Admixture Model Research Articles

Inferring Genome Ancestry and Estimating Molecular Relatedness Among 187 Chinese Maize Inbred Lines

The inference of genome ancestry and the estimation of molecular relatedness are of great importance for breeding efficiency and association studies. Seventy SSR loci, evenly distributed in 10 chromosomes, were assayed for polymorphism among 187 commonly used maize ( Zea mays L.) inbreds which represent the genetic diversity in China. The identified 290 alleles served as raw data for estimating population structure using the coalescent linked loci, based on the ADMIXTURE model. Population number, K, has been inferred to be between five and seven. Specifying five subpopulations ( K = 5) led to a distinct decrease and specifying K to be greater than six resulted in only minimal increases in the likelihood value. Therefore, population number, K, has been inferred into six subpopulations, which are PA, BSSS (includes Reid), PB, Lan (Lancaster Sure Crop), LRC (Luda Reb Cob, a Chinese landrace, and its derivatives), and SPT (Si-ping-tou, a Chinese landrace and its derivatives). The Kullback-Leibler distance of pairwise subpopulation was also inferred as n × p (187 × 6) Q matrices, which gave a detailed percentage of genetic composition of six subpopulations and molecular relatedness of each line. The genome-wide linkage disequilibrium (LD) indicated that the association studies in QTLs and/or candidate genes might avoid nonfunctional and spurious associations, as most of the LD blocks were broken among diverse germplasm. The defined population structure has given us a clear genetic structure of these lines for breeding practice and established a good basis for association analysis.

Structure of cattle, sheep, goat and buffalo populations using single nucleotide polymorphisms in genes affecting lipid metabolism

Single nucleotide polymorphism (SNPs) have the potential to become the genetic marker of choice in studies on the ecology and the conservation of natural populations because of their high frequency across the genome. Being biallelic markers, SNPs are intrinsically less variable than microsatellites, so individual locus information content is low. However, in population studies, this can be compensated for by the use of many more SNPs to obtain a suitable statistical power. SNPs in candidate genes could reveal chromosomal regions subject to natural or artificial selection directly or by linkage disequilibrium. In this study we used 69 SNPs, identified in 20 candidates genes involved in milk fat composition, to assess the genetic diversity within and across four ruminants species (cattle, sheep, goat and buffalo). SNPs were characterised and genotyped on 459 individuals belonging to 15 breeds and the allelic frequencies of the 2 alleles were calculated both at species and breed level. Individuals were clustered applying a parametric genetic mixture analysis implemented in the Structure 2.0 software. The number of genetic clusters (K) was tested using the no admixture model, a burning period of 100,000 followed by 200,000 MCMC repeats and considering SNPs frequencies correlated. The likelihood values and the variance of the bootstrap samples so obtained were plotted against K (ranging from 2 to 20, a number higher of the true number of breeds) in order to estimate the optimal K value. The method resolved 10 populations, being able to correctly identify breeds of cows, sheep and buffalo. Goat breeds remained unresolved, demonstrating the lower diversity found in this species in comparison to the other species. These findings are in agreement with other studies that used neutral markers like microsatellites.

Y chromosome genetic variation in the Italian peninsula is clinal and supports an admixture model for the Mesolithic–Neolithic encounter

The Italian peninsula, given its geographical location in the middle of the Mediterranean basin, was involved in the process of the peopling of Europe since the very beginning, with first settlements dating to the Upper Paleolithic. Later on, the Neolithic revolution left clear evidence in the archeological record, with findings going back to 7000 B.C. We have investigated the demographic consequences of the agriculture revolution in this area by genotyping Y chromosome markers for almost 700 individuals from 12 different regions. Data analysis showed a non-random distribution of the observed genetic variation, with more than 70% of the Y chromosome diversity distributed along a North–South axis. While the Greek colonisation during classical time appears to have left no significant contribution, the results support a male demic diffusion model, even if population replacement was not complete and the degree of Neolithic admixture with Mesolithic inhabitants was different in different areas of Italy.

Evidences of introgression from cultivated rice to Oryza rufipogon (Poaceae) populations based on SSR fingerprinting: implications for wild rice differentiation and conservation

Crop-to-wild introgression may play an important role in evolution of wild species. Asian cultivated rice (Oryza sativa L.) is of a particular concern because of its cross-compatibility with the wild ancestor, O. rufipogon Griff. The distribution of cultivated rice and O. rufipogon populations is extensively sympatric, particularly in Asia where many wild populations are surrounded by rice fields. Consequently, gene flow from cultivated rice may have a potential to alter genetic composition of wild rice populations in close proximity. In this study, we estimated introgression of cultivated rice with O. rufipogon based on analyses of 139 rice varieties (86 indica and 53 japonica ecotypes) and 336 wild individuals from 11 O. rufipogon populations in China. DNA fingerprinting based on 17 selected rice simple sequence repeat (SSR) primer pairs was adopted to measure allelic frequencies in rice varieties and O. rufipogon samples, and to estimate genetic associations between wild and cultivated rice through cluster analysis. We detected consanguinity of cultivated rice in O. rufipogon populations according to the admixture model of the STRUCTURE program. The analyses showedz that four wild rice populations, DX-P1, DX-P2, GZ-P2, and HL-P, contained some rare alleles that were commonly found in the rice varieties examined. In addition, the four wild rice populations that scattered among the rice varieties in the cluster analysis showed a closer affinity to the cultivars than the other wild populations. This finding supports the contention of substantial gene flow from crop to wild species when these species occur close to each other. The introgressive populations had slightly higher genetic diversity than those that were isolated from rice. Crop-to-wild introgression may have accumulative impacts on genetic variations in wild populations, leading to significant differentiation in wild species. Therefore, effective measure should be taken to avoid considerable introgression from cultivated rice, which may influence the effective in-situ conservation of wild rice species.

The WITCH experiment: towards weak interactions studies. Status and prospects

Primary goal of the WITCH experiment is to test the Standard Model for a possible ad-mixture of a scalar or tensor type interaction in beta-decay. This information will be inferred from the shape of the recoil energy spectrum. The experimental set-up was completed and is under intensive commissioning at ISOLDE (CERN). It combines a Penning trap to store the ions and a retardation spectrometer to probe the recoil ion energy. A brief overview of the WITCH set-up and the results of commissioning tests performed until now are presented. Finally, perspectives of the physics program are reviewed.

A classical likelihood based approach for admixture mapping using EM algorithm

Several disease-mapping methods have been proposed recently, which use the information generated by recent admixture of populations from historically distinct geographic origins. These methods include both classic likelihood and Bayesian approaches. In this study we directly maximize the likelihood function from the hidden Markov Model for admixture mapping using the EM algorithm, allowing for uncertainty in model parameters, such as the allele frequencies in the parental populations. We determined the robustness of the proposed method by examining the ancestral allele frequency estimate and individual marker-location specific ancestry when the data were generated by different population admixture models and no learning sample was used. The proposed method outperforms a widely used Bayesian MCMC strategy for data generated from various population admixture models. The multipoint information content for ancestry was derived based on the map provided by Smith et al. (2004) and the associated statistical power was calculated. We examined the distribution of admixture LD across the genome for both real and simulated data and established a threshold for genome wide significance applicable to admixture mapping studies. The software ADMIXPROGRAM for performing admixture mapping is available from authors.

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.

Bayesian identification of admixture events using multilocus molecular markers

Bayesian statistical methods for the estimation of hidden genetic structure of populations have gained considerable popularity in the recent years. Utilizing molecular marker data, Bayesian mixture models attempt to identify a hidden population structure by clustering individuals into genetically divergent groups, whereas admixture models target at separating the ancestral sources of the alleles observed in different individuals. We discuss the difficulties involved in the simultaneous estimation of the number of ancestral populations and the levels of admixture in studied individuals' genomes. To resolve this issue, we introduce a computationally efficient method for the identification of admixture events in the population history. Our approach is illustrated by analyses of several challenging real and simulated data sets. The software (baps), implementing the methods introduced here, is freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.

The Admixture Linkage Disequilibrium and Genetic Linkage Inference on the Gradual Admixture Population

Through the theoretical analysis of the admixture linkage disequilibrium (ALD) in the gradual admixture (GA) model, in which admixture occurs in every generation, the ALD is found to be proportional to the difference in marker allele frequencies, p 1- p 2, between two subpopulations. Based on this property, we can employ a strict monotonic function (Δ ker =Δ/( p 1- p 2), where Δ denotes the linkage disequilibrium (LD)) of the recombination fraction between the marker locus and the disease locus to infer the true genetic linkage. We construct a quasi likelihood ratio test (LRT) for the case-only data utilizing the information of unlinked markers in the human genome. The simulation results show that our tests can be used to fine map a disease locus. The effects of parameter values in the ALD mapping are also discussed.

Bayesian Analysis of an Admixture Model With Mutations and Arbitrarily Linked Markers

We introduce here a Bayesian analysis of a classical admixture model in which all parameters are simultaneously estimated. Our approach follows the approximate Bayesian computation (ABC) framework, relying on massive simulations and a rejection-regression algorithm. Although computationally intensive, this approach can easily deal with complex mutation models and partially linked loci, and it can be thoroughly validated without much additional computation cost. Compared to a recent maximum-likelihood (ML) method, the ABC approach leads to similarly accurate estimates of admixture proportions in the case of recent admixture events, but it is found superior when the admixture is more ancient. All other parameters of the admixture model such as the divergence time between parental populations, the admixture time, and the population sizes are also well estimated, unlike the ML method. The use of partially linked markers does not introduce any particular bias in the estimation of admixture, but ML confidence intervals are found too narrow if linkage is not specifically accounted for. The application of our method to an artificially admixed domestic bee population from northwest Italy suggests that the admixture occurred in the last 10-40 generations and that the parental Apis mellifera and A. ligustica populations were completely separated since the last glacial maximum.

The admixture model in linkage analysis

We study an appropriate version of the score statistic to test the hypothesis of no linkage in a sample of phase known meioses and show that accurate approximations to the genome wide significance level can be obtained by using a large deviation approximation to evaluate Rice's formula for the expected number of upcrossings of a smooth random process.

Linkage Analysis of a Complex Disease through Use of Admixed Populations

Linkage disequilibrium arising from the recent admixture of genetically distinct populations can be potentially useful in mapping genes for complex diseases. McKeigue has proposed a method that conditions on parental admixture to detect linkage. We show that this method tests for linkage only under specific assumptions, such as equal admixture in the parental generation and admixture that occurs in a single generation. In practice, these assumptions are unlikely to hold for natural populations, resulting in an inflation of the type I error rate when testing for linkage by this method. In this article, we generalize McKeigue's approach of testing for linkage to allow two different admixture models: (1) intermixture admixture and (2) continuous gene flow. We calculate the sample size required for a genomewide search by this method under different disease models: multiplicative, additive, recessive, and dominant. Our results show that the sample size required to obtain 90% power to detect a putative mutant allele at a genomewide significance level of 5% can usually be achieved in practice if informative markers are available at a density of 2 cM.

Estimating the impact of prehistoric admixture on the genome of Europeans.

We inferred past admixture processes in the European population from genetic diversity at eight loci, including autosomal, mitochondrial and Y-linked polymorphisms. Admixture coefficients were estimated from multilocus data, assuming that most current populations can be regarded as the result of a hybridization process among four or less potential parental populations. Two main components are apparent in the Europeans' genome, presumably corresponding to the contributions of the first, Paleolithic Europeans, and of the early, Neolithic farmers dispersing from the Near East. In addition, only a small fraction of the European alleles seems to come from North Africa, and a fourth component reflecting gene flow from Northern Asia is largely restricted to the northeast of the continent. The estimated Near Eastern contribution decreases as one moves from east to west, in agreement with the predictions of a model in which (Neolithic) immigrants from the Near East contributed a large share of the alleles in the genome of current Europeans. Several tests suggest that probable departures from the admixture models, due to factors such as choice of the putative parental populations and more complex demographic scenarios, may have affected our main estimates only to a limited extent.

Maximum-likelihood estimation of admixture proportions from genetic data.

For an admixed population, an important question is how much genetic contribution comes from each parental population. Several methods have been developed to estimate such admixture proportions, using data on genetic markers sampled from parental and admixed populations. In this study, I propose a likelihood method to estimate jointly the admixture proportions, the genetic drift that occurred to the admixed population and each parental population during the period between the hybridization and sampling events, and the genetic drift in each ancestral population within the interval between their split and hybridization. The results from extensive simulations using various combinations of relevant parameter values show that in general much more accurate and precise estimates of admixture proportions are obtained from the likelihood method than from previous methods. The likelihood method also yields reasonable estimates of genetic drift that occurred to each population, which translate into relative effective sizes (N(e)) or absolute average N(e)'s if the times when the relevant events (such as population split, admixture, and sampling) occurred are known. The proposed likelihood method also has features such as relatively low computational requirement compared with previous ones, flexibility for admixture models, and marker types. In particular, it allows for missing data from a contributing parental population. The method is applied to a human data set and a wolflike canids data set, and the results obtained are discussed in comparison with those from other estimators and from previous studies.

Qualitative semi-parametric test for genetic associations in case-control designs under structured populations.

Recently, statistical methods have been proposed using genomic markers to control for population stratification in genetic association studies. However, these methods either have unacceptable low power when population stratification becomes strong or cannot control for population stratification well under admixture population models. In this paper, we propose a semiparametric association test to detect genetic association between a candidate marker and a qualitative trait of interest in case-control designs. The performance of the test is compared to other existing methods through simulations. The results show that our method gives correct type I error rate both under discrete population models and admixture population models, and our method is robust to the extent of the population stratification. In most of the cases we considered, our method has higher power and, in some cases, substantially higher power than that of existing methods.

Relation of risk of systemic lupus erythematosus to west African admixture in a Caribbean population.

Risk of systemic lupus erythematosus (SLE) is higher in people of west African descent than in Europeans. The objective of this study was to distinguish between genetic and environmental explanations for this ethnic difference by examining the relationship of disease risk to individual admixture (defined as the proportion of the genome that is of west African ancestry); 124 cases of SLE and 219 matched controls resident in Trinidad were studied. Analysis of admixture was restricted to 52 cases and 107 controls who reported no Indian or Chinese ancestry. These individuals were typed with a panel of 26 single-nucleotide polymorphisms and five insertion/deletion polymorphisms chosen to have large allele frequency differentials between west African, European and Native American populations. A Bayesian model for population admixture, individual admixture and locus ancestry was fitted by Markov chain simulation. Mean west African admixture (M) was 0.81 in cases and 0.74 in controls (P=0.01). The risk ratio for SLE associated with unit change in M was estimated as 32.5 with a 95% confidence interval (CI) of 2.0-518. Adjustment for measures of socioeconomic status (household amenities in childhood and years of education) altered this risk ratio only slightly (adjusted risk ratio: 28.4, 95% CI 1.7-485). These results support an additive genetic model for the ethnic difference in risk of SLE between west Africans and Europeans, rather than an environmental explanation or an "overdominant" model in which risk is higher in heterozygous than in homozygous individuals. This conclusion lays a basis for localizing the genes underlying this ethnic difference in risk of SLE by admixture mapping.

Measuring and using admixture to study the genetics of complex diseases

Admixture is an important evolutionary force that can and should be used in efforts to apply genomic data and technology to the study of complex disease genetics. Admixture linkage disequilibrium (ALD) is created by the process of admixture and, in recently admixed populations, extends for substantial distances (of the order of 10 to 20 cM). The amount of ALD generated depends on the level of admixture, ancestry information content of markers and the admixture dynamics of the population, and thus influences admixture mapping (AM). The authors discuss different models of admixture and how these can have an impact on the success of AM studies. Selection of markers is important, since markers informative for parental population ancestry are required and these are uncommon. Rarely does the process of admixture result in a population that is uniform for individual admixture levels, but instead there is substantial population stratification. This stratification can be understood as variation in individual admixtures and can be both a source of statistical power for ancestry-phenotype correlation studies as well as a confounder in causing false-positives in gene association studies. Methods to detect and control for stratification in case/control and AM studies are reviewed, along with recent studies showing individual ancestry-phenotype correlations. Using skin pigmentation as a model phenotype, implications of AM in complex disease gene mapping studies are discussed. Finally, the article discusses some limitations of this approach that should be considered when designing an effective AM study.

Associations between cytoplasmic and nuclear Loci in hybridizing populations.

We extend current multilocus models to describe the effects of migration, recombination, selection, and nonrandom mating on sets of genes in diploids with varied modes of inheritance, allowing us to consider the patterns of nuclear and cytonuclear associations (disequilibria) under various models of migration. We show the relationship between the multilocus notation recently presented by Kirkpatrick, Johnson, and Barton (developed from previous work by Barton and Turelli) and the cytonuclear parameterization of Asmussen, Arnold, and Avise and extend this notation to describe associations between cytoplasmic elements and multiple nuclear genes. Under models with sexual symmetry, both nuclear-nuclear and cytonuclear disequilibria are equivalent. They differ, however, in cases involving some type of sexual asymmetry, which is then reflected in the asymmetric inheritance of cytoplasmic markers. An example given is the case of different migration rates in males and females; simulations using 2, 3, 4, or 5 unlinked autosomal markers with a maternally inherited cytoplasmic marker illustrate how nuclear-nuclear and cytonuclear associations can be used to separately estimate female and male migration rates. The general framework developed here allows us to investigate conditions where associations between loci with different modes of inheritance are not equivalent and to use this nonequivalence to test for deviations from simple models of admixture.

Multipoint linkage detection in the presence of heterogeneity.

Linkage heterogeneity is common for complex diseases. It is well known that loss of statistical power for detecting linkage will result if one assumes complete homogeneity in the presence of linkage heterogeneity. To this end, Smith (1963, Annals of Human Genetics 27, 175-182) proposed an admixture model to account for linkage heterogeneity. It is well known that for this model, the conventional chi-squared approximation to the likelihood ratio test for no linkage does not apply even when the sample size is large. By dealing with nuclear families and one marker at a time for genetic diseases with simple modes of inheritance, score-based test statistics (Liang and Rathouz, 1999, Biometrics 55, 65-74) and likelihood-ratio-based test statistics (Lemdani and Pons, 1995, Biometrics 51, 1033-1041) have been proposed which have a simple large-sample distribution under the null hypothesis of linkage. In this paper, we extend their work to more practical situations that include information from multiple markers and multi-generational pedigrees while allowing for a class of general genetic models. Three different approaches are proposed to eliminate the nuisance parameters in these test statistics. We show that all three approaches lead to the same asymptotic distribution under the null hypothesis of no linkage. Simulation results show that the proposed test statistics have adequate power to detect linkage and that the performances of these two classes of test statistics are quite comparable. We have applied the proposed method to a family study of asthma (Barnes et al., 1996), in which the score-based test shows evidence of linkage with p-value <0.0001 in the region of interest on chromosome 12. Additionally, we have implemented this score-based test within the frequently used computer package GENEHUNTER.

Search for new physics in beta-neutrino correlations with the WITCH spectrometer

The WITCH (Weak Interaction Trap for CHarged particles) experiment is a retardation spectrometer coupled to a Penning trap and measures the beta-neutrino angular correlation via the shape of the recoil energy spectrum. The present form of the Standard Model describes weak processes in terms of vector and axial-vector type interactions, but the possible presence of scalar and tensor interactions is not yet ruled out. The main aim of this experiment is a test of the Standard Model for possible admixture of scalar and tensor currents.