Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants

Regan, Kelly; Wang, Kanix; Doughty, Emily; Li, Haiquan; Lǐ, Jiànróng; Lee, Younghee; Kann, Maricel G.; Lussier, Yves A.

doi:10.1136/amiajnl-2011-000656

Cited by 16 publications

(12 citation statements)

References 57 publications

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“…We summarize the overlap and functional similarity between detected pathways from the three methods and the GS in Table 2 . Functional similarity between GO-BP was determine at a conservative cutoff of 0.7 using information theoretic similarity (ITS) methods we previously validated ( Gardeux et al , 2014a , b ; Li et al , 2012 ; Regan et al , 2012 ; Tao et al , 2007 ).…”

Section: Methodsmentioning

confidence: 99%

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival

Schissler

Gardeux

et al. 2015

Bioinformatics

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Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change).Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences).Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome).Availability and implementation: http://www.lussierlab.net/publications/N-of-1-pathways.Contact: yves@email.arizona.edu or piegorsch@math.arizona.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival

Schissler

Gardeux

et al. 2015

Bioinformatics

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“…We have previously shown that a GO-ITS score ≥ 0.7 robustly corresponds to highly similar GO terms using different computational biological validations: protein interaction [27, 28], human genetics [29], and Genome-Wide Association Studies [30]. GO-ITS was calculated on each distinct pair among the 3234 GO terms of size ≥15 and ≤500, leading to 10,458,756 pairs of which 59,577 have a GO-ITS ≥ 0.7 (≈5.6 out of 1,000).…”

Section: Methodsmentioning

confidence: 99%

Towards a PBMC “virogram assay” for precision medicine: Concordance between ex vivo and in vivo viral infection transcriptomes

Gardeux

Bosco

Lǐ

et al. 2015

Journal of Biomedical Informatics

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Background Understanding individual patient host-response to viruses is key to designing optimal personalized therapy. Unsurprisingly, in vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obviously limitations stemming from ethical considerations of the clinical risk. Objective In this rhinovirus study, we first hypothesized that ex vivo human cells response to virus can serve as proxy for otherwise controversial in vivo human experimentation. We further hypothesized that the N-of-1-pathways framework, previously validated in cancer, can be effective in understanding the more subtle individual transcriptomic response to viral infection. Method N-of-1-pathways computes a significance score for a given list of gene sets at the patient level, using merely the ‘omics profiles of two paired samples as input. We extracted the peripheral blood mononuclear cells (PBMC) of four human subjects, aliquoted in two paired samples, one subjected to ex vivo rhinovirus infection. Their dysregulated genes and pathways were then compared to those of 9 human subjects prior and after intranasal inoculation in vivo with rhinovirus. Additionally, we developed the Similarity Venn Diagram, a novel visualization method that goes beyond conventional overlap to show the similarity between two sets of qualitative measures. Results We evaluated the individual N-of-1-pathways results using two established cohort-based methods: GSEA and enrichment of differentially expressed genes. Similarity Venn Diagrams and individual patient ROC curves illustrate and quantify that the in vivo dysregulation is recapitulated ex vivo both at the gene and pathway level (p-values≤0.004). Conclusion We established the first evidence that an interpretable dynamic transcriptome metric, conducted as an ex vivo assays for a single subject, has the potential to predict individualized response to infectious disease without the clinical risks otherwise associated to in vivo challenges. These results serve as foundational work for personalized “virograms”.

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“…We have previously shown that an ITS score ≥ 0.7 robustly corresponds to highly similar GO terms using different computational biological validations: protein interaction [22,23], human genetics [24], and Genome-Wide Association Studies [25]. …”

Section: Methodsmentioning

confidence: 99%

Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study

et al. 2014

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BackgroundGenome-wide transcriptome profiling generated by microarray and RNA-Seq often provides deregulated genes or pathways applicable only to larger cohort. On the other hand, individualized interpretation of transcriptomes is increasely pursued to improve diagnosis, prognosis, and patient treatment processes. Yet, robust and accurate methods based on a single paired-sample remain an unmet challenge.Methods"N-of-1-pathways" translates gene expression data profiles into mechanism-level profiles on single pairs of samples (one p-value per geneset). It relies on three principles: i) statistical universe is a single paired sample, which serves as its own control; ii) statistics can be derived from multiple gene expression measures that share common biological mechanisms assimilated to genesets; iii) semantic similarity metric takes into account inter-mechanisms' relationships to better assess commonality and differences, within and cross study-samples (e.g. patients, cell-lines, tissues, etc.), which helps the interpretation of the underpinning biology.ResultsIn the context of underpowered experiments, N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within-and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets (Odds-Ratios≥13, p-values≤1 × 10−5), such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples.ConclusionsN-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods. Further, it identifies unique sample/ patient mechanisms, a requirement for precision medicine.

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Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants

Cited by 16 publications

References 57 publications

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival

Towards a PBMC “virogram assay” for precision medicine: Concordance between ex vivo and in vivo viral infection transcriptomes

Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study

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