ArticleScientific reports2015
Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A genome-wide association study to elucidate the genetic basis of the cottonseed protein content in Upland cotton (Breeding science · 2026Article
- Integrating Multi-omics Analysis to Decipher the Genetic Basis of Heterosis in Xieyou9308.Rice (New York, N.Y.) · 2025Article
- Loss-of-function mutations in the fruit softening geneHorticulture research · 2025Article
- mmGEBLUP: an advanced genomic prediction scheme for genetic improvement of complex traits in crops through integrative analysis of major genes, polygenes, and genotype-environment interactions.Briefings in bioinformatics · 2024Article
- Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing.Genome biology · 2023Article
- Compressed variance component mixed model reveals epistasis associated with flowering inFrontiers in plant science · 2023Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits.BMC plant biology · 2022Article
- Identification of superior parents with high fiber quality using molecular markers and phenotypes based on a core collection of upland cotton (Molecular breeding : new strategies in plant improvement · 2022Article
- Multi-omics analysis dissects the genetic architecture of seed coat content in Brassica napus.Genome biology · 2022Article
- Speeding up eQTL scans in the BXD population using GPUs.G3 (Bethesda, Md.) · 2021Article
- Article
- Conditional and unconditional genome-wide association study reveal complicate genetic architecture of human body weight and impacts of smoking.Scientific reports · 2020Article
- Genome-wide association study of maize plant architecture using FPlant molecular biology · 2019Article
- Dissection of complicate genetic architecture and breeding perspective of cottonseed traits by genome-wide association study.BMC genomics · 2018Article
- Full genetic analysis for genome-wide association study of Fangji: a powerful approach for effectively dissecting the molecular architecture of personalized traditional Chinese medicine.Acta pharmacologica Sinica · 2018Review
- Haplotype-based allele mining in the Japan-MAGIC rice population.Scientific reports · 2018Article
- Quantitative Trait Transcripts Mapping Coupled with Expression Quantitative Trait Loci Mapping Reveal the Molecular Network Regulating the Apetalous Characteristic inFrontiers in plant science · 2018Article
- Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information.Scientific reports · 2017Article
- Genetic dissection of yield traits in super hybrid rice Xieyou9308 using both unconditional and conditional genome-wide association mapping.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precise prediction for genetic architecture of complex traits is impeded by the limited understanding on genetic effects of complex traits, especially on gene-by-gene (GxG) and gene-by-environment (GxE) interaction. In the past decades, an explosion of high throughput technologies enables omics studies at multiple levels (such as genomics, transcriptomics, proteomics, and metabolomics). The analyses of large omics data, especially two-loci interaction analysis, are very time intensive. Integrating the diverse omics data and environmental effects in the analyses also remain challenges. We proposed mixed linear model approaches using GPU (Graphic Processing Unit) computation to simultaneously dissect various genetic effects. Analyses can be performed for estimating genetic main effects, GxG epistasis effects, and GxE environment interaction effects on large-scale omics data for complex traits, and for estimating heritability of specific genetic effects. Both mouse data analyses and Monte Carlo simulations demonstrated that genetic effects and environment interaction effects could be unbiasedly estimated with high statistical power by using the proposed approaches.
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