Genetic Variant Set-Based Tests Using the Generalized Berk-Jones Statistic with Application to a Genome-Wide Association Study of Breast Cancer.
Genetic Variant Set-Based Tests Using the Generalized Berk-Jones Statistic with Application to a Genome-Wide Association Study of Breast Cancer.
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DOI：
10.1080/01621459.2019.1660170
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发表时间：
2020
影响因子：
3.7
通讯作者：
Lin X
中科院分区：
文献类型：
--
作者：
Sun R;Lin X
研究单核苷酸多态性（SNP）组的影响，如在基因，遗传途径或网络中，可以为乳腺癌等复杂疾病提供新的见解，发现新的遗传关联，并增加可以从单独研究SNP中收集的信息。基于集合的遗传关联测试中的常见挑战包括弱效应大小、SNP集合中SNP之间的相关性以及信号的稀缺性，单个SNP效应的数量通常从极稀疏到中等稀疏不等。出于这些挑战的动机，我们提出了广义伯克-琼斯（GBJ）测试SNP集和结果之间的关联。GBJ通过考虑SNP之间的相关性扩展了Berk-Jones统计，并且当SNP集中的信号适度稀疏时，它提供了优于广义高级批评测试的优势。我们还提供了一个分析的p值计算SNP集的任何有限的大小，我们开发了一个综合统计，是强大的信号稀疏度。我们工作的另一个优势是能够使用来自全基因组关联研究（GWAS）的单个SNP汇总统计进行推断。我们评估有限样本性能的GBJ通过模拟和应用的方法来确定乳腺癌的风险基因在GWAS进行的癌症遗传标记的易感性协会。我们的研究结果表明FGFR 2与乳腺癌之间存在关联，并确定了其他潜在的易感基因，补充了传统的SNP水平分析。
Studying the effects of groups of single nucleotide polymorphisms (SNPs), as in a gene, genetic pathway, or network, can provide novel insight into complex diseases like breast cancer, uncovering new genetic associations and augmenting the information that can be gleaned from studying SNPs individually. Common challenges in set-based genetic association testing include weak effect sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with individual SNP effects often ranging from extremely sparse to moderately sparse in number. Motivated by these challenges, we propose the Generalized Berk-Jones (GBJ) test for the association between a SNP-set and outcome. The GBJ extends the Berk-Jones statistic by accounting for correlation among SNPs, and it provides advantages over the Generalized Higher Criticism test when signals in a SNP-set are moderately sparse. We also provide an analytic p-value calculation for SNP-sets of any finite size, and we develop an omnibus statistic that is robust to the degree of signal sparsity. An additional advantage of our work is the ability to conduct inference using individual SNP summary statistics from a genome-wide association study (GWAS). We evaluate the finite sample performance of the GBJ through simulation and apply the method to identify breast cancer risk genes in a GWAS conducted by the Cancer Genetic Markers of Susceptibility Consortium. Our results suggest evidence of association between FGFR2 and breast cancer and also identify other potential susceptibility genes, complementing conventional SNP-level analysis.
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