Browsing by Subject "Genome-wide association studies"

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  • Ostrander, E.A.; Lohi, H.; Dog10K Consortium (2019)
    Dogs are the most phenotypically diverse mammalian species, and they possess more known heritable disorders than any other non-human mammal. Efforts to catalog and characterize genetic variation across well-chosen populations of canines are necessary to advance our understanding of their evolutionary history and genetic architecture. To date, no organized effort has been undertaken to sequence the world's canid populations. The Dog10K Consortium (http://www.dog10kgenomes.org) is an international collaboration of researchers from across the globe who will generate 20× whole genomes from 10 000 canids in 5 years. This effort will capture the genetic diversity that underlies the phenotypic and geographical variability of modern canids worldwide. Breeds, village dogs, niche populations and extended pedigrees are currently being sequenced, and de novo assemblies of multiple canids are being constructed. This unprecedented dataset will address the genetic underpinnings of domestication, breed formation, aging, behavior and morphological variation. More generally, this effort will advance our understanding of human and canine health. © 2019 The Author(s) 2019. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.
  • van den Berg, Stephanie M.; de Moor, Marleen H. M.; McGue, Matt; Pettersson, Erik; Terracciano, Antonio; Verweij, Karin J. H.; Amin, Najaf; Derringer, Jaime; Esko, Tonu; van Grootheest, Gerard; Hansell, Narelle K.; Huffman, Jennifer; Konte, Bettina; Lahti, Jari; Luciano, Michelle; Matteson, Lindsay K.; Viktorin, Alexander; Wouda, Jasper; Agrawal, Arpana; Allik, Jueri; Bierut, Laura; Broms, Ulla; Campbell, Harry; Smith, George Davey; Eriksson, Johan G.; Ferrucci, Luigi; Franke, Barbera; Fox, Jean-Paul; de Geus, Eco J. C.; Giegling, Ina; Gow, Alan J.; Grucza, Richard; Hartmann, Annette M.; Heath, Andrew C.; Heikkilä, Kauko; Iacono, William G.; Janzing, Joost; Jokela, Markus; Kiemeney, Lambertus; Lehtimaki, Terho; Madden, Pamela A. F.; Magnusson, Patrik K. E.; Northstone, Kate; Nutile, Teresa; Ouwens, Klaasjan G.; Palotie, Aarno; Pattie, Alison; Pesonen, Anu-Katriina; Polasek, Ozren; Pulkkinen, Lea; Pulkki-Raback, Laura; Raitakari, Olli T.; Realo, Anu; Rose, Richard J.; Ruggiero, Daniela; Seppala, Ilkka; Slutske, Wendy S.; Smyth, David C.; Sorice, Rossella; Starr, John M.; Sutin, Angelina R.; Tanaka, Toshiko; Verhagen, Josine; Vermeulen, Sita; Vuoksimaa, Eero; Widen, Elisabeth; Willemsen, Gonneke; Wright, Margaret J.; Zgaga, Lina; Rujescu, Dan; Metspalu, Andres; Wilson, James F.; Ciullo, Marina; Hayward, Caroline; Rudan, Igor; Deary, Ian J.; Räikkönen, Katri; Vasquez, Alejandro Arias; Costa, Paul T.; Keltikangas-Jarvinen, Liisa; van Duijn, Cornelia M.; Penninx, Brenda W. J. H.; Krueger, Robert F.; Evans, David M.; Kaprio, Jaakko; Pedersen, Nancy L.; Martin, Nicholas G.; Boomsma, Dorret I. (2014)
  • Tabassum, Rubina; Ripatti, Samuli (2021)
    Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.
  • Ganel, Liron; Chen, Lei; Christ, Ryan; Vangipurapu, Jagadish; Young, Erica; Das, Indraniel; Kanchi, Krishna; Larson, David; Regier, Allison; Abel, Haley; Kang, Chul J.; Scott, Alexandra; Havulinna, Aki; Chiang, Charleston W. K.; Service, Susan; Freimer, Nelson; Palotie, Aarno; Ripatti, Samuli; Kuusisto, Johanna; Boehnke, Michael; Laakso, Markku; Locke, Adam; Stitziel, Nathan O.; Hall, Ira M. (BioMed Central, 2021)
    Abstract Background Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). Results We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10−8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10−8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10−21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. Conclusion These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.
  • Ganel, Liron; Chen, Lei; Christ, Ryan; Vangipurapu, Jagadish; Young, Erica; Das, Indraniel; Kanchi, Krishna; Larson, David; Regier, Allison; Abel, Haley; Kang, Chul Joo; Scott, Alexandra; Havulinna, Aki; Chiang, Charleston W. K.; Service, Susan; Freimer, Nelson; Palotie, Aarno; Ripatti, Samuli; Kuusisto, Johanna; Boehnke, Michael; Laakso, Markku; Locke, Adam; Stitziel, Nathan O.; Hall, Ira M. (2021)
    Background Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). Results We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 x 10(-8)), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 x 10(-8)), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 x 10(-21)) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. Conclusion These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.