A genome-wide analysis of admixture in Uyghurs and a high-density admixture map for disease-gene discovery

Following up on our previous study, we conducted a genome-wide analysis of admixture for two Uyghur population samples (HGDP-UG and PanAsia-UG), collected from the northern and southern regions of Xinjiang in China, respectively. Both HGDP-UG and PanAsia-UG showed a substantial admixture of East-Asian (EAS) and European (EUR) ancestries, with an empirical estimation of ancestry contribution of 53:47 (EAS:EUR) and 48:52 for HGDP-UG and PanAsia-UG, respectively. The effective admixture time under a model with a single pulse of admixture was estimated as 110 generations and 129 generations, or admixture events occurred about 2200 and 2580 years ago for HGDP-UG and PanAsia-UG, respectively, assuming an average of 20 yr per generation. Despite Uyghurs' earlier history compared to other admixture populations, admixture mapping, holds promise for this population, because of its large size and its mixture of ancestry from different continents. We screened multiple databases and identified a genome-wide single-nucleotide polymorphism panel that can distinguish EAS and EUR ancestry of chromosomal segments in Uyghurs. The panel contains 8150 ancestry-informative markers (AIMs) showing large frequency differences between EAS and EUR populations (F_ST > 0.25, mean F_ST = 0.43) but small frequency differences (7999 AIMs validated) within both populations (F_ST < 0.05, mean F_ST < 0.01). We evaluated the effectiveness of this admixture map for localizing disease genes in two Uyghur populations. To our knowledge, our map constitutes the first practical resource for admixture mapping in Uyghurs, and it will enable studies of diseases showing differences in genetic risk between EUR and EAS populations.     

Principal components analysis of population admixture

With the availability of high-density genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology. An important issue is how to make appropriate and correct inferences about population relationships from the results of PCA, especially when admixed individuals are included in the analysis. We extend our recently developed theoretical formulation of PCA to allow for admixed populations. Because the sampled individuals are treated as features, our generalized formulation of PCA directly relates the pattern of the scatter plot of the top eigenvectors to the admixture proportions and parameters reflecting the population relationships, and thus can provide valuable guidance on how to properly interpret the results of PCA in practice. Using our formulation, we theoretically justify the diagnostic of two-way admixture. More importantly, our theoretical investigations based on the proposed formulation yield a diagnostic of multi-way admixture. For instance, we found that admixed individuals with three parental populations are distributed inside the triangle formed by their parental populations and divide the triangle into three smaller triangles whose areas have the same proportions in the big triangle as the corresponding admixture proportions. We tested and illustrated these findings using simulated data and data from HapMap III and the Human Genome Diversity Project.     

Inferring admixture proportions from molecular data

We derive here two new estimators of admixture proportions based on acoalescent approach that explicitly takes into account molecularinformation as well as gene frequencies. These estimators can be applied toany type of molecular data (such as DNA sequences, restriction fragmentlength polymorphisms [RFLPs], or microsatellite data) for which the extentof molecular diversity is related to coalescent times. Monte Carlosimulation studies are used to analyze the behavior of our estimators. Weshow that one of them (mY) appears suitable for estimating admixture frommolecular data because of its absence of bias and relatively low variance.We then compare it to two conventional estimators that are based on genefrequencies. mY proves to be less biased than conventional estimators overa wide range of situations and especially for microsatellite data. However,its variance is larger than that of conventional estimators when parentalpopulations are not very differentiated. The variance of mY becomes smallerthan that of conventional estimators only if parental populations have beenkept separated for about N generations and if the mutation rate is high.Simulations also show that several loci should always be studied to achievea drastic reduction of variance and that, for microsatellite data, the meansquare error of mY rapidly becomes smaller than that of conventionalestimators if enough loci are surveyed. We apply our new estimator to thecase of admixed wolflike Canid populations tested for microsatellite data.     

Design and analysis of admixture mapping studies

Admixture between populations originating on different continents can be exploited to detect disease susceptibility loci at which risk alleles are distributed differentially between these populations. We first examine the statistical power and mapping resolution of this approach in the limiting situation in which gamete admixture and locus ancestry are measured without uncertainty. We show that, for a rare disease, the most efficient design is to study affected individuals only. In a typical African American population (two-way admixture proportions 0.8/0.2, ancestry crossover rate 2 per 100 cM), a study of 800 affected individuals has 90% power to detect at P values <10(-5) a locus that generates a risk ratio of 2 between populations, with an expected mapping resolution (size of 95% confidence region for the position of the locus) of 4 cM. In practice, to infer locus ancestry from marker data requires Bayesian computationally intensive methods, as implemented in the program ADMIXMAP. Affected-only study designs require strong prior information on the frequencies of each allele given locus ancestry. We show how data from unadmixed and admixed populations can be combined to estimate these ancestry-specific allele frequencies within the admixed population under study, allowing for variation between allele frequencies in unadmixed and admixed populations. Using simulated data based on the genetic structure of the African American population, we show that 60% of information can be extracted in a test for linkage using markers with an ancestry information content of 36% at 3-cM spacing. As in classic linkage studies, the most efficient strategy is to use markers at a moderate density for an initial genome search and then to saturate regions of putative linkage with additional markers, to extract nearly all information about locus ancestry.     

Estimating ethnic admixture from pedigree data

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.     

Ethnic admixture composition of two western Amazonian populations

A small riverine community, Portuchuelo (8 degrees 37'S, 63 degrees 49'W), and a rural county, Monte Negro (10 degrees 15'S, 63 degrees 18'W), both in the state of Rond?nia, Brazil, were studied for the purposes of ascertaining health conditions and the causes of the variability of some infectious diseases. The sample included 181 inhabitants of Portuchuelo and 924 of Monte Negro. Data on 11 blood polymorphisms (ABO, Rh, MNSs, Kell, Fy, haptoglobin, hemoglobin, ACP1, PGM1, GLO1, and CA2) were used to determine the ethnic composition of the inhabitants of Portuchuelo and Monte Negro. The contributions of Africans, Amerindians, and Europeans to the ethnic composition of the studied populations were, respectively, 0.21 +/- 0.046, 0.44 +/- 0.064, and 0.35 +/- 0.069 in Portuchuelo; and 0.25 +/- 0.032,0.12 +/- 0.046, and 0.63 +/- 0.054 in Monte Negro.     

Methods for high-density admixture mapping of disease genes

Admixture mapping (also known as mapping by admixture linkage disequilibrium, or MALD) has been proposed as an efficient approach to localizing disease-causing variants that differ in frequency (because of either drift or selection) between two historically separated populations. Near a disease gene, patient populations descended from the recent mixing of two or more ethnic groups should have an increased probability of inheriting the alleles derived from the ethnic group that carries more disease-susceptibility alleles. The central attraction of admixture mapping is that, since gene flow has occurred recently in modern populations (e.g., in African and Hispanic Americans in the past 20 generations), it is expected that admixture-generated linkage disequilibrium should extend for many centimorgans. High-resolution marker sets are now becoming available to test this approach, but progress will require (a). computational methods to infer ancestral origin at each point in the genome and (b). empirical characterization of the general properties of linkage disequilibrium due to admixture. Here we describe statistical methods to estimate the ancestral origin of a locus on the basis of the composite genotypes of linked markers, and we show that this approach accurately estimates states of ancestral origin along the genome. We apply this approach to show that strong admixture linkage disequilibrium extends, on average, for 17 cM in African Americans. Finally, we present power calculations under varying models of disease risk, sample size, and proportions of ancestry. Studying approximately 2500 markers in approximately 2500 patients should provide power to detect many regions contributing to common disease. A particularly important result is that the power of an admixture mapping study to detect a locus will be nearly the same for a wide range of mixture scenarios: the mixture proportion should be 10%-90% from both ancestral populations.     

Population admixture modulates risk for alcohol dependence

The admixture of different ancestral populations in America may have important implications for the risk for psychiatric disorders, as it appears to have for other medical disorders. The present study investigated the role of population admixture in risk for several psychiatric disorders in European-Americans (EAs) and African-Americans (AAs). This is a multisite study with 3,792 subjects recruited from across the United States, including 3,119 EAs and 673 AAs. These subjects included healthy controls and those with substance dependence (SD) [including alcohol dependence (AD), cocaine dependence, and opioid dependence], social phobia, affective disorders, and schizophrenia. In addition, DNA was included from 78 West Africans. The degree of admixture for each subject was estimated by analysis of a set of ancestry-informative genetic markers using the program STRUCTURE, and was compared between cases and controls. As noted previously, the degree of admixture in AAs was higher than EAs. In EAs, the degree of admixture (with African ancestry) was significantly lower in patients with SD (mainly AD) than controls (P = 0.009 for SD; P = 0.008 for AD). This finding suggests that population admixture may modulate risk for alcohol dependence. Population admixture might protect against alcohol dependence by increasing average heterozygosity and reducing the risk of deleterious recessive alleles. We cannot exclude the possibility that the results might have been influenced by selection bias due to the multisite nature of the study.     

Peripheral blood admixture in bone marrow aspirates

A simple method is described by which the degree to which a bone marrow aspirate is diluted with peripheral blood, the admixture of peripheral blood leukocytes in the aspirate, and the true cellularity of bone marrow at the aspiration site can be estimated quantitatively. A highly significant correlation is shown to exist between the true bone marrow cellularity and the number of hemopoietic cells in the bone marrow aspirate. This correlation can be described by an exponential regression equation (r = 0.6997; p less than or equal to 0.001). The degree of bone marrow dilution with peripheral blood does not depend on the initial bone marrow cellularity and is devoid of diagnostic value. It fluctuates over a wide range (1.2- to 6.3-fold). The admixture of peripheral blood leukocytes has been found to be small (0.05%-9.7%) in most cases studied and much higher in those cases where the cellularity is low while the dilution is high.     

A coalescent-based estimator of admixture from DNA sequences

A variety of estimators have been developed to use genetic marker information in inferring the admixture proportions (parental contributions) of a hybrid population. The majority of these estimators used allele frequency data, ignored molecular information that is available in markers such as microsatellites and DNA sequences, and assumed that mutations are absent since the admixture event. As a result, these estimators may fail to deliver an estimate or give rather poor estimates when admixture is ancient and thus mutations are not negligible. A previous molecular estimator based its inference of admixture proportions on the average coalescent times between pairs of genes taken from within and between populations. In this article I propose an estimator that considers the entire genealogy of all of the sampled genes and infers admixture proportions from the numbers of segregating sites in DNA sequence samples. By considering the genealogy of all sequences rather than pairs of sequences, this new estimator also allows the joint estimation of other interesting parameters in the admixture model, such as admixture time, divergence time, population size, and mutation rate. Comparative analyses of simulated data indicate that the new coalescent estimator generally yields better estimates of admixture proportions than the previous molecular estimator, especially when the parental populations are not highly differentiated. It also gives reasonably accurate estimates of other admixture parameters. A human mtDNA sequence data set was analyzed to demonstrate the method, and the analysis results are discussed and compared with those from previous studies.