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1.
  • 2017
  • swepub:Mat__t
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2.
  • van Leeuwen, F., et al. (creator_code:aut_t)
  • Gaia Data Release 1 : Open cluster astrometry: Performance, limitations, and future prospects
  • 2017
  • record:In_t: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 601
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry, the proper motion and parallax are calculated using Hipparcos and Tycho-2 positions in 1991.25 as prior information. Aims. We investigate the scientific potential and limitations of the TGAS component by means of the astrometric data for open clusters. Methods. Mean cluster parallax and proper motion values are derived taking into account the error correlations within the astrometric solutions for individual stars, an estimate of the internal velocity dispersion in the cluster, and, where relevant, the effects of the depth of the cluster along the line of sight. Internal consistency of the TGAS data is assessed. Results. Values given for standard uncertainties are still inaccurate and may lead to unrealistic unit-weight standard deviations of least squares solutions for cluster parameters. Reconstructed mean cluster parallax and proper motion values are generally in very good agreement with earlier Hipparcos-based determination, although the Gaia mean parallax for the Pleiades is a significant exception. We have no current explanation for that discrepancy. Most clusters are observed to extend to nearly 15 pc from the cluster centre, and it will be up to future Gaia releases to establish whether those potential cluster-member stars are still dynamically bound to the clusters. Conclusions. The Gaia DR1 provides the means to examine open clusters far beyond their more easily visible cores, and can provide membership assessments based on proper motions and parallaxes. A combined HR diagram shows the same features as observed before using the Hipparcos data, with clearly increased luminosities for older A and F dwarfs.
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3.
  • Abbafati, Cristiana, et al. (creator_code:aut_t)
  • 2020
  • swepub:Mat_article_t (swepub:level_refereed_t)
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4.
  • 2019
  • swepub:Mat_article_t (swepub:level_refereed_t)
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5.
  • Kattge, Jens, et al. (creator_code:aut_t)
  • TRY plant trait database - enhanced coverage and open access
  • 2020
  • record:In_t: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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6.
  • Hudson, Lawrence N, et al. (creator_code:aut_t)
  • The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project
  • 2017
  • record:In_t: Ecology and Evolution. - : John Wiley & Sons. - 2045-7758. ; 7:1, s. 145-188
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
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7.
  • Thomas, Minta, et al. (creator_code:aut_t)
  • Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.
  • 2020
  • record:In_t: American Journal of Human Genetics. - Cambridge : Elsevier BV. - 0002-9297 .- 1537-6605. ; 107:3, s. 432-444
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
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8.
  • Thomas, Minta, et al. (creator_code:aut_t)
  • Response to Li and Hopper
  • 2021
  • record:In_t: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297 .- 1537-6605. ; 108:3, s. 527-529
  • swepub:Mat_article_t (swepub:level_refereed_t)
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9.
  • Hudson, Lawrence N., et al. (creator_code:aut_t)
  • The PREDICTS database : a global database of how local terrestrial biodiversity responds to human impacts
  • 2014
  • record:In_t: Ecology and Evolution. - : Wiley. - 2045-7758. ; 4:24, s. 4701-4735
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - ). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.
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10.
  • Archambault, Alexi N., et al. (creator_code:aut_t)
  • Cumulative Burden of Colorectal Cancer Associated Genetic Variants Is More Strongly Associated With Early-Onset vs Late-Onset Cancer
  • 2020
  • record:In_t: Gastroenterology. - : Elsevier BV. - 0016-5085 .- 1528-0012. ; 158:5, s. 1274-1286.e12
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • BACKGROUND & AIMS: Early-onset colorectal cancer (CRC, in persons younger than 50 years old) is increasing in incidence; yet, in the absence of a family history of CRC, this population lacks harmonized recommendations for prevention. We aimed to determine whether a polygenic risk score (PRS) developed from 95 CRC-associated common genetic risk variants was associated with risk for early-onset CRC.METHODS: We studied risk for CRC associated with a weighted PRS in 12,197 participants younger than 50 years old vs 95,865 participants 50 years or older. PRS was calculated based on single nucleotide polymorphisms associated with CRC in a large-scale genome-wide association study as of January 2019. Participants were pooled from 3 large consortia that provided clinical and genotyping data: the Colon Cancer Family Registry, the Colorectal Transdisciplinary Study, and the Genetics and Epidemiology of Colorectal Cancer Consortium and were all of genetically defined European descent. Findings were replicated in an independent cohort of 72,573 participants.RESULTS: Overall associations with CRC per standard deviation of PRS were significant for early-onset cancer, and were stronger compared with late-onset cancer (P for interaction = .01); when we compared the highest PRS quartile with the lowest, risk increased 3.7-fold for early-onset CRC (95% CI 3.28-4.24) vs 2.9-fold for late-onset CRC (95% CI 2.80-3.04). This association was strongest for participants without a first-degree family history of CRC (P for interaction = 5.61 x 10(-5)). When we compared the highest with the lowest quartiles in this group, risk increased 4.3-fold for early-onset CRC (95% CI 3.61-5.01) vs 2.9-fold for late-onset CRC (95% CI 2.70-3.00). Sensitivity analyses were consistent with these findings.CONCLUSIONS: In an analysis of associations with CRC per standard deviation of PRS, we found the cumulative burden of CRC-associated common genetic variants to associate with early-onset cancer, and to be more strongly associated with early-onset than late-onset cancer, particularly in the absence of CRC family history. Analyses of PRS, along with environmental and lifestyle risk factors, might identify younger individuals who would benefit from preventive measures.
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