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Combining family- and population-based imputation data forassociation analysis of rare and common variants in largepedigrees

机译：结合基于家庭和人口的估算数据大的稀有和常见变体的关联分析血统书

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摘要

In the last two decades, complex traits have become the main focus of genetic studies. The hypothesis that both rare and common variants are associated with complex traits is increasingly being discussed. Family-based association studies using relatively large pedigrees are suitable for both rare and common variant identification. Because of the high cost of sequencing technologies, imputation methods are important for increasing the amount of information at low cost. A recent family-based imputation method, GIGI, is able to handle large pedigrees and accurately impute rare variants, but does less well for common variants where population-based methods perform better. Here, we propose a flexible approach to combine imputation data from both family- and population-based methods. We also extend the association test SKAT-RC, originally proposed for data from unrelated subjects, to family data in order to make use of such imputed data. We call this extension “famSKAT-RC”. We compare the performance of famSKAT-RC and several other existing burden and kernel association tests. In simulated pedigree sequence data, our results show an increase of imputation accuracy from use of our combining approach. Also, they show an increase of power of theassociation tests with this approach over the use of either family- orpopulation-based imputation methods alone, in the context of rare and commonvariants. Moreover, our results showed better performance of famSKAT-RC comparedto the other considered tests, in most scenarios investigated here.
机译：在过去的二十年中，复杂的性状已成为遗传研究的重点。越来越少地讨论稀有和常见变体都与复杂性状相关的假说。使用相对较大的谱系进行的基于家庭的关联研究适用于稀有和常见变异识别。由于测序技术的高成本，插补方法对于以低成本增加信息量很重要。最近的基于家庭的插补方法GIGI能够处理较大的谱系并准确插补稀有变体，但对于基于群体的方法效果更好的普通变体却效果不佳。在这里，我们提出了一种灵活的方法来组合基于家庭和基于人口的方法中的估算数据。我们还将最初针对不相关主题的数据提出的关联测试SKAT-RC扩展到家庭数据，以利用这种估算数据。我们将此扩展称为“ famSKAT-RC”。我们比较了famSKAT-RC和其他几个现有的负担和内核关联测试的性能。在模拟的谱系序列数据中，我们的结果表明，通过使用我们的组合方法，插补精度得以提高。而且，它们显示出使用家庭或家庭使用这种方法进行关联测试在稀有和常见的情况下，仅基于人口的插补方法变体。此外，我们的结果表明，与famSKAT-RC相比，famSKAT-RC的性能更好到其他考虑的测试，在这里调查的大多数情况下。

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