Evidence map›Paper›PMID 30537949›Full record

ArticleBMC genomics2018

Genetic ancestry, admixture and health determinants in Latin America.

Emily T Norris, Lu Wang, Andrew B Conley, Lavanya Rishishwar, Leonardo Mariño-Ramírez, Augusto Valderrama-Aguirre, I King Jordan

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 8 pooled it
10.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

81 citing papers in PubMed, 8 syntheses or guidelines pooled it, 142 citations in OpenAlex.

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  12. Spectrum and Clinical Interpretation ofInternational journal of molecular sciences · 2025
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21 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Emily T NorrisSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Lu WangSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Andrew B ConleyIHRC-Georgia Tech Applied Bioinformatics Laboratory (ABiL), Atlanta, GA, USA.
Lavanya RishishwarSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Leonardo Mariño-RamírezPanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia.
Augusto Valderrama-AguirrePanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia.
I King JordanSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA. king.jordan@biology.gatech.edu.
PanAmerican Bioinformatics Institute · COGeorgia Institute of Technology · USNational Center for Biotechnology Information · US

Funding

Genome Assemblies, Analyses, and ComparisonsZIALM082713 · NLM · NATIONAL LIBRARY OF MEDICINE · PI LANDSMAN, DAVID · 2013 to 2025
$3.5M
Intramural NIH HHS ZIA LM082713
6 · The paper itself

Abstract

backgroundModern Latin American populations were formed via genetic admixture among ancestral source populations from Africa, the Americas and Europe. We are interested in studying how combinations of genetic ancestry in admixed Latin American populations may impact genomic determinants of health and disease. For this study, we characterized the impact of ancestry and admixture on genetic variants that underlie health- and disease-related phenotypes in population genomic samples from Colombia, Mexico, Peru, and Puerto Rico.

resultsWe analyzed a total of 347 admixed Latin American genomes along with 1102 putative ancestral source genomes from Africans, Europeans, and Native Americans. We characterized the genetic ancestry, relatedness, and admixture patterns for each of the admixed Latin American genomes, finding a spectrum of ancestry proportions within and between populations. We then identified single nucleotide polymorphisms (SNPs) with anomalous ancestry-enrichment patterns, i.e. SNPs that exist in any given Latin American population at a higher frequency than expected based on the population's genetic ancestry profile. For this set of ancestry-enriched SNPs, we inspected their phenotypic impact on disease, metabolism, and the immune system. All four of the Latin American populations show ancestry-enrichment for a number of shared pathways, yielding evidence of similar selection pressures on these populations during their evolution. For example, all four populations show ancestry-enriched SNPs in multiple genes from immune system pathways, such as the cytokine receptor interaction, T cell receptor signaling, and antigen presentation pathways. We also found SNPs with excess African or European ancestry that are associated with ancestry-specific gene expression patterns and play crucial roles in the immune system and infectious disease responses. Genes from both the innate and adaptive immune system were found to be regulated by ancestry-enriched SNPs with population-specific regulatory effects.

conclusionsAncestry-enriched SNPs in Latin American populations have a substantial effect on health- and disease-related phenotypes. The concordant impact observed for same phenotypes across populations points to a process of adaptive introgression, whereby ancestry-enriched SNPs with specific functional utility appear to have been retained in modern populations by virtue of their effects on health and fitness.

Indexed as

Genetics, PopulationGenome, HumanPolymorphism, Single NucleotideBlack PeopleDiseaseEthnicityGenomicsHealth StatusHumansLatin AmericaWhite PeopleAdaptive introgressionAdmixtureAncestry-enrichmentDiseaseGenetic ancestryHealthImmune systemPopulation genetics

Identifiers

PMID30537949
PMCPMC6288849
OpenAlexW2904194155

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.