Fine-mapping additive and dominant SNP effects using group-LASSO and fractional resample model averaging.
Fine-mapping additive and dominant SNP effects using group-LASSO and fractional resample model averaging.
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DOI：
10.1002/gepi.21869
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发表时间：
2015-02
影响因子：
2.1
通讯作者：
Valdar, William
中科院分区：
文献类型：
--
作者：
Sabourin, Jeremy;Nobel, Andrew B.;Valdar, William
全基因组关联研究有时会确定潜在因果变异的数量和身份都不明确的基因座。在这种情况下，同时模拟多个SNP效应的统计方法可以帮助解开观察到的关联模式，并提供有关如何为后续研究优先考虑这些SNP的信息。然而，目前的多SNP方法倾向于假设SNP效应被加性遗传学很好地捕获;然而，当存在遗传显性时，这种假设转化为降低的功率和错误的优先级。我们描述了一个统计程序，优先在GWAS基因座，有效地模型加性和显性效应的SNP。我们的方法，LLARRMA-dawg，结合了一组LASSO程序的稀疏建模的多个SNP的影响与基于分数观测权重的resternation程序，它估计每个SNP的稳健性与表型的关联，既抽样变异和竞争的解释从其他SNP。在产生最能识别潜在真实信号的SNP优先级时，我们表明：我们的方法很容易优于单个标记分析;当存在仅加性信号时，我们的加性和显性联合模型相当于或仅略低于建模仅加性效应;并且，当存在显性信号时，即使与大量的加性效应相结合，我们的联合模型也无疑比假设加性的模型更强大。我们还描述了如何通过校准的随机惩罚来提高性能，并讨论了如何通过杂合子剂量或多重插补来整合未基因型SNP的优势。
Genomewide association studies sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple SNPs simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow-up studies. Current multi-SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA-dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights; it estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing a SNP prioritization that best identifies underlying true signals, we show that: our method easily outperforms a single marker analysis; when additive-only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive-only effects; and, when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation.
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