title: Genetic background of patients from a university medical center in Manhattan: implications for personalized medicine.
abstract: BACKGROUND
  The rapid progress currently being made in genomic science has created interest in potential clinical applications; however, formal translational research has been limited thus far. Studies of population genetics have demonstrated substantial variation in allele frequencies and haplotype structure at loci of medical relevance and the genetic background of patient cohorts may often be complex.
  
  
  RESULTS
  To describe the heterogeneity in an unselected clinical sample we used the Affymetrix 6.0 gene array chip to genotype self-identified European Americans (N = 326), African Americans (N = 324) and Hispanics (N = 327) from the medical practice of Mount Sinai Medical Center in Manhattan, NY. Additional data from US minority groups and Brazil were used for external comparison. Substantial variation in ancestral origin was observed for both African Americans and Hispanics; data from the latter group overlapped with both Mexican Americans and Brazilians in the external data sets. A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative. Selected loci harboring variants associated with medical traits and drug response confirmed substantial within- and between-group heterogeneity.
  
  
  CONCLUSIONS
  As a consequence of these complementary levels of heterogeneity group labels offered no guidance at the individual level. These findings demonstrate the complexity involved in clinical translation of the results from genome-wide association studies and suggest that in the genomic era conventional racial/ethnic labels are of little value.
authors.affiliation: Department of Preventive Medicine and Epidemiology, Loyola University Chicago Stritch School of Medicine, Maywood, Illinois, United States of America. btayo@lumc.edu
authors.forename: Bamidele O
authors.initials: BO
authors.lastname: Tayo
authors.forename: Marie
authors.initials: M
authors.lastname: Teil
authors.forename: Liping
authors.initials: L
authors.lastname: Tong
authors.forename: Huaizhen
authors.initials: H
authors.lastname: Qin
authors.forename: Gregory
authors.initials: G
authors.lastname: Khitrov
authors.forename: Weijia
authors.initials: W
authors.lastname: Zhang
authors.forename: Quinbin
authors.initials: Q
authors.lastname: Song
authors.forename: Omri
authors.initials: O
authors.lastname: Gottesman
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhu
authors.forename: Alexandre C
authors.initials: AC
authors.lastname: Pereira
authors.forename: Richard S
authors.initials: RS
authors.lastname: Cooper
authors.forename: Erwin P
authors.initials: EP
authors.lastname: Bottinger
mesh_terms: D000046:Academic Medical Centers; D000328:Adult; D001741:Black or African American; D015992:Body Mass Index; D005260:Female; D055106:Genome-Wide Association Study; D005838:Genotype; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics; D008297:Male; D008875:Middle Aged; D009519:New York City; D057285:Precision Medicine / Q000379:methods*; D025341:Principal Component Analysis; D044465:White People