Unravelling the hidden ancestry of American admixed populations.
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ABSTRACT:
SUBMITTER: Montinaro F
PROVIDER: S-EPMC4374169 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ancestry informative markers (AIMs) are used in forensic genetics to infer biogeographical ancestry (BGA) of individuals and may also have a prominent role in future police and identification investigations. In the last few years, many studies have been published reporting new AIM sets. These sets include markers (usually around 100 or less) selected with different purposes and different population resolutions. Regardless of the ability of these sets to separate populations from different continents or regions, the uncertainty associated with the estimates provided by these panels and their capacity to accurately report the different ancestral contributions in individuals of admixed populations has rarely been investigated. This issue is addressed in this study by evaluating different AIM sets. Ancestry inference was carried out in admixed South American populations, both at population and individual levels. The results of ancestry inferences using AIM sets with different numbers of markers among admixed reference populations were compared. To evaluate the performance of the different ancestry panels at the individual level, expected and observed estimates among families and their offspring were compared, considering that (1) the apportionment of ancestry in the offspring should be closer to the average ancestry of the parents, and (2) full siblings should present similar ancestry values. The results obtained illustrate the importance of having a good balance/compromise between not only the number of markers and their ability to differentiate ancestral populations, but also a balanced differentiation among reference groups, to obtain more precise values of genetic ancestry. This work also highlights the importance of estimating errors associated with the use of a limited number of markers. We demonstrate that although these errors have a moderate effect at the population level, they may have an important impact at the individual level. Considering that many AIM-sets are being described for inferences at the individual level and not at the population level, e.g., in association studies or the determination of a suspect's BGA, the results of this work point to the need of a more careful evaluation of the uncertainty associated with the ancestry estimates in admixed populations, when small AIM-sets are used.
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Project description:Inherited lung cancer risk, particularly in nonsmokers, is poorly understood. Genomic and ancestry analysis of 1,153 lung cancers from Latin America revealed striking associations between Native American ancestry and their somatic landscape, including tumor mutational burden, and specific driver mutations in EGFR, KRAS, and STK11. A local Native American ancestry risk score was more strongly correlated with EGFR mutation frequency compared with global ancestry correlation, suggesting that germline genetics (rather than environmental exposure) underlie these disparities. SIGNIFICANCE: The frequency of somatic EGFR and KRAS mutations in lung cancer varies by ethnicity, but we do not understand why. Our study suggests that the variation in EGFR and KRAS mutation frequency is associated with genetic ancestry and suggests further studies to identify germline alleles that underpin this association.See related commentary by Gomez et al., p. 534.This article is highlighted in the In This Issue feature, p. 521.
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Project description:The ancestry of the Colombian population comprises a large number of well differentiated Native communities belonging to diverse linguistic groups. In the late fifteenth century, a process of admixture was initiated with the arrival of the Europeans, and several years later, Africans also became part of the Colombian population. Therefore, the genepool of the current Colombian population results from the admixture of Native Americans, Europeans and Africans. This admixture occurred differently in each region of the country, producing a clearly stratified population. Considering the importance of population substructure in both clinical and forensic genetics, we sought to investigate and compare patterns of genetic ancestry in Colombia by studying samples from Native and non-Native populations living in its 5 continental regions: the Andes, Caribe, Amazonia, Orinoquía, and Pacific regions. For this purpose, 46 AIM-Indels were genotyped in 761 non-related individuals from current populations. Previously published genotype data from 214 Colombian Natives from five communities were used for population comparisons. Significant differences were observed between Native and non-Native populations, among non-Native populations from different regions and among Native populations from different ethnic groups. The Pacific was the region with the highest African ancestry, Amazonia harboured the highest Native ancestry and the Andean and Orinoquían regions showed the highest proportion of European ancestry. The Andean region was further sub-divided into 6 sub-regions: North East, Central West, Central East, West, South West and South East. Among these regions, the South West region showed a significantly lower European admixture than the other regions. Hardy-Weinberg equilibrium and variance values of ancestry among individuals within populations showed a potential stratification of the Pacific population.
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Project description:BackgroundAdmixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection.ResultsTo address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data.ConclusionsMI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.
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Project description: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.
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Project description:Population stratification is a growing concern in genetic-association studies. Averaged ancestry at the genome level (global ancestry) is insufficient for detecting the population substructures and correcting population stratifications in association studies. Local and phase stratification are needed for human genetic studies, but current technologies cannot be applied on the entire genome data due to various technical caveats. Here we developed a novel approach (aMAP, ancestry of Modern Admixed Populations) for inferring local phased ancestry. It took about 3 seconds on a desktop computer to finish a local ancestry analysis for each human genome with 1.4-million SNPs. This method also exhibits the scalability to larger datasets with respect to the number of SNPs, the number of samples, and the size of reference panels. It can detect the lack of the proxy of reference panels. The accuracy was 99.4%. The aMAP software has a capacity for analyzing 6-way admixed individuals. As the biomedical community continues to expand its efforts to increase the representation of diverse populations, and as the number of large whole-genome sequence datasets continues to grow rapidly, there is an increasing demand on rapid and accurate local ancestry analysis in genetics, pharmacogenomics, population genetics, and clinical diagnosis.
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Project description:Admixed populations offer a unique opportunity for mapping diseases that have large disease allele frequency differences between ancestral populations. However, association analysis in such populations is challenging because population stratification may lead to association with loci unlinked to the disease locus.We show that local ancestry at a test single nucleotide polymorphism (SNP) may confound with the association signal and ignoring it can lead to spurious association. We demonstrate theoretically that adjustment for local ancestry at the test SNP is sufficient to remove the spurious association regardless of the mechanism of population stratification, whether due to local or global ancestry differences among study subjects; however, global ancestry adjustment procedures may not be effective. We further develop two novel association tests that adjust for local ancestry. Our first test is based on a conditional likelihood framework which models the distribution of the test SNP given disease status and flanking marker genotypes. A key advantage of this test lies in its ability to incorporate different directions of association in the ancestral populations. Our second test, which is computationally simpler, is based on logistic regression, with adjustment for local ancestry proportion. We conducted extensive simulations and found that the Type I error rates of our tests are under control; however, the global adjustment procedures yielded inflated Type I error rates when stratification is due to local ancestry difference.
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Project description:Identifying the ancestry of chromosomal segments of distinct ancestry has a wide range of applications from disease mapping to learning about history. Most methods require the use of unlinked markers; but, using all markers from genome-wide scanning arrays, it should in principle be possible to infer the ancestry of even very small segments with exquisite accuracy. We describe a method, HAPMIX, which employs an explicit population genetic model to perform such local ancestry inference based on fine-scale variation data. We show that HAPMIX outperforms other methods, and we explore its utility for inferring ancestry, learning about ancestral populations, and inferring dates of admixture. We validate the method empirically by applying it to populations that have experienced recent and ancient admixture: 935 African Americans from the United States and 29 Mozabites from North Africa. HAPMIX will be of particular utility for mapping disease genes in recently admixed populations, as its accurate estimates of local ancestry permit admixture and case-control association signals to be combined, enabling more powerful tests of association than with either signal alone.
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Project description:Admixed American populations have different global proportions of European, Sub-Saharan African, and Native-American ancestry. However, individuals who display the same global ancestry could exhibit remarkable differences in the distribution of local ancestry blocks. We studied for the first time the distribution of local ancestry across the genome of 264 Brazilian admixed individuals, ascertained within the scope of the Brazilian Initiative on Precision Medicine. We found a decreased proportion of European ancestry together with an excess of Native-American ancestry on chromosome 8p23.1 and showed that this is due to haplotypes created by chromosomal inversion events. Furthermore, Brazilian non-inverted haplotypes were more similar to Native-American haplotypes than to European haplotypes, in contrast to what was found in other American admixed populations. We also identified signals of recent positive selection on chromosome 8p23.1, and one gene within this locus, PPP1R3B, is related to glycogenesis and has been associated with an increased risk of type 2 diabetes and obesity. These findings point to a selection event after admixture, which is still not entirely understood in recent admixture events.
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