Evidence map›Paper›PMID 17273979›Full record

ArticleAmerican journal of human genetics2007

Efficient association mapping of quantitative trait loci with selective genotyping.

B E Huang, D Y Lin

Abstract read
In one paragraph

Article in American journal of human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Article
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  14. Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling.Journal of the American Statistical Association · 2015
    Article
  15. Article
  16. Review
  17. Quantitative trait analysis in sequencing studies under trait-dependent sampling.Proceedings of the National Academy of Sciences of the United States of America · 2013
    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

2 authors.

B E HuangDepartment of Biostatistics, University of North Carolina, Chapel Hill 27599-7420, USA.
D Y Lin

Funding

Biostatistics for Research in Genomics and CancerT32CA106209 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI JOSEPH G IBRAHIM, Naim Ur Rashid · 2004 to 2026
$4.8M
NCI NIH HHS T32 CA106209
6 · The paper itself

Abstract

Selective genotyping (i.e., genotyping only those individuals with extreme phenotypes) can greatly improve the power to detect and map quantitative trait loci in genetic association studies. Because selection depends on the phenotype, the resulting data cannot be properly analyzed by standard statistical methods. We provide appropriate likelihoods for assessing the effects of genotypes and haplotypes on quantitative traits under selective-genotyping designs. We demonstrate that the likelihood-based methods are highly effective in identifying causal variants and are substantially more powerful than existing methods.

Indexed as

Chromosome MappingGenotypeModels, GeneticQuantitative Trait LociSelection, GeneticComputer SimulationData Interpretation, StatisticalHumansPhenotypePolymorphism, Single Nucleotide

Identifiers

PMID17273979
PMCPMC1821103

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.