HiGwas: How to compute longitudinal GWAS data in population designs

Zhong Wang, Nating Wang, Zilu Wang, Libo Jiang, Yaqun Wang, Jiahan Li, Rongling Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Summary: Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p ≪<FOR VERIFICATION> n. Although the integration of longitudinal information can significantly enhance a GWAS's power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiGwas, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiGwas provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiGwas through real data analysis and vignette document in the package.

Original languageEnglish (US)
Pages (from-to)4222-4224
Number of pages3
Issue number14
StatePublished - Jul 15 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


Dive into the research topics of 'HiGwas: How to compute longitudinal GWAS data in population designs'. Together they form a unique fingerprint.

Cite this