Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms.
Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms.
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DOI：
10.1186/1471-2164-12-460
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发表时间：
2011-09-23
期刊：
影响因子：
4.4
通讯作者：
Keller MC
中科院分区：
文献类型：
--
作者：
Howrigan DP;Simonson MA;Keller MC
研究全基因组SNP数据中纯合性运行（ROH）的一个中心目标是检测自合性（同一个体内两条同源染色体的延伸）对表型的影响。然而，目前尚不清楚哪种ROH检测程序以及给定程序中的哪组参数对于区分真正的纯合ROH与在标志物水平上纯合但在标志物之间的未测量变体上变化的ROH是最佳的。我们模拟了120 Mb的序列数据，以了解同源性的真实状态。然后，我们从该序列中提取常见变体以模拟SNP平台的特性，并使用三种流行的ROH检测程序PLINK、GERMLINE和BEAGLE进行ROH分析。我们改变了每个程序的检测阈值（例如，先验概率，ROH的长度），以了解它们对检测已知的同源性的影响。在每个程序的最佳阈值内，PLINK在检测来自遥远共同祖先的同源性方面优于GERMLINE和BEAGLE。PLINK的滑动窗口算法在使用针对连锁不平衡（LD）修剪的SNP数据时效果最好。我们的研究结果提供了一般和具体的建议，最大限度地提高全基因组SNP数据中的自合性检测，并应同样适用于研究全基因组自合性负担或研究特定的自合区域是否预测使用关联映射方法。
A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers. We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity. Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD). Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.
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