{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/129928"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/129928","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Allele-Specic QTL fine-mapping with PLASMA","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_html":"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_has_math":false,"creators":["Wang, Austin T."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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