Effect of genetic divergence in identifying ancestral origin using HAPAA

Abstract

The genome of an admixed individual with ancestors from isolated populations is a mosaic of chromosomal blocks, each following the statistical properties of variation seen in those populations. By analyzing polymorphisms in the admixed individual against those seen in representatives from the populations, we can infer the ancestral source of the individual's haploblocks. In this paper we describe a novel approach for ancestry inference, HAPAA (HMM-based analysis of polymorphisms in admixed ancestries), that models the allelic and haplotypic variation in the populations and captures the signal of correlation due to linkage disequilibrium, resulting in greatly improved accuracy. We also introduce a methodology for evaluating the effect of genetic divergence between ancestral populations and time-to-admixture on inference accuracy. Using HAPAA, we explore the limits of ancestry inference in closely related populations.[HAPAA is available at http://hapaa.stanford.edu.]Human population migration, adaptation, and admixture have a chaotic and mostly undocumented history. However, nature has auspiciously recorded its account of events within our genomes, and we are at the cusp of an era where we will be able to unlock these records. An individual's genome is a mosaic of ancestral haploblocks whose sizes depend on how far back in the ancestry we compare them. Because recombination can occur essentially anywhere in the genome, the precise boundaries and sources of these haploblocks cannot be easily inferred. However, if the haploblocks are derived from isolated human subpopulations, they will tend to follow the patterns of variation seen in those populations. Using these patterns, we can partition an admixed individual's genome into a mosaic of blocks derived from different populations. The inference of admixed ancestries is intriguing from a personal perspective because it speaks to an individual's origins. In addition, it can be used in association mapping studies to identify loci relevant in genetic disease (McKeigue 1998;Hoggart et al. 2004;Montana and Pritchard 2004;Patterson et al. 2004;Zhu et al. 2004Zhu et al. , 2005 and will help unravel some of the complexities in the history of human evolution.Although recent work suggests that human genomes differ significantly in many ways (Redon et al. 2006), single nucleotide polymorphisms (SNPs) are ubiquitous and can serve as markers for the variation. Recent advances in genotyping technology allow us to genotype hundreds of thousands of SNPs in a single experiment, making them a convenient vehicle for studying genome-wide variation. For example, the Illumina HumanHap550 genotyping chip can assay over 550,000 tag-SNP loci for a few hundred dollars (http://illumina.com/pages.ilmn?ID=154). Because linkage disequilibrium (LD) has a strong effect at short genetic distances, the high-density coverage of such genotyping chips makes it possible to infer much of the intervening genomic variation (Carlson et al. 2004). Using SNPs as a basis for variation, methods have been describ...

The Lengths of Admixture Tracts

“…Hoggart et al (2003) and Smith et al (2004) used HMMs for inferring admixture tracts with the purpose of admixture mapping and controlling for population stratification, similar to the method of Falush et al (2003). More recent publications have focused on admixture deconvolution for more general population-genetic purposes, such as Tang et al (2006) and Sundquist et al (2008).…”

“…Hoggart et al (2003) and Smith et al (2004) used HMMs for inferring admixture tracts with the purpose of admixture mapping and controlling for population stratification, similar to the method of Falush et al (2003). More recent publications have focused on admixture deconvolution for more general population-genetic purposes, such as Tang et al (2006) and Sundquist et al (2008).In HapMix (Price et al 2009), the HMM model of Li and Stephens (2003) for modeling linkage disequilibrium is extended to include admixture between two populations. HapMix uses a genotype-based state space and so does not require phased data.…”

Population Genetics in the Genomic Era

“…However, admixture mappings based on economic AIMs do not account for high-LD between markers, which makes it less powerful than studies inferring local ancestry based on genome-wide high-density data [181]. In recent years, various methods such as SABER [186], HAPAA [187] and HAPMIX [188] have been developed to infer locus-specific ancestry based on high density SNPs data. Especially, HAPMIX employs an explicit population genetic model to infer local ancestry based on finescale variation data for populations formed by two-way admixture [188].…”

Models, methods and tools for ancestry inference and admixture analysis

“…As such, HAPMIX is useless in estimating the ancestry of Latino and Hispanic populations, such as Mexicans and Puerto Ricans, because they are three-way admixed [51]. HAPAA (Hmm-based analysis of polymorphisms in admixed ancestries) [52], ChromoPainter [53], SWITCH-MHMM [54], MULTIMIX [55], and ALLOY [56] are all capable of analyzing admixtures of more than two populations.…”