Merino J, Dashti HS, Li SX, et al. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium. Mol Psychiatry. 2019;24(12):1920-1932. doi:10.1038/s41380-018-0079-4
Guo MH, Hirschhorn JN, Dauber A. Insights and Implications of Genome-Wide Association Studies of Height. J Clin Endocrinol Metab. 2018;103(9):3155-3168. doi:10.1210/jc.2018-01126
Speakman JR, Loos RJF, O’Rahilly S, Hirschhorn JN, Allison DB. GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity. Int J Obes (Lond). 2018;42(8):1524-1531. doi:10.1038/s41366-018-0147-5
Zekavat SM, Ruotsalainen S, Handsaker RE, et al. Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries. Nat Commun. 2018;9(1):2606. doi:10.1038/s41467-018-04668-w
Deming Y, Dumitrescu L, Barnes LL, et al. Sex-specific genetic predictors of Alzheimer’s disease biomarkers. Acta Neuropathol. 2018;136(6):857-872. doi:10.1007/s00401-018-1881-4
Gray KJ, Kovacheva VP, Mirzakhani H, et al. Gene-Centric Analysis of Preeclampsia Identifies Maternal Association at . Hypertension. 2018;72(2):408-416. doi:10.1161/HYPERTENSIONAHA.117.10688
Corbin LJ, Tan VY, Hughes DA, et al. Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference. Nat Commun. 2018;9(1):711. doi:10.1038/s41467-018-03109-y
Sung YJ, Winkler TW, Fuentes L de L, et al. A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. Am J Hum Genet. 2018;102(3):375-400. doi:10.1016/j.ajhg.2018.01.015
Peloso GM, Natarajan P. Insights from population-based analyses of plasma lipids across the allele frequency spectrum. Curr Opin Genet Dev. 2018;50:1-6. doi:10.1016/j.gde.2018.01.003
Anttila V, Wessman M, Kallela M, Palotie A. Genetics of migraine. Handb Clin Neurol. 2018;148:493-503. doi:10.1016/B978-0-444-64076-5.00031-4