Browsing by Subject "ARCHITECTURE"

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  • Ouni, Emna; Peaucelle, Alexis; Haas, Kalina T.; Van Kerk, Olivier; Dolmans, Marie-Madeleine; Tuuri, Timo; Otala, Marjut; Amorim, Christiani A. (2021)
    Although the first dissection of the human ovary dates back to the 17th century, its characterization is still limited. Here, the authors have unraveled a unique biophysical and topological phenotype of reproductive-age tissue, bridging biophysics and female fertility and providing a blueprint for the artificial ovary. Although the first dissection of the human ovary dates back to the 17(th) century, the biophysical characteristics of the ovarian cell microenvironment are still poorly understood. However, this information is vital to deciphering cellular processes such as proliferation, morphology and differentiation, as well as pathologies like tumor progression, as demonstrated in other biological tissues. Here, we provide the first readout of human ovarian fiber morphology, interstitial and perifollicular fiber orientation, pore geometry, topography and surface roughness, and elastic and viscoelastic properties. By determining differences between healthy prepubertal, reproductive-age, and menopausal ovarian tissue, we unravel and elucidate a unique biophysical phenotype of reproductive-age tissue, bridging biophysics and female fertility. While these data enable to design of more biomimetic scaffolds for the tissue-engineered ovary, our analysis pipeline is applicable for the characterization of other organs in physiological or pathological states to reveal their biophysical markers or design their bioinspired analogs.
  • Hu, Man; Pitkanen, Timo P.; Minunno, Francesco; Tian, Xianglin; Lehtonen, Aleksi; Makela, Annikki (2021)
    Background and Aims Branch biomass and other attributes are important for estimating the carbon budget of forest stands and characterizing crown structure. As destructive measuring is time-consuming and labour-intensive, terrestrial laser scanning (TLS) as a solution has been used to estimate branch biomass quickly and non-destructively. However, branch information extraction from TLS data alone is challenging due to occlusion and other defects, especially for estimating individual branch attributes in coniferous trees. Methods This study presents a method, entitled TSMtls, to estimate individual branch biomass non-destructively and accurately by combining tree structure models and TLS data. The TSMtls method constructs the stem-taper curve from TLS data, then uses tree structure models to determine the number, basal area and biomass of individual branches at whorl level. We estimated the tree structural model parameters from 122 destructively measured Scots pine (Pinus sylvestris) trees and tested the method on six Scots pine trees that were first TLS-scanned and later destructively measured. Additionally, we estimated the branch biomass using other TLS-based approaches for comparison. Key Results Tree-level branch biomass estimates derived from TSMtls showed the best agreement with the destructive measurements [coefficient of variation of root mean square error (CV-RMSE) = 9.66 % and concordance correlation coefficient (CCC) = 0.99], outperforming the other TLS-based approaches (CV-RMSE 12.97-57.45 % and CCC 0.43-0.98 ). Whorl-level individual branch attributes estimates produced from TSMtls showed more accurate results than those produced from TLS data directly. Conclusions The results showed that the TSMtls method proposed in this study holds promise for extension to more species and larger areas.
  • Myllymaki, Satu-Marja; Kämäräinen, Ulla-Reetta; Liu, Xiaonan; Cruz, Sara Pereira; Miettinen, Sini; Vuorela, Mikko; Varjosalo, Markku; Manninen, Aki (2019)
    Integrin-mediated laminin adhesions mediate epithelial cell anchorage to basement membranes and are critical regulators of epithelial cell polarity. Integrins assemble large multiprotein complexes that link to the cytoskeleton and convey signals into the cells. Comprehensive proteomic analyses of actin network-linked focal adhesions (FA) have been performed, but the molecular composition of intermediate filament-linked hemidesmosomes (HD) remains incompletely characterized. Here we have used proximity-dependent biotin identification (BioID) technology to label and characterize the interactome of epithelia-specific beta 4-integrin that, as alpha 6 beta 4-heterodimer, forms the core of HDs. The analysis identified similar to 150 proteins that were specifically labeled by BirA-tagged integrin-beta 4. In addition to known HDs proteins, the interactome revealed proteins that may indirectly link integrin-beta 4 to actin-connected protein complexes, such as FAs and dystrophin/dystroglycan complexes. The specificity of the screening approach was validated by confirming the HD localization of two candidate beta 4-interacting proteins, utrophin (UTRN) and ELKS/Rab6-interacting/CAST family member 1 (ERC1). Interestingly, although establishment of functional HDs depends on the formation of alpha 6 beta 4-heterodimers, the assembly of beta 4-interactome was not strictly dependent on alpha 6-integrin expression. Our survey to the HD interactome sets a precedent for future studies and provides novel insight into the mechanisms of HD assembly and function of the beta 4-integrin.
  • Salo, Raimo A.; Belevich, Ilya; Jokitalo, Eija; Gröhn, Olli; Sierra, Alejandra (2021)
    Validation and interpretation of diffusion magnetic resonance imaging (dMRI) requires detailed understanding of the actual microstructure restricting the diffusion of water molecules. In this study, we used serial block-face scanning electron microscopy (SBEM), a three-dimensional electron microscopy (3D-EM) technique, to image seven white and grey matter volumes in the rat brain. SBEM shows excellent contrast of cellular membranes, which are the major components restricting the diffusion of water in tissue. Additionally, we performed 3D structure tensor (3D-ST) analysis on the SBEM volumes and parameterised the resulting orientation distributions using Watson and angular central Gaussian (ACG) probability distributions as well as spherical harmonic (SH) decomposition. We analysed how these parameterisations described the underlying orientation distributions and compared their orientation and dispersion with corresponding parameters from two dMRI methods, neurite orientation dispersion and density imaging (NODDI) and constrained spherical deconvolution (CSD). Watson and ACG parameterisations and SH decomposition captured well the 3D-ST orientation distributions, but ACG and SH better represented the distributions due to its ability to model asymmetric dispersion. The dMRI parameters corresponded well with the 3D-ST parameters in the white matter volumes, but the correspondence was less evident in the more complex grey matter. SBEM imaging and 3D-ST analysis also revealed that the orientation distributions were often not axially symmetric, a property neatly captured by the ACG distribution. Overall, the ability of SBEM to image diffusion barriers in intricate detail, combined with 3D-ST analysis and parameterisation, provides a step forward toward interpreting and validating the dMRI signals in complex brain tissue microstructure.
  • Fedirko, Veronika; Jenab, Mazda; Meplan, Catherine; Jones, Jeb S.; Zhu, Wanzhe; Schomburg, Lutz; Siddiq, Afshan; Hybsier, Sandra; Overvad, Kim; Tjonneland, Anne; Omichessan, Hanane; Perduca, Vittorio; Boutron-Ruault, Marie-Christine; Kuehn, Tilman; Katzke, Verena; Aleksandrova, Krasimira; Trichopoulou, Antonia; Karakatsani, Anna; Kotanidou, Anastasia; Tumino, Rosario; Panico, Salvatore; Masala, Giovanna; Agnoli, Claudia; Naccarati, Alessio; Bueno-de-Mesquita, Bas; Vermeulen, Roel C. H.; Weiderpass, Elisabete; Skeie, Guri; Nost, Therese Haugdahl; Lujan-Barroso, Leila; Ramon Quiros, J.; Maria Huerta, Jose; Rodriguez-Barranco, Miguel; Barricarte, Aurelio; Gylling, Bjoern; Harlid, Sophia; Bradbury, Kathryn E.; Wareham, Nick; Khaw, Kay-Tee; Gunter, Marc; Murphy, Neil; Freisling, Heinz; Tsilidis, Kostas; Aune, Dagfinn; Riboli, Elio; Hesketh, John E.; Hughes, David J. (2019)
    Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (P-ACT = 0.10; P-ACT significance threshold was P <0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development.
  • Schillemans, Tessa; Tragante, Vinicius; Maitusong, Buamina; Gigante, Bruna; Cresci, Sharon; Laguzzi, Federica; Vikstrom, Max; Richards, Mark; Pilbrow, Anna; Cameron, Vicky; Foco, Luisa; Doughty, Robert N.; Kuukasjarvi, Pekka; Allayee, Hooman; Hartiala, Jaana A.; Tang, W. H. Wilson; Lyytikainen, Leo-Pekka; Nikus, Kjell; Laurikka, Jari O.; Srinivasan, Sundararajan; Mordi, Ify R.; Trompet, Stella; Kraaijeveld, Adriaan; van Setten, Jessica; Gijsberts, Crystel M.; Maitland-van der Zee, Anke H.; Saely, Christoph H.; Gong, Yan; Johnson, Julie A.; Cooper-DeHoff, Rhonda M.; Pepine, Carl J.; Casu, Gavino; Leiherer, Andreas; Drexel, Heinz; Horne, Benjamin D.; van der Laan, Sander W.; Marziliano, Nicola; Hazen, Stanley L.; Sinisalo, Juha; Kahonen, Mika; Lehtimaki, Terho; Lang, Chim C.; Burkhardt, Ralph; Scholz, Markus; Jukema, J. Wouter; Eriksson, Niclas; Akerblom, Axel; James, Stefan; Held, Claes; Hagstrom, Emil; Spertus, John A.; Algra, Ale; de Faire, Ulf; Akesson, Agneta; Asselbergs, Folkert W.; Patel, Riyaz S.; Leander, Karin (2022)
    Background: The knowledge of factors influencing disease progression in patients with established coronary heart disease (CHD) is still relatively limited. One potential pathway is related to peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A), a transcription factor linked to energy metabolism which may play a role in the heart function. Thus, its associations with subsequent CHD events remain unclear. We aimed to investigate the effect of three different SNPs in the PPARGC1A gene on the risk of subsequent CHD in a population with established CHD.Methods: We employed an individual-level meta-analysis using 23 studies from the GENetIcs of sUbSequent Coronary Heart Disease (GENIUS-CHD) consortium, which included participants (n = 80,900) with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. Three variants in the PPARGC1A gene (rs8192678, G482S; rs7672915, intron 2; and rs3755863, T528T) were tested for their associations with subsequent events during the follow-up using a Cox proportional hazards model adjusted for age and sex. The primary outcome was subsequent CHD death or myocardial infarction (CHD death/myocardial infarction). Stratified analyses of the participant or study characteristics as well as additional analyses for secondary outcomes of specific cardiovascular disease diagnoses and all-cause death were also performed.Results: Meta-analysis revealed no significant association between any of the three variants in the PPARGC1A gene and the primary outcome of CHD death/myocardial infarction among those with established CHD at baseline: rs8192678, hazard ratio (HR): 1.01, 95% confidence interval (CI) 0.98-1.05 and rs7672915, HR: 0.97, 95% CI 0.94-1.00; rs3755863, HR: 1.02, 95% CI 0.99-1.06. Similarly, no significant associations were observed for any of the secondary outcomes. The results from stratified analyses showed null results, except for significant inverse associations between rs7672915 (intron 2) and the primary outcome among 1) individuals aged >= 65, 2) individuals with renal impairment, and 3) antiplatelet users.Conclusion: We found no clear associations between polymorphisms in the PPARGC1A gene and subsequent CHD events in patients with established CHD at baseline.
  • Kivikoski, Mikko; Rastas, Pasi; Löytynoja, Ari; Merila, Juha (2021)
    We describe an integrative approach to improve contiguity and haploidy of a reference genome assembly and demonstrate its impact with practical examples. With two novel features of Lep-Anchor software and a combination of dense linkage maps, overlap detection and bridging long reads, we generated an improved assembly of the nine-spined stickleback (Pungitius pungitius) reference genome. We were able to remove a significant number of haplotypic contigs, detect more genetic variation and improve the contiguity of the genome, especially that of X chromosome. However, improved scaffolding cannot correct for mosaicism of erroneously assembled contigs, demonstrated by a de novo assembly of a 1.6-Mbp inversion. Qualitatively similar gains were obtained with the genome of three-spined stickleback (Gasterosteus aculeatus). Since the utility of genome-wide sequencing data in biological research depends heavily on the quality of the reference genome, the improved and fully automated approach described here should be helpful in refining reference genome assemblies.
  • Pers, Tune H.; Karjalainen, Juha M.; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R.; Yang, Jian; Lui, Julian C.; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K.; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S. N.; Hirschhorn, Joel N.; Franke, Lude; Genetic Invest Anthropometric Trai; Kaprio, Jaakko (2015)
    The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.
  • Patwardhan, Ardan; Brandt, Robert; Butcher, Sarah J.; Collinson, Lucy; Gault, David; Grunewald, Kay; Hecksel, Corey; Huiskonen, Juha T.; Iudin, Andrii; Jones, Martin L.; Korir, Paul K.; Koster, Abraham J.; Lagerstedt, Ingvar; Lawson, Catherine L.; Mastronarde, David; McCormick, Matthew; Parkinson, Helen; Rosenthal, Peter B.; Saalfeld, Stephan; Saibil, Helen R.; Sarntivijai, Sirarat; Valero, Irene Solanes; Subramaniam, Sriram; Swedlow, Jason R.; Tudose, Ilinca; Winn, Martyn; Kleywegt, Gerard J. (2017)
    The integration of cellular and molecular structural data is key to understanding the function of macromolecular assemblies and complexes in their in vivo context. Here we report on the outcomes of a workshop that discussed how to integrate structural data from a range of public archives. The workshop identified two main priorities: the development of tools and file formats to support segmentation (that is, the decomposition of a three-dimensional volume into regions that can be associated with defined objects), and the development of tools to support the annotation of biological structures.
  • Sihvonen, Aleksi J.; Särkämö, Teppo (2021)
    Patients with post-stroke impairments present often significant variation in response to therapeutic interventions. Recent studies have shown that daily music listening can aid post-stroke recovery of language and memory, but reliable predictors of treatment response are unknown. Utilizing data from the music intervention arms of a single-blind randomized controlled trial (RCT) on stroke patients (N = 31), we built regression models to predict the treatment response of a two-month music listening intervention on language skills and verbal memory with baseline demographic, clinical and musical data as well as fMRI data from a music listening task. Clinically, greater improvement in verbal memory and language skills after the music listening intervention were predicted by the severity of the initial deficit and educational level. Neurally, greater baseline fMRI activation during vocal music listening in the left parietal cortical and medial frontal areas predicted greater treatment-induced improvement in language skills and greater baseline engagement of the auditory network during instrumental music listening predicted improvement in both verbal memory and language skills. Our results suggest that clinical, demographic, and neuroimaging data predicts music listening treatment response. This data could be used clinically to target music-based treatments.
  • Kautt, Andreas F.; Kratochwil, Claudius F.; Nater, Alexander; Machado-Schiaffino, Gonzalo; Olave, Melisa; Henning, Frederico; Torres-Dowdall, Julian; Härer, Andreas; Hulsey, C. Darrin; Franchini, Paolo; Pippel, Martin; Myers, Eugene W.; Meyer, Axel (2020)
    Population genomic analyses of Midas cichlid fishes in young Nicaraguan crater lakes suggest that sympatric speciation is promoted by polygenic architectures. The transition from 'well-marked varieties' of a single species into 'well-defined species'-especially in the absence of geographic barriers to gene flow (sympatric speciation)-has puzzled evolutionary biologists ever since Darwin(1,2). Gene flow counteracts the buildup of genome-wide differentiation, which is a hallmark of speciation and increases the likelihood of the evolution of irreversible reproductive barriers (incompatibilities) that complete the speciation process(3). Theory predicts that the genetic architecture of divergently selected traits can influence whether sympatric speciation occurs(4), but empirical tests of this theory are scant because comprehensive data are difficult to collect and synthesize across species, owing to their unique biologies and evolutionary histories(5). Here, within a young species complex of neotropical cichlid fishes (Amphilophus spp.), we analysed genomic divergence among populations and species. By generating a new genome assembly and re-sequencing 453 genomes, we uncovered the genetic architecture of traits that have been suggested to be important for divergence. Species that differ in monogenic or oligogenic traits that affect ecological performance and/or mate choice show remarkably localized genomic differentiation. By contrast, differentiation among species that have diverged in polygenic traits is genomically widespread and much higher overall, consistent with the evolution of effective and stable genome-wide barriers to gene flow. Thus, we conclude that simple trait architectures are not always as conducive to speciation with gene flow as previously suggested, whereas polygenic architectures can promote rapid and stable speciation in sympatry.
  • Lwakatare, Lucy Ellen; Kilamo, Terhi; Karvonen, Teemu; Sauvola, Tanja; Heikkilä, Ville; Itkonen, Juha; Kuvaja, Pasi; Mikkonen, Tommi; Oivo, Markku; Lassenius, Casper (2019)
    Context: DevOps is considered important in the ability to frequently and reliably update a system in operational state. DevOps presumes cross-functional collaboration and automation between software development and operations. DevOps adoption and implementation in companies is non-trivial due to required changes in technical, organisational and cultural aspects. Objectives: This exploratory study presents detailed descriptions of how DevOps is implemented in practice. The context of our empirical investigation is web application and service development in small and medium sized companies. Method: A multiple-case study was conducted in five different development contexts with successful DevOps implementations since its benefits, such as quick releases and minimum deployment errors, were achieved. Data was mainly collected through interviews with 26 practitioners and observations made at the companies. Data was analysed by first coding each case individually using a set of predefined themes and thereafter perform a cross-case synthesis. Results: Our analysis yielded some of the following results: (I) software development team attaining ownership and responsibility to deploy software changes in production is crucial in DevOps. (ii) toolchain usage and support in deployment pipeline activities accelerates the delivery of software changes, bug fixes and handling of production incidents. (ii) the delivery speed to production is affected by context factors, such as manual approvals by the product owner (iii) steep learning curve for new skills is experienced by both software developers and operations staff, who also have to cope with working under pressure. Conclusion: Our findings contributes to the overall understanding of DevOps concept, practices and its perceived impacts, particularly in small and medium sized companies. We discuss two practical implications of the results.
  • Xu, Dianlei; Li, Tong; Li, Yong; Su, Xiang; Tarkoma, Sasu; Jiang, Tao; Crowcroft, Jon; Hui, Pan (2021)
    Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.
  • Li, Yuhong; Su, Xiang; Ding, Aaron Yi; Lindgren, Anders; Liu, Xiaoli; Prehofer, Christian; Riekki, Jukka; Rahmani, Rahim; Tarkoma, Sasu; Hui, Pan (2020)
    The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.
  • Broad Genomics Platform; DiscovEHR Collaboration; CHARGE; LuCamp; ProDiGY; GoT2D; ESP; SIGMA-T2D; T2D-GENES; AMP-T2D-GENES; Flannick, Jason; Mercader, Josep M.; Koistinen, Heikki A.; Kuusisto, Johanna; Groop, Leif; Tuomi, Tiinamaija; Tuomilehto, Jaakko; Boehnke, Michael (2019)
    Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 x 10(-3)) and candidate genes from knockout mice (P = 5.2 x 10(-3)). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.
  • Weissbrod, Omer; Hormozdiari, Farhad; Benner, Christian; Cui, Ran; Ulirsch, Jacob; Gazal, Steven; Schoech, Armin P.; van de Geijn, Bryce; Reshef, Yakir; Marquez-Luna, Carla; O'Connor, Luke; Pirinen, Matti; Finucane, Hilary K.; Price, Alkes L. (2020)
    Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome-not just genome-wide-significant loci-to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures. PolyFun is a computationally scalable framework for functionally informed fine-mapping that makes full use of genome-wide data. It prioritizes more variants than previous methods when applied to 49 complex traits from UK Biobank.
  • McLaughlin, Russell L.; Schijven, Dick; van Rheenen, Wouter; van Eijk, Kristel R.; O'Brien, Margaret; Kahn, Rene S.; Ophoff, Roel A.; Goris, An; Bradley, Daniel G.; Al-Chalabi, Ammar; van den Berg, Leonard H.; Luykx, Jurjen J.; Hardiman, Orla; Veldink, Jan H.; Project MinE GWAS Consortium; Schizophrenia Working Grp Psychiat; Palotie, Aarno (2017)
    We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P = 1 x 10(-4)) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P = 8.4 x 10(-7)). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.
  • Vinuela, Ana; Varshney, Arushi; van de Bunt, Martijn; Prasad, Rashmi B.; Asplund, Olof; Bennett, Amanda; Boehnke, Michael; Brown, Andrew A.; Erdos, Michael R.; Fadista, Joao; Hansson, Ola; Hatem, Gad; Howald, Cedric; Iyengar, Apoorva K.; Johnson, Paul; Krus, Ulrika; MacDonald, Patrick E.; Mahajan, Anubha; Manning Fox, Jocelyn E.; Narisu, Narisu; Nylander, Vibe; Orchard, Peter; Oskolkov, Nikolay; Panousis, Nikolaos I.; Payne, Anthony; Stitzel, Michael L.; Vadlamudi, Swarooparani; Welch, Ryan; Collins, Francis S.; Mohlke, Karen L.; Gloyn, Anna L.; Scott, Laura J.; Dermitzakis, Emmanouil T.; Groop, Leif; Parker, Stephen C. J.; McCarthy, Mark I. (2020)
    Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.
  • Momozawa, Yukihide; Merveille, Anne-Christine; Battaille, Geraldine; Wiberg, Maria; Koch, Jørgen; Willesen, Jakob Lundgren; Proschowsky, Helle Friis; Gouni, Vassiliki; Chetboul, Valerie; Tiret, Laurent; Fredholm, Merete; Seppälä, Eija H.; Lohi, Hannes; Georges, Michel; Lequarre, Anne-Sophie (2020)
    The domestic dog represents an ideal model for identifying susceptibility genes, many of which are shared with humans. In this study, we investigated the genetic contribution to individual differences in 40 clinically important measurements by a genome-wide association study (GWAS) in a multinational cohort of 472 healthy dogs from eight breeds. Meta-analysis using the binary effects model after breed-specific GWAS, identified 13 genome-wide significant associations, three of them showed experimental-wide significant associations. We detected a signal at chromosome 13 for the serum concentration of alanine aminotransferase (ALT) in which we detected four breed-specific signals. A large proportion of the variance of ALT (18.1-47.7%) was explained by this locus. Similarly, a single SNP was also responsible for a large proportion of the variance (6.8-78.4%) for other measurements such as fructosamine, stress during physical exam, glucose, and morphometric measurements. The genetic contribution of single variant was much larger than in humans. These findings illustrate the importance of performing meta-analysis after breed-specific GWAS to reveal the genetic contribution to individual differences in clinically important measurements, which would lead to improvement of veterinary medicine.
  • Francis, Deanne; Ghazanfar, Shila; Havula, Essi; Krycer, James R.; Strbenac, Dario; Senior, Alistair; Minard, Annabel Y.; Geddes, Thomas; Nelson, Marin E.; Weiss, Fiona; Stöckli, Jacqueline; Yang, Jean Y.H.; James, David E. (2021)
    Genetic and environmental factors play a major role in metabolic health. However, they do not act in isolation, as a change in an environmental factor such as diet may exert different effects based on an individual's genotype. Here, we sought to understand how such gene-diet interactions influenced nutrient storage and utilization, a major determinant of metabolic disease. We subjected 178 inbred strains from the Drosophila genetic reference panel (DGRP) to diets varying in sugar, fat, and protein. We assessed starvation resistance, a holistic phenotype of nutrient storage and utilization that can be robustly measured. Diet influenced the starvation resistance of most strains, but the effect varied markedly between strains such that some displayed better survival on a high carbohydrate diet (HCD) compared to a high-fat diet while others had opposing responses, illustrating a considerable gene x diet interaction. This demonstrates that genetics plays a major role in diet responses. Furthermore, heritability analysis revealed that the greatest genetic variability arose from diets either high in sugar or high in protein. To uncover the genetic variants that contribute to the heterogeneity in starvation resistance, we mapped 566 diet-responsive SNPs in 293 genes, 174 of which have human orthologs. Using whole-body knockdown, we identified two genes that were required for glucose tolerance, storage, and utilization. Strikingly, flies in which the expression of one of these genes, CG4607 a putative homolog of a mammalian glucose transporter, was reduced at the whole-body level, displayed lethality on a HCD. This study provides evidence that there is a strong interplay between diet and genetics in governing survival in response to starvation, a surrogate measure of nutrient storage efficiency and obesity. It is likely that a similar principle applies to higher organisms thus supporting the case for nutrigenomics as an important health strategy.