A Lasso multi-marker mixed model for association mapping with population structure correction
A Lasso multi-marker mixed model for association mapping with population structure correction
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DOI：
10.1093/bioinformatics/bts669
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发表时间：
2013-01-15
期刊：
影响因子：
5.8
通讯作者：
Borgwardt, Karsten
中科院分区：
文献类型：
--
作者：
Rakitsch, Barbara;Lippert, Christoph;Borgwardt, Karsten
动机：探索可遗传性状的遗传基础仍然是生物医学研究的核心挑战之一。在具有简单孟德尔遗传结构的性状中，单个多态性位点解释了表型变异的很大一部分。然而，许多感兴趣的性状似乎受到基因组位点群的多因素控制。准确检测这种多变量关联并非易事，并且常常因统计功效有限而受到影响。同时，诸如群体结构等混杂影响会导致虚假的关联信号，从而产生假阳性结果。
结果：我们提出线性混合模型LMM - Lasso，这是一种既允许多位点定位又能校正混杂效应的混合模型。我们的方法简单且无调整参数；它能有效控制群体结构，并可扩展到全基因组数据集。LMM - Lasso可同时发现可能的因果变异，并允许从基因型进行基于多标记的表型预测。我们展示了LMM - Lasso在拟南芥全基因组关联研究和小鼠连锁图谱分析中的实际应用，在这些应用中，我们的方法对91%所考虑的表型实现了更准确的表型预测。同时，我们的模型将表型变异分解为由单个单核苷酸多态性效应和群体结构导致的成分。对已知候选基因的富集表明，LMM - Lasso检索到的个体关联很可能是真实的。
Motivation: Exploring the genetic basis of heritable traits remains one of the central challenges in biomedical research. In traits with simple Mendelian architectures, single polymorphic loci explain a significant fraction of the phenotypic variability. However, many traits of interest seem to be subject to multifactorial control by groups of genetic loci. Accurate detection of such multivariate associations is non-trivial and often compromised by limited statistical power. At the same time, confounding influences, such as population structure, cause spurious association signals that result in false-positive findings.Results: We propose linear mixed models LMM-Lasso, a mixed model that allows for both multi-locus mapping and correction for confounding effects. Our approach is simple and free of tuning parameters; it effectively controls for population structure and scales to genome-wide datasets. LMM-Lasso simultaneously discovers likely causal variants and allows for multi-marker-based phenotype prediction from genotype. We demonstrate the practical use of LMM-Lasso in genome-wide association studies in Arabidopsis thaliana and linkage mapping in mouse, where our method achieves significantly more accurate phenotype prediction for 91% of the considered phenotypes. At the same time, our model dissects the phenotypic variability into components that result from individual single nucleotide polymorphism effects and population structure. Enrichment of known candidate genes suggests that the individual associations retrieved by LMM-Lasso are likely to be genuine.