ArticleNature communications2014
Warped linear mixed models for the genetic analysis of transformed phenotypes.
Article in Nature communications, 2014. 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.
- Optimizing phenotype scale improves genetic analyses in large-scale biobanks.bioRxiv : the preprint server for biology · 2026Article
- Integrative genomics ofbioRxiv : the preprint server for biology · 2026Article
- Genome-Wide Association Analyses Reveal the Genetic Basis of EMS Mutagenesis Efficiency in Rice.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026Article
- Semiparametric efficient estimation of small genetic effects in large-scale population cohorts.Biostatistics (Oxford, England) · 2025Article
- Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes.Communications biology · 2024Article
- Unraveling the genetics underlying micronutrient signatures of diversity panel present in brown rice through genome-ionome linkages.The Plant journal : for cell and molecular biology · 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, prediction and heritability in bacteria with application toNAR genomics and bioinformatics · 2022Article
- DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.Nature communications · 2022Article
- OsTPR boosts the superior grains through increase in upper secondary rachis branches without incurring a grain quality penalty.Plant biotechnology journal · 2021Article
- FLOURY ENDOSPERM19 encoding a class I glutamine amidotransferase affects grain quality in rice.Molecular breeding : new strategies in plant improvement · 2021Article
- Genome-wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness.Plant biotechnology journal · 2021Article
- Article
- Genome-Wide Association Mapping Identifies Novel Panicle Morphology Loci and Candidate Genes in Sorghum.Frontiers in plant science · 2021Article
- Identification of resistance loci in Chinese and Canadian canola/rapeseed varieties against Leptosphaeria maculans based on genome-wide association studies.BMC genomics · 2020Article
- Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes.BMC bioinformatics · 2020Article
- Integrating a genome-wide association study with a large-scale transcriptome analysis to predict genetic regions influencing the glycaemic index and texture in rice.Plant biotechnology journal · 2019Article
- Reverse GWAS: Using genetics to identify and model phenotypic subtypes.PLoS genetics · 2019Article
- Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests.Nature communications · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Linear mixed models (LMMs) are a powerful and established tool for studying genotype-phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction.
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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.