重鬱症為一種複雜性的病徵，是由遺傳因子與非遺傳因子兩者相互交叉作用所產生的疾病。截至目前為止，有許多資料庫與研究收集了非常大量的憂鬱症相關研究資料與結果，譬如遺傳相關性研究、連鎖分析以及基因表現型態分析等。這提供我們一個契機，設計一套優先化系統去結合不同面向的資料，整合歸納不同來源的候選基因，藉此建置一個依相關證據強弱排序的憂鬱症基因資料庫。首先，我們從五種來源去收集憂鬱症相關候選基因，分別是遺傳相關性研究、連鎖分析、基因表現型態、基因調控網路與文獻搜尋這五種平台，并包含人類以及動物模型研究。這些基因先初步依據其所屬來源種類給予分數。接著，我們使用一套兩階段的篩選系統去尋找其最理想的加權矩陣值，然後採用此加權矩陣去計算加總每個基因的總分數，最後所有的候選基因依據其分數大小進行排序。我們將這組優先化基因，利用全基因體掃瞄相關研究的數據，以及觀察其在人類組織中的表現情形，去評估經由這套系統優化過的高排名候選基因是否合理。結果顯示此套優先化系統是相當可行的。我們可以進一步研究這組優先化基因在人體或是動物實驗的再現性結果。

Major depressive disorder (MDD) is a complex and multifactorial trait with the interplay between genetic and nongenetic risk factors. So far, there have been many datasets and individual studies with massive information from multiple resources of genetic findings, including results in association studies, linkage scans, and gene expression studies for depression. This provides us an opportunity to conduct a prioritization system to utilize and combine multidimensional data to create an evidence-based gene set for depression. First, we collect susceptible genes for depression from five platforms: association study, linkage scan, gene expression, regulatory pathway, and literature search. Data resources included studies in both human and animal model. These genes are initially assigned scores by category-specific scoring method. Then, we use a two step approach to find an optimal weight matrix. Finally, susceptible genes are prioritized by their combined scores using the optimal weight matrix. We evaluate prioritized genes by an independent genome-wide association study and gene expression pattern in human tissues. Our results revealed that prioritized genes generated by such approach are promising for further biological experiment or replication.

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