Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation

Tao Wang, Yongzhuang Liu, Quanwei Yin, Jiaquan Geng, Jin Chen, Xipeng Yin, Yongtian Wang, Xuequn Shang, Chunwei Tian, Yadong Wang, Jiajie Peng

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Quantitative trait locus (QTL) analyses of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), have been widely used to infer the functional effects of genome variants. However, the QTL discovery is largely restricted by the limited study sample size, which demands higher threshold of minor allele frequency and then causes heavy missing molecular trait-variant associations. This happens prominently in single-cell level molecular QTL studies because of sample availability and cost. It is urgent to propose a method to solve this problem in order to enhance discoveries of current molecular QTL studies with small sample size. In this study, we presented an efficient computational framework called xQTLImp to impute missing molecular QTL associations. In the local-region imputation, xQTLImp uses multivariate Gaussian model to impute the missing associations by leveraging known association statistics of variants and the linkage disequilibrium (LD) around. In the genome-wide imputation, novel procedures are implemented to improve efficiency, including dynamically constructing a reused LD buffer, adopting multiple heuristic strategies and parallel computing. Experiments on various multiomic bulk and single-cell sequencing-based QTL datasets have demonstrated high imputation accuracy and novel QTL discovery ability of xQTLImp. Finally, a C++ software package is freely available at https://github.com/stormlovetao/QTLIMP.

Original languageEnglish
Article numberbbab370
JournalBriefings in Bioinformatics
Volume23
Issue number1
DOIs
StatePublished - Jan 1 2022

Bibliographical note

Funding Information:
National Natural Science Foundation of China (6210071334, 62072376); National Key Research and Development Programof China (2017YFC0907503, 2017YFC1201201); Aeronautical Science Foundation of China (2018ZD53047); Fundamental Research Funds for the Central Universities of China (G2021KY05112).

Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved.

Keywords

  • QTL analysis
  • imputation framework
  • single-cell
  • small sample size
  • summary statistics

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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