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Statistical Methods for Multivariate Genetic Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methods for Multivariate Genetic Analysis./
Author:	Ji, Soo Min.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	137 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
Subject:	Biostatistics. -
ISBN:	9798438756651

Statistical Methods for Multivariate Genetic Analysis.
Ji, Soo Min.

Statistical Methods for Multivariate Genetic Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 137 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2022.

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

This dissertation develops statistical and computational methods for human genetics. We consider modern solutions to estimate the power of proposed genetic studies, and propose an alternative to the mixed model framework for analysis on non-Gaussian distributions. The methods we develop are designed for multivariate simulation and analysis in high dimensions. We implement our methods in individual, open-sourced Julia packages. They are freely available to the scientific community through the OpenMendel platform.

ISBN: 9798438756651Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Human genetics

Statistical Methods for Multivariate Genetic Analysis.
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100 1 $a Ji, Soo Min. $3 3689177
245 1 0 $a Statistical Methods for Multivariate Genetic Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 137 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Lange, Kenneth L.;Zhou, Hua.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation develops statistical and computational methods for human genetics. We consider modern solutions to estimate the power of proposed genetic studies, and propose an alternative to the mixed model framework for analysis on non-Gaussian distributions. The methods we develop are designed for multivariate simulation and analysis in high dimensions. We implement our methods in individual, open-sourced Julia packages. They are freely available to the scientific community through the OpenMendel platform.
590 $a School code: 0031.
650 4 $a Biostatistics. $3 1002712
650 4 $a Systematic biology. $3 3173492
650 4 $a Genetics. $3 530508
653 $a Human genetics
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690 $a 0423
690 $a 0369
710 2 0 $a University of California, Los Angeles. $b Biostatistics 0132. $3 3550631
773 0 $t Dissertations Abstracts International $g 83-11B.
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791 $a Ph.D.
792 $a 2022
793 $a English