東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Imputation-based genetic association...

Duan, Qing.

Linked to FindBook

Google Book

Amazon

博客來

Imputation-based genetic association analysis of complex traits in admixed populations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Imputation-based genetic association analysis of complex traits in admixed populations./
Author:	Duan, Qing.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	127 p.
Notes:	Source: Dissertations Abstracts International, Volume: 78-01, Section: B.
Contained By:	Dissertations Abstracts International78-01B.
Subject:	African American Studies. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10120181
ISBN:	9781339813714

Imputation-based genetic association analysis of complex traits in admixed populations.
Duan, Qing.

Imputation-based genetic association analysis of complex traits in admixed populations. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 127 p.

Source: Dissertations Abstracts International, Volume: 78-01, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2016.

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

Genetic association studies in admixed populations have drawn increasing attention from the genetic community, as performing association analysis in diverse populations allows us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification due to admixture poses special challenges. To address the challenges, I conducted the following studies from the perspectives of enhancing genotype imputation quality and providing proper treatment of local ancestry in the association analysis. First, I provided a new resource of marker imputability information with commonly used reference panels to guide the choice of reference and genotyping platforms. To be specific, I systematically evaluated marker imputation quality using sequencing-based reference panels from the 1000 Genomes Project and released the information through a user-friendly and publicly available data portal. This is the first resource providing variant imputability information specific to each continental group and to each genotyping platform. Second, I established a paradigm for better imputation in African Americans using study-specific sequencing based reference panels. I built an internal reference panel consisting of variants derived from the NHLBI Exome Sequencing Project for African American subjects, which significantly increased effective sample size comparing with that from the 1000 Genomes Project. No loss of imputation quality was observed using a panel built from phenotypic extremes. In addition, I recommended using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination. Finally, I proposed a robust and powerful two-step testing procedure for association analysis in admixed populations. Through extensive numeric simulations, I demonstrated that our testing procedure robustly captures and pinpoints associations due to allele effect, ancestry effect or the existence of effect heterogeneity between the two ancestral populations. In particular, our testing procedure is more powerful in identifying the presence of effect heterogeneity than traditional cross-product interaction model. I further illustrated its usefulness by applying the two-step testing procedure to test for the association between genetic variants and hemoglobin trait in African American participates from CARe. Taken together, the above studies guide genotype imputation practice and substantially improve the power of imputation-based genetic association studies in admixed populations, leading to more accurate discovery of disease-associated variants and ultimately better therapeutic strategies in admixed populations.

ISBN: 9781339813714Subjects--Topical Terms:

1669123
African American Studies.
Subjects--Index Terms:

Admixed

Imputation-based genetic association analysis of complex traits in admixed populations.
LDR:04046nmm a2200397 4500 001 2268994
005 20200908082308.5
008 220629s2016 ||||||||||||||||| ||eng d
020 $a 9781339813714
035 $a (MiAaPQ)AAI10120181
035 $a (MiAaPQ)unc:16271
035 $a AAI10120181
040 $a MiAaPQ $c MiAaPQ
100 1 $a Duan, Qing. $3 1902766
245 1 0 $a Imputation-based genetic association analysis of complex traits in admixed populations.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 127 p.
500 $a Source: Dissertations Abstracts International, Volume: 78-01, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Mohlke, Karen L.;Li, Yun.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2016.
506 $a This item must not be sold to any third party vendors.
520 $a Genetic association studies in admixed populations have drawn increasing attention from the genetic community, as performing association analysis in diverse populations allows us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification due to admixture poses special challenges. To address the challenges, I conducted the following studies from the perspectives of enhancing genotype imputation quality and providing proper treatment of local ancestry in the association analysis. First, I provided a new resource of marker imputability information with commonly used reference panels to guide the choice of reference and genotyping platforms. To be specific, I systematically evaluated marker imputation quality using sequencing-based reference panels from the 1000 Genomes Project and released the information through a user-friendly and publicly available data portal. This is the first resource providing variant imputability information specific to each continental group and to each genotyping platform. Second, I established a paradigm for better imputation in African Americans using study-specific sequencing based reference panels. I built an internal reference panel consisting of variants derived from the NHLBI Exome Sequencing Project for African American subjects, which significantly increased effective sample size comparing with that from the 1000 Genomes Project. No loss of imputation quality was observed using a panel built from phenotypic extremes. In addition, I recommended using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination. Finally, I proposed a robust and powerful two-step testing procedure for association analysis in admixed populations. Through extensive numeric simulations, I demonstrated that our testing procedure robustly captures and pinpoints associations due to allele effect, ancestry effect or the existence of effect heterogeneity between the two ancestral populations. In particular, our testing procedure is more powerful in identifying the presence of effect heterogeneity than traditional cross-product interaction model. I further illustrated its usefulness by applying the two-step testing procedure to test for the association between genetic variants and hemoglobin trait in African American participates from CARe. Taken together, the above studies guide genotype imputation practice and substantially improve the power of imputation-based genetic association studies in admixed populations, leading to more accurate discovery of disease-associated variants and ultimately better therapeutic strategies in admixed populations.
590 $a School code: 0153.
650 4 $a African American Studies. $3 1669123
650 4 $a Biostatistics. $3 1002712
650 4 $a Genetics. $3 530508
650 4 $a Bioinformatics. $3 553671
653 $a Admixed
653 $a Genetic association
653 $a Imputation-based
653 $a Traits
690 $a 0296
690 $a 0308
690 $a 0369
690 $a 0715
710 2 $a The University of North Carolina at Chapel Hill. $b Bioinformatics and Computational Biology. $3 3183020
773 0 $t Dissertations Abstracts International $g 78-01B.
790 $a 0153
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10120181