Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype–phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.

中文翻译：

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结合随机森林和信号检测方法可实现基因型-表型关联的稳健检测


全基因组关联研究 (GWAS) 是一种成熟的方法，用于识别与生命科学所有分支中感兴趣的性状相关的基因组变异和基因。尽管这种方法已经成熟了很长时间，但基因型-表型关联的可靠检测对于许多数量性状来说仍然是一个挑战，这主要是因为大量基因组位点对所研究的性状的个体影响较弱。因此，可以假设，许多具有微小但真实影响的基因组变异在许多 GWAS 方法中仍然未被注意到。在这里，我们提出一个两步程序来解决这个问题。第一步，将三次样条拟合到测试统计值，并且具有高于偶然预期的样条峰值的基因组区域被视为数量性状基因座（QTL）。然后使用随机森林方法根据这些 QTL 中的 SNP 与表型的关联强度进行优先级排序。作为一个案例研究，我们将我们的程序应用于真实的数据集，并找到涉及各种鸡蛋质量性状的可靠数量的、部分新颖的基因组变异和基因。