Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, USA.
BMC Genomics. 2010 Jan 5;11:8. doi: 10.1186/1471-2164-11-8.
Though a variety of linkage disequilibrium tests have recently been introduced to measure the signal of recent positive selection, the statistical properties of the various methods have not been directly compared. While most applications of these tests have suggested that positive selection has played an important role in recent human history, the results of these tests have varied dramatically.
Here, we evaluate the performance of three statistics designed to detect incomplete selective sweeps, LRH and iHS, and ALnLH. To analyze the properties of these tests, we introduce a new computational method that can model complex population histories with migration and changing population sizes to simulate gene trees influenced by recent positive selection. We demonstrate that iHS performs substantially better than the other two statistics, with power of up to 0.74 at the 0.01 level for the variation best suited for full genome scans and a power of over 0.8 at the 0.01 level for the variation best suited for candidate gene tests. The performance of the iHS statistic was robust to complex demographic histories and variable recombination rates. Genome scans involving the other two statistics suffer from low power and high false positive rates, with false discovery rates of up to 0.96 for ALnLH. The difference in performance between iHS and ALnLH, did not result from the properties of the statistics, but instead from the different methods for mitigating the multiple comparison problem inherent in full genome scans.
We introduce a new method for simulating genealogies influenced by positive selection with complex demographic scenarios. In a power analysis based on this method, iHS outperformed LRH and ALnLH in detecting incomplete selective sweeps. We also show that the single-site iHS statistic is more powerful in a candidate gene test than the multi-site statistic, but that the multi-site statistic maintains a low false discovery rate with only a minor loss of power when applied to a scan of the entire genome. Our results highlight the need for careful consideration of multiple comparison problems when evaluating and interpreting the results of full genome scans for positive selection.
尽管最近已经引入了多种连锁不平衡测试来衡量近期正选择的信号，但各种方法的统计特性尚未直接比较。虽然这些测试的大多数应用都表明正选择在人类近代历史中发挥了重要作用，但测试结果差异巨大。
在这里，我们评估了三种设计用于检测不完全选择清除的统计数据的性能，LRH 和 iHS 以及 ALnLH。为了分析这些测试的特性，我们引入了一种新的计算方法，可以模拟具有迁移和不断变化的种群大小的复杂种群历史，以模拟受近期正选择影响的基因树。我们证明，iHS 的性能明显优于其他两种统计数据，在最适合全基因组扫描的变异中，其功率高达 0.74，在最适合候选基因测试的变异中，其功率超过 0.8。iHS 统计数据的性能对复杂的人口历史和可变的重组率具有鲁棒性。涉及其他两种统计数据的基因组扫描功率较低，假阳性率较高，ALnLH 的假发现率高达 0.96。iHS 和 ALnLH 之间的性能差异不是由于统计数据的特性所致，而是由于全基因组扫描中固有的多重比较问题的不同缓解方法所致。
我们引入了一种新方法，用于模拟具有复杂人口统计场景的正选择影响的系统发育。在基于该方法的功效分析中，iHS 在检测不完全选择清除方面优于 LRH 和 ALnLH。我们还表明，单点 iHS 统计数据在候选基因测试中比多点统计数据更有效，但多点统计数据在应用于整个基因组扫描时保持低假发现率，仅略有降低功效。我们的结果强调了在评估和解释全基因组扫描正选择结果时，需要仔细考虑多重比较问题。
