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A population-based approach for gene prioritization in understanding complex traits

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Abstract

Gene prioritization is the process of determining which variants and genes identified in genetic analyses are likely to cause a disease or a variation in a phenotype. For many genes, neither in vitro nor in vivo testing is available, thus assessing their pathogenic role could be challenging, leading to false-positive or false-negative results. In this paper, we propose an innovative score of gene prioritization based on the population of interest. We introduce the concept of singleton-cohort variants (SC variant), a variant that has allele count equal to one in the cohort under study. The difference between the normalized count of SC variants in the coding region and the normalized count of SC variants in the non-coding region should give a hint regarding the level of constraints for that gene in a specific population. This scoring system is negative when there are constraints that allow the presence of SC variants only in the non-coding region; on the contrary, it is positive when there are no constraints. A complimentary score is the sum of SC variants normalized count in both coding and non-coding regions, which could be used as a proxy of positive or strong purifying selection in a specific population. Our methodology showed a high level of constraining for genes such as USP34 in all subpopulations tested (1000 G dataset). In contrast, some genes showed a high negative score only in specific populations, e.g., MYT1L in Europeans, UBR5 in East Asians, and FBXO11 in Africans.

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Abbreviations

SC variants:

Singleton-cohort variants

SC_cds:

Normalized singleton-cohort variant count in the gene coding region

SC_ncds:

Normalized singleton-cohort variant count in the gene coding region

DSC score:

Delta singleton-cohort variant score

SSC score:

Sum singleton-cohort variant score

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Acknowledgements

A sincere thank you to Veronika Collovati and Eleonora Bernucci for proofreading this manuscript. We would like to thank the reviewers for their insightful comments.

Funding

This research was funded by the Italian Ministry of Health (5 × 1000 to Institute for Maternal and Child Health IRCCS “Burlo Garofolo”). The funders had no role in the design of the study, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Institute for Maternal and Child Health IRCCS Burlo Garofolo, Via dell’Istria 65/1, 34137, Trieste, Italy

    Massimo Mezzavilla, Massimiliano Cocca & Paolo Gasparini

  2. University of Padova, Padua, Italy

    Francesca Guidolin

  3. Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy

    Paolo Gasparini

Authors
  1. Massimo Mezzavilla
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  2. Massimiliano Cocca
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  3. Francesca Guidolin
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  4. Paolo Gasparini
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Correspondence to Massimo Mezzavilla.

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Mezzavilla, M., Cocca, M., Guidolin, F. et al. A population-based approach for gene prioritization in understanding complex traits. Hum Genet 139, 647–655 (2020). https://doi.org/10.1007/s00439-020-02152-4

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  • DOI: https://doi.org/10.1007/s00439-020-02152-4

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