Evidence map›Paper›PMID 25564017›Full record

ArticleGenome research2015

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data.

Anand Bhaskar, Y X Rachel Wang, Yun S Song

Abstract read
In one paragraph

Article in Genome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing 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

53 citing papers in PubMed.

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  14. Exact Decoding of a Sequentially Markov Coalescent Model in Genetics.Journal of the American Statistical Association · 2024
    Article
  15. Article
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  17. Scaling the Discrete-time Wright Fisher model to biobank-scale datasets.bioRxiv : the preprint server for biology · 2023
    Article
  18. Article
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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

3 authors.

Anand BhaskarSimons Institute for the Theory of Computing, Berkeley, California 94720, USA; Computer Science Division, University of California, Berkeley, California 94720, USA;
Y X Rachel WangDepartment of Statistics, University of California, Berkeley, California 94720, USA;
Yun S SongSimons Institute for the Theory of Computing, Berkeley, California 94720, USA; Computer Science Division, University of California, Berkeley, California 94720, USA; Department of Statistics, University of California, Berkeley, California 94720, USA; Department of Integrative Biology, University of California, Berkeley, California 94720, USA.

Funding

Mathematical Models and Statistical Methods for Large-Scale Population GenomicsR01GM094402 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SONG, YUN S · 2010 to 2018
$2.2M
Population Genetic Consequences of Recent Explosive Population Growth in HumansR01GM108805 · NIGMS · CORNELL UNIVERSITY · PI CLARK, ANDREW G, KEINAN, ALON · 2014 to 2017
$2.2M
NIGMS NIH HHS R01 GM094402NIGMS NIH HHS R01-GM094402NIGMS NIH HHS R01 GM108805NIGMS NIH HHS R01-GM108805
6 · The paper itself

Abstract

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal that is difficult to pick up with small sample sizes. Lastly, we use our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing data set of tens of thousands of individuals assayed at a few hundred genic regions.

Indexed as

Genetic LociGenetics, PopulationGenetic VariationGenomicsMutation RatePopulation DensityExomeHigh-Throughput Nucleotide SequencingHumansModels, GeneticModels, StatisticalReproducibility of Results

Identifiers

PMID25564017
PMCPMC4315300

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Registered trials

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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.