Evidence map›Paper›PMID 22443496›Full record

ArticleBMC genetics2012

Genetic mapping of complex traits by minimizing integrated square errors.

Song Wu, Guifang Fu, Yunmei Chen, Zhong Wang, Rongling Wu

Abstract read
In one paragraph

Article in BMC genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

5 authors.

Song WuDepartment of Applied Mathematics and Statistics, the State University of New York at Stony Brook, Stony Brook, NY 11790, USA.
Guifang Fu
Yunmei Chen
Zhong Wang
Rongling Wu

Funding

NCRR NIH HHS UL1RR0330184
6 · The paper itself

Abstract

backgroundGenetic mapping has been used as a tool to study the genetic architecture of complex traits by localizing their underlying quantitative trait loci (QTLs). Statistical methods for genetic mapping rely on a key assumption, that is, traits obey a parametric distribution. However, in practice real data may not perfectly follow the specified distribution.

resultsHere, we derive a robust statistical approach for QTL mapping that accommodates a certain degree of misspecification of the true model by incorporating integrated square errors into the genetic mapping framework. A hypothesis testing is formulated by defining a new test statistics--energy difference.

conclusionsSimulation studies were performed to investigate the statistical properties of this approach and compare these properties with those from traditional maximum likelihood and non-parametric QTL mapping approaches. Lastly, analyses of real examples were conducted to demonstrate the usefulness and utilization of the new approach in a practical genetic setting.

Indexed as

Models, GeneticChromosome MappingQuantitative Trait LociStatistics as Topic

Identifiers

PMID22443496
PMCPMC3353242

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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.