2026-09-20T12:13:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1299282026-06-06T00:56:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Manolis Kellis and Alexander Gusev.
Wang, Austin T.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-02-19T21:01:48Z
2021-02-19T21:01:48Z
2020
2020
https://hdl.handle.net/1721.1/129928
1237565656
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 35-37).
We introduce PLASMA (PopuLation Allele-Specic MApping), a statistical ne- mapping method that leverages allele-specic (AS) genomic data to improve detection of quantitative trait loci (QTLs) with causal effects on molecular traits. In simulations, PLASMA accurately prioritizes causal QTL variants over a wide range of genetic architectures. Applied to RNA-Seq data from 524 kidney tumor samples, PLASMA achieves a greater power at 50 samples than conventional QTL-based ne-mapping at 500 samples: with over 17% of loci ne-mapped to within 5 causal variants compared to 2% by QTL-based ne-mapping, and a 6.9-fold overall reduction in median credible set size. PLASMA offers high accuracy even at small sample sizes, yielding a 1.3-fold reduction in median credible set size compared to QTL-based ne-mapping when applied to H3K27AC ChIP-Seq from just 28 prostate tumor/normal samples. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data.
by Austin T. Wang.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
65 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Allele-Specic QTL fine-mapping with PLASMA
Allele-specic quantitative trait loci fine-mapping with PopuLation Allele-Specic MApping
Thesis
application/pdf
2021-02-19T21:01:18Z
Publication
Master
EECS
unknown
unknown
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<Abstract>We introduce PLASMA (PopuLation Allele-Specic MApping), a statistical ne- mapping method that leverages allele-specic (AS) genomic data to improve detection of quantitative trait loci (QTLs) with causal effects on molecular traits. In simulations, PLASMA accurately prioritizes causal QTL variants over a wide range of genetic architectures. Applied to RNA-Seq data from 524 kidney tumor samples, PLASMA achieves a greater power at 50 samples than conventional QTL-based ne-mapping at 500 samples: with over 17% of loci ne-mapped to within 5 causal variants compared to 2% by QTL-based ne-mapping, and a 6.9-fold overall reduction in median credible set size. PLASMA offers high accuracy even at small sample sizes, yielding a 1.3-fold reduction in median credible set size compared to QTL-based ne-mapping when applied to H3K27AC ChIP-Seq from just 28 prostate tumor/normal samples. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data.</Abstract>
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