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1.	Thomas, HS, et al. (author) 2019 swepub:Mat__t
2.	Bhangu, A, et al. (author) Mortality of emergency abdominal surgery in high-, middle- and low-income countries 2016 In: The British journal of surgery. - : Oxford University Press (OUP). - 1365-2168 .- 0007-1323. ; 103:8, s. 971-988 Journal article (peer-reviewed)
3.	Kinyoki, DK, et al. (author) Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017 2020 In: Nature medicine. - : Springer Science and Business Media LLC. - 1546-170X .- 1078-8956. ; 26:5, s. 750-759 Journal article (peer-reviewed)abstract A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic.
4.	Lanktree, MB, et al. (author) Meta-analysis of Dense Genecentric Association Studies Reveals Common and Uncommon Variants Associated with Height 2011 In: American journal of human genetics. - : Elsevier BV. - 1537-6605 .- 0002-9297. ; 88:1, s. 6-18 Journal article (peer-reviewed)
5.	Abbafati, C, et al. (author) Five insights from the Global Burden of Disease Study 2019 2020 In: Lancet (London, England). - 1474-547X .- 0140-6736. ; 396:10258, s. 1135-1159 Journal article (peer-reviewed)
6.	Randall, JC, et al. (author) Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits 2013 In: PLoS genetics. - : Public Library of Science (PLoS). - 1553-7404 .- 1553-7390. ; 9:6, s. e1003500- Journal article (peer-reviewed)
7.	Reiner, RC, et al. (author) Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 2020 In: Lancet (London, England). - 1474-547X. ; 395, s. 1779-1801 Journal article (peer-reviewed)
8.	Romagnoni, A, et al. (author) Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data 2019 In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351- Journal article (peer-reviewed)abstract Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
9.	Ruth, KS, et al. (author) Genetic insights into biological mechanisms governing human ovarian ageing 2021 In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 596:7872, s. 393-397 Journal article (peer-reviewed)
10.	Shrine, N, et al. (author) Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk 2023 In: Nature genetics. - 1546-1718. ; 55:10, s. 1778-1779 Journal article (other academic/artistic)

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Type of publication: journal article (94); conference paper (1)

Type of content: peer-reviewed (84); other academic/artistic (12)

Author/Editor: Hall, P (37); Easton, DF (34); Czene, K (33); Fasching, PA (29); Hopper, JL (26); Southey, MC (26); show more...; Schmidt, MK (26); Dunning, AM (25); Couch, FJ (25); Chang-Claude, J (25); Pharoah, PDP (25); Wang, Q. (24); Milne, RL (24); Bolla, MK (24); Andrulis, IL (24); Nevanlinna, H (24); Hamann, U (23); Bojesen, SE (23); Devilee, P (23); Garcia-Closas, M (23); Dennis, J (22); Giles, GG (22); Beckmann, MW (22); Mannermaa, A (22); Hooning, MJ (21); Wareham, NJ (21); Cox, A (20); Hunter, DJ (20); Esko, T (20); Lindblom, A (19); Guenel, P (19); Haiman, CA (19); Kraft, P (19); Montgomery, GW (18); Michailidou, K (18); Radice, P (18); Chanock, SJ (18); Jakubowska, A (18); Lubinski, J (18); Chenevix-Trench, G (18); Gieger, C (18); Hayward, C. (18); Kristensen, VN (18); Brenner, H (17); Martin, NG (17); Sawyer, EJ (17); Gago-Dominguez, M. (17); Wilson, JF (17); Metspalu, A (17); Saloustros, E (17); show less...

University: Karolinska Institutet (99); Lund University (19); Uppsala University (18); Umeå University (11); University of Gothenburg (7); Högskolan Dalarna (4); show more...; Stockholm University (2); Jönköping University (2); Stockholm School of Economics (2); Royal Institute of Technology (1); Örebro University (1); Linköping University (1); Swedish University of Agricultural Sciences (1); show less...

Language: English (104)

Research subject (UKÄ/SCB): Medical and Health Sciences (35); Natural sciences (4); Social Sciences (1)

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