Assessing the impact of global versus local ancestry in association studies.
BACKGROUND
To account for population stratification in association studies, principal-components analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. Here, we use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs.


METHODS
Standardized height residuals from unrelated adults from the FHS Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individual's ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20-Mbp regions within each chromosome. We assessed the extent to which there were differences in association studies of height depending on whether PCs for global, local, or both global and local ancestry were included as covariates.


RESULTS
The correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Genome-wide association tests without any ancestry adjustment demonstrated an inflated type I error rate that decreased with adjustment for local ancestry, global ancestry, or both. A known spurious association was replicated for SNPs within the lactase gene, and this false-positive association was abolished by adjustment with local or global ancestry PCs.


CONCLUSIONS
Population stratification is a potential source of bias in this seemingly homogenous FHS population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.
Kang
Sun Jung
SJ
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.
Larkin
Emma K
EK
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.|Center for Clinical Investigation, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.
Song
Yeunjoo
Y
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.
Barnholtz-Sloan
Jill
J
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.|Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, 11100 Euclid Avenue, Cleveland, Ohio 44106 USA.
Baechle
Dan
D
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.
Feng
Tao
T
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.
Zhu
Xiaofeng
X
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio 44106 USA.