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International Symposium on Bioinformatics Research and Applications

ISBRA 2017: Bioinformatics Research and Applications pp 150–161Cite as

PhAT-QTL: A Phase-Aware Test for QTL Detection

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

Abstract

Next generation sequencing based molecular assays have enabled unprecedented opportunities to quantitatively measure genome function. When combined with dense genetic data, quantitative trait loci (QTL) mapping of molecular traits is a fundamental tool for understanding the genetic basis of gene regulation. However, standard computational approaches for QTL mapping ignore the diploid nature of human genomes, testing for association between genotype and the total counts of sequencing reads mapping to both alleles at each genomic feature. In this work, we develop a new phase-aware test for QTL analysis (PhAT-QTL) leveraging the inherent single nucleotide resolution of sequencing reads to associate the alleles of each marker with the allele-specific counts (ASC) at a genomic feature. Through simulations, we show PhAT-QTL achieves increased power relative to standard genotype-based tests as a function of the number of heterozygotes for a given marker, the noise correlation between haplotypes, and the number of samples with detectable allele-specific counts at a genomic feature. Simulations further show that phasing error and error in quantifying ASC results in a loss of power as opposed to bias. Read simulations on varying haplotype structures (simulated from 1000 Genomes phased genomes) demonstrate that PhAT-QTL is able to detect 20% more QTLs while maintaining the same false positive rate as previous approaches. Applied to RNA-sequencing data, PhAT-QTL achieves similar performance as previous phase-aware methods in detecting cis expression QTLs (cis-eQTLs) but at a fraction of the computational cost.

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Authors and Affiliations

  1. UCSF Biological and Medical Informatics Graduate Program, San Francisco, USA

    Meena Subramaniam

  2. UCSF Department of Medicine, San Francisco, USA

    Meena Subramaniam & Noah Zaitlen

  3. UCSF Department of Biostatistics and Epidemiology, San Francisco, USA

    Meena Subramaniam & Jimmie Ye

  4. UCSF Department of Bioengineering and Therapeutic Sciences, San Francisco, USA

    Meena Subramaniam & Jimmie Ye

  5. UCSF Institute for Human Genetics, San Francisco, USA

    Meena Subramaniam, Noah Zaitlen & Jimmie Ye

Authors
  1. Meena Subramaniam
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  2. Noah Zaitlen
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  3. Jimmie Ye
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Correspondence to Jimmie Ye .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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© 2017 Springer International Publishing AG

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Subramaniam, M., Zaitlen, N., Ye, J. (2017). PhAT-QTL: A Phase-Aware Test for QTL Detection. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-59575-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

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