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Integration of Functional Genomic Da...

Chen, Siying.

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Integration of Functional Genomic Data in Genetic Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Integration of Functional Genomic Data in Genetic Analysis./
Author:	Chen, Siying.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	124 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
Contained By:	Dissertations Abstracts International82-07B.
Subject:	Bioinformatics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28261785
ISBN:	9798557083195

Integration of Functional Genomic Data in Genetic Analysis.
Chen, Siying.

Integration of Functional Genomic Data in Genetic Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 124 p.

Source: Dissertations Abstracts International, Volume: 82-07, Section: B.

Thesis (Ph.D.)--Columbia University, 2021.

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

Identifying disease risk genes is a central topic of human genetics. Cost-effective exome and whole genome sequencing enabled large-scale discovery of genetic variations. However, the statistical power of finding new risk genes through rare genetic variation is fundamentally limited by sample sizes. As a result, we have an incomplete understanding of genetic architecture and molecular etiology of most of human conditions and diseases. In this thesis, I developed new computational methods that integrate functional genomics data sets, such as epigenomic profiles and single-cell transcriptomics, to improve power for identifying genetic risks and gain more insights on etiology of developmental disorders. The overall hypothesis that disease risk genes contributing to developmental disorders are bottleneck genes under normal development and subject to precise transcriptional regulations to maintain spatiotemporal specific expression during development. In this thesis I describe two major research projects. The first project, Episcore, predicts haploinsufficient genes based on a large integrated epigenomic profiles from multiple tissues and cell lines by supervised machine learning methods. The second one, A-risk, predicts plausibility of being risk genes of autism spectrum disorder based on single-cell RNA-seq data collected in human fetal midbrain and prefrontal cortex. Both methods were shown to be able to improve gene discovery in analysis of de novo mutations in developmental disorders. Overall, my thesis represents an effort to integrate functional genomics data by machine learning to facilitate both discovery and interpretation of genetic studies of human diseases. We believe that such integrative analysis can help us better understand genetic variants and disease etiology.

ISBN: 9798557083195Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Genomic data

Integration of Functional Genomic Data in Genetic Analysis.
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300 $a 124 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
500 $a Advisor: Shen, Yufeng.
502 $a Thesis (Ph.D.)--Columbia University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Identifying disease risk genes is a central topic of human genetics. Cost-effective exome and whole genome sequencing enabled large-scale discovery of genetic variations. However, the statistical power of finding new risk genes through rare genetic variation is fundamentally limited by sample sizes. As a result, we have an incomplete understanding of genetic architecture and molecular etiology of most of human conditions and diseases. In this thesis, I developed new computational methods that integrate functional genomics data sets, such as epigenomic profiles and single-cell transcriptomics, to improve power for identifying genetic risks and gain more insights on etiology of developmental disorders. The overall hypothesis that disease risk genes contributing to developmental disorders are bottleneck genes under normal development and subject to precise transcriptional regulations to maintain spatiotemporal specific expression during development. In this thesis I describe two major research projects. The first project, Episcore, predicts haploinsufficient genes based on a large integrated epigenomic profiles from multiple tissues and cell lines by supervised machine learning methods. The second one, A-risk, predicts plausibility of being risk genes of autism spectrum disorder based on single-cell RNA-seq data collected in human fetal midbrain and prefrontal cortex. Both methods were shown to be able to improve gene discovery in analysis of de novo mutations in developmental disorders. Overall, my thesis represents an effort to integrate functional genomics data by machine learning to facilitate both discovery and interpretation of genetic studies of human diseases. We believe that such integrative analysis can help us better understand genetic variants and disease etiology.
590 $a School code: 0054.
650 4 $a Bioinformatics. $3 553671
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653 $a Genetic analysis
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