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Evaluating Runs of Homozygosity in Exome Sequencing Data - Utility in Disease Inheritance Model Selection and Variant Filtering

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Biomedical Engineering Systems and Technologies (BIOSTEC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 881))

Abstract

Runs of homozygosity (ROH) are regions consistently homozygous for genetic markers, which can occur throughout the human genome. Their size is dependent on the degree of shared parental ancestry, being longer in individuals descending from consanguineous marriages, or from inbred/isolated populations. Based on ROH existence, homozygosity mapping (HM) was developed as powerful tool for gene-discovery in human genetics. HM is based on the assumption that, through identity-by-descent, individuals affected by an autosomal recessive (AR) condition, are more likely to have homozygous markers surrounding the disease locus.

In this work, we reviewed some of the algorithms and bioinformatics tools available for HM and ROH detection, with special emphasis on those than can be applied to data from whole-exome sequencing (WES) data. Preliminary data is also shown demonstrating the relevance of performing ROH analysis, especially in sporadic cases. In this study, ROH from WES data of twelve unrelated patients was analyzed. Patients with AR diseases (n = 6) were subdivided into two groups: homozygous and compound heterozygous. ROH analysis was performed using the HomozygosityMapper software, varying the block length and collecting several parameters. Statistically significant differences between the two groups were identified for ROH total size and homozygosity score. The k-means clustering algorithm was then applied, where two clusters were identified, with statistically significant differences, corresponding to each predefined test group. Our results suggest that, in some cases, it may be possible to infer the most likely disease inheritance model from WES data alone, constituting a useful starting point for the subsequent variant filtering strategies.

J. Oliveira and R. Pereira—Equally contributing authors.

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Acknowledgements

The authors acknowledge support from: (i) Fundação para a Ciência e Tecnologia (FCT) [Grant ref.: PD/BD/105767/2014] (R.P.); (ii) Research grant attributed by “Fundo para a Investigação e Desenvolvimento do Centro Hospitalar do Porto” [Grant ref.: 336-13(196-DEFI/285-CES)] (J.O.). The work was also supported by the Institutions of the authors and in part by UMIB, which is funded by through FCT under the Pest-OE/SAU/UI0215/2014. The authors would like to thank the clinicians for patient referral.

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Authors and Affiliations

  1. Instituto de Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto, R. Jorge de Viterbo Ferreira nº 228, Porto, Portugal

    Jorge Oliveira, Rute Pereira & Mário Sousa

  2. Centro de Genética Médica Dr. Jacinto Magalhães, Centro Hospitalar do Porto, Praça Pedro Nunes nº 88, Porto, Portugal

    Jorge Oliveira & Rosário Santos

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  1. Jorge Oliveira
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  2. Rute Pereira
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  3. Rosário Santos
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  4. Mário Sousa
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Correspondence to Jorge Oliveira .

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Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Nathalia Peixoto

  2. Instituto Superior Técnico (IST), University of Lisbon, Lisbon, Portugal

    Margarida Silveira

  3. University of Nebraska at Omaha, Omaha, Nebraska, USA

    Hesham H. Ali

  4. University of Sao Paulo, Sao Carlos, SP, Brazil

    Carlos Maciel

  5. Utrecht University, Utrecht, The Netherlands

    Egon L. van den Broek

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Oliveira, J., Pereira, R., Santos, R., Sousa, M. (2018). Evaluating Runs of Homozygosity in Exome Sequencing Data - Utility in Disease Inheritance Model Selection and Variant Filtering. In: Peixoto, N., Silveira, M., Ali, H., Maciel, C., van den Broek, E. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2017. Communications in Computer and Information Science, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-319-94806-5_15

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  • DOI: https://doi.org/10.1007/978-3-319-94806-5_15

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