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Evaluating the Utility of Multiple T...

Fu, Jingyuan.

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Evaluating the Utility of Multiple Trait Methods for Estimating Polygenic Risk Scores.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evaluating the Utility of Multiple Trait Methods for Estimating Polygenic Risk Scores./
作者:	Fu, Jingyuan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	59 p.
附註:	Source: Masters Abstracts International, Volume: 82-01.
Contained By:	Masters Abstracts International82-01.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28001780
ISBN:	9781083510723

Evaluating the Utility of Multiple Trait Methods for Estimating Polygenic Risk Scores.
Fu, Jingyuan.

Evaluating the Utility of Multiple Trait Methods for Estimating Polygenic Risk Scores. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 59 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.S.)--University of California, Los Angeles, 2020.

This item must not be sold to any third party vendors.

Polygenic risk score (PRS) is a method that utilizes the effect sizes of genetic variants on a particular disease or trait to evaluate an overall genetic risk for a certain individual. Such effect sizes are often estimated using traditional genome-wide association study (GWAS) for the trait of interest. There are methods developed that aim to improve the predictive power of PRS by incorporating the genetic information from multiple related traits. One existing popular method is MTAG, which requires GWAS summary statistics from multiple traits and is based on strong assumptions about genetic correlation across traits. We developed some variations of MTAG and evaluated their performance for computing PRS against GWAS, using a variety of trait data from UK Biobank as well as simulated data.

ISBN: 9781083510723Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Computational genetics

Evaluating the Utility of Multiple Trait Methods for Estimating Polygenic Risk Scores.
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520 $a Polygenic risk score (PRS) is a method that utilizes the effect sizes of genetic variants on a particular disease or trait to evaluate an overall genetic risk for a certain individual. Such effect sizes are often estimated using traditional genome-wide association study (GWAS) for the trait of interest. There are methods developed that aim to improve the predictive power of PRS by incorporating the genetic information from multiple related traits. One existing popular method is MTAG, which requires GWAS summary statistics from multiple traits and is based on strong assumptions about genetic correlation across traits. We developed some variations of MTAG and evaluated their performance for computing PRS against GWAS, using a variety of trait data from UK Biobank as well as simulated data.
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