ArticleHuman heredity2015
Integrating Multiple Correlated Phenotypes for Genetic Association Analysis by Maximizing Heritability.
Article in Human heredity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Imputing Phenotypes for Genome-wide Association Studies.American journal of human genetics · 2016Pooled it
- Optimized phenotype definitions boost GWAS power.PLoS computational biology · 2026Article
- Computationally efficient methods for estimating phenome-wide coheritability of multi-type phenotypes using biobank data.Communications biology · 2025Article
- Genome-wide association analysis of composite sleep health scores in 413,904 individuals.Communications biology · 2025Article
- Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction.Nature genetics · 2024Article
- Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases.medRxiv : the preprint server for health sciences · 2023Article
- Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering.Genetic epidemiology · 2020Article
- Genetic Advances in Chronic Obstructive Pulmonary Disease. Insights from COPDGene.American journal of respiratory and critical care medicine · 2019Review
- A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.Bioinformatics (Oxford, England) · 2019Article
- Assessing pleiotropy and mediation in genetic loci associated with chronic obstructive pulmonary disease.Genetic epidemiology · 2019Article
- Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions.Annals of human genetics · 2018Article
- Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits.PLoS genetics · 2018Article
- Joint testing and false discovery rate control in high-dimensional multivariate regression.Biometrika · 2018Article
- A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.Genetic epidemiology · 2018Article
- AraPheno: a public database for Arabidopsis thaliana phenotypes.Nucleic acids research · 2017Article
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6 authors.
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Abstract
Many correlated disease variables are analyzed jointly in genetic studies in the hope of increasing power to detect causal genetic variants. One approach involves assessing the relationship between each phenotype and each SNP individually and using a Bonferroni correction for the effective number of tests conducted. Alternatively, one can apply a multivariate regression or a dimension reduction technique, such as principal component analysis, and test for the association with the principal components of the phenotypes rather than the individual phenotypes. Inspired by the previous approaches of combining phenotypes to maximize heritability at individual SNPs, in this paper, we propose to construct a maximally heritable (MaxH) phenotype by taking advantage of the estimated total heritability and co-heritability. The heritability and co-heritability only need to be estimated once; therefore, our method is applicable to genome-wide scans. The MaxH phenotype is a linear combination of the individual phenotypes with increased heritability and power over the phenotypes being combined. Simulations show that the heritability and power achieved agree well with the theory for large samples and two phenotypes. We compare our approach with commonly used methods and assess both the heritability and the power of the MaxH phenotype. Moreover, we provide suggestions for how to choose the phenotypes for combination. An application of our approach to a GWAS on chronic obstructive pulmonary disease shows its practical relevance.
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