<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Lyu C</submitter><funding>National Heart, Lung and Blood Institute</funding><funding>Eunice Kennedy Shriver National Institute of Child Health and Human Development</funding><funding>NICHD NIH HHS</funding><funding>NIDCR NIH HHS</funding><funding>National Institute of Dental and Craniofacial Research under award number</funding><funding>NIEHS NIH HHS</funding><funding>NHLBI NIH HHS</funding><pagination>3853-3862</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9364381</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>38(16)</volume><pubmed_abstract>&lt;h4>Motivation&lt;/h4>CpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another.&lt;h4>Results&lt;/h4>We propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through si</pubmed_abstract><journal>Bioinformatics (Oxford, England)</journal><pubmed_title>Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.</pubmed_title><pmcid>PMC9364381</pmcid><funding_grant_id>R01 HD039054</funding_grant_id><funding_grant_id>R03 HD092854</funding_grant_id><funding_grant_id>R03DE025646</funding_grant_id><funding_grant_id>K01 HL140333</funding_grant_id><funding_grant_id>R01HD039054</funding_grant_id><funding_grant_id>R03 DE025646</funding_grant_id><funding_grant_id>R03HD092854</funding_grant_id><funding_grant_id>P30 ES030285</funding_grant_id><funding_grant_id>K01HL140333</funding_grant_id><funding_grant_id>R03 DE024198</funding_grant_id><pubmed_authors>Witte JS</pubmed_authors><pubmed_authors>Chen Z</pubmed_authors><pubmed_authors>Liu N</pubmed_authors><pubmed_authors>Huang M</pubmed_authors><pubmed_authors>Lyu C</pubmed_authors><pubmed_authors>Li M</pubmed_authors><pubmed_authors>Lupo PJ</pubmed_authors><pubmed_authors>Tycko B</pubmed_authors><pubmed_authors>Hobbs CA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.</name><description>&lt;h4>Motivation&lt;/h4>CpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another.&lt;h4>Results&lt;/h4>We propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through si</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Aug</publication><modification>2025-04-21T22:12:48.043Z</modification><creation>2025-04-21T22:12:48.043Z</creation></dates><accession>S-EPMC9364381</accession><cross_references><pubmed>35781319</pubmed><doi>10.1093/bioinformatics/btac443</doi></cross_references></HashMap>