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Improving Understanding of Complex D...

Zhang, Wenmin,

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Improving Understanding of Complex Diseases Genetics With Bayesian Sparse Models and Variational Inference /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving Understanding of Complex Diseases Genetics With Bayesian Sparse Models and Variational Inference // Wenmin Zhang.
Author:	Zhang, Wenmin,
Description:	1 electronic resource (136 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Software. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31339270
ISBN:	9798384201656

Improving Understanding of Complex Diseases Genetics With Bayesian Sparse Models and Variational Inference /
Zhang, Wenmin,

Improving Understanding of Complex Diseases Genetics With Bayesian Sparse Models and Variational Inference /Wenmin Zhang. - 1 electronic resource (136 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Large biobanks and advanced sequencing technologies have enabled large-scale GWAS, yielding numerous associations between genetic variants and complex diseases. GWAS provide valuable resources for identifying causal variants, understanding the interplay between traits, and characterizing disease heterogeneity in populations with different environmental exposures.However, the interpretation of GWAS results is complicated by LD, as univariate regression models cannot account for correlation between variants. Additionally, the large number of genetic variants across the genome poses computational challenges and incurs a high burden of multiple testing. Building upon previous work, this thesis presents two novel Bayesian sparse models, along with efficient variational inference algorithms, to address these challenges and facilitate the interpretation of GWAS results.To begin, the thesis introduces the first method, SparsePro, to prioritize causal variants for experimental validation. By integrating GWAS associations and functional annotations, SparsePro demonstrated improved performance in simulations and identified biologically relevant causal variants. Next, the second method, SharePro, assesses whether two or more traits share the same genetic signals identified in GWAS. SharePro achieved improved power with well-controlled false positive rate and identified biologically plausible colocalizations missed by other methods. Lastly, SharePro is adapted for GxE analysis. By accounting for genetic effect heterogeneity in fine-mapping, SharePro can reduce multiple testing burden and improve power for detecting GxE. SharePro identified additional genetic effects on lung function modulating by smoking status and improved characterization of sex differentiated genetic effects on fat distribution.Taken together, this thesis provides valuable computational methods for improving understanding of complex disease genetics.

English

ISBN: 9798384201656Subjects--Topical Terms:

619355
Software.

Improving Understanding of Complex Diseases Genetics With Bayesian Sparse Models and Variational Inference /
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245 1 0 $a Improving Understanding of Complex Diseases Genetics With Bayesian Sparse Models and Variational Inference / $c Wenmin Zhang.
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500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisors: Dupuis, Josee; Najafabadi, Hamed S.
502 $b Ph.D. $c McGill University (Canada) $d 2023.
520 $a Large biobanks and advanced sequencing technologies have enabled large-scale GWAS, yielding numerous associations between genetic variants and complex diseases. GWAS provide valuable resources for identifying causal variants, understanding the interplay between traits, and characterizing disease heterogeneity in populations with different environmental exposures.However, the interpretation of GWAS results is complicated by LD, as univariate regression models cannot account for correlation between variants. Additionally, the large number of genetic variants across the genome poses computational challenges and incurs a high burden of multiple testing. Building upon previous work, this thesis presents two novel Bayesian sparse models, along with efficient variational inference algorithms, to address these challenges and facilitate the interpretation of GWAS results.To begin, the thesis introduces the first method, SparsePro, to prioritize causal variants for experimental validation. By integrating GWAS associations and functional annotations, SparsePro demonstrated improved performance in simulations and identified biologically relevant causal variants. Next, the second method, SharePro, assesses whether two or more traits share the same genetic signals identified in GWAS. SharePro achieved improved power with well-controlled false positive rate and identified biologically plausible colocalizations missed by other methods. Lastly, SharePro is adapted for GxE analysis. By accounting for genetic effect heterogeneity in fine-mapping, SharePro can reduce multiple testing burden and improve power for detecting GxE. SharePro identified additional genetic effects on lung function modulating by smoking status and improved characterization of sex differentiated genetic effects on fat distribution.Taken together, this thesis provides valuable computational methods for improving understanding of complex disease genetics.
520 $a Les grandes biobanques et les technologies de sequenc¸age avanc ees ont permis la r ealisation d'etudes d'association a l' echelle du g enome (GWAS) a grande echelle, aboutissant a de nom- breuses associations entre variants gen etiques et maladies complexes. Les GWAS fournissent des ressources precieuses pour identifier les variants causaux, comprendre les interactions entre les caracteristiques et caract eriser l'h et erog en eit e des maladies dans des populations presentant diff erentes expositions environnementales.Cependant, l'interpretation des r esultats des GWAS est compliqu ee par le d es equilibre de liai- son, car les modeles de r egression univari ee ne peuvent pas prendre en compte la corr elation entre les variants. De plus, le grand nombre de variants gen etiques a travers le g enome pose des defis computationnels et entra ine une charge elev ee de tests multiples. S'appuyant sur les travaux prec edents, cette th ese pr esente de deux nouveaux mod eles bay esiens parcimonieux, ainsi que des algorithmes d'inference variationnelle efficaces, pour relever ces d efis et faciliter l'interpretation des r esultats des GWAS.Pour commencer, cette these pr esente la premi ere methode, SparsePro, pour prioriser les variants causaux en vue d'une validation experimentale. En int egrant les associations GWAS et les annotations fonctionnelles, SparsePro a demontr e une am elioration des performances dans les simulations et a identifie des variants causaux biologiquement pertinents. Ensuite, la deuxieme methode, SharePro, evalue si deux traits ou plus partagent les m emes signaux gen etiques identifi es dans les GWAS. SharePro a obtenu une meilleure puissance avec un taux de fausses decouvertes bien contr ol e et a identifi e des colocalisations biologiquement plau- sibles qui etaient manqu ees par d'autres m ethodes. Enfin, SharePro est adapt e pour l'analyse des interactions gene-environnement. En tenant compte de l'h et erog en eit e des effets g en etiques dans la cartographie fine, SharePro reduit la charge des tests multiples et am eliore la puis- sance de detection des interactions g ene-environnement. SharePro a identifi e des effets g en etiques supplementaires sur la fonction pulmonaire modul es par le statut tabagique et a am elior e la caracterisation des effets g en etiques diff erenci es par sexe sur la distribution de l'adiposit e.Dans l'ensemble, cette these propose des m ethodes computationnelles pr ecieuses pour am eliorer la comprehension de la g en etique des maladies complexes.
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650 4 $a Genetics. $3 530508
650 4 $a Adaptation. $3 3562958
650 4 $a Cardiovascular disease. $3 3564561
650 4 $a Biomarkers. $3 2205735
650 4 $a Taxonomy. $3 3556303
650 4 $a Genomes. $3 592593
650 4 $a Privacy. $3 528582
650 4 $a Visualization. $3 586179
650 4 $a Medicine. $3 641104
650 4 $a Public health. $3 534748
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710 2 $a McGill University (Canada). $e degree granting institution. $3 3759190
720 1 $a Dupuis, Josee $e degree supervisor.
720 1 $a Najafabadi, Hamed S. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
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