ArticleHeredity2012
Generalized linear mixed models for mapping multiple quantitative trait loci.
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.
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Who cites it
6 citing papers in PubMed.
- Brain-Derived Neurotrophic Factor Gene Polymorphism Predicts Response to Continuous Theta Burst Stimulation in Chronic Stroke Patients.Neuromodulation : journal of the International Neuromodulation Society · 2022Article
- Statistical power in genome-wide association studies and quantitative trait locus mapping.Heredity · 2019Article
- MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL.Statistica Sinica · 2019Article
- Article
- Genomic prediction of dichotomous traits with Bayesian logistic models.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2013Article
- An efficient hierarchical generalized linear mixed model for mapping QTL of ordinal traits in crop cultivars.PloS one · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many biological traits are discretely distributed in phenotype but continuously distributed in genetics because they are controlled by multiple genes and environmental variants. Due to the quantitative nature of the genetic background, these multiple genes are called quantitative trait loci (QTL). When the QTL effects are treated as random, they can be estimated in a single generalized linear mixed model (GLMM), even if the number of QTL may be larger than the sample size. The GLMM in its original form cannot be applied to QTL mapping for discrete traits if there are missing genotypes. We examined two alternative missing genotype-handling methods: the expectation method and the overdispersion method. Simulation studies show that the two methods are efficient for multiple QTL mapping (MQM) under the GLMM framework. The overdispersion method showed slight advantages over the expectation method in terms of smaller mean-squared errors of the estimated QTL effects. The two methods of GLMM were applied to MQM for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets.
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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.