The American Journal of Human Genetics, Volume 82, Issue 3, 3 March 2008, Pages 748-755
Janet S. Sinsheimer, Christopher L. Plaisier, Adriana Huertas-Vazquez, Carlos Aguilar-Salinas, Teresa Tusie-Luna, Päivi Pajukanta and Kenneth Lange
This paper introduces a likelihood method of estimating ethnic admixture that uses individuals, pedigrees, or a combination of individuals and pedigrees. For each founder of a pedigree, admixture proportions are calculated by conditioning on the pedigree-wide genotypes at all ancestry-informative markers. These estimates are then propagated down the pedigree to the nonfounders by a simple averaging process. The large-sample standard errors of the founders' proportions can be similarly transformed into standard errors for the admixture proportions of the descendants. These standard errors are smaller than the corresponding standard errors when each individual is treated independently. Both hard and soft information on a founder's ancestry can be accommodated in this scheme, which has been implemented in the genetic software package Mendel. The utility of the method is demonstrated on simulated data and a real data example involving Mexican families of mixed Amerindian and Spanish ancestry.
Abstract | Full Text | PDF (188 kb)
The American Journal of Human Genetics, Volume 81, Issue 3, 1 September 2007, Pages 626-633
Hua Tang, Shweta Choudhry, Rui Mei, Martin Morgan, William Rodriguez-Cintron, Esteban González Burchard and Neil J. Risch
Recent studies have used dense markers to examine the human genome in ancestrally homogeneous populations for hallmarks of selection. No genomewide studies have focused on recently admixed groups—populations that have experienced admixing among continentally divided ancestral populations within the past 200–500 years. New World admixed populations are unique in that they represent the sudden confluence of geographically diverged genomes with novel environmental challenges. Here, we present a novel approach for studying selection by examining the genomewide distribution of ancestry in the genetically admixed Puerto Ricans. We find strong statistical evidence of recent selection in three chromosomal regions, including the human leukocyte antigen region on chromosome 6p, chromosome 8q, and chromosome 11q. Two of these regions harbor genes for olfactory receptors. Interestingly, all three regions exhibit deficiencies in the European-ancestry proportion.
Abstract | Full Text | PDF (1069 kb)
The American Journal of Human Genetics, Volume 82, Issue 2, 8 February 2008, Pages 352-365
Xiaofeng Zhu, Shengchao Li, Richard S. Cooper and Robert C. Elston
There are two common designs for association mapping of complex diseases: case-control and family-based designs. A case-control sample is more powerful to detect genetic effects than a family-based sample that contains the same numbers of affected and unaffected persons, although additional markers may be required to control for spurious association. When family and unrelated samples are available, statistical analyses are often performed in the family and unrelated samples separately, conditioning on parental information for the former, thus resulting in reduced power. In this report, we propose a unified approach that can incorporate both family and case-control samples and, provided the additional markers are available, at the same time corrects for population stratification. We apply the principal components of a marker matrix to adjust for the effect of population stratification. This unified approach makes it unnecessary to perform a conditional analysis of the family data and is more powerful than the separate analyses of unrelated and family samples, or a meta-analysis performed by combining the results of the usual separate analyses. This property is demonstrated in both a variety of simulation models and empirical data. The proposed approach can be equally applied to the analysis of both qualitative and quantitative traits.
Abstract | Full Text | PDF (1338 kb)
Copyright © 2008 The American Society of Human Genetics All rights reserved.
The American Journal of Human Genetics, Volume 82, Issue 2, 290-303, 8 February 2008
1 Computer Science Deptartment, University of California, Berkeley, Berkeley, CA 94720, USA
2 Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3 International Computer Science Institute, Berkeley, CA 94704, USA
Large-scale genotyping of SNPs has shown a great promise in identifying markers that could be linked to diseases. One of the major obstacles involved in performing these studies is that the underlying population substructure could produce spurious associations. Population substructure can be caused by the presence of two distinct subpopulations or a single pool of admixed individuals. In this work, we focus on the latter, which is significantly harder to detect in practice. New advances in this research direction are expected to play a key role in identifying loci that are different among different populations and are still associated with a disease. We evaluated current methods for inference of population substructure in such cases and show that they might be quite inaccurate even in relatively simple scenarios. We therefore introduce a new method, LAMP (Local Ancestry in adMixed Populations), which infers the ancestry of each individual at every single-nucleotide polymorphism (SNP). LAMP computes the ancestry structure for overlapping windows of contiguous SNPs and combines the results with a majority vote. Our empirical results show that LAMP is significantly more accurate and more efficient than existing methods for inferrring locus-specific ancestries, enabling it to handle large-scale datasets. We further show that LAMP can be used to estimate the individual admixture of each individual. Our experimental evaluation indicates that this extension yields a considerably more accurate estimate of individual admixture than state-of-the-art methods such as STRUCTURE or EIGENSTRAT, which are frequently used for the correction of population stratification in association studies.