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Genomic analysis tools for familial and case-control sequencing association studies. Huff, Chad
Description
Advances in high-throughput sequencing technologies are transforming the landscape of biomedical research. As the generation of genomic data becomes commoditized, research efforts are increasingly being shifted to data analysis and interpretation. However, the interpretation of large-scale genomic datasets in the context of human disease is greatly complicated by complexities inherent to the population genetics of disease-causing variation and heterogeneity in high-throughput sequencing technologies. In this talk, I will present an overview of the computational tools we have developed to analyze high-throughput sequencing data for identification and characterization of genetic variation influencing disease risk. Topics will include relationship estimation, pedigree reconstruction, functional variant prediction, familial and case-control rare variant association analysis, and strategies for leveraging phenotypic ontologies. A particular focus of the presentation will involve techniques to overcome biases inherent to heterogeneous sequencing technologies in association studies which combine sequencing datasets from multiple sources. These techniques have recently been implemented in the Cross-Platform Association Toolkit (XPAT), a software package which includes a suite of tools to support large-scale association studies. Features implemented in XPAT include cross-platform aware variant calling, quality control filtering, gene-based association testing, and rare variant effect size estimation. I will highlight examples throughout the talk from Mendelian and complex diseases, with an emphasis on common cancers.
Item Metadata
Title |
Genomic analysis tools for familial and case-control sequencing association studies.
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2018-11-05T11:05
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Description |
Advances in high-throughput sequencing technologies are transforming the landscape of biomedical research. As the generation of genomic data becomes commoditized, research efforts are increasingly being shifted to data analysis and interpretation. However, the interpretation of large-scale genomic datasets in the context of human disease is greatly complicated by complexities inherent to the population genetics of disease-causing variation and heterogeneity in high-throughput sequencing technologies. In this talk, I will present an overview of the computational tools we have developed to analyze high-throughput sequencing data for identification and characterization of genetic variation influencing disease risk. Topics will include relationship estimation, pedigree reconstruction, functional variant prediction, familial and case-control rare variant association analysis, and strategies for leveraging phenotypic ontologies. A particular focus of the presentation will involve techniques to overcome biases inherent to heterogeneous sequencing technologies in association studies which combine sequencing datasets from multiple sources. These techniques have recently been implemented in the Cross-Platform Association Toolkit (XPAT), a software package which includes a suite of tools to support large-scale association studies. Features implemented in XPAT include cross-platform aware variant calling, quality control filtering, gene-based association testing, and rare variant effect size estimation. I will highlight examples throughout the talk from Mendelian and complex diseases, with an emphasis on common cancers.
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Extent |
26.0
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: MD Anderson Cancer Center
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Series | |
Date Available |
2019-05-05
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0378586
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International