dc.contributor.author | Jiang, Z | |
dc.contributor.author | Zhang, H | |
dc.contributor.author | Ahearn, TU | |
dc.contributor.author | Garcia-Closas, M | |
dc.contributor.author | Chatterjee, N | |
dc.contributor.author | Zhu, H | |
dc.contributor.author | Zhan, X | |
dc.contributor.author | Zhao, N | |
dc.coverage.spatial | United States | |
dc.date.accessioned | 2023-08-04T13:37:07Z | |
dc.date.available | 2023-08-04T13:37:07Z | |
dc.date.issued | 2023-04-19 | |
dc.identifier.citation | Genetic Epidemiology, 2023, | en_US |
dc.identifier.issn | 0741-0395 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/5930 | |
dc.identifier.eissn | 1098-2272 | |
dc.identifier.eissn | 1098-2272 | |
dc.identifier.doi | 10.1002/gepi.22527 | |
dc.description.abstract | Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel set-based association analysis method, sequence kernel association test (SKAT)-MC, the sequence kernel association test for multicategorical outcomes (nominal or ordinal), which jointly evaluates the relationship between a set of variants (common and rare) and disease subtypes. Through comprehensive simulation studies, we showed that SKAT-MC effectively preserves the nominal type I error rate while substantially increases the statistical power compared to existing methods under various scenarios. We applied SKAT-MC to the Polish breast cancer study (PBCS), and identified gene FGFR2 was significantly associated with estrogen receptor (ER)+ and ER- breast cancer subtypes. We also investigated educational attainment using UK Biobank data ( N = 127 , 127 $N=127,127$ ) with SKAT-MC, and identified 21 significant genes in the genome. Consequently, SKAT-MC is a powerful and efficient analysis tool for genetic association studies with multicategorical outcomes. A freely distributed R package SKAT-MC can be accessed at https://github.com/Zhiwen-Owen-Jiang/SKATMC. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.language.iso | eng | en_US |
dc.publisher | WILEY | en_US |
dc.relation.ispartof | Genetic Epidemiology | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
dc.subject | SKAT | |
dc.subject | multicategorical data | |
dc.subject | the generalized logit model | |
dc.subject | the proportional odds model | |
dc.title | The sequence kernel association test for multicategorical outcomes. | en_US |
dc.type | Journal Article | |
dcterms.dateAccepted | 2023-03-30 | |
dc.date.updated | 2023-08-04T13:36:41Z | |
rioxxterms.version | VoR | en_US |
rioxxterms.versionofrecord | 10.1002/gepi.22527 | en_US |
rioxxterms.licenseref.startdate | 2023-04-19 | |
rioxxterms.type | Journal Article/Review | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/37078108 | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Genetics and Epidemiology | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Integrative Cancer Epidemiology | |
pubs.publication-status | Published online | |
pubs.publisher-url | http://dx.doi.org/10.1002/gepi.22527 | |
icr.researchteam | Integrative Cancer Epidem | en_US |
dc.contributor.icrauthor | Garcia-Closas, Montserrat | |
icr.provenance | Deposited by Mr Arek Surman on 2023-08-04. Deposit type is initial. No. of files: 1. Files: Genetic Epidemiology - 2023 - Jiang.pdf | |