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Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for Heritability

Regev Schweiger,Eyal Fisher,Elior Rahmani,Liat Shenhav,Saharon Rosset,E. Halperin

Published 2017 in Annual International Conference on Research in Computational Molecular Biology

ABSTRACT

Abstract Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense single-nucleotide polymorphism (SNP)-heritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard errors (SEs), which rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals (CIs). In addition, for larger data sets (e.g., tens of thousands of individuals), the construction of SEs itself may require considerable time, as it requires expensive matrix inversions and multiplications. Here, we present FIESTA (Fast confidence IntErvals using STochastic Approximation), a method for constructing accurate CIs. FIESTA is based on parametric bootstrap sampling, and, therefore, avoids unjustified assumptions on the distribution of the heritability estimator. FIESTA uses stochastic approximation techniques, which accelerate the construction of CIs by several orders of magnitude, compared with previous approaches as well as to the analytical approximation used by SEs. FIESTA builds accurate CIs rapidly, for example, requiring only several seconds for data sets of tens of thousands of individuals, making FIESTA a very fast solution to the problem of building accurate CIs for heritability for all data set sizes.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    Annual International Conference on Research in Computational Molecular Biology

  • Publication date

    2017-05-03

  • Fields of study

    Biology, Mathematics, Computer Science, Medicine

  • Identifiers
    DOI 10.1089/cmb.2018.0047PMID 29932739
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar, PubMed

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