File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: An enhanced machine learning tool for cis‐eQTL mapping with regularization and confounder adjustments

TitleAn enhanced machine learning tool for cis‐eQTL mapping with regularization and confounder adjustments
Authors
Keywordscis‐eQTL mapping
gene expression
least‐squares kernel machine
multiple variants
penalized
Issue Date2020
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/35841
Citation
Genetic Epidemiology, 2020, Epub 2020-07-22 How to Cite?
AbstractMany expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false‐discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine‐learning algorithm, lasso least‐squares kernel machine (LSKM‐LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM‐LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state‐of‐the‐art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype‐Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least‐squares kernel machine, LSKM‐LASSO provides a powerful tool for eQTL mapping and phenotype prediction.
Persistent Identifierhttp://hdl.handle.net/10722/286672
ISSN
2023 Impact Factor: 1.7
2023 SCImago Journal Rankings: 0.977
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYan, KK-
dc.contributor.authorZhao, H-
dc.contributor.authorWu, JT-
dc.contributor.authorPang, H-
dc.date.accessioned2020-09-04T13:28:50Z-
dc.date.available2020-09-04T13:28:50Z-
dc.date.issued2020-
dc.identifier.citationGenetic Epidemiology, 2020, Epub 2020-07-22-
dc.identifier.issn0741-0395-
dc.identifier.urihttp://hdl.handle.net/10722/286672-
dc.description.abstractMany expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false‐discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine‐learning algorithm, lasso least‐squares kernel machine (LSKM‐LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM‐LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state‐of‐the‐art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype‐Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least‐squares kernel machine, LSKM‐LASSO provides a powerful tool for eQTL mapping and phenotype prediction.-
dc.languageeng-
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/35841-
dc.relation.ispartofGenetic Epidemiology-
dc.rightsPreprint This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Postprint This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectcis‐eQTL mapping-
dc.subjectgene expression-
dc.subjectleast‐squares kernel machine-
dc.subjectmultiple variants-
dc.subjectpenalized-
dc.titleAn enhanced machine learning tool for cis‐eQTL mapping with regularization and confounder adjustments-
dc.typeArticle-
dc.identifier.emailWu, JT: joewu@hku.hk-
dc.identifier.emailPang, H: herbpang@hku.hk-
dc.identifier.authorityWu, JT=rp00517-
dc.identifier.authorityPang, H=rp01857-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/gepi.22341-
dc.identifier.pmid32700329-
dc.identifier.scopuseid_2-s2.0-85088381220-
dc.identifier.hkuros314126-
dc.identifier.volumeEpub 2020-07-22-
dc.identifier.isiWOS:000551077200001-
dc.publisher.placeUnited States-
dc.identifier.issnl0741-0395-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats