不同參考組下的基因型設算準確度分析

Abstract

基因型設算方法近期被廣泛應用於預測未觀察到的罕見變異基因型。我們以GAW18所提供的全基因組序列數據，分別運用BEAGLE跟IMPUTE v2兩種軟體作基因型設算，比較放入與研究組(墨西哥裔美國人)基因相關程度不同之參考組之基因設算準確程度。結果顯示利用與研究組基因相近的參考組可提高設算的準確性，但同時也增加遺失值的發生率。而參考組的樣本數越大越能降低設算的失誤與遺失值的發生。同時我們也發現對於混合基因型的研究，放入所有可能的參考基因型數據作為參考組能獲得較好的設算結果。

Genotype imputation approaches are now widely used to predict the genotypes for rare variants that are not directly genotyped in the study sample. Using whole genome sequence data from the Genetic Analysis Workshop 18 data set, this report applies BEAGLE and IMPUTE v2 to impute, and compares the genotype imputation accuracy among reference panels representing different degrees of genetic similarity to a study sample of admixed Mexican Americans. Results show that a reference panel that closely matches the ancestry of the study population can increase imputation accuracy, but it can also result more missing genotype calls. Having a reference panel with larger size can reduce imputation error and missing genotype, but the improvement can be limited. We also find that, for the admixed study sample, the composite reference panel combining all available reference data is more appropriate than others.