Evidence map›Paper›PMID 25990878›Full record

ArticleHeredity2015

Properties of different selection signature statistics and a new strategy for combining them.

Y Ma, X Ding, S Qanbari, S Weigend, Q Zhang, H Simianer

Abstract read
In one paragraph

Article in Heredity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.

0numbers the graph read from it
0cells of the map it votes in
103citing 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

103 citing papers in PubMed.

  1. Article
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  11. Selection scans and downstream analysis with selscan.Human population genetics and genomics · 2026
    Article
  12. Article
  13. Signature of Selection Analysis Reveals Candidate Genes Related to Climate Adaptation and Production Traits in Lao Native Goats.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Selection Signatures in the Genome of Dzhalgin Merino Sheep Breed.Animals : an open access journal from MDPI · 2025
    Article
  18. Article
  19. Article
  20. Article

43 more citing papers are in PubMed but not listed here.

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

6 authors.

Y MaKey Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College Animal Science and Technology, China Agricultural University, Beijing, China.
X DingKey Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College Animal Science and Technology, China Agricultural University, Beijing, China.
S QanbariAnimal Breeding and Genetics Group, Department of Animal Sciences, Georg-August University, Goettingen, Germany.
S WeigendInstitute for Animal Breeding, Federal Agricultural Research Centre, Mariensee, Germany.
Q ZhangKey Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College Animal Science and Technology, China Agricultural University, Beijing, China.
H SimianerAnimal Breeding and Genetics Group, Department of Animal Sciences, Georg-August University, Goettingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying signatures of recent or ongoing selection is of high relevance in livestock population genomics. From a statistical perspective, determining a proper testing procedure and combining various test statistics is challenging. On the basis of extensive simulations in this study, we discuss the statistical properties of eight different established selection signature statistics. In the considered scenario, we show that a reasonable power to detect selection signatures is achieved with high marker density (>1 SNP/kb) as obtained from sequencing, while rather small sample sizes (~15 diploid individuals) appear to be sufficient. Most selection signature statistics such as composite likelihood ratio and cross population extended haplotype homozogysity have the highest power when fixation of the selected allele is reached, while integrated haplotype score has the highest power when selection is ongoing. We suggest a novel strategy, called de-correlated composite of multiple signals (DCMS) to combine different statistics for detecting selection signatures while accounting for the correlation between the different selection signature statistics. When examined with simulated data, DCMS consistently has a higher power than most of the single statistics and shows a reliable positional resolution. We illustrate the new statistic to the established selective sweep around the lactase gene in human HapMap data providing further evidence of the reliability of this new statistic. Then, we apply it to scan selection signatures in two chicken samples with diverse skin color. Our analysis suggests that a set of well-known genes such as BCO2, MC1R, ASIP and TYR were involved in the divergent selection for this trait.

Indexed as

Models, GeneticSelection, GeneticAnimalsBreedingChickensComputer SimulationData Interpretation, StatisticalGene FrequencyHaplotypesHapMap ProjectHumansLikelihood FunctionsPolymorphism, Single Nucleotide

Identifiers

PMID25990878
PMCPMC4611237

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.