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- Anderson, Cynthia M., et al.
(författare)
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Permanent Genetic Resources added to Molecular Ecology Resources Database 1 December 2009-31 January 2010
- 2010
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Ingår i: Molecular Ecology Resources. - : Wiley. - 1755-098X .- 1755-0998. ; 10:3, s. 576-579
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Tidskriftsartikel (refereegranskat)abstract
- This article documents the addition of 220 microsatellite marker loci to the Molecular Ecology Resources Database. Loci were developed for the following species: Allanblackia floribunda, Amblyraja radiata, Bactrocera cucurbitae, Brachycaudus helichrysi, Calopogonium mucunoides, Dissodactylus primitivus, Elodea canadensis, Ephydatia fluviatilis, Galapaganus howdenae howdenae, Hoplostethus atlanticus, Ischnura elegans, Larimichthys polyactis, Opheodrys vernalis, Pelteobagrus fulvidraco, Phragmidium violaceum, Pistacia vera, and Thunnus thynnus. These loci were cross-tested on the following species: Allanblackia gabonensis, Allanblackia stanerana, Neoceratitis cyanescens, Dacus ciliatus, Dacus demmerezi, Bactrocera zonata, Ceratitis capitata, Ceratitis rosa, Ceratits catoirii, Dacus punctatifrons, Ephydatia mulleri, Spongilla lacustris, Geodia cydonium, Axinella sp., Ischnura graellsii, Ischnura ramburii, Ischnura pumilio, Pistacia integerrima and Pistacia terebinthus.
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| 2. |
- Furberg, Helena, et al.
(författare)
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Genome-wide meta-analyses identify multiple loci associated with smoking behavior
- 2010
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 42:5, s. 134-441
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Tidskriftsartikel (refereegranskat)abstract
- Consistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], b = 1.03, standard error (s.e.) = 0.053, beta = 2.8 x 10(-73)). Two 10q25 SNPs (rs1329650[G], b = 0.367, s. e. = 0.059, beta = 5.7 x 10(-10); and rs1028936[A], b = 0.446, s. e. = 0.074, beta = 1.3 x 10(-9)) and one 9q13 SNP in EGLN2 (rs3733829[G], b = 0.333, s. e. = 0.058, P = 1.0 x 10(-8)) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04-1.08, P = 1.8 x 10(-8)). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08-1.18, P = 3.6 x 10(-8)) was significantly associated with smoking cessation.
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| 5. |
- Haerens, L, et al.
(författare)
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Formative research to develop the IDEFICS physical activity intervention component: findings from focus groups with children and parents
- 2010
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Ingår i: Journal of Physical Activity and Health. - Champaign, IL : HUMAN KINETICS PUBL INC. - 1543-3080 .- 1543-5474. ; 7:2, s. 246-256
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The current study aimed at describing influencing factors for physical activity among young children to determine the best approaches for developing the IDEFICS community based intervention. METHODS: In 8 European sites a trained moderator conducted a minimum of 4 focus groups using standardized questioning guides. A total of 56 focus groups were conducted including 36 focus groups with parents and 20 focus groups with children, of which 74 were boys and 81 girls. Key findings were identified through independent reviews of focus group summary reports using content analysis methods. FINDINGS: Findings were generally consistent across countries. The greatest emphasis was on environmental physical (eg, seasonal influences, availability of facilities and safety), institutional (eg, length of breaks at school), and social factors (eg, role modeling of parents). Most cited personal factors by parents were age, social economical status, and perceived barriers. Both children and parents mentioned the importance of children's preferences. CONCLUSIONS: To increase physical activity levels of young children the intervention should aim at creating an environment (physical, institutional, social) supportive of physical activity. On the other hand strategies should take into account personal factors like age and social economical status and should consider personal barriers too.
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