Evidence map›Paper›PMID 38585733›Full record

ArticlebioRxiv : the preprint server for biology2024

A geographic history of human genetic ancestry.

Michael C Grundler, Jonathan Terhorst, Gideon S Bradburd

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael C GrundlerDepartment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0003-0729-6687
Jonathan TerhorstDepartment of Statistics, University of Michigan, Ann Arbor, MI, USA.
Gideon S BradburdDepartment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0001-8009-0154

Funding

Incorporating geography into statistical methods for analysis of population genomic DNAR35GM137919 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Gideon Bradburd · 2020 to 2026
$2.3M
Large-scale phylodynamics under non-neutral and non-treelike models of evolutionR35GM151145 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan G Terhorst · 2023 to 2026
$1.5M
NIGMS NIH HHS R35 GM137919NIGMS NIH HHS R35 GM151145
6 · The paper itself

Abstract

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. In humans, traditional approaches for describing population genetic variation often rely on discrete genetic ancestry labels, which, despite their utility, can obscure the complex, multi-faceted nature of human genetic history. These labels risk oversimplifying ancestry by ignoring its temporal depth and geographic continuity, and may therefore conflate notions of race, ethnicity, geography, and genetic ancestry. Here, we present a method that capitalizes on the rich genealogical information encoded in genomic tree sequences to infer the geographic locations of the shared ancestors of a sample of sequenced individuals. We use this method to infer the geographic history of genetic ancestry of a set of human genomes sampled from Europe, Asia, and Africa, accurately recovering major population movements on those continents. Our findings demonstrate the importance of defining the spatial-temporal context of genetic ancestry to describing human genetic variation and caution against the oversimplified interpretations of genetic data prevalent in contemporary discussions of race and ancestry.

Identifiers

PMID38585733
PMCPMC10996620

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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.