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Challenges in selecting admixture models and marker sets to infer genetic ancestry in a Brazilian admixed population.

ABSTRACT: The inference of genetic ancestry plays an increasingly prominent role in clinical, population, and forensic genetics studies. Several genotyping strategies and analytical methodologies have been developed over the last few decades to assign individuals to specific biogeographic regions. However, despite these efforts, ancestry inference in populations with a recent history of admixture, such as those in Brazil, remains a challenge. In admixed populations, proportion and components of genetic ancestry vary on different levels: (i) between populations; (ii) between individuals of the same population, and (iii) throughout the individual's genome. The present study evaluated 1171 admixed Brazilian samples to compare the genetic ancestry inferred by tri-/tetra-hybrid admixture models and evaluated different marker sets from those with small numbers of ancestry informative markers panels (AIMs), to high-density SNPs (HDSNP) and whole-genome-sequence (WGS) data. Analyses revealed greater variation in the correlation coefficient of ancestry components within and between admixed populations, especially for minority ancestral components. We also observed positive correlation between the number of markers in the AIMs panel and HDSNP/WGS. Furthermore, the greater the number of markers, the more accurate the tri-/tetra-hybrid admixture models.

SUBMITTER: Escher LM

PROVIDER: S-EPMC9731996 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Challenges in selecting admixture models and marker sets to infer genetic ancestry in a Brazilian admixed population.

Escher Luciana Maia LM Naslavsky Michel S MS Scliar Marília O MO Duarte Yeda A O YAO Zatz Mayana M Nunes Kelly K Oliveira Silviene F SF

Scientific reports 20221208 1

The inference of genetic ancestry plays an increasingly prominent role in clinical, population, and forensic genetics studies. Several genotyping strategies and analytical methodologies have been developed over the last few decades to assign individuals to specific biogeographic regions. However, despite these efforts, ancestry inference in populations with a recent history of admixture, such as those in Brazil, remains a challenge. In admixed populations, proportion and components of genetic an ...[more]

PMID: 36481695

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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/.

Project description:A current concern in genetic epidemiology studies in admixed populations is that population stratification can lead to spurious results. The Brazilian census classifies individuals according to self-reported "color", but several studies have demonstrated that stratifying according to "color" is not a useful strategy to control for population structure, due to the dissociation between self-reported "color" and genomic ancestry. We report the results of a study in a group of Brazilian siblings in which we measured skin pigmentation using a reflectometer, and estimated genomic ancestry using 21 Ancestry Informative Markers (AIMs). Self-reported "color", according to the Brazilian census, was also available for each participant. This made it possible to evaluate the relationship between self-reported "color" and skin pigmentation, self-reported "color" and genomic ancestry, and skin pigmentation and genomic ancestry. We observed that, although there were significant differences between the three "color" groups in genomic ancestry and skin pigmentation, there was considerable dispersion within each group and substantial overlap between groups. We also saw that there was no good agreement between the "color" categories reported by each member of the sibling pair: 30 out of 86 sibling pairs reported different "color", and in some cases, the sibling reporting the darker "color" category had lighter skin pigmentation. Socioeconomic status was significantly associated with self-reported "color" and genomic ancestry in this sample. This and other studies show that subjective classifications based on self-reported "color", such as the one that is used in the Brazilian census, are inadequate to describe the population structure present in recently admixed populations. Finally, we observed that one of the AIMs included in the panel (rs1426654), which is located in the known pigmentation gene SLC24A5, was strongly associated with skin pigmentation in this sample.

Dataset Information

Challenges in selecting admixture models and marker sets to infer genetic ancestry in a Brazilian admixed population.

Publications

Challenges in selecting admixture models and marker sets to infer genetic ancestry in a Brazilian admixed population.

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