ArticleScientific reports2016
Mapping small-effect and linked quantitative trait loci for complex traits in backcross or DH populations via a multi-locus GWAS methodology.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 57 citations in OpenAlex.
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- A novel trait to reduce the mechanical damage of peach fruits at harvest: The first genetic dissection study for peduncle length.Molecular breeding : new strategies in plant improvement · 2025Article
- BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies.Genomics, proteomics & bioinformatics · 2024Article
- Finding stable and closely linked QTLs against spot blotch in different planting dates during the adult stage in barley.Scientific reports · 2024Article
- Multi-locus genome-wide association study of fusarium head blight in relation to days to anthesis and plant height in a spring wheat association panel.Frontiers in plant science · 2023Article
- A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations.Computational and structural biotechnology journal · 2023Article
- 4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs.Biotechnology for biofuels and bioproducts · 2022Article
- A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an FPlant communications · 2022Article
- postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification.BMC research notes · 2022Article
- Identification and analysis of oil candidate genes reveals the molecular basis of cottonseed oil accumulation in Gossypium hirsutum L.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Genetic dissection of cassava brown streak disease in a genomic selection population.Frontiers in plant science · 2022Article
- Domestication and improvement genes reveal the differences of seed size- and oil-related traits in soybean domestication and improvement.Computational and structural biotechnology journal · 2022Article
- Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS.Scientific reports · 2021Article
- Identification of Main-Effect and Environmental Interaction QTL and Their Candidate Genes for Drought Tolerance in a Wheat RIL Population Between Two Elite Spring Cultivars.Frontiers in genetics · 2021Article
- QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize.Scientific reports · 2020Article
- QTL Mapping of Kernel Traits and Validation of a Major QTL for Kernel Length-Width Ratio Using SNP and Bulked Segregant Analysis in Wheat.Scientific reports · 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
- Identification of QTNs Controlling 100-Seed Weight in Soybean Using Multilocus Genome-Wide Association Studies.Frontiers in genetics · 2020Article
- Mapping dynamic QTL dissects the genetic architecture of grain size and grain filling rate at different grain-filling stages in barley.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Composite interval mapping (CIM) is the most widely-used method in linkage analysis. Its main feature is the ability to control genomic background effects via inclusion of co-factors in its genetic model. However, the result often depends on how the co-factors are selected, especially for small-effect and linked quantitative trait loci (QTL). To address this issue, here we proposed a new method under the framework of genome-wide association studies (GWAS). First, a single-locus random-SNP-effect mixed linear model method for GWAS was used to scan each putative QTL on the genome in backcross or doubled haploid populations. Here, controlling background via selecting markers in the CIM was replaced by estimating polygenic variance. Then, all the peaks in the negative logarithm P-value curve were selected as the positions of multiple putative QTL to be included in a multi-locus genetic model, and true QTL were automatically identified by empirical Bayes. This called genome-wide CIM (GCIM). A series of simulated and real datasets was used to validate the new method. As a result, the new method had higher power in QTL detection, greater accuracy in QTL effect estimation, and stronger robustness under various backgrounds as compared with the CIM and empirical Bayes methods.
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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.