The landscape of expression and alternative splicing variation across human traits

García-Pérez, Raquel; Ramírez, José Miguel; Ripoll-Cladellas, Aida; Chazarra-Gil, Ruben; Oliveros, Winona; Soldatkina, Oleksandra; Bosio, Mattia; Rognon, Paul Joris; Capella-Gutierrez, Salvador; Calvo, Miquel; Reverter, Ferrán; Guigó, Roderic; Aguet, François; Ferreira, Pedro G.; Ardlie, Kristin; Melé, Marta

doi:10.1016/j.xgen.2022.100244

Cited by 22 publications

(18 citation statements)

References 145 publications

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“…Irrespective of the global expression levels of a gene, changes in the relative expression of its isoforms influence protein abundance, which in turn modulates cellular processes. Furthermore, we detect minimal overlap between differentially expressed genes and differentially alternatively spliced genes, indicating that the genetic control of splicing and transcription are independent, as previously observed [17,21,22,30,[132][133][134]. Indeed, across our Indonesian dataset, splicing and expression affect different pathways, as DE and DAS genes were enriched for distinct biological processes and molecular functions.…”

Section: Discussionsupporting

confidence: 77%

“…To identify differential alternative splicing between the three groups, we corrected PSI values for batch effects and other technical confounders using fractional regression [41, 42]. Each splicing event was fit with logit-transformed PSI values ( glm function in the R stats package (R v4.3.3), setting family = quasibinomial(‘logit’)).…”

Section: Methodsmentioning

confidence: 99%

“…However, LCLs are known to trend towards a homogeneous pattern of gene expression that blunts the effect of inter-individual diversity [29], and thus this number may provide a lower-bound estimate of the true amount of variation. Indeed, the majority of AS events across GTEx tissues have been recently attributed to differences in ancestry between individuals, rather than to other demographic drivers such as age, sex or BMI [30].…”

Section: Introductionmentioning

confidence: 99%

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Profiling genetically driven alternative splicing across the Indonesian Archipelago

Ibeh,

Kusuma,

Crenna Darusallam

et al. 2024

Preprint

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One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterised across human populations, and it is unclear how genetic and environmental variation contribute to alternative splicing. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using a hierarchical event-based statistical model, we detected over 1,000 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci; sQTLs), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity revealed that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute towards elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.

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Section: Discussionsupporting

confidence: 77%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Profiling genetically driven alternative splicing across the Indonesian Archipelago

Ibeh,

Kusuma,

Crenna Darusallam

et al. 2024

Preprint

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“…Looking across regions, LINC01597 is a newly identified sex DE gene of particular interest, as it shows similar male-increased expression patterns across all models to that of an allosomal gene despite not having homology to any known Y region (Figure 1F). This extreme sex bias can be seen in other large human genetic studies (García-Pérez et al, 2023; Reynolds and Niedbalski, 2023). This could be a novel example of a uniquely regulated pseudoallosomal gene that may have important function, considering its high relative expression in the brain (Fagerberg et al, 2014) and pituitary (Carithers et al, 2015).…”

Section: Discussionsupporting

confidence: 54%

Relationship between sex biases in gene expression and sex biases in autism and Alzheimer’s disease

Fass,

Mulvey,

Yang

et al. 2023

Preprint

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Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions. In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-seq datasets using both within-region and pan-regional frameworks. We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-cell data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case-control gene expression studies suggests a female molecular predisposition towards Alzheimer's disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis. Finally, we provide region specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences.

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“…However, it is well-established that batch effects, which may stem from variations in sample treatment prior to RNA-seq library preparation, can introduce spurious gene expression differences between samples and result in confounding factors 74 . We therefore conducted an impartial and systematic search for potential batch effects.…”

Section: Read Count Data Pre-processingmentioning

confidence: 99%

voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues

Schneider

Saraiva-Agostinho

Barbosa‐Morais

2022

Preprint

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We herein introduce voyAGEr, an online graphical interface to explore age-related gene expression alterations in 48 human tissues. voyAGEr offers a visualization and statistical toolkit for the finding and functional exploration of sex- and tissue-specific transcriptomic changes with age. In its conception, we developed a novel bioinformatics pipeline leveraging RNA sequencing data, from the GTEx project, for more than 700 individuals. voyAGEr reveals transcriptomic signatures of the known asynchronous aging between tissues, allowing the observation of tissue-specific age-periods of major transcriptional changes, that likely reflect so-called digital aging, associated with alterations in different biological pathways, cellular composition, and disease conditions. voyAGEr therefore supports researchers with no expertise in bioinformatics in elaborating, testing and refining their hypotheses on the molecular nature of human aging and its association with pathologies, thereby also assisting in the discovery of novel therapeutic targets. voyAGEr is freely available at https://compbio.imm.medicina.ulisboa.pt/voyAGEr .

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The landscape of expression and alternative splicing variation across human traits

Cited by 22 publications

References 145 publications

Profiling genetically driven alternative splicing across the Indonesian Archipelago

Profiling genetically driven alternative splicing across the Indonesian Archipelago

Relationship between sex biases in gene expression and sex biases in autism and Alzheimer’s disease

voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues

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