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HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074024/ https://www.ncbi.nlm.nih.gov/pubmed/37033465 http://dx.doi.org/10.1093/bioadv/vbad038 |
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author | Yang, Yaoling Lawson, Daniel John |
author_facet | Yang, Yaoling Lawson, Daniel John |
author_sort | Yang, Yaoling |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10074024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-100740242023-04-06 HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype Yang, Yaoling Lawson, Daniel John Bioinform Adv Application Note 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. Oxford University Press 2023-03-23 /pmc/articles/PMC10074024/ /pubmed/37033465 http://dx.doi.org/10.1093/bioadv/vbad038 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Application Note Yang, Yaoling Lawson, Daniel John HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype |
title | HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype |
title_full | HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype |
title_fullStr | HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype |
title_full_unstemmed | HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype |
title_short | HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype |
title_sort | htrx: an r package for learning non-contiguous haplotypes associated with a phenotype |
topic | Application Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074024/ https://www.ncbi.nlm.nih.gov/pubmed/37033465 http://dx.doi.org/10.1093/bioadv/vbad038 |
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