遺傳相關性研究: 族層配對、基因體控制及多組DNA混集之整合策略

Abstract

背景: 複雜性疾病易感受性基因的相關研究非常仰賴流行病學的病例對照研究法。然而，族群分層偏差導致遺傳相關研究中，病例對照設計的研究結果備受爭議。基因定型花費龐大亦是一大問題。 方法: 本論文提出一族層配對、基因體控制及多組DNA混集之整合策略，以校正以族群為基礎的病例對照研究中所出現的族群分層偏差，並且節省基因定型之花費。 結果: 蒙地卡羅模擬顯示，在混集組數多或混集組數少時，其不同參數組合下，本方法的型一誤差率皆可得到良好控制。在檢力方面，不論在混集組數多或混集組數少時，隨著下列情況，檢力皆會隨之增加:(1)當族層配對越正確; (2)當使用越多的虛無標記基因來進行基因體控制；(3)DNA混集組數越多；以及(4) DNA混集的測量越正確。 結論: 族層配對、基因體控制及多組DNA混集之整合策略能校正族群分層偏差及節省基因定型之花費。

Backgrounds. The case-control association study has gained in popularity for mapping disease-susceptibility gene(s) of complex human diseases. However, the study is prone to population stratification bias. The high cost of typing genetic markers is also a problem. Methods. The authors propose a triple combination strategy of stratum matching, genomic controlling and multiple DNA pooling to deal with the pervasive problem of population stratification bias in population-based case-control association studies, and to save genotyping cost. Results. Monte-Carlo simulations show that the type I error rates are well controlled using this strategy under all scenarios and for large and small number of pooling sets. As for power, for large and small number of pooling sets it increases in the following situations: a) a more accurate stratum matching; b) a larger number of null markers for genomic control: c) a larger number of DNA pooling sets; and d) a more accurate DNA pooling measurement. Conclusions. The proposed triple combination strategy corrects population stratification bias and saves genotyping cost.