Exploring population admixture dynamics via empirical and simulated genome-wide distribution of ancestral chromosomal segments

Reconstructing complex admixture history using a hierarchical model

Various methods have been proposed to reconstruct admixture histories by analyzing the length of ancestral chromosomal tracts, such as estimating the admixture time and number of admixture events. However, available methods do not explicitly consider the complex admixture structure, which characterizes the joining and mixing patterns of different ancestral populations during the admixture process, and instead assume a simplified one-by-one sequential admixture model. In this study, we proposed a novel approach that considers the non-sequential admixture structure to reconstruct admixture histories. Specifically, we introduced a hierarchical admixture model that incorporated four ancestral populations and developed a new method, called HierarchyMix, which uses the length of ancestral tracts and the number of ancestry switches along genomes to reconstruct the four-way admixture history. By automatically selecting the optimal admixture model using the Bayesian information criterion principles, HierarchyMix effectively estimates the corresponding admixture parameters. Simulation studies confirmed the effectiveness and robustness of HierarchyMix. We also applied HierarchyMix to Uyghurs and Kazakhs, enabling us to reconstruct the admixture histories of Central Asians. Our results highlight the importance of considering complex admixture structures and demonstrate that HierarchyMix is a useful tool for analyzing complex admixture events.

Human genetic admixture through the lens of population genomics

Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, 'The Apportionment of Human Diversity'. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

AdmixSim: A Forward-Time Simulator for Various Complex Scenarios of Population Admixture

Background: Population admixture is a common phenomenon in humans, animals, and plants, and it plays a very important role in shaping individual genetic architecture and population genetic diversity. Inference of population admixture, however, is very challenging and typically relies on in silico simulation. We are aware of the lack of a computerized tool for such a purpose. A simulator capable of generating data under various complex admixture scenarios would facilitate the study of recombination, linkage disequilibrium, ancestry tracing, and admixture dynamics in admixed populations. We described such a simulator here.

Results: We developed a forward-time simulator (AdmixSim) under the standard Wright Fisher model. It can simulate the following admixed populations: (1) multiple ancestral populations; (2) multiple waves of admixture events; (3) fluctuating population size; and (4) admixtures of fluctuating proportions. Analysis of the simulated data by AdmixSim showed that our simulator can quickly and accurately generate data resembling real-world values. We included in AdmixSim all possible parameters that would allow users to modify and simulate any kind of admixture scenario easily, so it is very flexible. AdmixSim records recombination break points and traces of each chromosomal segment from different ancestral populations, with which users can easily perform further analysis and comparative studies with empirical data.

Conclusions:AdmixSim facilitates the study of population admixture by providing a simulation framework with the flexible implementation of various admixture models and parameters.

MultiWaver 2.0: modeling discrete and continuous gene flow to reconstruct complex population admixtures

Our goal in developing the MultiWaver software series was to be able to infer population admixture history under various complex scenarios. The earlier version of MultiWaver considered only discrete admixture models. Here, we report a newly developed version, MultiWaver 2.0, that implements a more flexible framework and is capable of inferring multiple-wave admixture histories under both discrete and continuous admixture models. MultiWaver 2.0 can automatically select an optimal admixture model based on the length distribution of ancestral tracks of chromosomes, and the program can estimate the corresponding parameters under the selected model. Specifically, for discrete admixture models, we used a likelihood ratio test (LRT) to determine the optimal discrete model and an expectation-maximization algorithm to estimate the parameters. In addition, according to the principles of the Bayesian Information Criterion (BIC), we compared the optimal discrete model with several continuous admixture models. In MultiWaver 2.0, we also applied a bootstrapping technique to provide levels of support for the chosen model and the confidence interval (CI) of the estimations of admixture time. Simulation studies validated the reliability and effectiveness of our method. Finally, the program performed well when applied to real datasets of typical admixed populations, such as African Americans, Uyghurs, and Hazaras.

Dating admixture events is unsolved problem in multi-way admixed populations

Advances in human sequencing technologies, coupled with statistical and computational tools, have fostered the development of methods for dating admixture events. These methods have merits and drawbacks in estimating admixture events in multi-way admixed populations. Here, we first provide a comprehensive review and comparison of current methods pertinent to dating admixture events. Second, we assess various admixture dating tools. We do so by performing various simulations. Third, we apply the top two assessed methods to real data of a uniquely admixed population from South Africa. Results reveal that current dating admixture models are not sufficiently equipped to estimate ancient admixtures events and to identify multi-faceted admixture events in complex multi-way admixed populations. We conclude with a discussion of research areas where further work on dating admixture-based methods is needed.

Modeling SNP array ascertainment with Approximate Bayesian Computation for demographic inference

Single nucleotide polymorphisms (SNPs) in commercial arrays have often been discovered in a small number of samples from selected populations. This ascertainment skews patterns of nucleotide diversity and affects population genetic inferences. We propose a demographic inference pipeline that explicitly models the SNP discovery protocol in an Approximate Bayesian Computation (ABC) framework. We simulated genomic regions according to a demographic model incorporating parameters for the divergence of three well-characterized HapMap populations and recreated the SNP distribution of a commercial array by varying the number of haploid samples and the allele frequency cut-off in the given regions. We then calculated summary statistics obtained from both the ascertained and genomic data and inferred ascertainment and demographic parameters. We implemented our pipeline to study the admixture process that gave rise to the present-day Mexican population. Our estimate of the time of admixture is closer to the historical dates than those in previous works which did not consider ascertainment bias. Although the use of whole genome sequences for demographic inference is becoming the norm, there are still underrepresented areas of the world from where only SNP array data are available. Our inference framework is applicable to those cases and will help with the demographic inference.

Inference of multiple-wave admixtures by length distribution of ancestral tracks

The ancestral tracks in admixed genomes are valuable for population history inference. While a few methods have been developed to infer admixture history based on ancestral tracks, these methods suffer the same flaw: only population admixture history under some specific models can be inferred. In addition, the inference of history might be biased or even unreliable if the specific model deviates from the real situation. To address this problem, we firstly proposed a general discrete admixture model to describe the admixture history with multiple ancestral populations and multiple-wave admixtures. We next deduced the length distribution of ancestral tracks under the general discrete admixture model. We further developed a new method, MultiWaver, to explore multiple-wave admixture histories. Our method could automatically determine an optimal admixture model based on the length distribution of ancestral tracks, and estimate the corresponding parameters under this optimal model. Specifically, we used a likelihood ratio test (LRT) to determine the number of admixture waves, and implemented an expectation-maximization (EM) algorithm to estimate parameters. We used simulation studies to validate the reliability and effectiveness of our method. Finally, good performance was observed when our method was applied to real data sets of African Americans and Mexicans, and new insights were gained into the admixture history of Uyghurs and Hazaras.

Genome-wide Ancestry and Demographic History of African-Descendant Maroon Communities from French Guiana and Suriname

The transatlantic slave trade was the largest forced migration in world history. However, the origins of the enslaved Africans and their admixture dynamics remain unclear. To investigate the demographic history of African-descendant Marron populations, we generated genome-wide data (4.3 million markers) from 107 individuals from three African-descendant populations in South America, as well as 124 individuals from six west African populations. Throughout the Americas, thousands of enslaved Africans managed to escape captivity and establish lasting communities, such as the Noir Marron. We find that this population has the highest proportion of African ancestry (∼98%) of any African-descendant population analyzed to date, presumably because of centuries of genetic isolation. By contrast, African-descendant populations in Brazil and Colombia harbor substantially more European and Native American ancestry as a result of their complex admixture histories. Using ancestry tract-length analysis, we detect different dates for the European admixture events in the African-Colombian (1749 CE; confidence interval [CI]: 1737-1764) and African-Brazilian (1796 CE; CI: 1789-1804) populations in our dataset, consistent with the historically attested earlier influx of Africans into Colombia. Furthermore, we find evidence for sex-specific admixture patterns, resulting from predominantly European paternal gene flow. Finally, we detect strong genetic links between the African-descendant populations and specific source populations in Africa on the basis of haplotype sharing patterns. Although the Noir Marron and African-Colombians show stronger affinities with African populations from the Bight of Benin and the Gold Coast, the African-Brazilian population from Rio de Janeiro has greater genetic affinity with Bantu-speaking populations from the Bight of Biafra and west central Africa.

Modeling Continuous Admixture Using Admixture-Induced Linkage Disequilibrium

Recent migrations and inter-ethnic mating of long isolated populations have resulted in genetically admixed populations. To understand the complex population admixture process, which is critical to both evolutionary and medical studies, here we used admixture induced linkage disequilibrium (LD) to infer continuous admixture events, which is common for most existing admixed populations. Unlike previous studies, we expanded the typical continuous admixture model to a more general scenario with isolation after a certain duration of continuous gene flow. Based on the new models, we developed a method, CAMer, to infer the admixture history considering continuous and complex demographic process of gene flow between populations. We evaluated the performance of CAMer by computer simulation and further applied our method to real data analysis of a few well-known admixed populations.

Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions

To infer the histories of population admixture, one important challenge with methods based on the admixture linkage disequilibrium (ALD) is to remove the effect of source LD (SLD), which is directly inherited from source populations. In previous methods, only the decay curve of weighted LD between pairs of sites whose genetic distance were larger than a certain starting distance was fitted by single or multiple exponential functions, for the inference of recent single- or multiple-wave admixture. However, the effect of SLD has not been well defined and no tool has been developed to estimate the effect of SLD on weighted LD decay. In this study, we defined the SLD in the formularized weighted LD statistic under the two-way admixture model and proposed a polynomial spectrum (p-spectrum) to study the weighted SLD and weighted LD. We also found that reference populations could be used to reduce the SLD in weighted LD statistics. We further developed a method, iMAAPs, to infer multiple-wave admixture by fitting ALD using a p-spectrum. We evaluated the performance of iMAAPs under various admixture models in simulated data and applied iMAAPs to the analysis of genome-wide single nucleotide polymorphism data from the Human Genome Diversity Project and the HapMap Project. We showed that iMAAPs is a considerable improvement over other current methods and further facilitates the inference of histories of complex population admixtures.

Ancestry variation and footprints of natural selection along the genome in Latin American populations

Latin American populations stem from the admixture of Europeans, Africans and Native Americans, which started over 400 years ago and had lasted for several centuries. Extreme deviation over the genome-wide average in ancestry estimations at certain genomic locations could reflect recent natural selection. We evaluated the distribution of ancestry estimations using 678 genome-wide microsatellite markers in 249 individuals from 13 admixed populations across Latin America. We found significant deviations in ancestry estimations including three locations with more than 3.5 times standard deviations from the genome-wide average: an excess of European ancestry at 1p36 and 14q32, and an excess of African ancestry at 6p22. Using simulations, we could show that at least the deviation at 6p22 was unlikely to result from genetic drift alone. By applying different linguistic groups as well as the most likely ancestral Native American populations as the ancestry, we showed that the choice of Native American ancestry could affect the local ancestry estimation. However, the signal at 6p22 consistently appeared in most of the analyses using various ancestral groups. This study provided important insights for recent natural selection in the context of the unique history of the New World and implications for disease mapping.

Population Genomics and the Statistical Values of Race: An Interdisciplinary Perspective on the Biological Classification of Human Populations and Implications for Clinical Genetic Epidemiological Research

The biological status and biomedical significance of the concept of race as applied to humans continue to be contentious issues despite the use of advanced statistical and clustering methods to determine continental ancestry. It is thus imperative for researchers to understand the limitations as well as potential uses of the concept of race in biology and biomedicine. This paper deals with the theoretical assumptions behind cluster analysis in human population genomics. Adopting an interdisciplinary approach, it demonstrates that the hypothesis that attributes the clustering of human populations to "frictional" effects of landform barriers at continental boundaries is empirically incoherent. It then contrasts the scientific status of the "cluster" and "cline" constructs in human population genomics, and shows how cluster may be instrumentally produced. It also shows how statistical values of race vindicate Darwin's argument that race is evolutionarily meaningless. Finally, the paper explains why, due to spatiotemporal parameters, evolutionary forces, and socio-cultural factors influencing population structure, continental ancestry may be pragmatically relevant to global and public health genomics. Overall, this work demonstrates that, from a biological systematic and evolutionary taxonomical perspective, human races/continental groups or clusters have no natural meaning or objective biological reality. In fact, the utility of racial categorizations in research and in clinics can be explained by spatiotemporal parameters, socio-cultural factors, and evolutionary forces affecting disease causation and treatment response.

Length Distribution of Ancestral Tracks under a General Admixture Model and Its Applications in Population History Inference

The length of ancestral tracks decays with the passing of generations which can be used to infer population admixture histories. Previous studies have shown the power in recovering the histories of admixed populations via the length distributions of ancestral tracks even under simple models. We believe that the deduction of length distributions under a general model will greatly elevate the power. Here we first deduced the length distributions under a general model and proposed general principles in parameter estimation and model selection with the deduced length distributions. Next, we focused on studying the length distributions and its applications under three typical special cases. Extensive simulations showed that the length distributions of ancestral tracks were well predicted by our theoretical framework. We further developed a new method, AdmixInfer, based on the length distributions and good performance was observed when it was applied to infer population histories under the three typical models. Notably, our method was insensitive to demographic history, sample size and threshold to discard short tracks. Finally, good performance was also observed when applied to some real datasets of African Americans, Mexicans and South Asian populations from the HapMap project and the Human Genome Diversity Project.

Dissecting the genetic structure and admixture of four geographical Malay populations

The Malay people are an important ethnic composition in Southeast Asia, but their genetic make-up and population structure remain poorly studied. Here we conducted a genome-wide study of four geographical Malay populations: Peninsular Malaysian Malay (PMM), Singaporean Malay (SGM), Indonesian Malay (IDM) and Sri Lankan Malay (SLM). All the four Malay populations showed substantial admixture with multiple ancestries. We identified four major ancestral components in Malay populations: Austronesian (17%–62%), Proto-Malay (15%–31%), East Asian (4%–16%) and South Asian (3%–34%). Approximately 34% of the genetic makeup of SLM is of South Asian ancestry, resulting in its distinct genetic pattern compared with the other three Malay populations. Besides, substantial differentiation was observed between the Malay populations from the north and the south, and between those from the west and the east. In summary, this study revealed that the genetic identity of the Malays comprises a mixed entity of multiple ancestries represented by Austronesian, Proto-Malay, East Asian and South Asian, with most of the admixture events estimated to have occurred 175 to 1,500 years ago, which in turn suggests that geographical isolation and independent admixture have significantly shaped the genetic architectures and the diversity of the Malay populations.

The peopling of the African continent and the diaspora into the new world

Africa is the birthplace of anatomically modern humans, and is the geographic origin of human migration across the globe within the last 100,000 years. The history of African populations has consisted of a number of demographic events that have influenced patterns of genetic and phenotypic variation across the continent. With the increasing amount of genomic data and corresponding developments in computational methods, researchers are able to explore long-standing evolutionary questions, expanding our understanding of human history within and outside of Africa. This review will summarize some of the recent findings regarding African demographic history, including the African Diaspora, and will briefly explore their implications for disease susceptibility in populations of African descent.

The influence of admixture and consanguinity on population genetic diversity in Middle East

The Middle East (ME) is an important crossroad where modern humans migrated 'out of Africa' and spread into Europe and Asia. After the initial peopling and long-term isolation leading to well-differentiated populations, the ME also had a crucial role in subsequent human migrations among Africa, Europe and Asia; thus, recent population admixture has been common in the ME. On the other hand, consanguinity, a well-known practice in the ME, often reduces genetic diversity and works in opposition to admixture. Here, we explored the degree to which admixture and consanguinity jointly affected genetic diversity in ME populations. Genome-wide single-nucleotide polymorphism data were generated in two representative ME populations (Arabian and Iranian), with comparisons made with populations worldwide. Our results revealed an overall higher genetic diversity in both ME populations relative to other non-African populations. We identified a much larger number of long runs of homozygosity in ME populations than in any other populations, which was most likely attributed to high levels of consanguineous marriages that significantly decreased both individual and population heterozygosity. Additionally, we were able to distinguish African, European and Asian ancestries in ME populations and quantify the impact of admixture and consanguinity with statistical approaches. Interestingly, genomic regions with significantly excessive ancestry from individual source populations are functionally enriched in olfactory pathways, which were suspected to be under natural selection. Our findings suggest that genetic admixture, consanguinity and natural selection have collectively shaped the genetic diversity of ME populations, which has important implications in both evolutionary studies and medical practices.

The role of local ancestry adjustment in association studies using admixed populations

Association analysis using admixed populations imposes challenges and opportunities for disease mapping. By developing some explicit results for the variance of an allele of interest conditional on either local or global ancestry and by simulation of recently admixed genomes we evaluate power and false-positive rates under a variety of scenarios concerning linkage disequilibrium (LD) and the presence of unmeasured variants. Pairwise LD patterns were compared between admixed and nonadmixed populations using the HapMap phase 3 data. Based on the above, we showed that as follows:

For causal variants with similar effect size in all populations, power is generally higher in a study using admixed population than using nonadmixed population, especially for highly differentiated SNPs. This gain of power is achieved with adjustment of global ancestry, which completely removes any cross-chromosome inflation of type I error rates, and addresses much of the intrachromosome inflation.

If reliably estimated, adjusting for local ancestry precisely recovers the localization that could have been achieved in a stratified analysis of source populations. Improved localization is most evident for highly differentiated SNPs; however, the advantage of higher power is lost on exactly the same differentiated SNPs.

In the real admixed populations such as African Americans and Latinos, the expansion of LD is not as dramatic as in our simulation.

While adjustment for global ancestry is required prior to announcing a novel association seen in an admixed population, local ancestry adjustment may best be regarded as a localization tool not strictly required for discovery purposes.

A comprehensive resequence-analysis of 250 kb region of 8q24.21 in men of African ancestry

BACKGROUND

Genome-wide association studies (GWAS) have identified that a ∼1 M region centromeric to the MYC oncogene on chromosome 8q24.21 harbors at least five independent loci associated with prostate cancer risk and additional loci associated with cancers of breast, colon, bladder, and chronic lymphocytic leukemia (CLL). Because GWAS identify genetic markers that may be indirectly associated with disease, fine-mapping based on sequence analysis provides important insights into patterns of linkage disequilibrium (LD) and is critical in defining the optimal variants to nominate for biological follow-up.

METHODS

To catalog variation in individuals of African ancestry, we resequenced a region (250 kb; chr8:128,050, 768–128, 300,801, hg19) containing several prostate cancer susceptibility loci as well as a locus associated with CLL. Our samples included 78 individuals from Ghana and 47 of African-Americans from Johns Hopkins University.

RESULTS

After quality control metrics were applied to next-generation sequence data, 1,838 SNPs were identified. Of these, 285 were novel and not yet reported in any public database. Using genotypes derived from sequencing, we refined the LD and recombination hotspots within the region and determined a set of tag SNPs to be used in future fine-mapping studies. Based on LD, we annotated putative risk loci and their surrogates using ENCODE data, which should help guide laboratory studies.

CONCLUSIONS

In comparison to the 1000 Genome Project data, we have identified additional variants that could be important in establishing priorities for future functional work designed to explain the biological basis of associations between SNPs and both prostate cancer and CLL.

A genome-wide association study of prostate cancer in West African men

Age-adjusted mortality rates for prostate cancer are higher for African-American men compared with those of European ancestry. Recent data suggest that West African men also have elevated risk for prostate cancer relative to European men. Genetic susceptibility to prostate cancer could account for part of this difference. We conducted a genome-wide association study (GWAS) of prostate cancer in West African men in the Ghana Prostate Study. Association testing was performed using multivariable logistic regression adjusted for age and genetic ancestry for 474 prostate cancer cases and 458 population-based controls on the Illumina HumanOmni-5 Quad BeadChip. The most promising association was at 10p14 within an intron of a long non-coding RNA (lncRNA RP11-543F8.2) 360 kb centromeric of GATA3 (p = 1.29E-7). In sub-analyses, SNPs at 5q31.3 were associated with high Gleason score (≥7) cancers, the strongest of which was a missense SNP in PCDHA1 (rs34575154, p = 3.66E-8), and SNPs at Xq28 (rs985081, p = 8.66E-9) and 6q21 (rs2185710, p = 5.95E-8) were associated with low Gleason score (<7) cancers. We sought to validate our findings in silico in the African Ancestry Prostate Cancer GWAS Consortium, but only one SNP, at 10p14, replicated at p < 0.05. Of the 90 prostate cancer loci reported from studies of men of European, Asian or African-American ancestry, we were able to test 81 in the Ghana Prostate Study, and 10 of these replicated at p < 0.05. Further genetic studies of prostate cancer in West African men are needed to confirm our promising susceptibility loci.

Distribution of ancestral chromosomal segments in admixed genomes and its implications for inferring population history and admixture mapping

The ancestral chromosomal segments in admixed genomes are of significant importance for both population history inference and admixture mapping, because they essentially provide the basic information for tracking genetic events. However, the distributions of the lengths of ancestral chromosomal segments (LACS) under some admixture models remain poorly understood. Here we introduced a theoretical framework on the distribution of LACS in two representative admixture models, that is, hybrid isolation (HI) model and gradual admixture (GA) model. Although the distribution of LACS in the GA model differs from that in the HI model, we demonstrated that the mean LACS in the HI model is approximately half of that in the GA model if both admixture proportion and admixture time in the two models are identical. We showed that the theoretical framework greatly facilitated the inference and understanding of population admixture history by analyzing African-American and Mexican empirical data. In addition, we found the peak of association signatures in the HI model was much narrower and sharper than that in the GA model, indicating that the identification of putative causal allele in the HI model is more efficient than that in the GA model. Thus admixture mapping with case-only data would be a reasonable and economical choice in the HI model due to the weak background noise. However, according to our previous studies, many populations are likely to be gradually admixed and have pretty high background linkage disequilibrium. Therefore, we suggest using a case-control approach rather than a case-only approach to conduct admixture mapping to retain the statistics power in recently admixed populations.

Human population admixture in Asia

Genetic admixture in human, the result of inter-marriage among people from different well-differentiated populations, has been extensively studied in the New World, where European colonization brought contact between peoples of Europe, Africa, and Asia and the Amerindian populations. In Asia, genetic admixing has been also prevalent among previously separated human populations. However, studies on admixed populations in Asia have been largely underrepresented in similar efforts in the New World. Here, I will provide an overview of population genomic studies that have been published to date on human admixture in Asia, focusing on population structure and population history.