Evidence map›Paper›PMID 12663553›Full record

ArticleGenetics2003

Mapping quantitative trait loci in the case of a spike in the phenotype distribution.

Karl W Broman

Abstract read
In one paragraph

Article in Genetics, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.

0numbers the graph read from it
0cells of the map it votes in
72citing 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

72 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Genetic determinism of prickles in rose.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. A New Diagnostic Resource forG3 (Bethesda, Md.) · 2017
    Article
  14. Article
  15. A Chromosome-Scale Assembly of theG3 (Bethesda, Md.) · 2017
    Article
  16. Article
  17. Quantitative trait loci controlling leaf appearance and curd initiation of cauliflower in relation to temperature.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016
    Article
  18. Article
  19. Article
  20. Article

12 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

1 author.

Karl W BromanDepartment of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, USA. kbroman@jhsph.edu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A common departure from the usual normality assumption in QTL mapping concerns a spike in the phenotype distribution. For example, in measurements of tumor mass, some individuals may exhibit no tumors; in measurements of time to death after a bacterial infection, some individuals may recover from the infection and fail to die. If an appreciable portion of individuals share a common phenotype value (generally either the minimum or the maximum observed phenotype), the standard approach to QTL mapping can behave poorly. We describe several alternative approaches for QTL mapping in the case of such a spike in the phenotype distribution, including the use of a two-part parametric model and a nonparametric approach based on the Kruskal-Wallis test. The performance of the proposed procedures is assessed via computer simulation. The procedures are further illustrated with data from an intercross experiment to identify QTL contributing to variation in survival of mice following infection with Listeria monocytogenes.

Indexed as

Models, GeneticQuantitative Trait LociComputer SimulationCrosses, GeneticInbreedingLod ScorePhenotype

Identifiers

PMID12663553
PMCPMC1462498

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.