Genetic Ancestry Inference and Its Application for the Genetic Mapping of Human Diseases

Suarez-Pajes, Eva; Usera, Ana Díaz-de; Marcelino-Rodríguez, Itahisa; Guillén‐Guío, Beatriz; Flores, Carlos

doi:10.3390/ijms22136962

Cited by 15 publications

(10 citation statements)

References 103 publications

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“…Usually, researchers adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principal components in statistical models. Conversely, researchers would conduct admixture mapping, which involves inferring ancestry at each genomic loci along the genome of an admixed individual (local ancestry inference), to increase the power to detect variants with an ancestry effect (30,31). This is sufficient in simple admixture scenarios, such as two-way admixed populations (African-Americans) and three-way admixed populations (Latinos/Hispanics) (1,32,33).…”

Section: Discussionmentioning

confidence: 99%

GWAS in the southern African context

Swart

Eeden

Uren

et al. 2022

Preprint

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Researchers would generally adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principle components. Alternatively, researchers would conduct admixture mapping to increase the power to detect variants with an ancestry effect. This is sufficient in simple admixture scenarios, however, populations from southern Africa can be complex multi-way admixed populations. Duan et al. (2018) first described local ancestry adjusted allelic (LAAA) analysis as a robust method for discovering association signals, while producing minimal false-positives. Their simulation study, however, was limited to a two-way admixed population. Realizing that their findings might not translate to other admixture scenarios, we simulated a three- and five-way admixed population to compare the LAAA model to other models commonly used in GWAS. We found that, given our admixture scenarios, the LAAA model identifies the most causal variants in most of the phenotypes we tested across both the three-way and five-way admixed populations. The LAAA model also produced a high number of false-positives which was potentially caused by the ancestry effect size that we assumed. Considering the extent to which the various models tested differed in their results and considering that the source of a given association is unknown, we recommend that researchers use multiple GWAS models when analysing populations with complex ancestry.

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Section: Discussionmentioning

confidence: 99%

GWAS in the southern African context

Swart

Eeden

Uren

et al. 2022

Preprint

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“…This probably occurs because the genome of an admixed individual is a mosaic composed of segments from different parental sources. Over generations, due to the process of meiotic recombination, the components of distinct ancestry are shu ed between homologous chromosomes [11,51]. Thus, the greater the number of generations since the admixture beginning, the smaller the size of the genomic segments of the ancestry will be.…”

Section: Discussionmentioning

confidence: 99%

“…The inference of genetic ancestry has also been an important tool in the study of admixed populations. It has been widely used to control the effect of population strati cation in association studies [10] and for the identi cation of disease-associated genes (review in [11]). In addition to its important role in revealing the past history of admixed populations, such as origin, number of migratory waves and the dating of migratory events [12,13].…”

Section: Introductionmentioning

confidence: 99%

Genetic Ancestry Inference: from AIMS to WGS application and challenges in admixed population

Escher

Naslavsky

Scliar

et al. 2022

Preprint

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Increasingly, the inference of genetic ancestry plays a prominent role in clinical, population and forensic genetics studies. Over the last few decades, several genotyping strategies and analytical methodologies have been developed in order to assign individuals to specific biogeographic regions. However, despite all these efforts, the ancestry inference in populations with a recent history of admixture, such as those in America, is still a challenge. In admixed populations, proportion and components of genetic ancestry vary at different levels: (i) between populations; (ii) between individuals of the same population; (iii) throughout the individual's genome. In the present study we compared and evaluated different sets of markers, from those with small numbers of ancestry informative markers panels (AIMs), to high-density SNPs (HDSNP) and whole-genome-sequence (WGS) data. To this end, we evaluated 1,675 admixed samples from America, using a tetrahybrid admixture model (Native American, European, African and East Asian). Analyses show greater variation in the correlation coefficient of ancestry components within and between admixed populations, especially for minority ancestral components. We also observed, the higher the number of markers in the AIMs panel, the higher the correlation with HDSNP and WGS. In addition, the greater the number of markers, the more robust was the tetrahybrid admixture model.

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“…Differential assortment of ancestral allele frequencies across a genome results from selection, mutation or genetic drift, when previously isolated populations interbreed [ 1 , 2 ]. Taking advantage of these differences in allele frequencies can help identify population-specific disease risk alleles associated with disease phenotypes due to various ancestries being exposed to distinct environments and pathogens [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

GWAS in the southern African context

et al. 2022

View full text Add to dashboard Cite

Researchers would generally adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principle components. Alternatively, researchers would conduct admixture mapping to increase the power to detect variants with an ancestry effect. This is sufficient in simple admixture scenarios, however, populations from southern Africa can be complex multi-way admixed populations. Duan et al. (2018) first described local ancestry adjusted allelic (LAAA) analysis as a robust method for discovering association signals, while producing minimal false positive hits. Their simulation study, however, was limited to a two-way admixed population. Realizing that their findings might not translate to other admixture scenarios, we simulated a three- and five-way admixed population to compare the LAAA model to other models commonly used in genome-wide association studies (GWAS). We found that, given our admixture scenarios, the LAAA model identifies the most causal variants in most of the phenotypes we tested across both the three-way and five-way admixed populations. The LAAA model also produced a high number of false positive hits which was potentially caused by the ancestry effect size that we assumed. Considering the extent to which the various models tested differed in their results and considering that the source of a given association is unknown, we recommend that researchers use multiple GWAS models when analysing populations with complex ancestry.

show abstract

Genetic Ancestry Inference and Its Application for the Genetic Mapping of Human Diseases

Cited by 15 publications

References 103 publications

GWAS in the southern African context

GWAS in the southern African context

Genetic Ancestry Inference: from AIMS to WGS application and challenges in admixed population

GWAS in the southern African context

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