國立虎尾科技大學 |

New Computational Approaches for Analyzing Admixed Populations.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	New Computational Approaches for Analyzing Admixed Populations./
Author:	Park, Danny.
Description:	1 online resource (140 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Genetics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355143874

New Computational Approaches for Analyzing Admixed Populations.
Park, Danny.

New Computational Approaches for Analyzing Admixed Populations. - 1 online resource (140 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--University of California, San Francisco, 2017.

Includes bibliographical references

This work revolves around developing, applying, and evaluating computational methods for the analysis of genomic datasets of recently admixed populations. Individuals from recently admixed populations, such as African Americans and Hispanic Latinos, derive their genomes from multiple genetically distinct ancestral populations. For example, African Americans have locus-specific ancestry from African and European genomes, which reflects demographic history and influences disease predisposition. Genomic studies of admixed populations therefore provide an enormous opportunity to investigate the influence of genetic variation on human phenotypic diversity. Furthermore, such studies offer a framework to test the generalizability of findings on genotype-phenotype relationships originally obtained in more homogeneous populations (i.e. Europeans), potentially yielding insights into underlying mechanisms. Here we present four novel statistical/computational approaches that leverage the unique genetic makeup of admixed populations to aid in deepening our understanding of the effect of human genetic variation on the phenome.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355143874Subjects--Topical Terms:

578972
Genetics.
Index Terms--Genre/Form:

554714
Electronic books.

New Computational Approaches for Analyzing Admixed Populations.
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245 1 0 $a New Computational Approaches for Analyzing Admixed Populations.
264 0 $c 2017
300 $a 1 online resource (140 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Noah Zaitlen.
502 $a Thesis (Ph.D.)--University of California, San Francisco, 2017.
504 $a Includes bibliographical references
520 $a This work revolves around developing, applying, and evaluating computational methods for the analysis of genomic datasets of recently admixed populations. Individuals from recently admixed populations, such as African Americans and Hispanic Latinos, derive their genomes from multiple genetically distinct ancestral populations. For example, African Americans have locus-specific ancestry from African and European genomes, which reflects demographic history and influences disease predisposition. Genomic studies of admixed populations therefore provide an enormous opportunity to investigate the influence of genetic variation on human phenotypic diversity. Furthermore, such studies offer a framework to test the generalizability of findings on genotype-phenotype relationships originally obtained in more homogeneous populations (i.e. Europeans), potentially yielding insights into underlying mechanisms. Here we present four novel statistical/computational approaches that leverage the unique genetic makeup of admixed populations to aid in deepening our understanding of the effect of human genetic variation on the phenome.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Genetics. $3 578972
650 4 $a Bioinformatics. $3 583857
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0369
690 $a 0715
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Francisco. $b Pharmaceutical Sciences and Pharmacogenomics. $3 1184261
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10599025 $z click for full text (PQDT)