Evidence map›Paper›PMID 30104759›Full record

ArticleNature genetics2018

High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability.

Pier Francesco Palamara, Jonathan Terhorst, Yun S Song, Alkes L Price

Abstract read
In one paragraph

Article in Nature genetics, 2018. 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.

  1. Review
  2. Article
  3. Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Identity-by-descent segments in large samples.Theoretical population biology · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. 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

4 authors.

Pier Francesco PalamaraDepartment of Statistics, University of Oxford, Oxford, UK. palamara@stats.ox.ac.uk.ORCID http://orcid.org/0000-0002-7999-1972
Jonathan TerhorstDepartment of Statistics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-7765-2101
Yun S SongDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
Alkes L PriceDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.ORCID http://orcid.org/0000-0002-2971-7975

Funding

Statistical methods for studies of rare variantsR01MH101244 · NIMH · HARVARD MEDICAL SCHOOL · PI Benjamin Michael Neale, ALKES L PRICE · 2013 to 2026
$9.4M
Methods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024
$6.3M
Functionally specialized components of disease heritability in ENCODE dataU01HG009379 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L, RAYCHAUDHURI, SOUMYA · 2017 to 2021
$2.5M
Mathematical Models and Statistical Methods for Large-Scale Population GenomicsR01GM094402 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SONG, YUN S · 2010 to 2018
$2.2M
Improving Polygenic Prediction using Next-Generation Data SetsR01GM105857 · NIGMS · BRIGHAM AND WOMEN'S HOSPITAL · PI SUNYAEV, SHAMIL · 2014 to 2017
$2.0M
PCA-based selection scans in very large samplesR03ES027902 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2018 to 2019
$170k
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG006399NHGRI NIH HHS U01 HG009379NIEHS NIH HHS R03 ES027902NIGMS NIH HHS R01 GM094402NIGMS NIH HHS R01 GM105857NIMH NIH HHS R01 MH101244
6 · The paper itself

Abstract

Interest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequencing data. Here we introduce a powerful new method, ASMC, that can estimate coalescence times using only SNP array data, and is orders of magnitude faster than previous approaches. We applied ASMC to detect recent positive selection in 113,851 phased British samples from the UK Biobank, and detected 12 genome-wide significant signals, including 6 novel loci. We also applied ASMC to sequencing data from 498 Dutch individuals to detect background selection at deeper time scales. We detected strong heritability enrichment in regions of high background selection in an analysis of 20 independent diseases and complex traits using stratified linkage disequilibrium score regression, conditioned on a broad set of functional annotations (including other background selection annotations). These results underscore the widespread effects of background selection on the genetic architecture of complex traits.

Indexed as

DiseaseGenome-Wide Association StudyHigh-Throughput Screening AssaysHumansLinkage DisequilibriumModels, GeneticMolecular Sequence AnnotationMultifactorial InheritancePolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID30104759
PMCPMC6145075

What OpenQuestion holds

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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.