Evidence map›Paper›PMID 39363040›Full record

ArticleJournal of mathematical biology2024

Understanding admixture fractions: theory and estimation of gene-flow.

Mason Liang, Mikhail Shishkin, Vladimir Shchur, Rasmus Nielsen

Abstract read
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Article in Journal of mathematical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Mason Liang *Department of Integrative Biology, University of California, Valley Life Sciences Building, Berkeley, CA, 94720, USA.
Mikhail Shishkin *International laboratory of statistical and computational genomics, Faculty of computer science, HSE University, Pokrovskiy Boulevard, 11, Moscow, Russian Federation, 109028.
Vladimir ShchurInternational laboratory of statistical and computational genomics, Faculty of computer science, HSE University, Pokrovskiy Boulevard, 11, Moscow, Russian Federation, 109028. vshchur@hse.ru.ORCID 0000-0002-1431-5260
Rasmus NielsenDepartment of Integrative Biology, University of California, Valley Life Sciences Building, Berkeley, CA, 94720, USA. rasmus_nielsen@berkeley.edu.

Funding

Enabling Precision Genomics Using Adaptive VariationR01GM138634 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI NIELSEN, RASMUS · 2020 to 2023
$1.7M
Inference and application of graphs for genomic dataR35GM153400 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI RASMUS NIELSEN · 2024 to 2026
$1.3M
NIGMS NIH HHS R01 GM138634NIGMS NIH HHS R35 GM153400NIH HHS R01GM138634Russian Science Foundation 22-71-10056
6 · The paper itself

Abstract

Estimation of admixture proportions has become one of the most commonly used computational tools in population genomics. However, there is remarkably little population genetic theory on statistical properties of these variables. We develop theoretical results that can accurately predict means and variances of admixture proportions within a population using models with recombination and genetic drift. Based on established theory on measures of multilocus disequilibrium, we show that there is a set of recurrence relations that can be used to derive expectations for higher moments of the admixture proportions distribution. We obtain closed form solutions for some special cases. Using these results, we develop a method for estimating admixture parameters from estimated admixture proportions obtained from programs such as Structure or Admixture. We apply this method to HapMap 3 data and find that the population history of African Americans, as expected, is not best explained by a single admixture event between people of European and African ancestry. The model of constant gene flow starting at 8 generations and ending at 2 generations before present gives the best fit.

Indexed as

Gene FlowGenetics, PopulationLinkage DisequilibriumMathematical ConceptsModels, GeneticWhiteBlack or African AmericanGenetic DriftHumansRecombination, GeneticAdmixtureAdmixture fractionsContinuous migrationLocal ancestryMigration

Identifiers

PMID39363040
PMCPMC12042791

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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.