Brown Robert, Kichaev Gleb, Mancuso Nicholas, Boocock James, Pasaniuc Bogdan
Bioinformatics IDP, University of California Los Angeles, Los Angeles, CA, USA.
Department of Pathology and Laboratory Medicine.
Bioinformatics. 2017 Aug 1;33(15):2307-2313. doi: 10.1093/bioinformatics/btx142.
Expression quantitative trait loci (eQTLs), genetic variants associated with gene expression levels, are identified in eQTL mapping studies. Such studies typically test for an association between single nucleotide polymorphisms (SNPs) and expression under an additive model, which ignores interaction and haplotypic effects. Mismatches between the model tested and the underlying genetic architecture can lead to a loss of association power. Here we introduce a new haplotype-based test for eQTL studies that looks for haplotypic effects on expression levels. Our test is motivated by compound heterozygous architectures, a common disease model for recessive monogenic disorders, where two different alleles can have the same effect on a gene's function.
When the underlying true causal architecture for a simulated gene is a compound heterozygote, our method is better able to capture the signal than the marginal SNP method. When the underlying model is a single SNP, there is no difference in the power of our method relative to the marginal SNP method. We apply our method to empirical gene expression data measured in 373 European individuals from the GEUVADIS study and find 29 more eGenes (genes with at least one association) than the standard marginal SNP method. Furthermore, in 974 of the 3529 total eGenes, our haplotype-based method results in a stronger association signal than the standard marginal SNP method. This demonstrates our method both increases power over the standard method and provides evidence of haplotypic architectures regulating gene expression.
http://bogdan.bioinformatics.ucla.edu/software/.
表达数量性状基因座（eQTL），即与基因表达水平相关的遗传变异，是在eQTL定位研究中确定的。此类研究通常在加性模型下测试单核苷酸多态性（SNP）与表达之间的关联，而忽略了相互作用和单倍型效应。所测试的模型与潜在遗传结构之间的不匹配可能导致关联能力的丧失。在此，我们为eQTL研究引入了一种新的基于单倍型的测试方法，该方法可寻找单倍型对表达水平的影响。我们的测试方法是由复合杂合结构激发的，复合杂合结构是隐性单基因疾病的常见疾病模型，其中两个不同的等位基因可对基因功能产生相同影响。
当模拟基因的潜在真实因果结构为复合杂合子时，我们的方法比边际SNP方法更能捕捉信号。当潜在模型为单个SNP时，我们的方法与边际SNP方法在功效上没有差异。我们将我们的方法应用于来自GEUVADIS研究的373名欧洲个体中测量的经验性基因表达数据，发现比标准边际SNP方法多29个e基因（至少有一个关联的基因）。此外，在总共3529个e基因中的974个基因中，我们基于单倍型的方法产生的关联信号比标准边际SNP方法更强。这表明我们的方法不仅比标准方法提高了功效，还提供了单倍型结构调节基因表达的证据。
http://bogdan.bioinformatics.ucla.edu/software/。
