서브메뉴
검색
Leveraging Multi-Scale Genomic Information to Enhance Prediction and Understanding of Complex Traits
Leveraging Multi-Scale Genomic Information to Enhance Prediction and Understanding of Complex Traits
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105419
- ISBN
- 9798270297527
- DDC
- 574
- 서명/저자
- Leveraging Multi-Scale Genomic Information to Enhance Prediction and Understanding of Complex Traits
- 발행사항
- [Sl] : The University of North Carolina at Chapel Hill, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 174 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-07, Section: B.
- 주기사항
- Advisor: Li, Yun.
- 학위논문주기
- Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2025.
- 초록/해제
- 요약Understanding the mechanisms underlying complex diseases is essential for developing effective treatments. Advances in sequencing technologies and declining costs have led to an exponential growth in genomic data. To effectively analyze these large datasets and identify the genetic basis of diseases, it is crucial to develop new statistical tools that combine information from multiple data sources. Integrating information from within a single omics layer, such as leveraging genetically related traits or combining total and allele-specific gene expression, can enhance prediction and increase power in biological research. Multi-trait polygenic risk score (PRS) methods improve disease risk prediction by utilizing the shared genetic architecture among correlated traits. However, these methods do not account for vertical pleiotropy, where one trait acts as a mediator for another. For my first project, I present endoPRS, a weighted lasso framework that incorporates information from relevant endophenotypes, measurable biological traits with genetic links to disease progression. Simulation analyses demonstrate the robustness of endoPRS across complex genetic frameworks. By leveraging a genome-wide association study (GWAS) of eosinophil count, endoPRS significantly improves the prediction of childhood-onset asthma. Quantitative trait loci (QTL) mapping identifies genomic regions associated with quantitative traits such as gene expression. Approaches that simultaneously leverage total gene expression and allele-specific gene expression can increase QTL detection power but are limited to bi-allelic variants. They cannot capture haplotype effects, which are essential to analyzing the genetics of multiparent populations. For my second project, I extend two QTL mapping methods, TReCASE and mixQTL, to model founder haplotype effects. These methods substantially increase the power to detect QTL compared to conventional methods in both simulated and real gene expression data from Collaborative Cross Recombinant Intercross (CC-RIX) mice. As access to individual-level genotype-phenotype data is often restricted, my third project extends endoPRS to endoPRS-SS, which estimates PRS coefficients using GWAS summary statistics and LD reference panels. I also extend the endoPRS-SS framework to allow for multiple endophenotypes. Simulations and real data analysis show that endoPRS-SS performs comparably to endoPRS. Moreover, by integrating both monocyte count and executive function scores, endoPRS-SS improves Alzheimer's disease risk prediction compared to using either endophenotype alone.
- 일반주제명
- Biostatistics
- 일반주제명
- Bioinformatics
- 일반주제명
- Genetics
- 키워드
- Complex traits
- 키워드
- Genomics
- 기타저자
- The University of North Carolina at Chapel Hill Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 87-07B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017360289
■00520260202105419
■006m o d
■007cr#unu||||||||
■020 ▼a9798270297527
■035 ▼a(MiAaPQ)AAI32281432
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aKharitonova, Elena.
■24510▼aLeveraging Multi-Scale Genomic Information to Enhance Prediction and Understanding of Complex Traits
■260 ▼a[Sl]▼bThe University of North Carolina at Chapel Hill▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a174 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-07, Section: B.
■500 ▼aAdvisor: Li, Yun.
■5021 ▼aThesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2025.
■520 ▼aUnderstanding the mechanisms underlying complex diseases is essential for developing effective treatments. Advances in sequencing technologies and declining costs have led to an exponential growth in genomic data. To effectively analyze these large datasets and identify the genetic basis of diseases, it is crucial to develop new statistical tools that combine information from multiple data sources. Integrating information from within a single omics layer, such as leveraging genetically related traits or combining total and allele-specific gene expression, can enhance prediction and increase power in biological research. Multi-trait polygenic risk score (PRS) methods improve disease risk prediction by utilizing the shared genetic architecture among correlated traits. However, these methods do not account for vertical pleiotropy, where one trait acts as a mediator for another. For my first project, I present endoPRS, a weighted lasso framework that incorporates information from relevant endophenotypes, measurable biological traits with genetic links to disease progression. Simulation analyses demonstrate the robustness of endoPRS across complex genetic frameworks. By leveraging a genome-wide association study (GWAS) of eosinophil count, endoPRS significantly improves the prediction of childhood-onset asthma. Quantitative trait loci (QTL) mapping identifies genomic regions associated with quantitative traits such as gene expression. Approaches that simultaneously leverage total gene expression and allele-specific gene expression can increase QTL detection power but are limited to bi-allelic variants. They cannot capture haplotype effects, which are essential to analyzing the genetics of multiparent populations. For my second project, I extend two QTL mapping methods, TReCASE and mixQTL, to model founder haplotype effects. These methods substantially increase the power to detect QTL compared to conventional methods in both simulated and real gene expression data from Collaborative Cross Recombinant Intercross (CC-RIX) mice. As access to individual-level genotype-phenotype data is often restricted, my third project extends endoPRS to endoPRS-SS, which estimates PRS coefficients using GWAS summary statistics and LD reference panels. I also extend the endoPRS-SS framework to allow for multiple endophenotypes. Simulations and real data analysis show that endoPRS-SS performs comparably to endoPRS. Moreover, by integrating both monocyte count and executive function scores, endoPRS-SS improves Alzheimer's disease risk prediction compared to using either endophenotype alone.
■590 ▼aSchool code: 0153.
■650 4▼aBiostatistics
■650 4▼aBioinformatics
■650 4▼aGenetics
■653 ▼aComplex traits
■653 ▼aGenomics
■653 ▼aPolygenic risk score
■653 ▼aQuantitative trait loci
■653 ▼aStatistical genetics
■690 ▼a0308
■690 ▼a0369
■690 ▼a0715
■71020▼aThe University of North Carolina at Chapel Hill▼bBiostatistics.
■7730 ▼tDissertations Abstracts International▼g87-07B.
■790 ▼a0153
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360289▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
