ArticleHeredity2012
Bias correction for estimated QTL effects using the penalized maximum likelihood method.
Article in Heredity, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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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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Who cites it
6 citing papers in PubMed.
- Average semivariance yields accurate estimates of the fraction of marker-associated genetic variance and heritability in complex trait analyses.PLoS genetics · 2021Article
- A Fast Multi-Locus Ridge Regression Algorithm for High-Dimensional Genome-Wide Association Studies.Frontiers in genetics · 2021Article
- TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.Scientific reports · 2019Article
- pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.Heredity · 2017Article
- Interacted QTL mapping in partial NCII design provides evidences for breeding by design.PloS one · 2015Article
- Integrated phenotypes: understanding trait covariation in plants and animals.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.
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Registered trials
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.