PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Genomics & Informatics10.5808/gi.2016.14.4.1492016144149Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass IndexSunghwan Bae, Sungkyoung Choi, Sung Min Kim, Taesung Parkhttps://synapse.koreamed.org/pdf/10.5808/GI.2016.14.4.149, https://synapse.koreamed.org/DOIx.php?id=10.5808/GI.2016.14.4.149, https://synapse.koreamed.org/DOIx.php?id=10.5808/GI.2016.14.4.149
10.21203/rs.3.rs-701491/v12021Body Mass Index and Healthcare Costs: Using Genetic Variants from the HUNT Study as Instrumental VariablesChristina Hansen Edwards, Gunnhild Åberge Vie, Christina Hansen Edwardshttps://www.researchsquare.com/article/rs-701491/v1, https://www.researchsquare.com/article/rs-701491/v1.html
Investigative Opthalmology & Visual Science10.1167/iovs.61.14.322020611432Rare and Common Genetic Variants, Smoking, and Body Mass Index: Progression and Earlier Age of Developing Advanced Age-Related Macular DegenerationJohanna M. Seddon, Rafael Widjajahakim, Bernard Rosnerhttps://iovs.arvojournals.org/article.aspx?articleid=2772117
BMC Health Services Research10.1186/s12913-022-07597-z2022221Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variablesChristina Hansen Edwards, Gunnhild Åberge Vie, Jonas Minet Kingehttps://link.springer.com/content/pdf/10.1186/s12913-022-07597-z.pdf, https://link.springer.com/article/10.1186/s12913-022-07597-z/fulltext.html, https://link.springer.com/content/pdf/10.1186/s12913-022-07597-z.pdf
Genes10.3390/genes70100022016712Detecting the Common and Individual Effects of Rare Variants on Quantitative Traits by Using Extreme Phenotype SamplingYa-Jing Zhou, Yong Wang, Li-Li Chenhttp://www.mdpi.com/2073-4425/7/1/2/pdf
Genetic Epidemiology10.1002/gepi.22355202045164-81MF‐TOWmuT: Testing an optimally weighted combination of common and rare variants with multiple traits using family dataCheng Gao, Qiuying Sha, Shuanglin Zhang, Kui Zhanghttps://onlinelibrary.wiley.com/doi/pdf/10.1002/gepi.22355, https://onlinelibrary.wiley.com/doi/full-xml/10.1002/gepi.22355, https://onlinelibrary.wiley.com/doi/pdf/10.1002/gepi.22355
Health Economics10.1002/hec.428520213081933-1949The relationship between body mass index and income: Using genetic variants from HUNT as instrumental variablesChristina Hansen Edwards, Johan Håkon Bjørngaard, Jonas Minet Kingehttps://onlinelibrary.wiley.com/doi/pdf/10.1002/hec.4285, https://onlinelibrary.wiley.com/doi/full-xml/10.1002/hec.4285, https://onlinelibrary.wiley.com/doi/pdf/10.1002/hec.4285
PLoS ONE10.1371/journal.pone.0073802201389e73802Meal Frequencies Modify the Effect of Common Genetic Variants on Body Mass Index in Adolescents of the Northern Finland Birth Cohort 1986Anne Jääskeläinen, Ursula Schwab, Marjukka Kolehmainen, Marika Kaakinen, Markku J. Savolainen, Philippe Froguel, Stéphane Cauchi, Marjo-Riitta Järvelin, Jaana Laitinenhttp://dx.plos.org/10.1371/journal.pone.0073802
Proceedings of the 2017 International Conference on Cloud and Big Data Computing - ICCBDC 201710.1145/3141128.31411502017A Support Vector Regression Based Model for the Quantitative Prediction of Age and Body Mass Index by using Epigenetic Information from Peripheral BloodFerdi Sarac, Huseyin Seker, Ahmed Bouridanehttp://dl.acm.org/ft_gateway.cfm?id=3141150&ftid=1936230&dwn=1
Genetic Epidemiology10.1002/gepi.13701206281995126689-706Genetic analysis of a common oligogenic trait with quantitative correlates: Summary of GAW9 resultsJ. Blangerohttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fgepi.1370120628, https://onlinelibrary.wiley.com/doi/full/10.1002/gepi.1370120628