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Vestal BE, Wynn E, Moore CM. lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models. BMC Bioinformatics 2022; 23:489. [DOI: 10.1186/s12859-022-05019-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 10/28/2022] [Indexed: 11/17/2022] Open
Abstract
Abstract
Background
Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity. This R package contains a set of utilities to fit linear mixed effects models to transformed RNA-Seq counts that properly account for this dependence when performing statistical analyses.
Results
In a simulation study comparing lmerSeq and two existing methodologies that also work with transformed RNA-Seq counts, we found that lmerSeq was comprehensively better in terms of nominal error rate control and statistical power.
Conclusions
Existing R packages for analyzing transformed RNA-Seq data with linear mixed models are limited in the variance structures they allow and/or the transformation methods they support. The lmerSeq package offers more flexibility in both of these areas and gave substantially better results in our simulations.
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Bastiaanse H, Henry IM, Tsai H, Lieberman M, Canning C, Comai L, Groover A. A systems genetics approach to deciphering the effect of dosage variation on leaf morphology in Populus. THE PLANT CELL 2021; 33:940-960. [PMID: 33793772 PMCID: PMC8226299 DOI: 10.1093/plcell/koaa016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/30/2020] [Indexed: 05/05/2023]
Abstract
Gene copy number variation is frequent in plant genomes of various species, but the impact of such gene dosage variation on morphological traits is poorly understood. We used a large population of Populus carrying genomically characterized insertions and deletions across the genome to systematically assay the effect of gene dosage variation on a suite of leaf morphology traits. A systems genetics approach was used to integrate insertion and deletion locations, leaf morphology phenotypes, gene expression, and transcriptional network data, to provide an overview of how gene dosage influences morphology. Dosage-sensitive genomic regions were identified that influenced individual or pleiotropic morphological traits. We also identified cis-expression quantitative trait loci (QTL) within these dosage QTL regions, a subset of which modulated trans-expression QTL as well. Integration of data types within a gene co-expression framework identified co-expressed gene modules that are dosage sensitive, enriched for dosage expression QTL, and associated with morphological traits. Functional description of these modules linked dosage-sensitive morphological variation to specific cellular processes, as well as candidate regulatory genes. Together, these results show that gene dosage variation can influence morphological variation through complex changes in gene expression, and suggest that frequently occurring gene dosage variation has the potential to likewise influence quantitative traits in nature.
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Affiliation(s)
- Héloïse Bastiaanse
- Present address: VIB Center for Plant Systems Biology, Ghent University, 9052 Ghent, Belgium
| | - Isabelle M Henry
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
| | - Helen Tsai
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
| | - Meric Lieberman
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
| | - Courtney Canning
- Pacific Southwest Research Station, US Forest Service, Davis, California 95618
| | - Luca Comai
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
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Ponsuksili S, Oster M, Reyer H, Hadlich F, Trakooljul N, Rodehutscord M, Camarinha-Silva A, Bennewitz J, Wimmers K. Genetic regulation and heritability of miRNA and mRNA expression link to phosphorus utilization and gut microbiome. Open Biol 2021; 11:200182. [PMID: 33593158 PMCID: PMC8061690 DOI: 10.1098/rsob.200182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Improved utilization of phytates and mineral phosphorus (P) in monogastric animals contributes significantly to preserving the finite resource of mineral P and mitigating environmental pollution. In order to identify pathways and to prioritize candidate genes related to P utilization (PU), the genomic heritability of 77 and 80 trait-dependent expressed miRNAs and mRNAs in 482 Japanese quail were estimated and eQTL (expression quantitative trait loci) were detected. In total, 104 miR-eQTL (microRNA expression quantitative traits loci) were associated with SNP markers (false discovery rate less than 10%) including 41 eQTL of eight miRNAs. Similarly, 944 mRNA-eQTL were identified at the 5% False discovery rate threshold, with 573 being cis-eQTL of 36 mRNAs. High heritabilities of miRNA and mRNA expression coincide with highly significant eQTL. Integration of phenotypic data with transcriptome and microbiome data of the same animals revealed genetic regulated mRNA and miRNA transcripts (SMAD3, CAV1, ENNPP6, ATP2B4, miR-148a-3p, miR-146b-5p, miR-16-5p, miR-194, miR-215-5p, miR-199-3p, miR-1388a-3p) and microbes (Candidatus Arthromitus, Enterococcus) that are associated with PU. The results reveal novel insights into the role of mRNAs and miRNAs in host gut tissue functions, which are involved in PU and other related traits, in terms of the genetic regulation and inheritance of their expression and in association with microbiota components.
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Affiliation(s)
- Siriluck Ponsuksili
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Michael Oster
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Henry Reyer
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Frieder Hadlich
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Nares Trakooljul
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Markus Rodehutscord
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
| | | | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
| | - Klaus Wimmers
- Leibniz Institute for Farm Animal Biology, Institute for Genome Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany.,Faculty of Agricultural and Environmental Sciences, University Rostock, 18059 Rostock, Germany
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Radcliffe RA, Dowell R, Odell AT, Richmond PA, Bennett B, Larson C, Kechris K, Saba LM, Rudra P, Wen S. Systems genetics analysis of the LXS recombinant inbred mouse strains:Genetic and molecular insights into acute ethanol tolerance. PLoS One 2020; 15:e0240253. [PMID: 33095786 PMCID: PMC7584226 DOI: 10.1371/journal.pone.0240253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/22/2020] [Indexed: 11/18/2022] Open
Abstract
We have been using the Inbred Long- and Short-Sleep mouse strains (ILS, ISS) and a recombinant inbred panel derived from them, the LXS, to investigate the genetic underpinnings of acute ethanol tolerance which is considered to be a risk factor for alcohol use disorders (AUDs). Here, we have used RNA-seq to examine the transcriptome of whole brain in 40 of the LXS strains 8 hours after a saline or ethanol "pretreatment" as in previous behavioral studies. Approximately 1/3 of the 14,184 expressed genes were significantly heritable and many were unique to the pretreatment. Several thousand cis- and trans-eQTLs were mapped; a portion of these also were unique to pretreatment. Ethanol pretreatment caused differential expression (DE) of 1,230 genes. Gene Ontology (GO) enrichment analysis suggested involvement in numerous biological processes including astrocyte differentiation, histone acetylation, mRNA splicing, and neuron projection development. Genetic correlation analysis identified hundreds of genes that were correlated to the behaviors. GO analysis indicated that these genes are involved in gene expression, chromosome organization, and protein transport, among others. The expression profiles of the DE genes and genes correlated to AFT in the ethanol pretreatment group (AFT-Et) were found to be similar to profiles of HDAC inhibitors. Hdac1, a cis-regulated gene that is located at the peak of a previously mapped QTL for AFT-Et, was correlated to 437 genes, most of which were also correlated to AFT-Et. GO analysis of these genes identified several enriched biological process terms including neuron-neuron synaptic transmission and potassium transport. In summary, the results suggest widespread genetic effects on gene expression, including effects that are pretreatment-specific. A number of candidate genes and biological functions were identified that could be mediating the behavioral responses. The most prominent of these was Hdac1 which may be regulating genes associated with glutamatergic signaling and potassium conductance.
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Affiliation(s)
- Richard A. Radcliffe
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder CO, United States of America
| | - Robin Dowell
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, United States of America
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, United States of America
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States of America
| | - Aaron T. Odell
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, United States of America
| | - Phillip A. Richmond
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, United States of America
| | - Beth Bennett
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Colin Larson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Laura M. Saba
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Pratyaydipta Rudra
- Department of Statistics, Oklahoma State University, Stillwater, OK, United States of America
| | - Shi Wen
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
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Vestal BE, Moore CM, Wynn E, Saba L, Fingerlin T, Kechris K. MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments. BMC Bioinformatics 2020; 21:375. [PMID: 32859148 PMCID: PMC7455910 DOI: 10.1186/s12859-020-03715-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 08/18/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference. RESULTS In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements. CONCLUSIONS Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.
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Affiliation(s)
- Brian E. Vestal
- Center for Genes, Environment and Health, National Jewish Health, 1400 Jackson St, Denver, 80206 CO USA
| | - Camille M. Moore
- Center for Genes, Environment and Health, National Jewish Health, 1400 Jackson St, Denver, 80206 CO USA
| | - Elizabeth Wynn
- Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO USA
| | - Laura Saba
- Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO USA
| | - Tasha Fingerlin
- Center for Genes, Environment and Health, National Jewish Health, 1400 Jackson St, Denver, 80206 CO USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO USA
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Quigley KM, Bay LK, van Oppen MJH. Genome-wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol Ecol 2020; 29:2176-2188. [PMID: 32453867 DOI: 10.1111/mec.15482] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 05/13/2020] [Indexed: 12/18/2022]
Abstract
Marine heat waves are increasing in magnitude, duration, and frequency as a result of climate change and are the principal global driver of mortality in reef-building corals. Resilience-based genetic management may increase coral heat tolerance, but it is unclear how temperature responses are regulated at the genome level and thus how corals may adapt to warming naturally or through selective breeding. Here we combine phenotypic, pedigree, and genomic marker data from colonies sourced from a warm reef on the Great Barrier Reef reproductively crossed with conspecific colonies from a cooler reef to produce combinations of warm purebreds and warm-cool hybrid larvae and juveniles. Interpopulation breeding created significantly greater genetic diversity across the coral genome compared to breeding between populations and maintained diversity in key regions associated with heat tolerance and fitness. High-density genome-wide scans of single nucleotide polymorphisms (SNPs) identified alleles significantly associated with larval families reared at 27.5°C (87-2,224 loci), including loci putatively associated with proteins involved in responses to heat stress (cell membrane formation, metabolism, and immune responses). Underlying genetics of these families explained 43% of PCoA multilocus variation in survival, growth, and bleaching responses at 27.5°C and 31°C at the juvenile stage. Genetic marker contribution to total variation in fitness traits (narrow-sense heritability) was high for survival but not for growth and bleaching in juveniles, with heritability of these traits being higher at 31°C relative to 27.5°C. While based on only a limited number of crosses, the mechanistic understanding presented here demonstrates that allele frequencies are affected by one generation of selective breeding, key information for the assessments of genetic intervention feasibility and modelling of reef futures.
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Affiliation(s)
- Kate M Quigley
- Australian Institute of Marine Science, Townsville, Qld, Australia
| | - Line K Bay
- Australian Institute of Marine Science, Townsville, Qld, Australia
| | - Madeleine J H van Oppen
- Australian Institute of Marine Science, Townsville, Qld, Australia.,School of BioSciences, The University of Melbourne, Parkville, Vic, Australia
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DOĞAN İ, DOGAN N. Kalıtım Derecesinin Tahmini ve İnsan Hastalıklarının/Özelliklerinin Kalıtsallığı. DÜZCE ÜNIVERSITESI SAĞLIK BILIMLERI ENSTITÜSÜ DERGISI 2020. [DOI: 10.33631/duzcesbed.679732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Abstract
Background micro RNA (miRNA) are important regulators of gene expression and may influence phenotypes and disease traits. The connection between genetics and miRNA expression can be determined through expression quantitative loci (eQTL) analysis, which has been extensively used in a variety of tissues, and in both human and model organisms. miRNA play an important role in brain-related diseases, but eQTL studies of miRNA in brain tissue are limited. We aim to catalog miRNA eQTL in brain tissue using miRNA expression measured on a recombinant inbred mouse panel. Because samples were collected without any intervention or treatment (naïve), the panel allows characterization of genetic influences on miRNAs’ expression levels. We used brain RNA expression levels of 881 miRNA and 1416 genomic locations to identify miRNA eQTL. To address multiple testing, we employed permutation p-values and subsequent zero permutation p-value correction. We also investigated the underlying biology of miRNA regulation using additional analyses, including hotspot analysis to search for regions controlling multiple miRNAs, and Bayesian network analysis to identify scenarios where a miRNA mediates the association between genotype and mRNA expression. We used addiction related phenotypes to illustrate the utility of our results. Results Thirty-eight miRNA eQTL were identified after appropriate multiple testing corrections. Ten of these miRNAs had target genes enriched for brain-related pathways and mapped to four miRNA eQTL hotspots. Bayesian network analysis revealed four biological networks relating genetic variation, miRNA expression and gene expression. Conclusions Our extensive evaluation of miRNA eQTL provides valuable insight into the role of miRNA regulation in brain tissue. Our miRNA eQTL analysis and extended statistical exploration identifies miRNA candidates in brain for future study.
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Rudra P, Shi WJ, Russell P, Vestal B, Tabakoff B, Hoffman P, Kechris K, Saba L. Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse. BMC Genomics 2018; 19:639. [PMID: 30157779 PMCID: PMC6114181 DOI: 10.1186/s12864-018-5004-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 08/08/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) are small non-coding RNAs that bind messenger RNAs and promote their degradation or repress their translation. There is increasing evidence of miRNAs playing an important role in alcohol related disorders. However, the role of miRNAs as mediators of the genetic effect on alcohol phenotypes is not fully understood. We conducted a high-throughput sequencing study to measure miRNA expression levels in alcohol naïve animals in the LXS panel of recombinant inbred (RI) mouse strains. We then combined the sequencing data with genotype data, microarry gene expression data, and data on alcohol-related behavioral phenotypes such as 'Drinking in the dark', 'Sleep time', and 'Low dose activation' from the same RI panel. SNP-miRNA-gene triplets with strong association within the triplet that were also associated with one of the 4 alcohol phenotypes were selected and a Bayesian network analysis was used to aggregate results into a directed network model. RESULTS We found several triplets with strong association within the triplet that were also associated with one of the alcohol phenotypes. The Bayesian network analysis found two networks where a miRNA mediates the genetic effect on the alcohol phenotype. The miRNAs were found to influence the expression of protein-coding genes, which in turn influences the quantitative phenotypes. The pathways in which these genes are enriched have been previously associated with alcohol-related traits. CONCLUSION This work enhances association studies by identifying miRNAs that may be mediating the association between genetic markers (SNPs) and the alcohol phenotypes. It suggests a mechanism of how genetic variants are affecting traits of interest through the modification of miRNA expression.
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Affiliation(s)
- Pratyaydipta Rudra
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, 80045 CO USA
| | - Wen J. Shi
- Department of Pharmacology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, 80045 CO USA
| | - Pamela Russell
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, 80045 CO USA
| | - Brian Vestal
- Center for Genes, Environment and Health, National Jewish Health, Denver, 80206 CO USA
| | - Boris Tabakoff
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, 80045 CO USA
| | - Paula Hoffman
- Department of Pharmacology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, 80045 CO USA
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, 80045 CO USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, 80045 CO USA
| | - Laura Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, 80045 CO USA
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