ArticleGenetics, selection, evolution : GSE2018
Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis.
Article in Genetics, selection, evolution : GSE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 21 citations in OpenAlex.
- Bayesian Genome-Wide Association Study of Feed Efficiency Traits in Pigs.Animals : an open access journal from MDPI · 2026Article
- Application of Bayesian genomic prediction methods to genome-wide association analyses.Genetics, selection, evolution : GSE · 2022Review
- Genomic prediction using a reference population of multiple pure breeds and admixed individuals.Genetics, selection, evolution : GSE · 2021Article
- Genome-Wide Identification of Candidate Genes for Milk Production Traits in Korean Holstein Cattle.Animals : an open access journal from MDPI · 2021Article
- Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study.Frontiers in genetics · 2021Article
- Genomic regions influencing intramuscular fat in divergently selected rabbit lines.Animal genetics · 2020Article
- Assessing Accuracy of Genomic Predictions for Resistance to Infectious Hematopoietic Necrosis Virus With Progeny Testing of Selection Candidates in a Commercial Rainbow Trout Breeding Population.Frontiers in veterinary science · 2020Article
- Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits.Genetics · 2019Article
- Statistical power in genome-wide association studies and quantitative trait locus mapping.Heredity · 2019Article
- Genome-wide association analysis and accuracy of genome-enabled breeding value predictions for resistance to infectious hematopoietic necrosis virus in a commercial rainbow trout breeding population.Genetics, selection, evolution : GSE · 2019Article
- Five genomic regions have a major impact on fat composition in Iberian pigs.Scientific reports · 2019Article
- Two novel genomic regions associated with fearfulness in dogs overlap human neuropsychiatric loci.Translational psychiatry · 2019Article
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3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPopulation stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method.
methodsWe conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison.
resultsSMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate.
conclusionsThis study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.
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