Gene-based burden tests are a popular and powerful approach for analysis of exome-wide association studies. These approaches combine sets of variants within a gene into a single burden score that is then tested for association. Typically, a range of burden scores are calculated and tested across a range of annotation classes and frequency bins. Correlation between these tests can complicate the multiple testing correction and hamper interpretation of the results. We introduce a method called the sparse burden association test (SBAT) that tests the joint set of burden scores under the assumption that causal burden scores act in the same effect direction. The method simultaneously assesses the significance of the model fit and selects the set of burden scores that best explain the association at the same time. Using simulated data, we show that the method is well calibrated and highlight scenarios where the test outperforms existing gene-based tests. We apply the method to 73 quantitative traits from the UK Biobank, showing that SBAT is a valuable additional gene-based test when combined with other existing approaches. This test is implemented in the REGENIE software.

中文翻译：

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使用非负最小二乘法联合检验罕见变异负担评分


基于基因的负荷检测是分析外显子组范围关联研究的一种常用且强大的方法。这些方法将基因内的变异集组合成一个负担评分，然后测试相关性。通常，会在一系列注释类和频率区间中计算和测试一系列负担分数。这些测试之间的相关性会使多重测试校正复杂化，并妨碍对结果的解释。我们引入了一种称为稀疏负担关联测试 （SBAT） 的方法，该方法在因果负担分数作用相同的效果方向下测试联合负担分数集。该方法同时评估模型拟合的显著性，并同时选择最能解释关联的负担分值集。使用模拟数据，我们表明该方法经过良好校准，并突出了该测试优于现有基于基因的测试的情况。我们将该方法应用于英国生物样本库的 73 个数量性状，表明 SBAT 与其他现有方法相结合时，是一种有价值的基于基因的附加测试。该测试在 REGENIE 软件中实现。