Jeannette Simino1, Rezart Kume1, Aldi T Kraja2, Stephen T Turner3, Craig L Hanis4, Wayne Sheu5, Ida Chen6, Cashell Jaquish7, Richard S Cooper8, Aravinda Chakravarti9, Thomas Quertermous10, Eric Boerwinkle4, Steven C Hunt11, D C Rao1,12. 1. Division of Biostatistics, Washington University in St. Louis, School of Medicine, Saint Louis, Missouri, USA. 2. Division of Statistical Genomics Washington University in St. Louis, School of Medicine, Saint Louis, Missouri, USA. 3. Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA. 4. Human Genetics Center, University of Texas Health Science Center, Houston, Texas, USA. 5. Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan. 6. Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA 90502. 7. Division of Cardiovascular Sciences, National Heart, Lung, Blood Institute, Bethesda, Maryland, USA. 8. Department of Preventive Medicine and Epidemiology, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA. 9. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA. 10. Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, USA. 11. Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA. 12. Also Departments of Genetics, Psychiatry, and Mathematics, Washington University in St. Louis, School of Medicine, Missouri, USA.
Abstract
OBJECTIVE: To detect novel loci with age-dependent effects on fasting (≥ 8 h) levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides using 3600 African Americans, 1283 Asians, 3218 European Americans, and 2026 Mexican Americans from the Family Blood Pressure Program (FBPP). METHODS: Within each subgroup (defined by network, race, and sex), we employed stepwise linear regression (retention p ≤ 0.05) to adjust lipid levels for age, age-squared, age-cubed, body-mass-index, current smoking status, current drinking status, field center, estrogen therapy (females only), as well as antidiabetic, antihypertensive, and antilipidemic medication use. For each trait, we pooled the standardized male and female residuals within each network and race and fit a generalized variance components model that incorporated gene-age interactions. We conducted FBPP-wide and race-specific meta-analyses by combining the p-values of each linkage marker across subgroups using a modified Fisher's method. RESULTS: We identified seven novel loci with age-dependent effects; four total cholesterol loci from the meta-analysis of Mexican Americans (on chromosomes 2q24.1, 4q21.21, 8q22.2, and 12p11.23) and three high-density lipoprotein loci from the meta-analysis of all FBPP subgroups (on chromosomes 1p12, 14q11.2, and 21q21.1). These loci lacked significant genome-wide linkage or association evidence in the literature and had logarithm of odds (LOD) score ≥ 3 in the meta-analysis with LOD ≥ 1 in at least two network and race subgroups (exclusively of non-European descent). CONCLUSION: Incorporating gene-age interactions into the analysis of lipids using multi-ethnic cohorts can enhance gene discovery. These interaction loci can guide the selection of families for sequencing studies of lipid-associated variants.
OBJECTIVE: To detect novel loci with age-dependent effects on fasting (≥ 8 h) levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides using 3600 African Americans, 1283 Asians, 3218 European Americans, and 2026 Mexican Americans from the Family Blood Pressure Program (FBPP). METHODS: Within each subgroup (defined by network, race, and sex), we employed stepwise linear regression (retention p ≤ 0.05) to adjust lipid levels for age, age-squared, age-cubed, body-mass-index, current smoking status, current drinking status, field center, estrogen therapy (females only), as well as antidiabetic, antihypertensive, and antilipidemic medication use. For each trait, we pooled the standardized male and female residuals within each network and race and fit a generalized variance components model that incorporated gene-age interactions. We conducted FBPP-wide and race-specific meta-analyses by combining the p-values of each linkage marker across subgroups using a modified Fisher's method. RESULTS: We identified seven novel loci with age-dependent effects; four total cholesterol loci from the meta-analysis of Mexican Americans (on chromosomes 2q24.1, 4q21.21, 8q22.2, and 12p11.23) and three high-density lipoprotein loci from the meta-analysis of all FBPP subgroups (on chromosomes 1p12, 14q11.2, and 21q21.1). These loci lacked significant genome-wide linkage or association evidence in the literature and had logarithm of odds (LOD) score ≥ 3 in the meta-analysis with LOD ≥ 1 in at least two network and race subgroups (exclusively of non-European descent). CONCLUSION: Incorporating gene-age interactions into the analysis of lipids using multi-ethnic cohorts can enhance gene discovery. These interaction loci can guide the selection of families for sequencing studies of lipid-associated variants.
Authors: Rector Arya; Ravindranath Duggirala; Laura Almasy; David L Rainwater; Michael C Mahaney; Shelley Cole; Thomas D Dyer; Ken Williams; Robin J Leach; James E Hixson; Jean W MacCluer; Peter O'Connell; Michael P Stern; John Blangero Journal: Nat Genet Date: 2001-12-17 Impact factor: 38.330
Authors: Andrew L Carvalho-Wells; Kim G Jackson; Rosalynn Gill; Estibaliz Olano-Martin; Julie A Lovegrove; Christine M Williams; Anne M Minihane Journal: Atherosclerosis Date: 2010-06-30 Impact factor: 5.162
Authors: Donald W Bowden; S Sandy An; Nicholette D Palmer; W Mark Brown; Jill M Norris; Stephen M Haffner; Gregory A Hawkins; Xiuqing Guo; Jerome I Rotter; Y-D Ida Chen; Lynne E Wagenknecht; Carl D Langefeld Journal: Hum Mol Genet Date: 2010-08-05 Impact factor: 6.150
Authors: Dina L Newman; Mark Abney; Harvey Dytch; Rodney Parry; Mary Sara McPeek; Carole Ober Journal: Hum Mol Genet Date: 2003-01-15 Impact factor: 6.150
Authors: Analabha Basu; Hua Tang; Cora E Lewis; Kari North; J David Curb; Thomas Quertermous; Thomas H Mosley; Eric Boerwinkle; Xiaofeng Zhu; Neil J Risch Journal: Hum Mol Genet Date: 2009-03-20 Impact factor: 6.150
Authors: Jeannette Simino; Zhiying Wang; Jan Bressler; Vincent Chouraki; Qiong Yang; Steven G Younkin; Sudha Seshadri; Myriam Fornage; Eric Boerwinkle; Thomas H Mosley Journal: PLoS One Date: 2017-07-13 Impact factor: 3.240
Authors: Jose V Sorlí; Rocío Barragán; Oscar Coltell; Olga Portolés; Eva C Pascual; Carolina Ortega-Azorín; José I González; Ramon Estruch; Carmen Saiz; Alejandro Pérez-Fidalgo; Jose M Ordovas; Dolores Corella Journal: Nutrients Date: 2020-10-29 Impact factor: 5.717