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1,778 results on '"AKI, S."'

1. Greengenes2 unifies microbial data in a single reference tree

Author

McDonald, Daniel, Jiang, Yueyu, Balaban, Metin, Cantrell, Kalen, Zhu, Qiyun, Gonzalez, Antonio, Morton, James T, Nicolaou, Giorgia, Parks, Donovan H, Karst, Søren M, Albertsen, Mads, Hugenholtz, Philip, DeSantis, Todd, Song, Se Jin, Bartko, Andrew, Havulinna, Aki S, Jousilahti, Pekka, Cheng, Susan, Inouye, Michael, Niiranen, Teemu, Jain, Mohit, Salomaa, Veikko, Lahti, Leo, Mirarab, Siavash, and Knight, Rob

Subjects
Microbiology, Biological Sciences, Genetics, Human Genome, RNA, Ribosomal, 16S, Phylogeny, Metagenomics, Bacteria, Databases, Genetic, Metagenome
Abstract

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

Published
2024

3. Genome-wide association meta-analysis identifies five loci associated with postpartum hemorrhage

Author

Westergaard, David, Steinthorsdottir, Valgerdur, Stefansdottir, Lilja, Rohde, Palle Duun, Wu, Xiaoping, Geller, Frank, Tyrmi, Jaakko, Havulinna, Aki S., Solé-Navais, Pol, Flatley, Christopher, Ostrowski, Sisse Rye, Pedersen, Ole Birger, Erikstrup, Christian, Sørensen, Erik, Mikkelsen, Christina, Bruun, Mie Topholm, Aagaard Jensen, Bitten, Brodersen, Thorsten, Ullum, Henrik, Magnus, Per, Andreassen, Ole A., Njolstad, Pål R., Kolte, Astrid Marie, Krebs, Lone, Nyegaard, Mette, Hansen, Thomas Folkmann, Feenstra, Bjarke, Daly, Mark, Lindgren, Cecilia M., Thorleifsson, Gudmar, Stefansson, Olafur A., Sveinbjornsson, Gardar, Gudbjartsson, Daniel F., Thorsteinsdottir, Unnur, Banasik, Karina, Jacobsson, Bo, Laisk, Triin, Laivuori, Hannele, Stefansson, Kari, Brunak, Søren, and Nielsen, Henriette Svarre

Published
2024
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4. Functional EPAS1/HIF2A missense variant is associated with hematocrit in Andean highlanders

Author

Lawrence, Elijah S, Gu, Wanjun, Bohlender, Ryan J, Anza-Ramirez, Cecilia, Cole, Amy M, Yu, James J, Hu, Hao, Heinrich, Erica C, O’Brien, Katie A, Vasquez, Carlos A, Cowan, Quinn T, Bruck, Patrick T, Mercader, Kysha, Alotaibi, Mona, Long, Tao, Hall, James E, Moya, Esteban A, Bauk, Marco A, Reeves, Jennifer J, Kong, Mitchell C, Salem, Rany M, Vizcardo-Galindo, Gustavo, Macarlupu, Jose-Luis, Figueroa-Mujíca, Rómulo, Bermudez, Daniela, Corante, Noemi, Gaio, Eduardo, Fox, Keolu P, Salomaa, Veikko, Havulinna, Aki S, Murray, Andrew J, Malhotra, Atul, Powel, Frank L, Jain, Mohit, Komor, Alexis C, Cavalleri, Gianpiero L, Huff, Chad D, Villafuerte, Francisco C, and Simonson, Tatum S

Subjects
Biological Sciences, Genetics, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Humans, Adaptation, Physiological, Altitude, East Asian People, Hematocrit, Hypoxia, Mutation, Missense, South American People
Abstract

Hypoxia-inducible factor pathway genes are linked to adaptation in both human and nonhuman highland species. EPAS1, a notable target of hypoxia adaptation, is associated with relatively lower hemoglobin concentration in Tibetans. We provide evidence for an association between an adaptive EPAS1 variant (rs570553380) and the same phenotype of relatively low hematocrit in Andean highlanders. This Andean-specific missense variant is present at a modest frequency in Andeans and absent in other human populations and vertebrate species except the coelacanth. CRISPR-base-edited human cells with this variant exhibit shifts in hypoxia-regulated gene expression, while metabolomic analyses reveal both genotype and phenotype associations and validation in a lowland population. Although this genocopy of relatively lower hematocrit in Andean highlanders parallels well-replicated findings in Tibetans, it likely involves distinct pathway responses based on a protein-coding versus noncoding variants, respectively. These findings illuminate how unique variants at EPAS1 contribute to the same phenotype in Tibetans and a subset of Andean highlanders despite distinct evolutionary trajectories.

Published
2024

7. Associations between gut microbiota and incident fractures in the FINRISK cohort

Author

Louise Grahnemo, Oleg Kambur, Leo Lahti, Pekka Jousilahti, Teemu Niiranen, Rob Knight, Veikko Salomaa, Aki S. Havulinna, and Claes Ohlsson

Subjects
Microbial ecology, QR100-130
Abstract

Abstract The gut microbiota (GM) can regulate bone mass, but its association with incident fractures is unknown. We used Cox regression models to determine whether the GM composition is associated with incident fractures in the large FINRISK 2002 cohort (n = 7043, 1092 incident fracture cases, median follow-up time 18 years) with information on GM composition and functionality from shotgun metagenome sequencing. Higher alpha diversity was associated with decreased fracture risk (hazard ratio [HR] 0.92 per standard deviation increase in Shannon index, 95% confidence interval 0.87–0.96). For beta diversity, the first principal component was associated with fracture risk (Aitchison distance, HR 0.90, 0.85–0.96). In predefined phyla analyses, we observed that the relative abundance of Proteobacteria was associated with increased fracture risk (HR 1.14, 1.07–1.20), while the relative abundance of Tenericutes was associated with decreased fracture risk (HR 0.90, 0.85–0.96). Explorative sub-analyses within the Proteobacteria phylum showed that higher relative abundance of Gammaproteobacteria was associated with increased fracture risk. Functionality analyses showed that pathways related to amino acid metabolism and lipopolysaccharide biosynthesis associated with fracture risk. The relative abundance of Proteobacteria correlated with pathways for amino acid metabolism, while the relative abundance of Tenericutes correlated with pathways for butyrate synthesis. In conclusion, the overall GM composition was associated with incident fractures. The relative abundance of Proteobacteria, especially Gammaproteobacteria, was associated with increased fracture risk, while the relative abundance of Tenericutes was associated with decreased fracture risk. Functionality analyses demonstrated that pathways known to regulate bone health may underlie these associations.

Published
2024
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8. Divergent trends in the incidence and mortality of acute myocardial ischaemic syndrome, especially in women. Evidence from Finland in 1996–2021

Author

Atte Kallström, Ida Holopainen, Oleg Kambur, Markus Perola, Veikko Salomaa, Aki S. Havulinna, Markus Ramste, and Juha Sinisalo

Subjects
epidemiology, acute myocardial ischaemic syndrome, incidence, mortality, women, Medicine
Abstract

Objective Although the incidence and case fatality (CF) of acute myocardial ischaemic syndrome (AMIS) have declined in recent decades, some studies have suggested a potential stagnation in this decline. We examined if a similar development in AMIS trends can be observed in Finland from 1996 to 2021 among persons aged 35–74 years.Methods We linked Finnish country-wide Hospital Discharge- and Causes of Death- Registers covering the first non-fatal and fatal myocardial ischaemic events (total 69 906 442 person-years at risk). We analyzed the incidence, mortality, and 28-day CF and their trends using negative binomial, Poisson, segmented and logistic regression adjusting for age and sex.Results The analysis consisted of 186 489 non-fatal and 72 907 fatal myocardial ischaemic events. AMIS incidence declined in men (annual percentage change (APC) −2.0%) and in older women (APC of 55–64 years −1.5%; 65–74 years −3.3%) during the study period. However, the incidence decline slowed down over the last decade in oldest age groups and stopped overall in women. Incidence was unchanged during the study period in younger women aged 35-54 years. AMIS mortality and CF declined (APC of mortality in men −4.4%; in women −5.0%; APC of CF in men −2.7%; in women −3.3%).Conclusions AMIS mortality declined in all groups, but the decline in AMIS incidence slowed down and even stopped in women. Incidence was unchanged during the study period in women aged 35-54 years. These results emphasize the need for further efforts in prevention of cardiovascular disease, particularly in young and middle-aged women.

Published
2024
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9. Correction for Zhu et al., 'Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy'

Author

Qiyun Zhu, Shi Huang, Antonio Gonzalez, Imran McGrath, Daniel McDonald, Niina Haiminen, George Armstrong, Yoshiki Vázquez-Baeza, Julian Yu, Justin Kuczynski, Gregory D. Sepich-Poore, Austin D. Swafford, Promi Das, Justin P. Shaffer, Franck Lejzerowicz, Pedro Belda-Ferre, Aki S. Havulinna, Guillaume Méric, Teemu Niiranen, Leo Lahti, Veikko Salomaa, Ho-Cheol Kim, Mohit Jain, Michael Inouye, Jack A. Gilbert, and Rob Knight

Subjects
Microbiology, QR1-502
Published
2024
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10. Gut microbiome and atrial fibrillation—results from a large population-based study

Author

Palmu, Joonatan, Börschel, Christin S, Ortega-Alonso, Alfredo, Markó, Lajos, Inouye, Michael, Jousilahti, Pekka, Salido, Rodolfo A, Sanders, Karenina, Brennan, Caitriona, Humphrey, Gregory C, Sanders, Jon G, Gutmann, Friederike, Linz, Dominik, Salomaa, Veikko, Havulinna, Aki S, Forslund, Sofia K, Knight, Rob, Lahti, Leo, Niiranen, Teemu, and Schnabel, Renate B

Subjects
Epidemiology, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Cardiovascular, Heart Disease, Prevention, Genetics, 2.1 Biological and endogenous factors, Aetiology, Humans, Atrial Fibrillation, Gastrointestinal Microbiome, Heart, Bacteria, Aging, Incidence, Atrial fibrillation, Gut microbiome, Metagenomics, Public Health and Health Services, Clinical sciences
Abstract

BackgroundAtrial fibrillation (AF) is an important heart rhythm disorder in aging populations. The gut microbiome composition has been previously related to cardiovascular disease risk factors. Whether the gut microbial profile is also associated with the risk of AF remains unknown.MethodsWe examined the associations of prevalent and incident AF with gut microbiota in the FINRISK 2002 study, a random population sample of 6763 individuals. We replicated our findings in an independent case-control cohort of 138 individuals in Hamburg, Germany.FindingsMultivariable-adjusted regression models revealed that prevalent AF (N = 116) was associated with nine microbial genera. Incident AF (N = 539) over a median follow-up of 15 years was associated with eight microbial genera with false discovery rate (FDR)-corrected P

Published
2023

11. The gut microbiome is a significant risk factor for future chronic lung disease

Author

Liu, Yang, Teo, Shu Mei, Méric, Guillaume, Tang, Howard HF, Zhu, Qiyun, Sanders, Jon G, Vázquez-Baeza, Yoshiki, Verspoor, Karin, Vartiainen, Ville A, Jousilahti, Pekka, Lahti, Leo, Niiranen, Teemu, Havulinna, Aki S, Knight, Rob, Salomaa, Veikko, and Inouye, Michael

Subjects
Asthma, Lung, Chronic Obstructive Pulmonary Disease, Clinical Research, Prevention, Respiratory, Adult, Humans, Gastrointestinal Microbiome, Prospective Studies, Pulmonary Disease, Chronic Obstructive, Risk Factors, COPD, Gut, asthma, metagenomics, microbiome, Immunology, Allergy
Abstract

BackgroundThe gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults.ObjectiveWe sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD).MethodsShallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status.ResultsA total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities.ConclusionsThe gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

Published
2023

13. Potential and risks of water reuse in Brandenburg (Germany) – an interdisciplinary case study

Author

Daniel Dittmann, Alina H. Seelig, Mogens Thalmann, Theresa Wilkes, Veikko Junghans, Daniel Zahn, Sondra Klitzke, Andre Peters, Jens Haberkamp, Thorsten Reemtsma, and Aki S. Ruhl

Subjects
diatrizoic acid, domestic wastewater, emerging contaminants, farmland, organic trace substance, Environmental technology. Sanitary engineering, TD1-1066
Abstract

For Brandenburg, a region in Germany with increasing water shortage and drought events, water reuse can counteract competition scenarios between drinking water supply, agricultural irrigation, and industrial use. Centralized and decentralized sources for reclaimed water are found to potentially substitute 245 or 28% of irrigation water, respectively, in agriculture production in Brandenburg. For such a reuse scenario, the fate of organic micro-pollutants is examined for diatrizoate (DZA) and carbamazepine (CBZ). Retention in local sandy soil and transfer into roots and leaves of arugula are analyzed in lysimeter studies and greenhouse pot experiments. Vertical transport was found for DZA and accumulation in or on arugula roots with a root concentration factor of 1,925 ± 34% but a low bioconcentration factor due to intrinsic molecule properties. CBZ was not found to be mobile in the sandy soil but accumulates in arugula roots and leaves by factors of 70 ± 7% and 155 ± 12%, respectively. Further research on potential plant uptake and groundwater enrichment for more substances is highly recommended as well as tertiary wastewater treatment prior to water reuse. HIGHLIGHTS The volume of treated wastewater exceeds irrigation water by a factor of 2.5 in Brandenburg.; Water reuse could reduce competition between drinking water supply, industry and agriculture.; Organic micro-pollutant pathways for sandy soil to groundwater and to plants are assessed.; Carbamazepine did not reach 10 cm depth in lysimeter studies probably due to acidic soil pH.; Diatrizoate obtained very high concentrations in arugula roots but were low in leaves.;

Published
2024
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14. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author

Ramdas, Shweta, Judd, Jonathan, Graham, Sarah E, Kanoni, Stavroula, Wang, Yuxuan, Surakka, Ida, Wenz, Brandon, Clarke, Shoa L, Chesi, Alessandra, Wells, Andrew, Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Zajac, Greg JM, Wu, Kuan-Han H, Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T, Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Havulinna, Aki S, Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A, Lingren, Todd, Feng, QiPing, Kullo, Iftikhar J, Narita, Akira, Takayama, Jun, Martin, Hilary C, Hunt, Karen A, Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Rasheed, Asif, Hindy, George, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A, Yao, Jie, Manichaikul, Ani, Lee, Wen-Jane, Hsiung, Chao Agnes, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, and Sabater-Lleal, Maria

Subjects
Human Genome, Biotechnology, Genetics, 2.1 Biological and endogenous factors, Aetiology, Chromatin, Genome-Wide Association Study, Genomics, Humans, Lipids, Polymorphism, Single Nucleotide, Million Veterans Program, Global Lipids Genetics Consortium, complex traits, fine-mapping, functional genomics, lipid biology, post-GWAS, regulatory mechanism, variant prioritization, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Published
2022

15. Association between butyrate-producing gut bacteria and the risk of infectious disease hospitalisation: results from two observational, population-based microbiome studies

Author

Kullberg, Robert F J, Wikki, Irina, Haak, Bastiaan W, Kauko, Anni, Galenkamp, Henrike, Peters-Sengers, Hessel, Butler, Joe M, Havulinna, Aki S, Palmu, Joonatan, McDonald, Daniel, Benchraka, Chouaib, Abdel-Aziz, Mahmoud I, Prins, Maria, Maitland van der Zee, Anke H, van den Born, Bert-Jan, Jousilahti, Pekka, de Vos, Willem M, Salomaa, Veikko, Knight, Rob, Lahti, Leo, Nieuwdorp, Max, Niiranen, Teemu, and Wiersinga, W Joost

Published
2024
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17. Serum MMP-8 and TIMP-1 concentrations in a population-based cohort: effects of age, gender, and health status

Author

Julia Ranta, Aki S. Havulinna, Taina Tervahartiala, Katriina Niemi, Ghazal Aarabi, Terhi Vihervaara, Veikko Salomaa, Timo Sorsa, Pirkko J. Pussinen, and Aino Salminen

Subjects
PMNL collagenase, neutrophil collagenase, biomarker, risk factor, coronary artery disease, circulating, Dentistry, RK1-715
Abstract

BackgroundMatrix-degrading proteinases and their regulators, such as matrix metalloproteinase 8 (MMP-8) and tissue inhibitor of matrix metalloproteinase 1 (TIMP-1), may contribute to various pathological events. Elevated MMP-8 concentrations have been associated with e.g., cardiovascular diseases and periodontitis. However, there is little knowledge on the physiological concentrations of these molecules in serum, or the effect of demographic or lifestyle factors on their levels.Design and methodsWe investigated the effect of various demographic characteristics and behavioral habits, such as aging, sex, smoking, and BMI, on serum concentrations of MMP-8 and TIMP-1. We used the FINRISK97 cohort (n = 8,446), which has comprehensive information on demographic and lifestyle factors, clinical data, laboratory measurements, and register data available. Further, we investigated the concentrations of MMP-8, TIMP-1, and the MMP-8/TIMP-1 ratio in different age groups of healthy and diseased participants. A t-test was used to compare log-transformed mean levels in different groups and linear regression was used to evaluate the association between MMP-8 and TIMP-1 and selected diseases and background variables.ResultsMMP-8 levels decreased with increasing age in the whole population and for women, while TIMP-1 concentration increased slightly with age for the whole population and both genders separately (p for linear trend

Published
2024
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18. Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy

Author

Zhu, Qiyun, Huang, Shi, Gonzalez, Antonio, McGrath, Imran, McDonald, Daniel, Haiminen, Niina, Armstrong, George, Vázquez-Baeza, Yoshiki, Yu, Julian, Kuczynski, Justin, Sepich-Poore, Gregory D, Swafford, Austin D, Das, Promi, Shaffer, Justin P, Lejzerowicz, Franck, Belda-Ferre, Pedro, Havulinna, Aki S, Méric, Guillaume, Niiranen, Teemu, Lahti, Leo, Salomaa, Veikko, Kim, Ho-Cheol, Jain, Mohit, Inouye, Michael, Gilbert, Jack A, and Knight, Rob

Subjects
Human Genome, Genetics, Biotechnology, Life Below Water, Humans, Phylogeny, Metagenome, RNA, Ribosomal, 16S, Microbiota, Ecology, operational genomic unit, taxonomy independent, reference phylogeny, UniFrac, supervised learning, metagenomics
Abstract

We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

Published
2022

19. Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults

Author

Ruuskanen, Matti O, Erawijantari, Pande P, Havulinna, Aki S, Liu, Yang, Méric, Guillaume, Tuomilehto, Jaakko, Inouye, Michael, Jousilahti, Pekka, Salomaa, Veikko, Jain, Mohit, Knight, Rob, Lahti, Leo, and Niiranen, Teemu J

Subjects
Nutrition, Diabetes, Prevention, Metabolic and endocrine, Adult, Cohort Studies, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Female, Finland, Gastrointestinal Microbiome, Humans, Male, Middle Aged
Abstract

ObjectiveTo examine the previously unknown long-term association between gut microbiome composition and incident type 2 diabetes in a representative population cohort.Research design and methodsWe collected fecal samples from 5,572 Finns (mean age 48.7 years; 54.1% women) in 2002 who were followed up for incident type 2 diabetes until 31 December 2017. The samples were sequenced using shotgun metagenomics. We examined associations between gut microbiome composition and incident diabetes using multivariable-adjusted Cox regression models. We first used the eastern Finland subpopulation to obtain initial findings and validated these in the western Finland subpopulation.ResultsAltogether, 432 cases of incident diabetes occurred over the median follow-up of 15.8 years. We detected four species and two clusters consistently associated with incident diabetes in the validation models. These four species were Clostridium citroniae (hazard ratio [HR] 1.21; 95% CI 1.04-1.42), C. bolteae (HR 1.20; 95% CI 1.04-1.39), Tyzzerella nexilis (HR 1.17; 95% CI 1.01-1.36), and Ruminococcus gnavus (HR 1.17; 95% CI 1.01-1.36). The positively associated clusters, cluster 1 (HR 1.18; 95% CI 1.02-1.38) and cluster 5 (HR 1.18; 95% CI 1.02-1.36), mostly consisted of these same species.ConclusionsWe observed robust species-level taxonomic features predictive of incident type 2 diabetes over long-term follow-up. These findings build on and extend previous mainly cross-sectional evidence and further support links between dietary habits, metabolic diseases, and type 2 diabetes that are modulated by the gut microbiome. The gut microbiome can potentially be used to improve disease prediction and uncover novel therapeutic targets for diabetes.

Published
2022

20. Prioritization of Kidney Cell Types Highlights Myofibroblast Cells in Regulating Human Blood Pressure

Author

Evangelou, Evangelos, Warren, Helen R., Gao, He, Ntritsos, Georgios, Dimou, Niki, Esko, Tonu, Mägi, Reedik, Milani, Lili, Almgren, Peter, Boutin, Thibaud, Debette, Stéphanie, Ding, Jun, Giulianini, Franco, Holliday, Elizabeth G., Jackson, Anne U., Li -Gao, Ruifang, Lin, Wei -Yu, Luan, Jian'an, Mangino, Massimo, Oldmeadow, Christopher, Prins, Bram Peter, Qian, Yong, Sargurupremraj, Muralidharan, Shah, Nabi, Surendran, Praveen, Thériault, Sébastien, Verweij, Niek, Willems, Sara M., Zhao, Jing -Hua, Amouyel, Philippe, Connell, John, de Mutsert, Renée, Doney, Alex S.F., Farrall, Martin, Menni, Cristina, Morris, Andrew D., Noordam, Raymond, Paré, Guillaume, Poulter, Neil R., Shields, Denis C., Stanton, Alice, Thom, Simon, Abecasis, Gonçalo, Amin, Najaf, Arking, Dan E., Ayers, Kristin L., Barbieri, Caterina M., Batini, Chiara, Bis, Joshua C., Blake, Tineka, Bochud, Murielle, Boehnke, Michael, Boerwinkle, Eric, Boomsma, Dorret I., Bottinger, Erwin P., Braund, Peter S., Brumat, Marco, Campbell, Archie, Campbell, Harry, Chakravarti, Aravinda, Chambers, John C., Chauhan, Ganesh, Ciullo, Marina, Cocca, Massimiliano, Collins, Francis, Cordell, Heather J., Davies, Gail, de Borst, Martin H., de Geus, Eco J., Deary, Ian J., Deelen, Joris, Del Greco M, Fabiola, Demirkale, Cumhur Yusuf, Dörr, Marcus, Ehret, Georg B., Elosua, Roberto, Enroth, Stefan, Erzurumluoglu, A. Mesut, Ferreira, Teresa, Frånberg, Mattias, Franco, Oscar H., Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Gieger, Christian, Girotto, Giorgia, Goel, Anuj, Gow, Alan J., Gudnason, Vilmundur, Guo, Xiuqing, Gyllensten, Ulf, Hamsten, Anders, Harris, Tamara B., Harris, Sarah E., Hartman, Catharina A., Havulinna, Aki S., Hicks, Andrew A., Hofer, Edith, Hofman, Albert, Hottenga, Jouke-Jan, Huffman, Jennifer E., Hwang, Shih-Jen, Ingelsson, Erik, James, Alan, Jansen, Rick, Jarvelin, Marjo -Riitta, Joehanes, Roby, Johansson, Åsa, Johnson, Andrew D., Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Jula, Antti, Kähönen, Mika, Kathiresan, Sekar, Keavney, Bernard D., Khaw, Kay-Tee, Knekt, Paul, Knight, Joanne, Kolcic, Ivana, Kooner, Jaspal S., Koskinen, Seppo, Kristiansson, Kati, Kutalik, Zoltan, Laan, Maris, Larson, Marty, Launer, Lenore J., Lehne, Benjamin, Lehtimäki, Terho, Liewald, David C.M., Lin, Li, Lind, Lars, Lindgren, Cecilia M., Liu, YongMei, Loos, Ruth J.F., Lopez, Lorna M., Lu, Yingchang, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamasoula, Chrysovalanto, Marrugat, Jaume, Marten, Jonathan, Milaneschi, Yuri, Morgan, Anna, Morris, Andrew P., Morrison, Alanna C., Munson, Peter J., Nalls, Mike A., Nandakumar, Priyanka, Nelson, Christopher P., Niiranen, Teemu, Nolte, Ilja M., Nutile, Teresa, Oldehinkel, Albertine J., Oostra, Ben A., O'Reilly, Paul F., Org, Elin, Padmanabhan, Sandosh, Palmas, Walter, Palotie, Aarno, Pattie, Alison, Penninx, Brenda W.J.H., Perola, Markus, Peters, Annette, Polasek, Ozren, Pramstaller, Peter P., Nguyen, Quang Tri, Raitakari, Olli T., Rettig, Rainer, Rice, Kenneth, Ridker, Paul M., Ried, Janina S., Riese, Harriëtte, Ripatti, Samuli, Robino, Antonietta, Rose, Lynda M., Rotter, Jerome I., Rudan, Igor, Ruggiero, Daniela, Saba, Yasaman, Sala, Cinzia F., Salomaa, Veikko, Samani, Nilesh J., Sarin, Antti-Pekka, Schmidt, Reinhold, Schmidt, Helena, Shrine, Nick, Siscovick, David, Smith, Albert V., Snieder, Harold, Sõber, Siim, Sorice, Rossella, Starr, John M., Stott, David J., Strachan, David P., Strawbridge, Rona J., Sundström, Johan, Swertz, Morris A., Taylor, Kent D., Teumer, Alexander, Tobin, Martin D., Tomaszewski, Maciej, Toniolo, Daniela, Traglia, Michela, Trompet, Stella, Tuomilehto, Jaakko, Tzourio, Christophe, Uitterlinden, André G., Vaez, Ahmad, van der Most, Peter J., van Duijn, Cornelia M., Verwoert, Germaine C., Vitart, Veronique, Völker, Uwe, Vollenweider, Peter, Vuckovic, Dragana, Watkins, Hugh, Wild, Sarah H., Willemsen, Gonneke, Wilson, James F., Wright, Alan F., Yao, Jie, Zemunik, Tatijana, Zhang, Weihua, Attia, John R., Butterworth, Adam S., Chasman, Daniel I., Conen, David, Cucca, Francesco, Danesh, John, Hayward, Caroline, Howson, Joanna M.M., Laakso, Markku, Lakatta, Edward G., Langenberg, Claudia, Melander, Olle, Mook-Kanamori, Dennis O., Palmer, Colin N.A., Risch, Lorenz, Scott, Robert A., Scott, Rodney J., Sever, Peter, Spector, Tim D., van der Harst, Pim, Wareham, Nicholas J., Zeggini, Eleftheria, Levy, Daniel, Munroe, Patricia B., Newton-Cheh, Christopher, Brown, Morris J., Metspalu, Andres, Psaty, Bruce M., Wain, Louise V., Elliott, Paul, Caulfield, Mark J., Ganji-Arjenaki, Mahboube, Kamali, Zoha, Sardari, Soroush, and de Borst, Martin

Published
2024
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21. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort

Author

Qin, Youwen, Havulinna, Aki S, Liu, Yang, Jousilahti, Pekka, Ritchie, Scott C, Tokolyi, Alex, Sanders, Jon G, Valsta, Liisa, Brożyńska, Marta, Zhu, Qiyun, Tripathi, Anupriya, Vázquez-Baeza, Yoshiki, Loomba, Rohit, Cheng, Susan, Jain, Mohit, Niiranen, Teemu, Lahti, Leo, Knight, Rob, Salomaa, Veikko, Inouye, Michael, and Méric, Guillaume

Subjects
Microbiology, Biological Sciences, Genetics, Prevention, Digestive Diseases, Nutrition, Human Genome, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Oral and gastrointestinal, ABO Blood-Group System, Bifidobacterium, Clostridiales, Cohort Studies, Colorectal Neoplasms, Depressive Disorder, Major, Diet, Dietary Fiber, Enterococcus faecalis, Gastrointestinal Microbiome, Gastrointestinal Tract, Genetic Variation, Genome-Wide Association Study, Host Microbial Interactions, Humans, Lactase, Mediator Complex, Mendelian Randomization Analysis, Metagenome, Morganella, Polymorphism, Single Nucleotide, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.

Published
2022

22. Author Correction: Greengenes2 unifies microbial data in a single reference tree

Author

McDonald, Daniel, Jiang, Yueyu, Balaban, Metin, Cantrell, Kalen, Zhu, Qiyun, Gonzalez, Antonio, Morton, James T., Nicolaou, Giorgia, Parks, Donovan H., Karst, Søren M., Albertsen, Mads, Hugenholtz, Philip, DeSantis, Todd, Song, Se Jin, Bartko, Andrew, Havulinna, Aki S., Jousilahti, Pekka, Cheng, Susan, Inouye, Michael, Niiranen, Teemu, Jain, Mohit, Salomaa, Veikko, Lahti, Leo, Mirarab, Siavash, and Knight, Rob

Published
2024
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23. The power of genetic diversity in genome-wide association studies of lipids

Author

Graham, Sarah E, Clarke, Shoa L, Wu, Kuan-Han H, Kanoni, Stavroula, Zajac, Greg JM, Ramdas, Shweta, Surakka, Ida, Ntalla, Ioanna, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Hwang, Mi Yeong, Han, Sohee, Narita, Akira, Choudhury, Ananyo, Bentley, Amy R, Ekoru, Kenneth, Verma, Anurag, Trivedi, Bhavi, Martin, Hilary C, Hunt, Karen A, Hui, Qin, Klarin, Derek, Zhu, Xiang, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Ruotsalainen, Sanni E, Havulinna, Aki S, Veturi, Yogasudha, Feng, QiPing, Rosenthal, Elisabeth A, Lingren, Todd, Pacheco, Jennifer Allen, Pendergrass, Sarah A, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Hindy, George, Rasheed, Asif, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Willemsen, Gonneke, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Yao, Jie, Manichaikul, Ani, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Sidore, Carlo, Fiorillo, Edoardo, McDaid, Aaron F, Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, and Sattar, Naveed

Subjects
Heart Disease, Prevention, Human Genome, Genetics, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Cardiovascular Diseases, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Population Groups, VA Million Veteran Program, Global Lipids Genetics Consortium*, General Science & Technology
Abstract

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

Published
2021

24. Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes

Author

Armstrong, George, Cantrell, Kalen, Huang, Shi, McDonald, Daniel, Haiminen, Niina, Carrieri, Anna Paola, Zhu, Qiyun, Gonzalez, Antonio, McGrath, Imran, Beck, Kristen L, Hakim, Daniel, Havulinna, Aki S, Méric, Guillaume, Niiranen, Teemu, Lahti, Leo, Salomaa, Veikko, Jain, Mohit, Inouye, Michael, Swafford, Austin D, Kim, Ho-Cheol, Parida, Laxmi, Vázquez-Baeza, Yoshiki, and Knight, Rob

Subjects
Microbiology, Biological Sciences, Aging, Microbiota, Phylogeny, Medical and Health Sciences, Bioinformatics, Genetics
Abstract

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Published
2021

28. 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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29. Associations of healthy food choices with gut microbiota profiles

Author

Koponen, Kari K, Salosensaari, Aaro, Ruuskanen, Matti O, Havulinna, Aki S, Männistö, Satu, Jousilahti, Pekka, Palmu, Joonatan, Salido, Rodolfo, Sanders, Karenina, Brennan, Caitriona, Humphrey, Gregory C, Sanders, Jon G, Meric, Guillaume, Cheng, Susan, Inouye, Michael, Jain, Mohit, Niiranen, Teemu J, Valsta, Liisa M, Knight, Rob, and Salomaa, Veikko V

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Prevention, 3.3 Nutrition and chemoprevention, Prevention of disease and conditions, and promotion of well-being, Metabolic and endocrine, Adult, Aged, Bacteria, Choice Behavior, Diet Surveys, Diet, Healthy, Female, Food, Gastrointestinal Microbiome, Humans, Male, Middle Aged, cross-sectional study, dietary score, epidemiology, healthy diet, metagenomics, microbiology, microbiota, nutrition, Engineering, Medical and Health Sciences, Nutrition & Dietetics, Clinical sciences, Nutrition and dietetics
Abstract

BackgroundDiet has a major influence on the human gut microbiota, which has been linked to health and disease. However, epidemiological studies on associations of a healthy diet with the microbiota utilizing a whole-diet approach are still scant.ObjectivesTo assess associations between healthy food choices and human gut microbiota composition, and to determine the strength of association with functional potential.MethodsThis population-based study sample consisted of 4930 participants (ages 25-74; 53% women) in the FINRISK 2002 study. Intakes of recommended foods were assessed using a food propensity questionnaire, and responses were transformed into healthy food choices (HFC) scores. Microbial diversity (alpha diversity) and compositional differences (beta diversity) and their associations with the HFC score and its components were assessed using linear regression. Multiple permutational multivariate ANOVAs were run from whole-metagenome shallow shotgun-sequenced samples. Associations between specific taxa and HFC were analyzed using linear regression. Functional associations were derived from Kyoto Encyclopedia of Genes and Genomes orthologies with linear regression models.ResultsBoth microbial alpha diversity (β/SD, 0.044; SE, 6.18 × 10-5; P = 2.21 × 10-3) and beta diversity (R2, 0.12; P ≤ 1.00 × 10-3) were associated with the HFC score. For alpha diversity, the strongest associations were observed for fiber-rich breads, poultry, fruits, and low-fat cheeses (all positive). For beta diversity, the most prominent associations were observed for vegetables, followed by berries and fruits. Genera with fiber-degrading and SCFA-producing capacities were positively associated with the HFC score. The HFC score was associated positively with functions such as SCFA metabolism and synthesis, and inversely with functions such as fatty acid biosynthesis and the sulfur relay system.ConclusionsOur results from a large, population-based survey confirm and extend findings of other, smaller-scale studies that plant- and fiber-rich dietary choices are associated with a more diverse and compositionally distinct microbiota, and with a greater potential to produce SCFAs.

Published
2021

30. Taxonomic signatures of cause-specific mortality risk in human gut microbiome.

Author

Salosensaari, Aaro, Laitinen, Ville, Havulinna, Aki S, Meric, Guillaume, Cheng, Susan, Perola, Markus, Valsta, Liisa, Alfthan, Georg, Inouye, Michael, Watrous, Jeramie D, Long, Tao, Salido, Rodolfo A, Sanders, Karenina, Brennan, Caitriona, Humphrey, Gregory C, Sanders, Jon G, Jain, Mohit, Jousilahti, Pekka, Salomaa, Veikko, Knight, Rob, Lahti, Leo, and Niiranen, Teemu

Subjects
Feces, Humans, Enterobacteriaceae, RNA, Ribosomal, 16S, Population Surveillance, Cause of Death, Risk Factors, Cohort Studies, Cross-Sectional Studies, Adolescent, Adult, Aged, Middle Aged, Finland, Female, Male, Young Adult, Gastrointestinal Microbiome
Abstract

The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.

Published
2021

31. Association of structural variation with cardiometabolic traits in Finns

Author

Chen, Lei, Abel, Haley J, Das, Indraniel, Larson, David E, Ganel, Liron, Kanchi, Krishna L, Regier, Allison A, Young, Erica P, Kang, Chul Joo, Scott, Alexandra J, Chiang, Colby, Wang, Xinxin, Lu, Shuangjia, Christ, Ryan, Service, Susan K, Chiang, Charleston WK, Havulinna, Aki S, Kuusisto, Johanna, Boehnke, Michael, Laakso, Markku, Palotie, Aarno, Ripatti, Samuli, Freimer, Nelson B, Locke, Adam E, Stitziel, Nathan O, and Hall, Ira M

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Alleles, Cardiovascular Diseases, Cholesterol, DNA Copy Number Variations, Female, Finland, Genome, Human, Genomic Structural Variation, Genotype, High-Throughput Nucleotide Sequencing, Humans, Male, Mitochondrial Proteins, Promoter Regions, Genetic, Pyruvate Dehydrogenase (Lipoamide)-Phosphatase, Pyruvic Acid, Serum Albumin, Human, Finnish population, cardiometabolic traits, genome-wide association study, structural variation, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10-54) and is also associated with increased levels of total cholesterol (p = 1.22 × 10-28) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10-21) and alanine (p = 6.14 × 10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10-10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

Published
2021

35. Links between gut microbiome composition and fatty liver disease in a large population sample

Author

Ruuskanen, Matti O, Åberg, Fredrik, Männistö, Ville, Havulinna, Aki S, Méric, Guillaume, Liu, Yang, Loomba, Rohit, Vázquez-Baeza, Yoshiki, Tripathi, Anupriya, Valsta, Liisa M, Inouye, Michael, Jousilahti, Pekka, Salomaa, Veikko, Jain, Mohit, Knight, Rob, Lahti, Leo, and Niiranen, Teemu J

Subjects
Microbiology, Biological Sciences, Liver Disease, Human Genome, Chronic Liver Disease and Cirrhosis, Genetics, Digestive Diseases, Oral and gastrointestinal, Good Health and Well Being, Adult, Age Factors, Bacteria, Clostridium, Cohort Studies, Ethanol, Fatty Liver, Female, Fermentation, Gastrointestinal Microbiome, Genome, Bacterial, Humans, Male, Metagenome, Middle Aged, Phylogeny, Sex Factors, Metagenomics, human gut, fatty liver, fatty liver index, population sample
Abstract

Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 y, but this association remains mostly unstudied in the general population because of underdiagnosis and small sample sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population sample of 6,269 Finnish participants. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between individuals with a high FLI (≥60, indicates likely liver steatosis) and low FLI (

Published
2021

36. Author Correction: Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort

Author

Qin, Youwen, Havulinna, Aki S., Liu, Yang, Jousilahti, Pekka, Ritchie, Scott C., Tokolyi, Alex, Sanders, Jon G., Valsta, Liisa, Brożyńska, Marta, Zhu, Qiyun, Tripathi, Anupriya, Vázquez-Baeza, Yoshiki, Loomba, Rohit, Cheng, Susan, Jain, Mohit, Niiranen, Teemu, Lahti, Leo, Knight, Rob, Salomaa, Veikko, Inouye, Michael, and Méric, Guillaume

Published
2024
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37. 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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38. 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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39. A toolbox of machine learning software to support microbiome analysis

Author

Laura Judith Marcos-Zambrano, Víctor Manuel López-Molina, Burcu Bakir-Gungor, Marcus Frohme, Kanita Karaduzovic-Hadziabdic, Thomas Klammsteiner, Eliana Ibrahimi, Leo Lahti, Tatjana Loncar-Turukalo, Xhilda Dhamo, Andrea Simeon, Alina Nechyporenko, Gianvito Pio, Piotr Przymus, Alexia Sampri, Vladimir Trajkovik, Blanca Lacruz-Pleguezuelos, Oliver Aasmets, Ricardo Araujo, Ioannis Anagnostopoulos, Önder Aydemir, Magali Berland, M. Luz Calle, Michelangelo Ceci, Hatice Duman, Aycan Gündoğdu, Aki S. Havulinna, Kardokh Hama Najib Kaka Bra, Eglantina Kalluci, Sercan Karav, Daniel Lode, Marta B. Lopes, Patrick May, Bram Nap, Miroslava Nedyalkova, Inês Paciência, Lejla Pasic, Meritxell Pujolassos, Rajesh Shigdel, Antonio Susín, Ines Thiele, Ciprian-Octavian Truică, Paul Wilmes, Ercument Yilmaz, Malik Yousef, Marcus Joakim Claesson, Jaak Truu, and Enrique Carrillo de Santa Pau

Subjects
microbiome, machine learning, software, feature generation, feature analysis, data integration, Microbiology, QR1-502
Abstract

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Published
2023
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41. Association Between the Gut Microbiota and Blood Pressure in a Population Cohort of 6953 Individuals

Author

Palmu, Joonatan, Salosensaari, Aaro, Havulinna, Aki S, Cheng, Susan, Inouye, Michael, Jain, Mohit, Salido, Rodolfo A, Sanders, Karenina, Brennan, Caitriona, Humphrey, Gregory C, Sanders, Jon G, Vartiainen, Erkki, Laatikainen, Tiina, Jousilahti, Pekka, Salomaa, Veikko, Knight, Rob, Lahti, Leo, and Niiranen, Teemu J

Subjects
Cardiovascular, Hypertension, Nutrition, Digestive Diseases, Adult, Aged, Blood Pressure, Cohort Studies, Female, Gastrointestinal Microbiome, Humans, Lactobacillus, Male, Metagenome, Middle Aged, Sodium, Dietary, blood pressure, gastrointestinal microbiota, hypertension, salt intake, Cardiorespiratory Medicine and Haematology
Abstract

Background Several small-scale animal studies have suggested that gut microbiota and blood pressure (BP) are linked. However, results from human studies remain scarce and conflicting. We wanted to elucidate the multivariable-adjusted association between gut metagenome and BP in a large, representative, well-phenotyped population sample. We performed a focused analysis to examine the previously reported inverse associations between sodium intake and Lactobacillus abundance and between Lactobacillus abundance and BP. Methods and Results We studied a population sample of 6953 Finns aged 25 to 74 years (mean age, 49.2±12.9 years; 54.9% women). The participants underwent a health examination, which included BP measurement, stool collection, and 24-hour urine sampling (N=829). Gut microbiota was analyzed using shallow shotgun metagenome sequencing. In age- and sex-adjusted models, the α (within-sample) and β (between-sample) diversities of taxonomic composition were strongly related to BP indexes (P

Published
2020

42. Inflammatory and infectious upper respiratory diseases associate with 41 genomic loci and type 2 inflammation

Author

Elmo C. Saarentaus, Juha Karjalainen, Joel T. Rämö, Tuomo Kiiskinen, Aki S. Havulinna, Juha Mehtonen, Heidi Hautakangas, Sanni Ruotsalainen, Max Tamlander, Nina Mars, FINNGEN, Sanna Toppila-Salmi, Matti Pirinen, Mitja Kurki, Samuli Ripatti, Mark Daly, Tuula Palotie, Antti Mäkitie, and Aarno Palotie

Subjects
Science
Abstract

The shared genetics between upper respiratory diseases have not been well studied. Here, the authors find shared and distinct genetic loci for pharyngeal and sinonasal inflammatory conditions, which show shared heritability with autoimmune conditions and immune deficiency, highlighting the TNFR2 pathway.

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

Author

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

Subjects
Science
Abstract

Otosclerosis is a common form of hearing loss, with an unclear genetic etiology. Here, the authors perform a genome-wide association study meta-analysis of otosclerosis identifying 27 genetic loci, pointing to genes involved in bone remodeling, skeletal disorders and transforming growth factor β signaling.

Published
2023
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44. Stroke genetics informs drug discovery and risk prediction across ancestries

Author

Mishra, Aniket, Malik, Rainer, Hachiya, Tsuyoshi, Jürgenson, Tuuli, Namba, Shinichi, Posner, Daniel C., Kamanu, Frederick K., Koido, Masaru, Le Grand, Quentin, Shi, Mingyang, He, Yunye, Georgakis, Marios K., Caro, Ilana, Krebs, Kristi, Liaw, Yi-Ching, Vaura, Felix C., Lin, Kuang, Winsvold, Bendik Slagsvold, Srinivasasainagendra, Vinodh, Parodi, Livia, Bae, Hee-Joon, Chauhan, Ganesh, Chong, Michael R., Tomppo, Liisa, Akinyemi, Rufus, Roshchupkin, Gennady V., Habib, Naomi, Jee, Yon Ho, Thomassen, Jesper Qvist, Abedi, Vida, Cárcel-Márquez, Jara, Nygaard, Marianne, Leonard, Hampton L., Yang, Chaojie, Yonova-Doing, Ekaterina, Knol, Maria J., Lewis, Adam J., Judy, Renae L., Ago, Tetsuro, Amouyel, Philippe, Armstrong, Nicole D., Bakker, Mark K., Bartz, Traci M., Bennett, David A., Bis, Joshua C., Bordes, Constance, Børte, Sigrid, Cain, Anael, Ridker, Paul M., Cho, Kelly, Chen, Zhengming, Cruchaga, Carlos, Cole, John W., de Jager, Phil L., de Cid, Rafael, Endres, Matthias, Ferreira, Leslie E., Geerlings, Mirjam I., Gasca, Natalie C., Gudnason, Vilmundur, Hata, Jun, He, Jing, Heath, Alicia K., Ho, Yuk-Lam, Havulinna, Aki S., Hopewell, Jemma C., Hyacinth, Hyacinth I., Inouye, Michael, Jacob, Mina A., Jeon, Christina E., Jern, Christina, Kamouchi, Masahiro, Keene, Keith L., Kitazono, Takanari, Kittner, Steven J., Konuma, Takahiro, Kumar, Amit, Lacaze, Paul, Launer, Lenore J., Lee, Keon-Joo, Lepik, Kaido, Li, Jiang, Li, Liming, Manichaikul, Ani, Markus, Hugh S., Marston, Nicholas A., Meitinger, Thomas, Mitchell, Braxton D., Montellano, Felipe A., Morisaki, Takayuki, Mosley, Thomas H., Nalls, Mike A., Nordestgaard, Børge G., O’Donnell, Martin J., Okada, Yukinori, Onland-Moret, N. Charlotte, Ovbiagele, Bruce, Peters, Annette, Psaty, Bruce M., Rich, Stephen S., Rosand, Jonathan, Sabatine, Marc S., Sacco, Ralph L., Saleheen, Danish, Sandset, Else Charlotte, Salomaa, Veikko, Sargurupremraj, Muralidharan, Sasaki, Makoto, Satizabal, Claudia L., Schmidt, Carsten O., Shimizu, Atsushi, Smith, Nicholas L., Sloane, Kelly L., Sutoh, Yoichi, Sun, Yan V., Tanno, Kozo, Tiedt, Steffen, Tatlisumak, Turgut, Torres-Aguila, Nuria P., Tiwari, Hemant K., Trégouët, David-Alexandre, Trompet, Stella, Tuladhar, Anil Man, Tybjærg-Hansen, Anne, van Vugt, Marion, Vibo, Riina, Verma, Shefali S., Wiggins, Kerri L., Wennberg, Patrik, Woo, Daniel, Wilson, Peter W. F., Xu, Huichun, Yang, Qiong, Yoon, Kyungheon, Millwood, Iona Y., Gieger, Christian, Ninomiya, Toshiharu, Grabe, Hans J., Jukema, J. Wouter, Rissanen, Ina L., Strbian, Daniel, Kim, Young Jin, Chen, Pei-Hsin, Mayerhofer, Ernst, Howson, Joanna M. M., Irvin, Marguerite R., Adams, Hieab, Wassertheil-Smoller, Sylvia, Christensen, Kaare, Ikram, Mohammad A., Rundek, Tatjana, Worrall, Bradford B., Lathrop, G. Mark, Riaz, Moeen, Simonsick, Eleanor M., Kõrv, Janika, França, Paulo H. C., Zand, Ramin, Prasad, Kameshwar, Frikke-Schmidt, Ruth, de Leeuw, Frank-Erik, Liman, Thomas, Haeusler, Karl Georg, Ruigrok, Ynte M., Heuschmann, Peter Ulrich, Longstreth, W. T., Jung, Keum Ji, Bastarache, Lisa, Paré, Guillaume, Damrauer, Scott M., Chasman, Daniel I., Rotter, Jerome I., Anderson, Christopher D., Zwart, John-Anker, Niiranen, Teemu J., Fornage, Myriam, Liaw, Yung-Po, Seshadri, Sudha, Fernández-Cadenas, Israel, Walters, Robin G., Ruff, Christian T., Owolabi, Mayowa O., Huffman, Jennifer E., Milani, Lili, Kamatani, Yoichiro, Dichgans, Martin, and Debette, Stephanie

Published
2022
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45. A saturated map of common genetic variants associated with human height

Author

Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U., Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D., Graham, Sarah E., Mukamel, Ronen E., Spracklen, Cassandra N., Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H., Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E., Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S., Akiyama, Masato, Allison, Matthew A., Alvarez, Marcus, Andersen, Mette K., Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F., Bollepalli, Sailalitha, Bonnycastle, Lori L., Bork-Jensen, Jette, Bradfield, Jonathan P., Bradford, Yuki, Braund, Peter S., Brody, Jennifer A., Burgdorf, Kristoffer S., Cade, Brian E., Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E. Warwick, Degenhard, Frauke, Delgado, Graciela E., Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B., Engmann, Jorgen E., Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D., Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E., Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P., Goel, Anuj, Gordon, Scott D., Gorski, Mathias, Grove, Jakob, Guo, Xiuqing, Gustafsson, Stefan, Haessler, Jeffrey, Hansen, Thomas F., Havulinna, Aki S., Haworth, Simon J., He, Jing, Heard-Costa, Nancy, Hebbar, Prashantha, Hindy, George, Ho, Yuk-Lam A., Hofer, Edith, Holliday, Elizabeth, Horn, Katrin, Hornsby, Whitney E., Hottenga, Jouke-Jan, Huang, Hongyan, Huang, Jie, Huerta-Chagoya, Alicia, Huffman, Jennifer E., Hung, Yi-Jen, Huo, Shaofeng, Hwang, Mi Yeong, Iha, Hiroyuki, Ikeda, Daisuke D., Isono, Masato, Jackson, Anne U., Jäger, Susanne, Jansen, Iris E., Johansson, Ingegerd, Jonas, Jost B., Jonsson, Anna, Jørgensen, Torben, Kalafati, Ioanna-Panagiota, Kanai, Masahiro, Kanoni, Stavroula, Kårhus, Line L., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kawaguchi, Takahisa, Kember, Rachel L., Kentistou, Katherine A., Kim, Han-Na, Kim, Young Jin, Kleber, Marcus E., Knol, Maria J., Kurbasic, Azra, Lauzon, Marie, Le, Phuong, Lea, Rodney, Lee, Jong-Young, Leonard, Hampton L., Li, Shengchao A., Li, Xiaohui, Li, Xiaoyin, Liang, Jingjing, Lin, Honghuang, Lin, Shih-Yi, Liu, Jun, Liu, Xueping, Lo, Ken Sin, Long, Jirong, Lores-Motta, Laura, Luan, Jian’an, Lyssenko, Valeriya, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamakou, Vasiliki, Mangino, Massimo, Manichaikul, Ani, Marten, Jonathan, Mattheisen, Manuel, Mavarani, Laven, McDaid, Aaron F., Meidtner, Karina, Melendez, Tori L., Mercader, Josep M., Milaneschi, Yuri, Miller, Jason E., Millwood, Iona Y., Mishra, Pashupati P., Mitchell, Ruth E., Møllehave, Line T., Morgan, Anna, Mucha, Soeren, Munz, Matthias, Nakatochi, Masahiro, Nelson, Christopher P., Nethander, Maria, Nho, Chu Won, Nielsen, Aneta A., Nolte, Ilja M., Nongmaithem, Suraj S., Noordam, Raymond, Ntalla, Ioanna, Nutile, Teresa, Pandit, Anita, Christofidou, Paraskevi, Pärna, Katri, Pauper, Marc, Petersen, Eva R. B., Petersen, Liselotte V., Pitkänen, Niina, Polašek, Ozren, Poveda, Alaitz, Preuss, Michael H., Pyarajan, Saiju, Raffield, Laura M., Rakugi, Hiromi, Ramirez, Julia, Rasheed, Asif, Raven, Dennis, Rayner, Nigel W., Riveros, Carlos, Rohde, Rebecca, Ruggiero, Daniela, Ruotsalainen, Sanni E., Ryan, Kathleen A., Sabater-Lleal, Maria, Saxena, Richa, Scholz, Markus, Sendamarai, Anoop, Shen, Botong, Shi, Jingchunzi, Shin, Jae Hun, Sidore, Carlo, Sitlani, Colleen M., Slieker, Roderick C., Smit, Roelof A. J., Smith, Albert V., Smith, Jennifer A., Smyth, Laura J., Southam, Lorraine, Steinthorsdottir, Valgerdur, Sun, Liang, Takeuchi, Fumihiko, Tallapragada, Divya Sri Priyanka, Taylor, Kent D., Tayo, Bamidele O., Tcheandjieu, Catherine, Terzikhan, Natalie, Tesolin, Paola, Teumer, Alexander, Theusch, Elizabeth, Thompson, Deborah J., Thorleifsson, Gudmar, Timmers, Paul R. H. J., Trompet, Stella, Turman, Constance, Vaccargiu, Simona, van der Laan, Sander W., van der Most, Peter J., van Klinken, Jan B., van Setten, Jessica, Verma, Shefali S., Verweij, Niek, Veturi, Yogasudha, Wang, Carol A., Wang, Chaolong, Wang, Lihua, Wang, Zhe, Warren, Helen R., Bin Wei, Wen, Wickremasinghe, Ananda R., Wielscher, Matthias, Wiggins, Kerri L., Winsvold, Bendik S., Wong, Andrew, Wu, Yang, Wuttke, Matthias, Xia, Rui, Xie, Tian, Yamamoto, Ken, Yang, Jingyun, Yao, Jie, Young, Hannah, Yousri, Noha A., Yu, Lei, Zeng, Lingyao, Zhang, Weihua, Zhang, Xinyuan, Zhao, Jing-Hua, Zhao, Wei, Zhou, Wei, Zimmermann, Martina E., Zoledziewska, Magdalena, Adair, Linda S., Adams, Hieab H. H., Aguilar-Salinas, Carlos A., Al-Mulla, Fahd, Arnett, Donna K., Asselbergs, Folkert W., Åsvold, Bjørn Olav, Attia, John, Banas, Bernhard, Bandinelli, Stefania, Bennett, David A., Bergler, Tobias, Bharadwaj, Dwaipayan, Biino, Ginevra, Bisgaard, Hans, Boerwinkle, Eric, Böger, Carsten A., Bønnelykke, Klaus, Boomsma, Dorret I., Børglum, Anders D., Borja, Judith B., Bouchard, Claude, Bowden, Donald W., Brandslund, Ivan, Brumpton, Ben, Buring, Julie E., Caulfield, Mark J., Chambers, John C., Chandak, Giriraj R., Chanock, Stephen J., Chaturvedi, Nish, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Ching-Yu, Christophersen, Ingrid E., Ciullo, Marina, Cole, John W., Collins, Francis S., Cooper, Richard S., Cruz, Miguel, Cucca, Francesco, Cupples, L. Adrienne, Cutler, Michael J., Damrauer, Scott M., Dantoft, Thomas M., de Borst, Gert J., de Groot, Lisette C. P. G. M., De Jager, Philip L., de Kleijn, Dominique P. V., Janaka de Silva, H., Dedoussis, George V., den Hollander, Anneke I., Du, Shufa, Easton, Douglas F., Elders, Petra J. M., Eliassen, A. Heather, Ellinor, Patrick T., Elmståhl, Sölve, Erdmann, Jeanette, Evans, Michele K., Fatkin, Diane, Feenstra, Bjarke, Feitosa, Mary F., Ferrucci, Luigi, Ford, Ian, Fornage, Myriam, Franke, Andre, Franks, Paul W., Freedman, Barry I., Gasparini, Paolo, Gieger, Christian, Girotto, Giorgia, Goddard, Michael E., Golightly, Yvonne M., Gonzalez-Villalpando, Clicerio, Gordon-Larsen, Penny, Grallert, Harald, Grant, Struan F. A., Grarup, Niels, Griffiths, Lyn, Gudnason, Vilmundur, Haiman, Christopher, Hakonarson, Hakon, Hansen, Torben, Hartman, Catharina A., Hattersley, Andrew T., Hayward, Caroline, Heckbert, Susan R., Heng, Chew-Kiat, Hengstenberg, Christian, Hewitt, Alex W., Hishigaki, Haretsugu, Hoyng, Carel B., Huang, Paul L., Huang, Wei, Hunt, Steven C., Hveem, Kristian, Hyppönen, Elina, Iacono, William G., Ichihara, Sahoko, Ikram, M. Arfan, Isasi, Carmen R., Jackson, Rebecca D., Jarvelin, Marjo-Riitta, Jin, Zi-Bing, Jöckel, Karl-Heinz, Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Kähönen, Mika, Kamatani, Yoichiro, Kang, Kui Dong, Kaprio, Jaakko, Kardia, Sharon L. R., Karpe, Fredrik, Kato, Norihiro, Kee, Frank, Kessler, Thorsten, Khera, Amit V., Khor, Chiea Chuen, Kiemeney, Lambertus A. L. M., Kim, Bong-Jo, Kim, Eung Kweon, Kim, Hyung-Lae, Kirchhof, Paulus, Kivimaki, Mika, Koh, Woon-Puay, Koistinen, Heikki A., Kolovou, Genovefa D., Kooner, Jaspal S., Kooperberg, Charles, Köttgen, Anna, Kovacs, Peter, Kraaijeveld, Adriaan, Kraft, Peter, Krauss, Ronald M., Kumari, Meena, Kutalik, Zoltan, Laakso, Markku, Lange, Leslie A., Langenberg, Claudia, Launer, Lenore J., Le Marchand, Loic, Lee, Hyejin, Lee, Nanette R., Lehtimäki, Terho, Li, Huaixing, Li, Liming, Lieb, Wolfgang, Lin, Xu, Lind, Lars, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Loeffler, Markus, London, Barry, Lubitz, Steven A., Lye, Stephen J., Mackey, David A., Mägi, Reedik, Magnusson, Patrik K. E., Marcus, Gregory M., Vidal, Pedro Marques, Martin, Nicholas G., März, Winfried, Matsuda, Fumihiko, McGarrah, Robert W., McGue, Matt, McKnight, Amy Jayne, Medland, Sarah E., Mellström, Dan, Metspalu, Andres, Mitchell, Braxton D., Mitchell, Paul, Mook-Kanamori, Dennis O., Morris, Andrew D., Mucci, Lorelei A., Munroe, Patricia B., Nalls, Mike A., Nazarian, Saman, Nelson, Amanda E., Neville, Matt J., Newton-Cheh, Christopher, Nielsen, Christopher S., Nöthen, Markus M., Ohlsson, Claes, Oldehinkel, Albertine J., Orozco, Lorena, Pahkala, Katja, Pajukanta, Päivi, Palmer, Colin N. A., Parra, Esteban J., Pattaro, Cristian, Pedersen, Oluf, Pennell, Craig E., Penninx, Brenda W. J. H., Perusse, Louis, Peters, Annette, Peyser, Patricia A., Porteous, David J., Posthuma, Danielle, Power, Chris, Pramstaller, Peter P., Province, Michael A., Qi, Qibin, Qu, Jia, Rader, Daniel J., Raitakari, Olli T., Ralhan, Sarju, Rallidis, Loukianos S., Rao, Dabeeru C., Redline, Susan, Reilly, Dermot F., Reiner, Alexander P., Rhee, Sang Youl, Ridker, Paul M., Rienstra, Michiel, Ripatti, Samuli, Ritchie, Marylyn D., Roden, Dan M., Rosendaal, Frits R., Rotter, Jerome I., Rudan, Igor, Rutters, Femke, Sabanayagam, Charumathi, Saleheen, Danish, Salomaa, Veikko, Samani, Nilesh J., Sanghera, Dharambir K., Sattar, Naveed, Schmidt, Börge, Schmidt, Helena, Schmidt, Reinhold, Schulze, Matthias B., Schunkert, Heribert, Scott, Laura J., Scott, Rodney J., Sever, Peter, Shiroma, Eric J., Shoemaker, M. Benjamin, Shu, Xiao-Ou, Simonsick, Eleanor M., Sims, Mario, Singh, Jai Rup, Singleton, Andrew B., Sinner, Moritz F., Smith, J. Gustav, Snieder, Harold, Spector, Tim D., Stampfer, Meir J., Stark, Klaus J., Strachan, David P., ‘t Hart, Leen M., Tabara, Yasuharu, Tang, Hua, Tardif, Jean-Claude, Thanaraj, Thangavel A., Timpson, Nicholas J., Tönjes, Anke, Tremblay, Angelo, Tuomi, Tiinamaija, Tuomilehto, Jaakko, Tusié-Luna, Maria-Teresa, Uitterlinden, Andre G., van Dam, Rob M., van der Harst, Pim, Van der Velde, Nathalie, van Duijn, Cornelia M., van Schoor, Natasja M., Vitart, Veronique, Völker, Uwe, Vollenweider, Peter, Völzke, Henry, Wacher-Rodarte, Niels H., Walker, Mark, Wang, Ya Xing, Wareham, Nicholas J., Watanabe, Richard M., Watkins, Hugh, Weir, David R., Werge, Thomas M., Widen, Elisabeth, Wilkens, Lynne R., Willemsen, Gonneke, Willett, Walter C., Wilson, James F., Wong, Tien-Yin, Woo, Jeong-Taek, Wright, Alan F., Wu, Jer-Yuarn, Xu, Huichun, Yajnik, Chittaranjan S., Yokota, Mitsuhiro, Yuan, Jian-Min, Zeggini, Eleftheria, Zemel, Babette S., Zheng, Wei, Zhu, Xiaofeng, Zmuda, Joseph M., Zonderman, Alan B., Zwart, John-Anker, Chasman, Daniel I., Cho, Yoon Shin, Heid, Iris M., McCarthy, Mark I., Ng, Maggie C. Y., O’Donnell, Christopher J., Rivadeneira, Fernando, Thorsteinsdottir, Unnur, Sun, Yan V., Tai, E. Shyong, Boehnke, Michael, Deloukas, Panos, Justice, Anne E., Lindgren, Cecilia M., Loos, Ruth J. F., Mohlke, Karen L., North, Kari E., Stefansson, Kari, Walters, Robin G., Winkler, Thomas W., Young, Kristin L., Loh, Po-Ru, Yang, Jian, Esko, Tõnu, Assimes, Themistocles L., Auton, Adam, Abecasis, Goncalo R., Willer, Cristen J., Locke, Adam E., Berndt, Sonja I., Lettre, Guillaume, Frayling, Timothy M., Okada, Yukinori, Wood, Andrew R., Visscher, Peter M., and Hirschhorn, Joel N.

Published
2022
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47. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author

Stavroula Kanoni, Sarah E. Graham, Yuxuan Wang, Ida Surakka, Shweta Ramdas, Xiang Zhu, Shoa L. Clarke, Konain Fatima Bhatti, Sailaja Vedantam, Thomas W. Winkler, Adam E. Locke, Eirini Marouli, Greg J. M. Zajac, Kuan-Han H. Wu, Ioanna Ntalla, Qin Hui, Derek Klarin, Austin T. Hilliard, Zeyuan Wang, Chao Xue, Gudmar Thorleifsson, Anna Helgadottir, Daniel F. Gudbjartsson, Hilma Holm, Isleifur Olafsson, Mi Yeong Hwang, Sohee Han, Masato Akiyama, Saori Sakaue, Chikashi Terao, Masahiro Kanai, Wei Zhou, Ben M. Brumpton, Humaira Rasheed, Aki S. Havulinna, Yogasudha Veturi, Jennifer Allen Pacheco, Elisabeth A. Rosenthal, Todd Lingren, QiPing Feng, Iftikhar J. Kullo, Akira Narita, Jun Takayama, Hilary C. Martin, Karen A. Hunt, Bhavi Trivedi, Jeffrey Haessler, Franco Giulianini, Yuki Bradford, Jason E. Miller, Archie Campbell, Kuang Lin, Iona Y. Millwood, Asif Rasheed, George Hindy, Jessica D. Faul, Wei Zhao, David R. Weir, Constance Turman, Hongyan Huang, Mariaelisa Graff, Ananyo Choudhury, Dhriti Sengupta, Anubha Mahajan, Michael R. Brown, Weihua Zhang, Ketian Yu, Ellen M. Schmidt, Anita Pandit, Stefan Gustafsson, Xianyong Yin, Jian’an Luan, Jing-Hua Zhao, Fumihiko Matsuda, Hye-Mi Jang, Kyungheon Yoon, Carolina Medina-Gomez, Achilleas Pitsillides, Jouke Jan Hottenga, Andrew R. Wood, Yingji Ji, Zishan Gao, Simon Haworth, Noha A. Yousri, Ruth E. Mitchell, Jin Fang Chai, Mette Aadahl, Anne A. Bjerregaard, Jie Yao, Ani Manichaikul, Chii-Min Hwu, Yi-Jen Hung, Helen R. Warren, Julia Ramirez, Jette Bork-Jensen, Line L. Kårhus, Anuj Goel, Maria Sabater-Lleal, Raymond Noordam, Pala Mauro, Floris Matteo, Aaron F. McDaid, Pedro Marques-Vidal, Matthias Wielscher, Stella Trompet, Naveed Sattar, Line T. Møllehave, Matthias Munz, Lingyao Zeng, Jianfeng Huang, Bin Yang, Alaitz Poveda, Azra Kurbasic, Claudia Lamina, Lukas Forer, Markus Scholz, Tessel E. Galesloot, Jonathan P. Bradfield, Sanni E. Ruotsalainen, EWarwick Daw, Joseph M. Zmuda, Jonathan S. Mitchell, Christian Fuchsberger, Henry Christensen, Jennifer A. Brody, Miguel Vazquez-Moreno, Mary F. Feitosa, Mary K. Wojczynski, Zhe Wang, Michael H. Preuss, Massimo Mangino, Paraskevi Christofidou, Niek Verweij, Jan W. Benjamins, Jorgen Engmann, Noah L. Tsao, Anurag Verma, Roderick C. Slieker, Ken Sin Lo, Nuno R. Zilhao, Phuong Le, Marcus E. Kleber, Graciela E. Delgado, Shaofeng Huo, Daisuke D. Ikeda, Hiroyuki Iha, Jian Yang, Jun Liu, Ayşe Demirkan, Hampton L. Leonard, Jonathan Marten, Mirjam Frank, Börge Schmidt, Laura J. Smyth, Marisa Cañadas-Garre, Chaolong Wang, Masahiro Nakatochi, Andrew Wong, Nina Hutri-Kähönen, Xueling Sim, Rui Xia, Alicia Huerta-Chagoya, Juan Carlos Fernandez-Lopez, Valeriya Lyssenko, Suraj S. Nongmaithem, Swati Bayyana, Heather M. Stringham, Marguerite R. Irvin, Christopher Oldmeadow, Han-Na Kim, Seungho Ryu, Paul R. H. J. Timmers, Liubov Arbeeva, Rajkumar Dorajoo, Leslie A. Lange, Gauri Prasad, Laura Lorés-Motta, Marc Pauper, Jirong Long, Xiaohui Li, Elizabeth Theusch, Fumihiko Takeuchi, Cassandra N. Spracklen, Anu Loukola, Sailalitha Bollepalli, Sophie C. Warner, Ya Xing Wang, Wen B. Wei, Teresa Nutile, Daniela Ruggiero, Yun Ju Sung, Shufeng Chen, Fangchao Liu, Jingyun Yang, Katherine A. Kentistou, Bernhard Banas, Giuseppe Giovanni Nardone, Karina Meidtner, Lawrence F. Bielak, Jennifer A. Smith, Prashantha Hebbar, Aliki-Eleni Farmaki, Edith Hofer, Maoxuan Lin, Maria Pina Concas, Simona Vaccargiu, Peter J. van der Most, Niina Pitkänen, Brian E. Cade, Sander W. van der Laan, Kumaraswamy Naidu Chitrala, Stefan Weiss, Amy R. Bentley, Ayo P. Doumatey, Adebowale A. Adeyemo, Jong Young Lee, Eva R. B. Petersen, Aneta A. Nielsen, Hyeok Sun Choi, Maria Nethander, Sandra Freitag-Wolf, Lorraine Southam, Nigel W. Rayner, Carol A. Wang, Shih-Yi Lin, Jun-Sing Wang, Christian Couture, Leo-Pekka Lyytikäinen, Kjell Nikus, Gabriel Cuellar-Partida, Henrik Vestergaard, Bertha Hidalgo, Olga Giannakopoulou, Qiuyin Cai, Morgan O. Obura, Jessica van Setten, Xiaoyin Li, Jingjing Liang, Hua Tang, Natalie Terzikhan, Jae Hun Shin, Rebecca D. Jackson, Alexander P. Reiner, Lisa Warsinger Martin, Zhengming Chen, Liming Li, Takahisa Kawaguchi, Joachim Thiery, Joshua C. Bis, Lenore J. Launer, Huaixing Li, Mike A. Nalls, Olli T. Raitakari, Sahoko Ichihara, Sarah H. Wild, Christopher P. Nelson, Harry Campbell, Susanne Jäger, Toru Nabika, Fahd Al-Mulla, Harri Niinikoski, Peter S. Braund, Ivana Kolcic, Peter Kovacs, Tota Giardoglou, Tomohiro Katsuya, Dominique de Kleijn, Gert J. de Borst, Eung Kweon Kim, Hieab H. H. Adams, M. Arfan Ikram, Xiaofeng Zhu, Folkert W. Asselbergs, Adriaan O. Kraaijeveld, Joline W. J. Beulens, Xiao-Ou Shu, Loukianos S. Rallidis, Oluf Pedersen, Torben Hansen, Paul Mitchell, Alex W. Hewitt, Mika Kähönen, Louis Pérusse, Claude Bouchard, Anke Tönjes, Yii-Der Ida Chen, Craig E. Pennell, Trevor A. Mori, Wolfgang Lieb, Andre Franke, Claes Ohlsson, Dan Mellström, Yoon Shin Cho, Hyejin Lee, Jian-Min Yuan, Woon-Puay Koh, Sang Youl Rhee, Jeong-Taek Woo, Iris M. Heid, Klaus J. Stark, Martina E. Zimmermann, Henry Völzke, Georg Homuth, Michele K. Evans, Alan B. Zonderman, Ozren Polasek, Gerard Pasterkamp, Imo E. Hoefer, Susan Redline, Katja Pahkala, Albertine J. Oldehinkel, Harold Snieder, Ginevra Biino, Reinhold Schmidt, Helena Schmidt, Stefania Bandinelli, George Dedoussis, Thangavel Alphonse Thanaraj, Sharon L. R. Kardia, Patricia A. Peyser, Norihiro Kato, Matthias B. Schulze, Giorgia Girotto, Carsten A. Böger, Bettina Jung, Peter K. Joshi, David A. Bennett, Philip L. De Jager, Xiangfeng Lu, Vasiliki Mamakou, Morris Brown, Mark J. Caulfield, Patricia B. Munroe, Xiuqing Guo, Marina Ciullo, Jost B. Jonas, Nilesh J. Samani, Jaakko Kaprio, Päivi Pajukanta, Teresa Tusié-Luna, Carlos A. Aguilar-Salinas, Linda S. Adair, Sonny Augustin Bechayda, H. Janaka de Silva, Ananda R. Wickremasinghe, Ronald M. Krauss, Jer-Yuarn Wu, Wei Zheng, Anneke Iden Hollander, Dwaipayan Bharadwaj, Adolfo Correa, James G. Wilson, Lars Lind, Chew-Kiat Heng, Amanda E. Nelson, Yvonne M. Golightly, James F. Wilson, Brenda Penninx, Hyung-Lae Kim, John Attia, Rodney J. Scott, D. C. Rao, Donna K. Arnett, Steven C. Hunt, Mark Walker, Heikki A. Koistinen, Giriraj R. Chandak, Josep M. Mercader, Maria C. Costanzo, Dongkeun Jang, Noël P. Burtt, Clicerio Gonzalez Villalpando, Lorena Orozco, Myriam Fornage, EShyong Tai, Rob M. van Dam, Terho Lehtimäki, Nish Chaturvedi, Mitsuhiro Yokota, Jianjun Liu, Dermot F. Reilly, Amy Jayne McKnight, Frank Kee, Karl-Heinz Jöckel, Mark I. McCarthy, Colin N. A. Palmer, Veronique Vitart, Caroline Hayward, Eleanor Simonsick, Cornelia M. van Duijn, Zi-Bing Jin, Jia Qu, Haretsugu Hishigaki, Xu Lin, Winfried März, Vilmundur Gudnason, Jean-Claude Tardif, Guillaume Lettre, Leen M.‘t Hart, Petra J. M. Elders, Scott M. Damrauer, Meena Kumari, Mika Kivimaki, Pim van der Harst, Tim D. Spector, Ruth J. F. Loos, Michael A. Province, Esteban J. Parra, Miguel Cruz, Bruce M. Psaty, Ivan Brandslund, Peter P. Pramstaller, Charles N. Rotimi, Kaare Christensen, Samuli Ripatti, Elisabeth Widén, Hakon Hakonarson, Struan F. A. Grant, Lambertus A. L. M. Kiemeney, Jacqueline de Graaf, Markus Loeffler, Florian Kronenberg, Dongfeng Gu, Jeanette Erdmann, Heribert Schunkert, Paul W. Franks, Allan Linneberg, J. Wouter Jukema, Amit V. Khera, Minna Männikkö, Marjo-Riitta Jarvelin, Zoltan Kutalik, Cucca Francesco, Dennis O. Mook-Kanamori, Ko Willems van Dijk, Hugh Watkins, David P. Strachan, Niels Grarup, Peter Sever, Neil Poulter, Lee-Ming Chuang, Jerome I. Rotter, Thomas M. Dantoft, Fredrik Karpe, Matt J. Neville, Nicholas J. Timpson, Ching-Yu Cheng, Tien-Yin Wong, Chiea Chuen Khor, Hengtong Li, Charumathi Sabanayagam, Annette Peters, Christian Gieger, Andrew T. Hattersley, Nancy L. Pedersen, Patrik K. E. Magnusson, Dorret I. Boomsma, Allegonda H. M. Willemsen, LAdrienne Cupples, Joyce B. J. van Meurs, Mohsen Ghanbari, Penny Gordon-Larsen, Wei Huang, Young Jin Kim, Yasuharu Tabara, Nicholas J. Wareham, Claudia Langenberg, Eleftheria Zeggini, Johanna Kuusisto, Markku Laakso, Erik Ingelsson, Goncalo Abecasis, John C. Chambers, Jaspal S. Kooner, Paul S. de Vries, Alanna C. Morrison, Scott Hazelhurst, Michèle Ramsay, Kari E. North, Martha Daviglus, Peter Kraft, Nicholas G. Martin, John B. Whitfield, Shahid Abbas, Danish Saleheen, Robin G. Walters, Michael V. Holmes, Corri Black, Blair H. Smith, Aris Baras, Anne E. Justice, Julie E. Buring, Paul M. Ridker, Daniel I. Chasman, Charles Kooperberg, Gen Tamiya, Masayuki Yamamoto, David A. van Heel, Richard C. Trembath, Wei-Qi Wei, Gail P. Jarvik, Bahram Namjou, M. Geoffrey Hayes, Marylyn D. Ritchie, Pekka Jousilahti, Veikko Salomaa, Kristian Hveem, Bjørn Olav Åsvold, Michiaki Kubo, Yoichiro Kamatani, Yukinori Okada, Yoshinori Murakami, Bong-Jo Kim, Unnur Thorsteinsdottir, Kari Stefansson, Jifeng Zhang, YEugene Chen, Yuk-Lam Ho, Julie A. Lynch, Daniel J. Rader, Philip S. Tsao, Kyong-Mi Chang, Kelly Cho, Christopher J. O’Donnell, John M. Gaziano, Peter W. F. Wilson, Timothy M. Frayling, Joel N. Hirschhorn, Sekar Kathiresan, Karen L. Mohlke, Yan V. Sun, Andrew P. Morris, Michael Boehnke, Christopher D. Brown, Pradeep Natarajan, Panos Deloukas, Cristen J. Willer, Themistocles L. Assimes, and Gina M. Peloso

Subjects
Cholesterol, Lipids, Genetics, Genome-wide association study, GWAS, Biology (General), QH301-705.5, QH426-470
Abstract

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Published
2022
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48. Use of antibiotics and risk of type 2 diabetes, overweight and obesity: the Cardiovascular Risk in Young Finns Study and the national FINRISK study

Author

Joel Nuotio, Teemu Niiranen, Tomi T. Laitinen, Jessica Miller, Matthew A. Sabin, Aki S. Havulinna, Jorma S. A. Viikari, Tapani Rönnemaa, Nina Hutri-Kähönen, Tomi P. Laitinen, Päivi Tossavainen, Veikko Salomaa, Olli T. Raitakari, David P. Burgner, and Markus Juonala

Subjects
Antibiotics, Type 2 diabetes, Obesity, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

Abstract Purpose To investigate whether exposure to systemic antibiotics influences the risk of developing type 2 diabetes and overweight/obesity. Methods The study sample comprised 2209 (110 with incident diabetes) participants from the population-based Cardiovascular Risk in Young Finns Study (YFS) aged 24–39 years in 2001. The exposure was national linked register data on purchased antibiotic courses between 1993 and 2001. Clinical examinations including BMI were conducted in 2001, 2007 and 2011. Participants with prevalent diabetes in 2001 were excluded. Data on type 2 diabetes was also obtained from two national registers until 2017. Data from four population-based National FINRISK studies were used for replication (N = 24,674, 1866 with incident diabetes). Results Prior antibiotic exposure (> 5 versus 0–1 antibiotic courses) was associated with subsequent type 2 diabetes in both YFS (OR 2.29; 95%CI 1.33–3.96) and FINRISK (HR 1.73; 95%CI 1.51–1.99). An increased risk for type 2 diabetes was observed in YFS (OR 1.043; 95%CI 1.013–1.074) and FINRISK (HR 1.022; 95%CI 1.016–1.029) per course. Exposure to antibiotics increased the risk of overweight/obesity (BMI > 25 kg/m2) after a 10-year follow-up in YFS (OR 1.043; 95%CI 1.019–1.068) and in FINRISK (OR 1.023; 95%CI 1.018–1.029) at baseline per antibiotic course. Adjustments for confounders from early life in YFS and at baseline in FINRISK, including BMI, socioeconomic status, smoking, insulin, blood pressure, and physical activity, did not appreciably alter the findings. Conclusion Our results show that exposure to antibiotics was associated with increased risk for future type 2 diabetes and overweight/obesity and support judicious antibiotic prescribing.

Published
2022
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49. Gene–gene interaction detection with deep learning

Author

Tianyu Cui, Khaoula El Mekkaoui, Jaakko Reinvall, Aki S. Havulinna, Pekka Marttinen, and Samuel Kaski

Subjects
Biology (General), QH301-705.5
Abstract

An open-source framework combines deep learning and permutations of gene interaction neural networks to detect complex gene–gene interactions and their significance in contributions to phenotypes.

Published
2022
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50. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

Author

Domenica D’Elia, Jaak Truu, Leo Lahti, Magali Berland, Georgios Papoutsoglou, Michelangelo Ceci, Aldert Zomer, Marta B. Lopes, Eliana Ibrahimi, Aleksandra Gruca, Alina Nechyporenko, Marcus Frohme, Thomas Klammsteiner, Enrique Carrillo-de Santa Pau, Laura Judith Marcos-Zambrano, Karel Hron, Gianvito Pio, Andrea Simeon, Ramona Suharoschi, Isabel Moreno-Indias, Andriy Temko, Miroslava Nedyalkova, Elena-Simona Apostol, Ciprian-Octavian Truică, Rajesh Shigdel, Jasminka Hasić Telalović, Erik Bongcam-Rudloff, Piotr Przymus, Naida Babić Jordamović, Laurent Falquet, Sonia Tarazona, Alexia Sampri, Gaetano Isola, David Pérez-Serrano, Vladimir Trajkovik, Lubos Klucar, Tatjana Loncar-Turukalo, Aki S. Havulinna, Christian Jansen, Randi J. Bertelsen, and Marcus Joakim Claesson

Subjects
microbiome, machine learning, artificial intelligence, standards, best practices, Microbiology, QR1-502
Abstract

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Published
2023
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