OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data

Haselimashhadi, Hamed; Mason, Jeremy; Mallon, Ann‐Marie; Smedley, Damian; Meehan, Terrence F.; Parkinson, Helen

doi:10.1371/journal.pone.0242933

Cited by 15 publications

(12 citation statements)

References 37 publications

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“…To do this, we used a subset of the genes for which we had continuous measurements (Supp Table 3) to calculate the Euclidean distance from the individual gene-trait associations. The effect size is calculated using the Cohen method (Haselimashhadi et al 2020). Our results based on the extensive IMPC data set are consistent with previous observations.…”

Section: Detecting Pleiotropysupporting

confidence: 88%

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

et al. 2021

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Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

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Section: Detecting Pleiotropysupporting

confidence: 88%

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

et al. 2021

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“…In line with [3], the sexual dimorphism effect is tested for all 22 haematology traits, independently for WT mice from each of the 11 centres, corresponding to the same mouse strain and metadata group split. We used a windowed linear mixed model described in [26,27] and implemented in the software R [28], packages OpenStats and SmoothWin [29,30]. As in [3], Sex and Body Weight in the fixed effect terms and Batch (the date when the test is performed on mice) in the random effect term.…”

Section: Resultsmentioning

confidence: 99%

A consensus score to combine inferences from multiple centres

Mashhadi

Babalola

Wilson

et al. 2022

Preprint

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Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators are challenging in design, data collection and inferences. This may lead to inconsistent results across the resources. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology.

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“…We then pruned the resulting data to only include ‘Successful’ analyses, and removed experiments that included the skull. To generate the Gpatch1 boxplot, we obtained raw data using from IMPC’s ‘statistical-raw-data’ SOLR database for Gpatch1 , and analyzed the data in the same manner as IMPC, using the ‘OpenStats’ R package (version 1.0.2), using the method = ‘MM’ and MM_BodyWeightIncluded = TRUE arguments ( Haselimashhadi et al, 2020 ). Finally, mouse genes were converted to their human syntenic counterparts using Ensembl’s ‘hsapiens_gene_ensembl’ and ‘mmusculus_gene_ensembl’ datasets through biomaRt.…”

Section: Methodsmentioning

confidence: 99%

Transcriptome-wide association study and eQTL colocalization identify potentially causal genes responsible for human bone mineral density GWAS associations

Al‐Barghouthi

Rosenow

et al. 2022

eLife

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Genome-wide association studies (GWASs) for bone mineral density (BMD) in humans have identified over 1100 associations to date. However, identifying causal genes implicated by such studies has been challenging. Recent advances in the development of transcriptome reference datasets and computational approaches such as transcriptome-wide association studies (TWASs) and expression quantitative trait loci (eQTL) colocalization have proven to be informative in identifying putatively causal genes underlying GWAS associations. Here, we used TWAS/eQTL colocalization in conjunction with transcriptomic data from the Genotype-Tissue Expression (GTEx) project to identify potentially causal genes for the largest BMD GWAS performed to date. Using this approach, we identified 512 genes as significant using both TWAS and eQTL colocalization. This set of genes was enriched for regulators of BMD and members of bone relevant biological processes. To investigate the significance of our findings, we selected PPP6R3, the gene with the strongest support from our analysis which was not previously implicated in the regulation of BMD, for further investigation. We observed that Ppp6r3 deletion in mice decreased BMD. In this work, we provide an updated resource of putatively causal BMD genes and demonstrate that PPP6R3 is a putatively causal BMD GWAS gene. These data increase our understanding of the genetics of BMD and provide further evidence for the utility of combined TWAS/colocalization approaches in untangling the genetics of complex traits.

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OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data

Cited by 15 publications

References 37 publications

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

A consensus score to combine inferences from multiple centres

Transcriptome-wide association study and eQTL colocalization identify potentially causal genes responsible for human bone mineral density GWAS associations

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