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HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype.
Yang, Yaoling; Lawson, Daniel John.
Afiliação
  • Yang Y; Department of Statistical Science, School of Mathematics, University of Bristol, Bristol BS8 1UG, UK.
  • Lawson DJ; Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK.
Bioinform Adv ; 3(1): vbad038, 2023.
Article em En | MEDLINE | ID: mdl-37033465
ABSTRACT

Summary:

Haplotype Trend Regression with eXtra flexibility (HTRX) is an R package to learn sets of interacting features that explain variance in a phenotype. Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits and diseases, but finding the true causal signal from a high linkage disequilibrium block is challenging. We focus on the simpler task of quantifying the total variance explainable not just with main effects but also interactions and tagging, using haplotype-based associations. HTRX identifies haplotypes composed of non-contiguous SNPs associated with a phenotype and can naturally be performed on regions with a GWAS hit before or after fine-mapping. To reduce the space and computational complexity when investigating many features, we constrain the search by growing good feature sets using 'Cumulative HTRX', and limit the maximum complexity of a feature set. As the computational time scales linearly with the number of SNPs, HTRX has the potential to be applied to large chromosome regions. Availability and implementation HTRX is implemented in R and is available under GPL-3 licence from CRAN (https//cran.r-project.org/web/packages/HTRX/readme/README.html). The development version is maintained on GitHub (https//github.com/YaolingYang/HTRX). Contact yaoling.yang@bristol.ac.uk. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article