Genetic Analysis of Obstructive Sleep Apnea and Its Relationship with Severe COVID-19.
Annals of the American Thoracic Society
Strausz, S., Agafonova, E., Tiullinen, V., Kiiskinen, T., Broberg, M., Ruotsalainen, S. E., Koskela, J., Bachour, A., Sofer, T., Gottlieb, D. J., Palotie, A., Palotie, T., Ripatti, S., Ollila, H. M.
2024
Abstract
While patients with obstructive sleep apnea (OSA) have a higher risk for COVID-19 hospitalization, the causal relationship has remained unexplored.To understand the causal relationship between OSA and COVID-19 leveraging data from vaccination and electronic health records, genetic risk factors from genome-wide association studies (GWAS) and Mendelian randomization.We elucidated genetic risk factors for OSA using FinnGen (N total = 377,277 individuals) performing genome-wide association. We used the associated variants as instruments for univariate and multivariate Mendelian randomization (MR) analyses and computed absolute risk reduction (ARR) against COVID-19 hospitalization with or without vaccination.We identified 9 novel loci for OSA and replicated our findings in the Million Veterans Program. Furthermore, MR analysis showed that OSA was a causal risk factor for severe COVID-19 (P=9.41x10-4). Probabilistic modelling showed that the strongest genetic risk factor for OSA at the FTO locus reflected a signal of higher BMI, whereas BMI independent association was seen with the earlier reported SLC9A4 locus and a MECOM locus which is a transcriptional regulator with 210-fold enrichment in the Finnish population. Similarly, Multivariate MR (MVMR) analysis showed that the causality for severe COVID-19 was driven by body mass index (BMI), (P MVMR = 5.97x10-6, beta=0.47). Finally, vaccination reduced the risk for COVID-19 hospitalization more in the OSA patients than in the non-OSA controls: ARR = 13.3% vs. ARR = 6.3% in the OSA vs. non-OSA population.Our analysis identified novel genetic risk factors for OSA and showed that OSA is a causal risk factor for severe COVID-19. The effect is predominantly explained by higher BMI and suggests BMI-dependent effects at the level of individual variants and at the level of comorbid causality.
View details for DOI 10.1513/AnnalsATS.202303-215OC
View details for PubMedID 38330144
The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections.
EBioMedicine
Jones, S. E., Maisha, F. I., Strausz, S. J., Lammi, V., Cade, B. E., Tervi, A., Helaakoski, V., Broberg, M. E., FinnGen, Lane, J. M., Redline, S., Saxena, R., Ollila, H. M., Palotie, A., Daly, M., Riley-Gills, B., Jacob, H., Paul, D., Petrovski, S., Runz, H., John, S., Okafo, G., Lawless, N., Salminen-Mankonen, H., Plenge, R., Maranville, J., McCarthy, M., Ehm, M. G., Auro, K., Longerich, S., Malarstig, A., Klinger, K., Chatelain, C., Gossel, M., Estrada, K., Graham, R., Yang, R., O Donnell, C., Makela, T. P., Kaprio, J., Virolainen, P., Hakanen, A., Kilpi, T., Perola, M., Partanen, J., Pitkaranta, A., Raivio, T., Tikkanen, J., Serpi, R., Laitinen, T., Kosma, V., Laukkanen, J., Hautalahti, M., Tuovila, O., Pakkanen, R., Waring, J., Riley-Gillis, B., Rahimov, F., Tachmazidou, I., Chen, C., Runz, H., Ding, Z., Jung, M., Biswas, S., Pendergrass, R., Ehm, M. G., Pulford, D., Raghavan, N., Huertas-Vazquez, A., Sul, J., Malarstig, A., Hu, X., Hedman, A., Klinger, K., Graham, R., Rivas, M., Waterworth, D., Renaud, N., Obeidat, M. E., Ripatti, S., Schleutker, J., Perola, M., Arvas, M., Carpen, O., Hinttala, R., Kettunen, J., Mannermaa, A., Aalto-Setala, K., Kahonen, M., Laukkanen, J., Makela, J., Kalviainen, R., Julkunen, V., Soininen, H., Remes, A., Hiltunen, M., Peltola, J., Raivio, M., Tienari, P., Rinne, J., Kallionpaa, R., Partanen, J., Abbasi, A., Ziemann, A., Smaoui, N., Lehtonen, A., Eaton, S., Runz, H., Lahdenpera, S., Biswas, S., Bowers, N., Teng, E., Pendergrass, R., Xu, F., Pulford, D., Auro, K., Addis, L., Eicher, J., Li, Q. S., He, K., Khramtsova, E., Raghavan, N., Farkkila, M., Koskela, J., Pikkarainen, S., Jussila, A., Kaukinen, K., Blomster, T., Kiviniemi, M., Voutilainen, M., Daly, M., Abbasi, A., Waring, J., Smaoui, N., Rahimov, F., Lehtonen, A., Lu, T., Bowers, N., Pendergrass, R., McCarthy, L., Hart, A., Guan, M., Miller, J., Kalpala, K., Miller, M., Hu, X., Eklund, K., Palomaki, A., Isomaki, P., Pirila, L., Kaipiainen-Seppanen, O., Huhtakangas, J., Mars, N., Abbasi, A., Waring, J., Rahimov, F., Lertratanakul, A., Smaoui, N., Lehtonen, A., Viollet, C., Hochfeld, M., Bowers, N., Pendergrass, R., Gordillo, J. E., Auro, K., Waterworth, D., Farias, F., Kalpala, K., Bing, N., Hu, X., Laitinen, T., Pelkonen, M., Kauppi, P., Kankaanranta, H., Harju, T., Lahesmaa, R., Smaoui, N., Viollet, C., Eaton, S., Chen, H., Pendergrass, R., Bowers, N., Betts, J., Auro, K., Mishra, R., Mouded, M., Ngo, D., Niiranen, T., Vaura, F., Salomaa, V., Metsarinne, K., Aittokallio, J., Kahonen, M., Hernesniemi, J., Gordin, D., Sinisalo, J., Taskinen, M., Tuomi, T., Hiltunen, T., Laukkanen, J., Elliott, A., Reeve, M. P., Ruotsalainen, S., Paul, D., Bowers, N., Pendergrass, R., Chu, A., Auro, K., Reilly, D., Mendelson, M., Parkkinen, J., Miller, M., Meretoja, T., Joensuu, H., Carpen, O., Mattson, J., Salminen, E., Auranen, A., Karihtala, P., Auvinen, P., Elenius, K., Schleutker, J., Pitkanen, E., Mars, N., Daly, M., Popovic, R., Waring, J., Riley-Gillis, B., Lehtonen, A., Fabre, M., Schutzman, J., Bowers, N., Pendergrass, R., Kulkarni, D., Auro, K., Porello, A., Loboda, A., Lehtonen, H., McDonough, S., Vuoti, S., Kaarniranta, K., Turunen, J. A., Ollila, T., Uusitalo, H., Karjalainen, J., Pitkanen, E., Liu, M., Runz, H., Loomis, S., Strauss, E., Bowers, N., Chen, H., Pendergrass, R., Tasanen, K., Huilaja, L., Hannula-Jouppi, K., Salmi, T., Peltonen, S., Koulu, L., Smaoui, N., Rahimov, F., Lehtonen, A., Choy, D., Pendergrass, R., Waterworth, D., Kalpala, K., Wu, Y., Pussinen, P., Salminen, A., Salo, T., Rice, D., Nieminen, P., Palotie, U., Siponen, M., Suominen, L., Mantyla, P., Gursoy, U., Anttonen, V., Sipila, K., Pendergrass, R., Laivuori, H., Kurra, V., Kotaniemi-Talonen, L., Heikinheimo, O., Kalliala, I., Aaltonen, L., Jokimaa, V., Kettunen, J., Vaarasmaki, M., Uimari, O., Morin-Papunen, L., Niinimaki, M., Piltonen, T., Kivinen, K., Widen, E., Tukiainen, T., Reeve, M. P., Daly, M., Valimaki, N., Laakkonen, E., Tyrmi, J., Silven, H., Sliz, E., Arffman, R., Savukoski, S., Laisk, T., Pujol, N., Liu, M., Riley-Gillis, B., Pendergrass, R., Kumar, J., Auro, K., Hovatta, I., Chen, C., Isometsa, E., Ollila, H., Suvisaari, J., Als, T. D., Makitie, A., Bizaki-Vallaskangas, A., Toppila-Salmi, S., Willberg, T., Saarentaus, E., Aarnisalo, A., Salminen, E., Rahikkala, E., Kettunen, J., Aittomaki, K., Aberg, F., Kurki, M., Ripatti, S., Daly, M., Karjalainen, J., Havulinna, A., Mehtonen, J., Palta, P., Hassan, S., Della Briotta Parolo, P., Zhou, W., Maasha, M., Hassan, S., Lemmela, S., Rivas, M., Palotie, A., Liu, A., Lehisto, A., Ganna, A., Llorens, V., Laivuori, H., Tukiainen, T., Reeve, M. P., Heyne, H., Mars, N., Ramo, J., Saarentaus, E., Ollila, H., Rodosthenous, R., Strausz, S., Palotie, T., Palin, K., Garcia-Tabuenca, J., Siirtola, H., Kiiskinen, T., Lee, J., Tsuo, K., Elliott, A., Kristiansson, K., Arvas, M., Hyvarinen, K., Ritari, J., Carpen, O., Kettunen, J., Pylkas, K., Sliz, E., Karjalainen, M., Mantere, T., Kangasniemi, E., Heikkinen, S., Mannermaa, A., Laakkonen, E., Pitkanen, N., Lessard, S., Chatelain, C., Kallio, L., Wahlfors, T., Partanen, J., Punkka, E., Serpi, R., Siltanen, S., Kosma, V., Kuopio, T., Jalanko, A., Shen, H., Kajanne, R., Aavikko, M., Leinonen, R., Palin, H., Linna, M., Kurki, M., Karjalainen, J., Della Briotta Parolo, P., Lehisto, A., Mehtonen, J., Zhou, W., Kanai, M., Maasha, M., Zheng, Z., Laivuori, H., Havulinna, A., Lemmela, S., Kiiskinen, T., Lahtela, L. E., Kaunisto, M., Kilpelainen, E., Sipila, T. P., Dada, O. A., Ghazal, A., Kytola, A., Weldatsadik, R., Ruotsalainen, S., Donner, K., Sipila, T. P., Loukola, A., Laiho, P., Sistonen, T., Kaiharju, E., Laukkanen, M., Jarvensivu, E., Lahteenmaki, S., Mannikko, L., Wong, R., Toivola, A., Brunfeldt, M., Mattsson, H., Kristiansson, K., Lemmela, S., Koskelainen, S., Hiekkalinna, T., Paajanen, T., Palta, P., Parn, K., Kals, M., Luo, S., Laitinen, T., Reeve, M. P., Padmanabhuni, S. S., Niemi, M., Siirtola, H., Gracia-Tabuenca, J., Helminen, M., Luukkaala, T., Vahatalo, I., Tammerluoto, J., Hautalahti, M., Makela, J., Smith, S., Southerington, T., Lehto, P.
2023: 104630
Abstract
BACKGROUND: Poor sleep is associated with an increased risk of infections and all-cause mortality but the causal direction between poor sleep and respiratory infections has remained unclear. We examined if poor sleep contributes as a causal risk factor to respiratory infections.METHODS: We used data on insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital records in the UK Biobank (N231,000) and FinnGen (N392,000). We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and performed Mendelian randomization analyses to assess causality.FINDINGS: Utilizing 23 years of registry data and follow-up, we discovered that insomnia diagnosis associated with increased risk for infections (FinnGen influenza Cox's proportional hazard (CPH) HR=4.34 [3.90, 4.83], P=4.16*10-159, UK Biobank influenza CPH HR=1.54 [1.37, 1.73], P=2.49*10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (inverse-variance weighted (IVW) OR=1.65, P=5.86*10-7), URI (IVW OR=1.94, P=8.14*10-31), COVID-19 infection (IVW OR=1.08, P=0.037) and risk of hospitalization from COVID-19 (IVW OR=1.47, P=4.96*10-5).INTERPRETATION: Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens.FUNDING: Instrumentarium Science Foundation, Academy of Finland, Signe and Ane Gyllenberg Foundation, National Institutes of Health.
View details for DOI 10.1016/j.ebiom.2023.104630
View details for PubMedID 37301713
Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy.
Nature communications
Ollila, H. M., Sharon, E., Lin, L., Sinnott-Armstrong, N., Ambati, A., Yogeshwar, S. M., Hillary, R. P., Jolanki, O., Faraco, J., Einen, M., Luo, G., Zhang, J., Han, F., Yan, H., Dong, X. S., Li, J., Zhang, J., Hong, S. C., Kim, T. W., Dauvilliers, Y., Barateau, L., Lammers, G. J., Fronczek, R., Mayer, G., Santamaria, J., Arnulf, I., Knudsen-Heier, S., Bredahl, M. K., Thorsby, P. M., Plazzi, G., Pizza, F., Moresco, M., Crowe, C., Van den Eeden, S. K., Lecendreux, M., Bourgin, P., Kanbayashi, T., Martínez-Orozco, F. J., Peraita-Adrados, R., Benetó, A., Montplaisir, J., Desautels, A., Huang, Y. S., Jennum, P., Nevsimalova, S., Kemlink, D., Iranzo, A., Overeem, S., Wierzbicka, A., Geisler, P., Sonka, K., Honda, M., Högl, B., Stefani, A., Coelho, F. M., Mantovani, V., Feketeova, E., Wadelius, M., Eriksson, N., Smedje, H., Hallberg, P., Hesla, P. E., Rye, D., Pelin, Z., Ferini-Strambi, L., Bassetti, C. L., Mathis, J., Khatami, R., Aran, A., Nampoothiri, S., Olsson, T., Kockum, I., Partinen, M., Perola, M., Kornum, B. R., Rueger, S., Winkelmann, J., Miyagawa, T., Toyoda, H., Khor, S. S., Shimada, M., Tokunaga, K., Rivas, M., Pritchard, J. K., Risch, N., Kutalik, Z., O'Hara, R., Hallmayer, J., Ye, C. J., Mignot, E. J.
2023; 14 (1): 2709
Abstract
Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.
View details for DOI 10.1038/s41467-023-36120-z
View details for PubMedID 37188663
View details for PubMedCentralID PMC10185546
Genome-wide screen of otosclerosis in population biobanks: 27 loci and shared associations with skeletal structure.
Nature communications
Ramo, J. T., Kiiskinen, T., Seist, R., Krebs, K., Kanai, M., Karjalainen, J., Kurki, M., Hamalainen, E., Happola, P., Havulinna, A. S., Hautakangas, H., FinnGen, Magi, R., Palta, P., Esko, T., Metspalu, A., Pirinen, M., Karczewski, K. J., Ripatti, S., Milani, L., Stankovic, K. M., Makitie, A., Daly, M. J., Palotie, A.
2023; 14 (1): 157
Abstract
Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 3504 cases and 861,198 controls. We identify 23 novel risk loci (p<5*10-8) and report an association in RELN and three previously reported candidate gene or linkage regions (TGFB1, MEPE, and OTSC7). We demonstrate developmental stage-dependent immunostaining patterns of MEPE and RUNX2 in mouse otic capsules. In most association loci, the nearest protein-coding genes are implicated in bone remodelling, mineralization or severe skeletal disorders. We highlight multiple genes involved in transforming growth factor beta signalling for follow-up studies.
View details for DOI 10.1038/s41467-022-32936-3
View details for PubMedID 36653343
Author Correction: Genetics of 35 blood and urine biomarkers in the UK Biobank.
Nature genetics
Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Benner, C., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Kiiskinen, T., Havulinna, A. S., Pirruccello, J. P., Qian, J., Shcherbina, A., FinnGen, Rodriguez, F., Assimes, T. L., Agarwala, V., Tibshirani, R., Hastie, T., Ripatti, S., Pritchard, J. K., Daly, M. J., Rivas, M. A.
2021
Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome.
Circulation. Genomic and precision medicine
Lee, J., Kiiskinen, T., Mars, N., Jukarainen, S., Ingelsson, E., Neale, B., Ripatti, S., Natarajan, P., Ganna, A.
2021
Abstract
Background - Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease (CHD). Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS. Methods - We explored the association between 405 clinical conditions diagnosed before baseline and 9,080 incident cases of ACS in 387,832 individuals from the UK Biobank. Results were replicated in 6,430 incident cases of ACS in 177,876 individuals from FinnGen. Results - We identified 80 conventional (e.g., stable angina pectoris (SAP), type 2 diabetes mellitus) and unconventional (e.g., diaphragmatic hernia, inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of SAP yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction p-value=2.87*10-8) risk for ACS in individuals with SAP (HR=1.163 [95% CI: 1.082-1.251]) compared to individuals without SAP (HR=1.531 [95% CI: 1.497-1.565]). These findings were replicated in FinnGen (interaction p-value=1.38*10-6). Conclusions - In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable CHD. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for CHD.
View details for DOI 10.1161/CIRCGEN.120.003283
View details for PubMedID 34232692
Genetics of 35 blood and urine biomarkers in the UK Biobank.
Nature genetics
Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Benner, C., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Kiiskinen, T., Havulinna, A. S., Pirruccello, J. P., Qian, J., Shcherbina, A., FinnGen, Rodriguez, F., Assimes, T. L., Agarwala, V., Tibshirani, R., Hastie, T., Ripatti, S., Pritchard, J. K., Daly, M. J., Rivas, M. A.
2021
Abstract
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n=363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n=135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
View details for DOI 10.1038/s41588-020-00757-z
View details for PubMedID 33462484
Sleep apnoea is a risk factor for severe COVID-19.
BMJ open respiratory research
Strausz, S., Kiiskinen, T., Broberg, M., Ruotsalainen, S., Koskela, J., Bachour, A., FinnGen, Palotie, A., Palotie, T., Ripatti, S., Ollila, H. M., Palotie, A., Daly, M., Jacob, H., Matakidou, A., Runz, H., John, S., Plenge, R., McCarthy, M., Hunkapiller, J., Ehm, M., Waterworth, D., Fox, C., Malarstig, A., Klinger, K., Call, K., Makela, T., Kaprio, J., Virolainen, P., Pulkki, K., Kilpi, T., Perola, M., Partanen, J., Pitkaranta, A., Kaarteenaho, R., Vainio, S., Savinainen, K., Kosma, V., Kujala, U., Tuovila, O., Hendolin, M., Pakkanen, R., Waring, J., Riley-Gillis, S. B., Matakidou, A., Runz, H., Liu, J., Biswas, S., Hunkapiller, J., Waterworth, D., Ehm, M., Diogo, D., Fox, C., Pfizer, A., Marshall, C., Hu, X., Call, K., Klinger, K., Gossel, M., Ripatti, S., Schleutker, J., Perola, M., Arvas, M., Carpen, O., Hinttala, R., Kettunen, J., Laaksonen, R., Mannermaa, A., Paloneva, J., Kujala, U., Tuovila, O., Hendolin, M., Pakkanen, R., Soininen, H., Julkunen, V., Remes, A., Kalviainen, R., Hiltunen, M., Peltola, J., Tienari, P., Rinne, J., Ziemann, A., Waring, J., Esmaeeli, S., Smaoui, N., Lehtonen, A., Eaton, S., Runz, H., Lahdenpera, S., Biswas, S., Michon, J., Kerchner, G., Hunkapiller, J., Bowers, N., Teng, E., Merck, J., Mehta, V., Gormley, P., Linden, K., Whelan, C., Xu, F., Pulford, D., Farkkila, M., Pikkarainen, S., Jussila, A., Blomster, T., Kiviniemi, M., Voutilainen, M., Georgantas, B., Heap, G., Waring, J., Smaoui, N., Rahimov, F., Lehtonen, A., Usiskin, K., Maranville, J., Lu, T., Bowers, N., Oh, D., Michon, J., Mehta, V., Kalpala, K., Miller, M., Hu, X., McCarthy, L., Eklund, K., Palomaki, A., Isomaki, P., Pirila, L., Kaipiainen-Seppanen, O., Huhtakangas, J., Georgantas, B., Waring, J., Rahimov, F., Lertratanakul, A., Smaoui, N., Close, D., Hochfeld, M., Bowers, N., Michon, J., Diogo, D., Mehta, V., Kalpala, K., Bing, N., Hu, X., Gordillo, J. E., Mars, N., Laitinen, T., Pelkonen, M., Kauppi, P., Kankaanranta, H., Harju, T., Smaoui, N., Close, D., GreenbergCelgene, S., Chen, H., Bowers, N., Michon, J., Mehta, V., Betts, J., Ghosh, S., Salomaa, V., Niiranen, T., Juonala, M., Metsarinne, K., Kahonen, M., Junttila, J., Laakso, M., Pihlajamaki, J., Sinisalo, J., Taskinen, M., Tuomi, T., Laukkanen, J., Ben Challis, A. P., Hunkapiller, J., Bowers, N., Michon, J., Diogo, D., Chu, A., Mehta, V., Parkkinen, J., Miller, M., Muslin, A., Waterworth, D., Joensuu, H., Meretoja, T., Carpen, O., Aaltonen, L., Auranen, A., Karihtala, P., Kauppila, S., Auvinen, P., Elenius, K., Popovic, R., Waring, J., Riley-Gillis, B., Lehtonen, A., Matakidou, A., Schutzman, J., Hunkapiller, J., Bowers, N., Michon, J., Mehta, V., Loboda, A., Chhibber, A., Lehtonen, H., McDonough, S., Crohns, M., Kulkarni, D., Kaarniranta, K., Turunen, J., Ollila, T., Seitsonen, S., Uusitalo, H., Aaltonen, V., Uusitalo-Jarvinen, H., Luodonpaa, M., Hautala, N., Runz, H., Strauss, E., Bowers, N., Chen, H., Michon, J., Podgornaia, A., Mehta, V., Diogo, D., Hoffman, J., Tasanen, K., Huilaja, L., Hannula-Jouppi, K., Salmi, T., Peltonen, S., Koulu, L., Harvima, I., Kalpala, K., Wu, Y., Choy, D., Michon, J., Smaoui, N., Rahimov, F., Lehtonen, A., Waterworth, D., Jalanko, A., Kajanne, R., Lyhs, U., Kaunisto, M., Davis, J., Riley-Gillis, B., Quarless, D., Petrovski, S., Liu, J., Chen, C., Bronson, P., Yang, R., Maranville, J., Biswas, S., Chang, D., Hunkapiller, J., Bhangale, T., Bowers, N., Diogo, D., Holzinger, E., Gormley, P., Wang, X., Chen, X., Hedman, A., Auro, K., Wang, C., Xu, E., Auge, F., Chatelain, C., Kurki, M., Ripatti, S., Daly, M., Karjalainen, J., Havulinna, A., Jalanko, A., Palin, K., Palta, P., Della, P., Zhou, W., Lemmela, S., Rivas, M., Harju, J., Palotie, A., Lehisto, A., Ganna, A., Llorens, V., Karlsson, A., Kristiansson, K., Arvas, M., Hyvarinen, K., Ritari, J., Wahlfors, T., Koskinen, M., Carpen, O., Kettunen, J., Pylkas, K., Kalaoja, M., Karjalainen, M., Mantere, T., Kangasniemi, E., Heikkinen, S., Mannermaa, A., Laakkonen, E., Kononen, J., Loukola, A., Laiho, P., Sistonen, T., Kaiharju, E., Laukkanen, M., Jarvensivu, E., Lahteenmaki, S., Mannikko, L., Wong, R., Kristiansson, K., Mattsson, H., Lemmela, S., Hiekkalinna, T., Jimenez, M., Donner, K., Palta, P., Parn, K., Nunez-Fontarnau, J., Harju, J., Kilpelainen, E., Sipila, T., Brein, G., Dada, A., Awaisa, G., Shcherban, A., Sipila, T., Laivuori, H., Havulinna, A., Lemmela, S., Kiiskinen, T., Laitinen, T., Siirtola, H., Tabuenca, J., Kallio, L., Soini, S., Partanen, J., Pitkanen, K., Vainio, S., Savinainen, K., Kosma, V., Kuopio, T.
2021; 8 (1)
Abstract
BACKGROUND: Obstructive sleep apnoea (OSA) is associated with higher body mass index (BMI), diabetes, older age and male gender, which are all risk factors for severe COVID-19.We aimed to study if OSA is an independent risk factor for COVID-19 infection or for severe COVID-19.METHODS: OSA diagnosis and COVID-19 infection were extracted from the hospital discharge, causes of death and infectious diseases registries in individuals who participated in the FinnGen study (n=260 405). Severe COVID-19 was defined as COVID-19 requiring hospitalisation. Multivariate logistic regression model was used to examine association. Comorbidities for either COVID-19 or OSA were selected as covariates. We performed a meta-analysis with previous studies.RESULTS: We identified 445 individuals with COVID-19, and 38 (8.5%) of them with OSA of whom 19 out of 91 (20.9%) were hospitalised. OSA associated with COVID-19 hospitalisation independent from age, sex, BMI and comorbidities (p-unadjusted=5.13*10-5, OR-adjusted=2.93 (95% CI 1.02 to 8.39), p-adjusted=0.045). OSA was not associated with the risk of contracting COVID-19 (p=0.25). A meta-analysis of OSA and severe COVID-19 showed association across 15 835 COVID-19 positive controls, and n=1294 patients with OSA with severe COVID-19 (OR=2.37 (95% 1.14 to 4.95), p=0.021).CONCLUSION: Risk for contracting COVID-19 was the same for patients with OSA and those without OSA. In contrast, among COVID-19 positive patients, OSA was associated with higher risk for hospitalisation. Our findings are in line with earlier works and suggest OSA as an independent risk factor for severe COVID-19.
View details for DOI 10.1136/bmjresp-2020-000845
View details for PubMedID 33436406
Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices.
Nature communications
Natarajan, P., Pampana, A., Graham, S. E., Ruotsalainen, S. E., Perry, J. A., de Vries, P. S., Broome, J. G., Pirruccello, J. P., Honigberg, M. C., Aragam, K., Wolford, B., Brody, J. A., Antonacci-Fulton, L., Arden, M., Aslibekyan, S., Assimes, T. L., Ballantyne, C. M., Bielak, L. F., Bis, J. C., Cade, B. E., Do, R., Doddapaneni, H., Emery, L. S., Hung, Y., Irvin, M. R., Khan, A. T., Lange, L., Lee, J., Lemaitre, R. N., Martin, L. W., Metcalf, G., Montasser, M. E., Moon, J., Muzny, D., O'Connell, J. R., Palmer, N. D., Peralta, J. M., Peyser, P. A., Stilp, A. M., Tsai, M., Wang, F. F., Weeks, D. E., Yanek, L. R., Wilson, J. G., Abecasis, G., Arnett, D. K., Becker, L. C., Blangero, J., Boerwinkle, E., Bowden, D. W., Chang, Y., Chen, Y. I., Choi, W. J., Correa, A., Curran, J. E., Daly, M. J., Dutcher, S. K., Ellinor, P. T., Fornage, M., Freedman, B. I., Gabriel, S., Germer, S., Gibbs, R. A., He, J., Hveem, K., Jarvik, G. P., Kaplan, R. C., Kardia, S. L., Kenny, E., Kim, R. 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X., Zhao, W., Zhi, D., Zhou, X., Zhu, X., Zody, M., Zoellner, S., Palotie, A., Daly, M., Jacob, H., Matakidou, A., Runz, H., John, S., Plenge, R., McCarthy, M., Hunkapiller, J., Ehm, M., Waterworth, D., Fox, C., Malarstig, A., Klinger, K., Call, K., Mkel, T., Kaprio, J., Virolainen, P., Pulkki, K., Kilpi, T., Perola, M., Partanen, J., Pitkranta, A., Kaarteenaho, R., Vainio, S., Savinainen, K., Kosma, V., Kujala, U., Tuovila, O., Hendolin, M., Pakkanen, R., Waring, J., Riley-Gillis, B., Liu, J., Biswas, S., Diogo, D., Marshall, C., Hu, X., Gossel, M., Ripatti, S., Schleutker, J., Arvas, M., Carpen, O., Hinttala, R., Kettunen, J., Laaksonen, R., Mannermaa, A., Paloneva, J., Soininen, H., Julkunen, V., Remes, A., Klviinen, R., Hiltunen, M., Peltola, J., Tienari, P., Rinne, J., Ziemann, A., Waring, J., Esmaeeli, S., Smaoui, N., Lehtonen, A., Eaton, S., Lahdenper, S., Michon, J., Kerchner, G., Bowers, N., Teng, E., Eicher, J., Mehta, V., Gormley, P., Linden, K., Whelan, C., Xu, F., Pulford, D., Frkkil, M., Pikkarainen, S., Jussila, A., Blomster, T., Kiviniemi, M., Voutilainen, M., Georgantas, B., Heap, G., Rahimov, F., Usiskin, K., Maranville, J., Lu, T., Oh, D., Kalpala, K., Miller, M., McCarthy, L., Eklund, K., Palomki, A., Isomki, P., Piril, L., Kaipiainen-Seppnen, O., Huhtakangas, J., Lertratanakul, A., Close, D., Hochfeld, M., Bing, N., Gordillo, J. 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2021; 12 (1): 2182
Abstract
Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P=8.5*10-72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P=1.7*10-4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P=1.4*10-5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.
View details for DOI 10.1038/s41467-021-22339-1
View details for PubMedID 33846329
Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma.
PLoS genetics
Tanigawa, Y., Wainberg, M., Karjalainen, J., Kiiskinen, T., Venkataraman, G., Lemmela, S., Turunen, J. A., Graham, R. R., Havulinna, A. S., Perola, M., Palotie, A., FinnGen, Daly, M. J., Rivas, M. A.
2020; 16 (5): e1008682
Abstract
Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (beta = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.
View details for DOI 10.1371/journal.pgen.1008682
View details for PubMedID 32369491
Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma
PLOS GENETICS
Tanigawa, Y., Wainberg, M., Karjalainen, J., Kiiskinen, T., Venkataraman, G., Lemmela, S., Turunen, J. A., Graham, R. R., Havulinna, A. S., Perola, M., Palotie, A., Gen, F., Daly, M. J., Rivas, M. A.
2020; 16 (5)
Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health.
The European respiratory journal
Strausz, S. n., Ruotsalainen, S. n., Ollila, H. M., Karjalainen, J. n., Kiiskinen, T. n., Reeve, M. n., Kurki, M. n., Mars, N. n., Havulinna, A. S., Luonsi, E. n., Mansour Aly, D. n., Ahlqvist, E. n., Teder-Laving, M. n., Palta, P. n., Groop, L. n., Mägi, R. n., Mäkitie, A. n., Salomaa, V. n., Bachour, A. n., Tuomi, T. n., Palotie, A. n., Palotie, T. n., Ripatti, S. n.
2020
Abstract
There is currently limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). We aimed at identifying genetic loci associated with OSA risk and to test if OSA and its comorbidities share a common genetic background.We conducted the first large-scale genome-wide association study of OSA using FinnGen Study (217 955 individuals) with 16 761 OSA patients identified using nationwide health registries.We estimated 8.3% [0.06-0.11] heritability and identified five loci associated with OSA (p<5.0×10-8): rs4837016 near GTPase activating protein and VPS9 domains 1 (GAPVD1), rs10928560 near C-X-C motif chemokine receptor 4 (CXCR4), rs185932673 near Calcium/calmodulin-dependent protein kinase ID (CAMK1D) and rs9937053 near Fat mass and obesity-associated protein (FTO) - a variant previously associated with body mass index (BMI). In a BMI-adjusted analysis, an association was observed for rs10507084 near Rhabdomyosarcoma 2 associated transcript (RMST)/NEDD1 gamma-tubulin ring complex targeting factor (NEDD1). We found high genetic correlations between OSA and BMI (rg=0.72 [0.62-0.83]) and with comorbidities including hypertension, type 2 diabetes (T2D), coronary heart disease (CHD), stroke, depression, hypothyroidism, asthma and inflammatory rheumatic diseases (IRD) (rg>0.30). Polygenic risk score (PRS) for BMI showed 1.98-fold increased OSA risk between the highest and the lowest quintile and Mendelian randomisation supported a causal relationship between BMI and OSA.Our findings support the causal link between obesity and OSA and joint genetic basis between OSA and comorbidities.
View details for DOI 10.1183/13993003.03091-2020
View details for PubMedID 33243845
Genetic architecture of human plasma lipidome and its link to cardiovascular disease.
Nature communications
Tabassum, R., Ramo, J. T., Ripatti, P., Koskela, J. T., Kurki, M., Karjalainen, J., Palta, P., Hassan, S., Nunez-Fontarnau, J., Kiiskinen, T. T., Soderlund, S., Matikainen, N., Gerl, M. J., Surma, M. A., Klose, C., Stitziel, N. O., Laivuori, H., Havulinna, A. S., Service, S. K., Salomaa, V., Pirinen, M., FinnGen Project, Jauhiainen, M., Daly, M. J., Freimer, N. B., Palotie, A., Taskinen, M., Simons, K., Ripatti, S., Jalanko, A., Kaprio, J., Donner, K., Kaunisto, M., Mars, N., Dada, A., Shcherban, A., Ganna, A., Lehisto, A., Kilpelainen, E., Brein, G., Awaisa, G., Harju, J., Parn, K., Parolo, P. D., Kajanne, R., Lemmela, S., Sipila, T. P., Sipila, T., Lyhs, U., Llorens, V., Niiranen, T., Kristiansson, K., Mannikko, L., Jimenez, M. G., Perola, M., Wong, R., Kilpi, T., Hiekkalinna, T., Jarvensivu, E., Kaiharju, E., Mattsson, H., Laukkanen, M., Laiho, P., Lahteenmaki, S., Sistonen, T., Soini, S., Ziemann, A., Lehtonen, A., Lertratanakul, A., Georgantas, B., Riley-Gillis, B., Quarless, D., Rahimov, F., Heap, G., Jacob, H., Waring, J., Davis, J. W., Smaoui, N., Popovic, R., Esmaeeli, S., Waring, J., Matakidou, A., Challis, B., Close, D., Petrovski, S., Karlsson, A., Schleutker, J., Pulkki, K., Virolainen, P., Kallio, L., Mannermaa, A., Heikkinen, S., Kosma, V., Chen, C., Runz, H., Liu, J., Bronson, P., John, S., Lahdenpera, S., Eaton, S., Zhou, W., Hendolin, M., Tuovila, O., Pakkanen, R., Maranville, J., Usiskin, K., Hochfeld, M., Plenge, R., Yang, R., Biswas, S., Greenberg, S., Laakkonen, E., Kononen, J., Paloneva, J., Kujala, U., Kuopio, T., Laukkanen, J., Kangasniemi, E., Savinainen, K., Laaksonen, R., Arvas, M., Ritari, J., Partanen, J., Hyvarinen, K., Wahlfors, T., Peterson, A., Oh, D., Chang, D., Teng, E., Strauss, E., Kerchner, G., Chen, H., Chen, H., Schutzman, J., Michon, J., Hunkapiller, J., McCarthy, M., Bowers, N., Lu, T., Bhangale, T., Pulford, D., Waterworth, D., Kulkarni, D., Xu, F., Betts, J., Gordillo, J. E., Hoffman, J., Auro, K., McCarthy, L., Ghosh, S., Ehm, M., Pitkanen, K., Makela, T., Loukola, A., Joensuu, H., Sinisalo, J., Eklund, K., Aaltonen, L., Farkkila, M., Carpen, O., Kauppi, P., Tienari, P., Ollila, T., Tuomi, T., Meretoja, T., Pitkaranta, A., Turunen, J., Hannula-Jouppi, K., Pikkarainen, S., Seitsonen, S., Koskinen, M., Palomaki, A., Rinne, J., Metsarinne, K., Elenius, K., Pirila, L., Koulu, L., Voutilainen, M., Juonala, M., Peltonen, S., Aaltonen, V., Loboda, A., Podgornaia, A., Chhibber, A., Chu, A., Fox, C., Diogo, D., Holzinger, E., Eicher, J., Gormley, P., Mehta, V., Wang, X., Kettunen, J., Pylkas, K., Kalaoja, M., Karjalainen, M., Hinttala, R., Kaarteenaho, R., Vainio, S., Mantere, T., Vainio, S., Remes, A., Huhtakangas, J., Junttila, J., Tasanen, K., Huilaja, L., Luodonpaa, M., Hautala, N., Karihtala, P., Kauppila, S., Harju, T., Blomster, T., Soininen, H., Harvima, I., Pihlajamaki, J., Kaarniranta, K., Pelkonen, M., Laakso, M., Hiltunen, M., Kiviniemi, M., Kaipiainen-Seppanen, O., Auvinen, P., Kalviainen, R., Julkunen, V., Malarstig, A., Hedman, A., Marshall, C., Whelan, C., Lehtonen, H., Parkkinen, J., Linden, K., Kalpala, K., Miller, M., Bing, N., McDonough, S., Chen, X., Hu, X., Wu, Y., Auranen, A., Jussila, A., Uusitalo-Jarvinen, H., Kankaanranta, H., Uusitalo, H., Peltola, J., Kahonen, M., Isomaki, P., Laitinen, T., Salmi, T., Muslin, A., Wang, C., Chatelain, C., Xu, E., Auge, F., Call, K., Klinger, K., Crohns, M., Gossel, M., Palin, K., Rivas, M., Siirtola, H., Tabuenca, J. G.
2019; 10 (1): 4329
Abstract
Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n=2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n=511,700 individuals). We identify 35 lipid-species-associated loci (P<5*10-8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.
View details for DOI 10.1038/s41467-019-11954-8
View details for PubMedID 31551469
