ArticleBioinformatics (Oxford, England)2019
Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies.
Article in Bioinformatics (Oxford, England), 2019. 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.
- Article
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- A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.Cell reports methods · 2025Article
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- Pervasive findings of directional selection realize the promise of ancient DNA to elucidate human adaptation.bioRxiv : the preprint server for biology · 2024Article
- DNA Methylation Carries Signatures of Sublethal Effects Under Thermal Stress in Loggerhead Sea Turtles.Evolutionary applications · 2024Article
- Mediterranean diet protects against a neuroinflammatory cortical transcriptome: Associations with brain volumetrics, peripheral inflammation, social isolation, and anxiety in nonhuman primates (Macaca fascicularis).Brain, behavior, and immunity · 2024Article
- SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data.PLoS genetics · 2023Article
- SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics.Genome biology · 2023Article
- A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data.Bioinformatics (Oxford, England) · 2023Article
- Permutation-based significance analysis reduces the type 1 error rate in bisulphite sequencing data analysis of human umbilical cord blood samples.Epigenetics · 2022Article
- Spatially aware dimension reduction for spatial transcriptomics.Nature communications · 2022Article
- SpaceX: gene co-expression network estimation for spatial transcriptomics.Bioinformatics (Oxford, England) · 2022Article
- Spatially informed cell-type deconvolution for spatial transcriptomics.Nature biotechnology · 2022Article
- Trade-offs of Linear Mixed Models in Genome-Wide Association Studies.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Effective and scalable single-cell data alignment with non-linear canonical correlation analysis.Nucleic acids research · 2022Article
- SpatialMap: Spatial Mapping of Unmeasured Gene Expression Profiles in Spatial Transcriptomic Data Using Generalized Linear Spatial Models.Frontiers in genetics · 2022Article
- Placental DNA methylation marks are associated with maternal depressive symptoms during early pregnancy.Neurobiology of stress · 2021Article
- Genetic prediction of complex traits with polygenic scores: a statistical review.Trends in genetics : TIG · 2021Review
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6 authors.
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Abstract
Motivation: Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets. Results: Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites. Availability and implementation: PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html. Supplementary information: Supplementary data are available at Bioinformatics online.
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