlsouer	2.0k 0.7×	2.6k 1.1×	712 0.6×	804 0.8×	208 0.2×	156	9.9k
John A. Lawson	1.5k 0.6×	3.3k 1.4×	2.5k 1.9×	1.2k 1.2×	2.2k 2.2×	250	15.4k
Shelley S. Tworoger	2.0k 0.7×	2.3k 1.0×	2.3k 1.8×	578 0.6×	425 0.4×	340	13.4k
Yong‐Bing Xiang	925 0.3×	2.4k 1.0×	1.5k 1.2×	798 0.8×	396 0.4×	284	9.2k
Marie‐Claude Vohl	2.3k 0.9×	3.8k 1.6×	3.1k 2.4×	1.8k 1.8×	965 1.0×	327	10.2k

Countries citing papers authored by Samuli Ripatti

Since Specialization

Citations

This map shows the geographic impact of Samuli Ripatti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Samuli Ripatti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuli Ripatti more than expected).

Fields of papers citing papers by Samuli Ripatti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuli Ripatti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Samuli Ripatti. The network helps show where Samuli Ripatti may publish in the future.

Co-authorship network of co-authors of Samuli Ripatti

This figure shows the co-authorship network connecting the top 25 collaborators of Samuli Ripatti. A scholar is included among the top collaborators of Samuli Ripatti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Samuli Ripatti. Samuli Ripatti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Strausz, Satu, Martin Broberg, Samuel E. Jones, et al.. (2025). Genetic associations between serotonin receptor 1F (HTR1F) regulatory variation and sleep apnoea in non-obese individuals: insights from GWAS and eQTL analyses. European Respiratory Journal. 66(3). 2401778–2401778.

Mykkänen, Juha, Saku Ruohonen, Katja Pahkala, et al.. (2024). Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data. BMC Medical Informatics and Decision Making. 24(1). 116–116. 14 indexed citations

Koivunen, Kaisa, Teemu Palviainen, Urho M. Kujala, et al.. (2024). Genome-Wide Polygenic Score for Muscle Strength Predicts Risk for Common Diseases and Lifespan: A Prospective Cohort Study. The Journals of Gerontology Series A. 79(4). 1 indexed citations

Tabassum, Rubina, Sanni Ruotsalainen, Mathias J. Gerl, et al.. (2022). Lipidome‐ and Genome‐Wide Study to Understand Sex Differences in Circulatory Lipids. Journal of the American Heart Association. 11(19). e027103–e027103. 38 indexed citations

Moustgaard, Heta, et al.. (2022). Association between genetic risk of alcohol consumption and alcohol‐related morbidity and mortality under different alcohol policy conditions: Evidence from the Finnish alcohol price reduction of 2004. Addiction. 118(4). 678–685. 3 indexed citations

Kullo, Iftikhar J., Cathryn M. Lewis, Michael Inouye, et al.. (2022). Polygenic scores in biomedical research. Nature Reviews Genetics. 23(9). 524–532. 82 indexed citations

Widén, Elisabeth, Nella Junna, Sanni Ruotsalainen, et al.. (2022). How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior: an Observational Follow-up Study. Circulation Genomic and Precision Medicine. 15(2). e003459–e003459. 69 indexed citations

Strausz, Satu, Tuomo Kiiskinen, Martin Broberg, et al.. (2021). Sleep apnoea is a risk factor for severe COVID-19. BMJ Open Respiratory Research. 8(1). e000845–e000845. 74 indexed citations

Lee, Jiwoo, Tuomo Kiiskinen, Nina Mars, et al.. (2021). Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome. Circulation Genomic and Precision Medicine. 14(4). e003283–e003283. 4 indexed citations

10.

Silventoinen, Karri, Kaarina Korhonen, Aline Jelenkovic, et al.. (2021). Joint associations of depression, genetic susceptibility and the area of residence for coronary heart disease incidence. Journal of Epidemiology & Community Health. 76(3). 281–284. 1 indexed citations

11.

Saarentaus, Elmo, Aki S. Havulinna, Nina Mars, et al.. (2021). Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants. Molecular Psychiatry. 26(9). 4884–4895. 7 indexed citations

12.

Ripatti, Pietari, Joel Rämö, Nina Mars, et al.. (2020). Polygenic Hyperlipidemias and Coronary Artery Disease Risk. UTUPub (University of Turku). 55 indexed citations

13.

Ganna, Andrea, Mitja Kurki, Aki S. Havulinna, et al.. (2018). Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum. Työväentutkimus Vuosikirja. 54 indexed citations

14.

Pirinen, Matti, Christian Benner, Pekka Marttinen, et al.. (2017). biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements. Bioinformatics. 33(15). 2405–2407. 5 indexed citations

15.

Cichońska, Anna, Juho Rousu, Pekka Marttinen, et al.. (2016). metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics. 32(13). 1981–1989. 78 indexed citations

16.

Benner, Christian, Chris C. A. Spencer, Aki S. Havulinna, et al.. (2016). FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 32(10). 1493–1501. 350 indexed citations breakdown →

17.

Granér, Marit, Emmi Tikkanen, Samuli Ripatti, et al.. (2013). Diagnostic efficacy of myeloperoxidase to identify acute coronary syndrome in subjects with chest pain. Annals of Medicine. 45(4). 322–327. 5 indexed citations

18.

Würtz, Peter, Ville‐Petteri Mäkinen, Pasi Soininen, et al.. (2012). Metabolic Signatures of Insulin Resistance in 7,098 Young Adults. Diabetes. 61(6). 1372–1380. 235 indexed citations

19.

Peltonen, Leena, Jussi Naukkarinen, Ida Surakka, et al.. (2010). . DSpace@MIT (Massachusetts Institute of Technology). 57 indexed citations

20.

Surakka, Ida, Kati Kristiansson, Michael Inouye, et al.. (2010). Founder population-specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging. STM:n Hallinnonalan avoin julkaisuarkisto (Julkari). 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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