Evidence map›Paper›PMID 40071816›Full record

ReviewAmerican journal of biological anthropology2024

Computational Genomics and Its Applications to Anthropological Questions.

Kelsey E Witt, Fernando A Villanea

Abstract readReview
In one paragraph

Review in American journal of biological anthropology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. A legacy of genetic entanglement with wolves shapes modern dogs.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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

2 authors.

Kelsey E WittDepartment of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Clemson, South Carolina, USA.ORCID 0000-0002-3242-2123
Fernando A VillaneaDepartment of Anthropology, University of Colorado Boulder, Boulder, Colorado, USA.ORCID 0000-0002-6661-0368

Funding

Statistical Methods for Gene Regulatory Analysis From Single Cell Genomics DataP20GM139769 · NIGMS · CLEMSON UNIVERSITY · PI ALEXANDROV, ANDREI · 2021 to 2025
$10.8M
NIGMS NIH HHS P20 GM139769
6 · The paper itself

Abstract

The advent of affordable genome sequencing and the development of new computational tools have established a new era of genomic knowledge. Sequenced human genomes number in the tens of thousands, including thousands of ancient human genomes. The abundance of data has been met with new analysis tools that can be used to understand populations' demographic and evolutionary histories. Thus, a variety of computational methods now exist that can be leveraged to answer anthropological questions. This includes novel likelihood and Bayesian methods, machine learning techniques, and a vast array of population simulators. These computational tools provide powerful insights gained from genomic datasets, although they are generally inaccessible to those with less computational experience. Here, we outline the theoretical workings behind computational genomics methods, limitations and other considerations when applying these computational methods, and examples of how computational methods have already been applied to anthropological questions. We hope this review will empower other anthropologists to utilize these powerful tools in their own research.

Indexed as

AnthropologyComputational BiologyGenome, HumanGenomicsBayes TheoremHumansMachine Learningcomputational genomicsgenetic anthropologypopulation genetics

Identifiers

PMID40071816
PMCPMC11898561

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.