HRS Bibliography

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Davies G, Lam M, Harris SE, et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications. 2018;9(1):2098. doi:10.1038/s41467-018-04362-x.
Deelen J, Evans DS, Arking DE, et al. A meta-analysis of genome-wide association studies identifies multiple longevity genes. Nature Communications. 2019;10(1):3669. doi:10.1038/s41467-019-11558-2.
http://www.ncbi.nlm.nih.gov/pubmed/31413261?dopt=Abstract
Direk N, Williams S, Smith JA, et al. An Analysis of Two Genome-wide Association Meta-analyses Identifies a New Locus for Broad Depression Phenotype. Biological Psychiatry. 2017;82(5):322-329. doi:10.1016/j.biopsych.2016.11.013.

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Joshi PK, Esko T, Mattsson H, et al. Directional dominance on stature and cognition in diverse human populations. Nature. 2015;523(7561):459-62. doi:10.1038/nature14618.
http://www.ncbi.nlm.nih.gov/pubmed/26131930?dopt=Abstract

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Okbay A, Beauchamp JP, Fontana MAlan, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533(7604):539-42. doi:10.1038/nature17671.
http://www.ncbi.nlm.nih.gov/pubmed/27225129?dopt=Abstract

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Schmitz LL, Gard AM, Ware EB. Examining sex differences in pleiotropic effects for depression and smoking using polygenic and gene-region aggregation techniques. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2019;180(6):448 - 468. doi:10.1002/ajmg.v180.610.1002/ajmg.b.32748.
Schmitz LL, Gard AM, Ware EB. Examining sex differences in pleiotropic effects for depression and smoking using polygenic and gene-region aggregation techniques. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics. 2019. doi:10.1002/ajmg.b.32748.
http://www.ncbi.nlm.nih.gov/pubmed/31219244?dopt=Abstract
Sun D, Richard M, Musani SK, et al. Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits. Human Genetics and Genomics Advances. 2021;2(1):100013. doi:10.1016/j.xhgg.2020.100013.

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Turcot V, Lu Y, Highland HM, et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. 2018;50(1):26-41. doi:10.1038/s41588-017-0011-x.
http://www.ncbi.nlm.nih.gov/pubmed/29273807?dopt=Abstract

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Wang H, Nandakumar P, Tekola-Ayele F, et al. Combined linkage and association analysis identifies rare and low frequency variants for blood pressure at 1q31. European Journal of Human Genetics. 2019;27(2):269-277. doi:10.1038/s41431-018-0277-1.
Ware EB, Morataya C, Fu M, Bakulski KM. Type 2 Diabetes and Cognitive Status in the Health and Retirement Study: A Mendelian Randomization Approach. Frontiers in Genetics. 2021;12:634767. doi:10.3389/fgene.2021.634767.
Ware EB, Mukherjee B, Sun YV, Diez-Roux AV, Kardia SLR. Comparative genome-wide association studies of a depressive symptom phenotype in a repeated measures setting by race/ethnicity in the Multi-Ethnic Study of Atherosclerosis. BMC Genet. 2015;16:118. doi:10.1186/s12863-015-0274-0.
http://www.ncbi.nlm.nih.gov/pubmed/26459564?dopt=Abstract
Ware EB, Schmitz LL, Faul J. HRS Polygenic Scores: 2006-2010 Genetic Data. Ann Arbor, Michigan: Survey Research Center, Institute for Social Research, University of Michigan; 2017.PDF icon Download PDF (599.95 KB)
Ware EB, Faul J. Genomic data measures and methods: a primer for social scientists. In: Ferraro KF, Carr D, eds. Handbook of Aging and the Social Sciences (Ninth Edition)Handbooks of Aging. Handbook of Aging and the Social Sciences (Ninth Edition)Handbooks of Aging. Academic Press; 2021:49-62. doi:https://doi.org/10.1016/B978-0-12-815970-5.00004-8.