利用親本子代三元體資料進行指標式探索數量性狀基因座與標識基因間相關

Abstract

利用群體資料或家族資料進行相關研究，已經廣泛應用於複雜疾病的基因定位。Karlin (1979)提出一種主效基因指標(MGI)利用核心家庭資料探索數量性狀的遺傳結構方式，然而，該指標卻可能因為次方轉換參數選取的不適當以及性狀的遺傳率低導致謬誤的結論。為了解決此問題，本文利用親本－子代三元體資料建構二種新主效基因指標，能更成功地探索出數量性狀的遺傳結構方式。另外也建構二種條件主效基因指標(CMGIs)以探索數量性狀與標識基因的遺傳相關訊息。透過模擬研究以評估二種新主效基因指標與二種條件主效基因指標之行為表現。

Association studies, based on either population data or familial data, have been widely applied to mapping of genes for complex disease. Karlin (1979) proposed a major gene index (MGI) for exploring the inheritance mode for quantitative traits using nuclear familial data. However, the index may lead to spurious results due to the inappropriate selection of a power transformation parameter and the low heritability of a trait. In order to resolve this problem, in this thesis, we use parent-offspring triad data to construct two new MGIs that can more successfully explore the mode of inheritance for quantitative traits. In addition, we also construct two conditional major gene indices (CMGIs) to explore the genetic association information between a quantitative trait and a marker. Simulations are conducted to evaluate the performance of the two new MGIs and the two CMGIs.