ArticleGenetics2003
Estimating polygenic effects using markers of the entire genome.
Article in Genetics, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers.
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.
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.
Who cites it
179 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Reversible jumps for the joint modeling of the dimension of pseudo-functional models in chromosomal windows in genome-wide selection.BMC genomics · 2026Article
- Genomic prediction in a backcross population using relationship matrices.Archives animal breeding · 2025Article
- Powerful QTL mapping and favorable allele mining in an all-in-one population: a case study of heading date.National science review · 2024Article
- Article
- Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system.Genetics, selection, evolution : GSE · 2023Article
- Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane.Plants (Basel, Switzerland) · 2022Review
- Estimating genetic variance contributed by a quantitative trait locus: A random model approach.PLoS computational biology · 2022Article
- Genome-Wide Identification of Candidate Genes for Milk Production Traits in Korean Holstein Cattle.Animals : an open access journal from MDPI · 2021Article
- A statistical framework for QTL hotspot detection.G3 (Bethesda, Md.) · 2021Article
- FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.Frontiers in genetics · 2021Article
- Interest of using imputation for genomic evaluation in layer chicken.Poultry science · 2020Article
- Genome-wide association studies using binned genotypes.Heredity · 2020Article
- QTL.gCIMapping.GUI v2.0: An R software for detecting small-effect and linked QTLs for quantitative traits in bi-parental segregation populations.Computational and structural biotechnology journal · 2020Article
- Exploring the Link Between Additive Heritability and Prediction Accuracy From a Ridge Regression Perspective.Frontiers in genetics · 2020Article
- Training Population Optimization for Genomic Selection.The plant genome · 2019Article
- Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.Scientific reports · 2019Article
- An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2.Briefings in bioinformatics · 2019Article
- Polygenic prediction via Bayesian regression and continuous shrinkage priors.Nature communications · 2019Article
- Genomic prediction using an iterative conditional expectation algorithm for a fast BayesC-like model.Genetica · 2018Article
119 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Molecular markers have been used to map quantitative trait loci. However, they are rarely used to evaluate effects of chromosome segments of the entire genome. The original interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic effects of the entire genome because they require evaluation of multiple models and model selection. Here we present a Bayesian regression method to simultaneously estimate genetic effects associated with markers of the entire genome. With the Bayesian method, we were able to handle situations in which the number of effects is even larger than the number of observations. The key to the success is that we allow each marker effect to have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, we were able to handle a large number of markers and most of the markers may have negligible effects. As a result, it is possible to evaluate the distribution of the marker effects. Using data from the North American Barley Genome Mapping Project in double-haploid barley, we found that the distribution of gene effects follows closely an L-shaped Gamma distribution, which is in contrast to the bell-shaped Gamma distribution when the gene effects were estimated from interval mapping. In addition, we show that the Bayesian method serves as an alternative or even better QTL mapping method because it produces clearer signals for QTL. Similar results were found from simulated data sets of F(2) and backcross (BC) families.
Indexed as
Identifiers
What OpenQuestion holds
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.