Data from: A powerful test of independent assortment that determines genome-wide significance quickly and accurately

In the analysis of DNA sequences on related individuals, most methods strive to incorporate as much information as possible, with little or no attention paid to the issue of statistical significance. For example, a modern workstation can easily handle the computations needed to perform a large-scale genome-wide inheritance-by-descent (IBD) scan, but accurate assessment of the significance of that scan is often hindered by inaccurate approximations and computationally intensive simulation. To address these issues, we developed gLOD-a test of co-segregation that, for large samples, models chromosome-specific IBD statistics as a collection of stationary Gaussian processes. With this simple model, the parametric bootstrap yields an accurate and rapid assessment of significance-the genome-wide corrected P-value. Furthermore, we show that (i) under the null hypothesis, the limiting distribution of the gLOD is the standard Gumbel distribution; (ii) our parametric bootstrap simulator is approximately 40 000 times faster than gene-dropping methods, and it is more powerful than methods that approximate the adjusted P-value; and, (iii) the gLOD has the same statistical power as the widely used maximum Kong and Cox LOD. Thus, our approach gives researchers the ability to determine quickly and accurately the significance of most large-scale IBD scans, which may contain multiple traits, thousands of families and tens of thousands of DNA sequences.在针对亲缘个体的DNA序列分析中，绝大多数方法都致力于纳入尽可能多的信息，却极少或未关注统计学显著性问题。例如，现代工作站可轻松完成大规模全基因组血缘同源（inheritance-by-descent, IBD）扫描所需的计算，但对该扫描显著性的精准评估，常因近似方法不够精准以及计算密集型模拟而受阻。为解决这些问题，我们开发了gLOD——一种共分离检验方法：针对大样本，该方法将染色体特异性IBD统计量建模为平稳高斯过程的集合。借助这一简洁模型，参数自助法可实现精准且快速的显著性评估，即获得基因组校正P值。此外，我们还证实：（i）在原假设下，gLOD的极限分布为标准耿贝尔分布；（ii）我们的参数自助法模拟器相较于基因抛散法，速度提升约40000倍，且性能优于近似校正P值的方法；（iii）gLOD的统计效力与广泛使用的Kong和Cox最大LOD值一致。综上，本方法可帮助研究人员快速、精准地评估绝大多数大规模IBD扫描的显著性，这类扫描可涵盖多种性状、数千个家系以及数万个DNA序列。