全基因體定序技術除了能幫助我們得到人類身上 30 億對鹼基對 (base pair) 的定序結果，也因定序技術的突破其所花費的成本也大幅降低，因此帶動了關聯分析(association analysis) 方法的蓬勃發展，從此基因變異位點與疾病風險之間相關性的研究也開始被廣泛進行。本研究採用原始基因定序資料，針對每一個單核苷酸多態性 (single­nucleotide polymorphism, SNP) 位點擷取測序深度及次要等位基因個數的資訊，計算突變等位基因頻率 (variant allele frequency, VAF) 作為新的風險指標，並在基因組關聯分析 (set based association test) 的基礎上，使用廣義可加模型 (generalized additive model, GAM) 在校正患者之年齡及身體質量指數的影響下，探討產生神經病變與風險指標集合之間的相關性。另外，針對現行已提的方法 VEGAS(versatile gene­ based association study)，藉由統計模擬及實際資料的比較分析，結果可以說明當多位點關聯呈非線性時，本研究提出的方法具有較高的檢定力。

The whole genome sequencing (WGS) technology can help clinical cancer researchers obtain the sequencing results of 3 billion base pairs in humans, which has led to the rapid development of association analysis methods. Researchers have begun to work on the association between disease risks and SNP sites. Our research extracts information from raw WGS data to calculate variant allele frequency (VAF) as a new risk score. To conduct set based association analysis, we use generalized additive model (GAM) to determine the association between VAFs set and disease status. Through statistical simulation and analysis of real data, we compare our method to versatile gene based association study(VEGAS) and conclude that our method GAM has higher power when the relationship is non­linear.

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