{"id":50987,"date":"2024-12-11T14:54:06","date_gmt":"2024-12-11T14:54:06","guid":{"rendered":""},"modified":"2024-12-11T14:54:06","modified_gmt":"2024-12-11T14:54:06","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-50987","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-50987\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0646\u0642\u0634\u0647\u200c\u0628\u0631\u062f\u0627\u0631\u06cc \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u0627\u062b\u0631\u0627\u062a \u0698\u0646\u062a\u06cc\u06a9\u06cc \u0633\u06cc\u0633 \u0628\u0631 \u0628\u06cc\u0627\u0646 \u0698\u0646 \u062f\u0631 \u062c\u0645\u0639\u06cc\u062a\u200c\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641\u060c \u0698\u0646\u200c\u0647\u0627\u06cc \u062d\u06cc\u0627\u062a\u06cc \u0628\u06cc\u0645\u0627\u0631\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f."},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPowerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0646\u0642\u0634\u0647\u200c\u0628\u0631\u062f\u0627\u0631\u06cc \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u0627\u062b\u0631\u0627\u062a \u0698\u0646\u062a\u06cc\u06a9\u06cc \u0633\u06cc\u0633 \u0628\u0631 \u0628\u06cc\u0627\u0646 \u0698\u0646 \u062f\u0631 \u062c\u0645\u0639\u06cc\u062a\u200c\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641\u060c \u0698\u0646\u200c\u0647\u0627\u06cc \u062d\u06cc\u0627\u062a\u06cc \u0628\u06cc\u0645\u0627\u0631\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f.<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n ProfileKai Akamatsu, Stephen Golzari, ProfileTiffany Amariuta<\/td>\n<\/tr>\n
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\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n43<\/td>\n<\/tr>\n
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\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nGenetic and Genomic Medicine
\n\u062f\u0627\u0631\u0648\u06cc \u0698\u0646\u062a\u06cc\u06a9\u06cc \u0648 \u0698\u0646\u0648\u0645\u06cc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
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\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO\u2019s ability to more accurately estimate causal eQTL effect sizes (p < 3.98 \u00d7 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.<\/p>\n

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\u062f\u0647\u062f \u0648 \u0628\u0647 \u0637\u0648\u0631 \u0645\u062f\u0627\u0648\u0645 \u0627\u0632 \u0647\u0631 \u06cc\u06a9 \u0627\u0632 6 \u0631\u0648\u0634 \u0631\u0642\u06cc\u0628 \u062f\u0631 \u06a9\u0627\u0631\u0647\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0628\u06cc\u0627\u0646 \u0698\u0646 \u0628\u0647\u062a\u0631 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\u062f.\u0627\u062f\u063a\u0627\u0645 \u0628\u0627 \u0622\u0645\u0627\u0631 \u062e\u0644\u0627\u0635\u0647 GWAS \u062f\u0631 66 \u0635\u0641\u062a \u067e\u06cc\u0686\u06cc\u062f\u0647 (\u0646\u0645\u0627\u06cc\u0627\u0646\u06af\u0631 22 \u0641\u0646\u0648\u062a\u06cc\u067e \u0648 3 \u0627\u062c\u062f\u0627\u062f) \u0645\u0646\u062c\u0631 \u0628\u0647 2\u060c331 \u0627\u0646\u062c\u0645\u0646 \u062c\u062f\u06cc\u062f \u0698\u0646 \u0634\u0627\u062e\u0635 \u0634\u062f \u060c \u06a9\u0647 \u0628\u0633\u06cc\u0627\u0631\u06cc \u0627\u0632 \u0622\u0646\u0647\u0627 \u062f\u0631 \u0628\u06cc\u0646 \u0627\u062c\u062f\u0627\u062f \u0645\u062a\u0639\u062f\u062f \u060c \u0627\u0632 \u062c\u0645\u0644\u0647 PHTF1 \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0634\u0645\u0627\u0631\u0634 \u06af\u0644\u0628\u0648\u0644\u0647\u0627\u06cc \u0633\u0641\u06cc\u062f \u060c \u0698\u0646\u06cc \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u062f\u0631 \u0644\u0648\u0633\u0645\u06cc \u0628\u06cc\u0627\u0646 \u0645\u06cc \u0634\u0648\u062f\u0628\u06cc\u0645\u0627\u0631\u0627\u0646.MagePro \u0647\u0645\u0686\u0646\u06cc\u0646 \u06cc\u0627\u0641\u062a\u0647 \u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0628\u06cc\u0648\u0644\u0648\u0698\u06cc\u06a9\u06cc \u0642\u0627\u0628\u0644 \u0642\u0628\u0648\u0644 \u060c \u0645\u0627\u0646\u0646\u062f PIGB \u060c \u06cc\u06a9 \u0645\u0624\u0644\u0641\u0647 \u0627\u0633\u0627\u0633\u06cc \u0628\u06cc\u0648\u0633\u0646\u062a\u0632 GPI \u060c \u0647\u0645\u0631\u0627\u0647 \u0628\u0627 \u0646\u0627\u0631\u0633\u0627\u06cc\u06cc \u0642\u0644\u0628\u06cc \u0631\u0627 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u06a9\u0631\u062f \u060c \u06a9\u0647 \u0642\u0628\u0644\u0627\u064b \u062a\u0648\u0633\u0637 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0646\u062a\u06cc\u062c\u0647 \u0628\u0627\u0644\u06cc\u0646\u06cc \u0645\u0634\u0647\u0648\u062f \u0628\u0648\u062f.\u0628\u0647 \u0637\u0648\u0631 \u06a9\u0644\u06cc \u060c MagePro \u0627\u0628\u0632\u0627\u0631\u06cc \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u0628\u0631\u0627\u06cc \u062a\u0642\u0648\u06cc\u062a \u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u0627\u062b\u0631\u0627\u062a \u0646\u0638\u0627\u0631\u062a\u06cc \u0698\u0646 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u062d\u062a \u0642\u062f\u0631\u062a \u0627\u0633\u062a \u0648 \u062f\u0631\u06a9 \u0645\u0627 \u0627\u0632 \u0627\u062b\u0631\u0627\u062a \u0698\u0646\u062a\u06cc\u06a9\u06cc \u062e\u0627\u0635 \u0648 \u0645\u0634\u062a\u0631\u06a9 \u062c\u0645\u0639\u06cc\u062a \u0631\u0627 \u0628\u0631 \u0631\u0648\u06cc \u0635\u0641\u0627\u062a \u067e\u06cc\u0686\u06cc\u062f\u0647 \u0628\u0647\u0628\u0648\u062f \u0628\u062e\u0634\u06cc\u062f\u0647 \u0627\u0633\u062a.\n\n\n[sc name=\"papertranslation\"][\/sc]\n<\/p>\n","protected":false},"excerpt":{"rendered":"

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