Holzinger Emily R, Verma Shefali S, Moore Carrie B, Hall Molly, De Rishika, Gilbert-Diamond Diane, Lanktree Matthew B, Pankratz Nathan, Amuzu Antoinette, Burt Amber, Dale Caroline, Dudek Scott, Furlong Clement E, Gaunt Tom R, Kim Daniel Seung, Riess Helene, Sivapalaratnam Suthesh, Tragante Vinicius, van Iperen Erik P A, Brautbar Ariel, Carrell David S, Crosslin David R, Jarvik Gail P, Kuivaniemi Helena, Kullo Iftikhar J, Larson Eric B, Rasmussen-Torvik Laura J, Tromp Gerard, Baumert Jens, Cruickshanks Karen J, Farrall Martin, Hingorani Aroon D, Hovingh G K, Kleber Marcus E, Klein Barbara E, Klein Ronald, Koenig Wolfgang, Lange Leslie A, Mӓrz Winfried, North Kari E, Charlotte Onland-Moret N, Reiner Alex P, Talmud Philippa J, van der Schouw Yvonne T, Wilson James G, Kivimaki Mika, Kumari Meena, Moore Jason H, Drenos Fotios, Asselbergs Folkert W, Keating Brendan J, Ritchie Marylyn D
Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institute for General Medical Sciences, National Institutes of Health, Baltimore, MD USA.
The Center for Systems Genomics, The Pennsylvania State University, University Park, State College, PA USA.
BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017.
The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG).
Our analysis consisted of a discovery phase using a merged dataset of five different cohorts ( = 12,853 to = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing.
These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.
人类脂质定量性状的遗传病因尚未完全阐明，变异之间的相互作用可能起作用。我们针对四种不同的脂质性状进行了以基因为中心的相互作用研究：低密度脂蛋白胆固醇（LDL-C）、高密度脂蛋白胆固醇（HDL-C）、总胆固醇（TC）和甘油三酯（TG）。
我们的分析包括一个发现阶段，使用了五个不同队列的合并数据集（根据脂质表型，样本量从12,853到16,849不等），以及一个复制阶段，涉及十个独立队列，总共增加了36,938个样本。在进行相互作用测试之前，通常会应用筛选器来校正测试所有成对相互作用的负担。我们使用了两种不同的筛选器：1. 一种筛选器，仅测试在前一项关联研究中主效应P < 0.001的单核苷酸多态性（SNP）。2. 一种筛选器，仅测试由Biofilter 2.0识别的相互作用。在发现数据集中达到相互作用显著性水平P < 0.001的成对模型进行复制测试。在考虑多重检验后，我们确定了13个SNP-SNP模型在多个复制队列中具有显著性。
这些结果可能揭示脂质水平遗传病因的新见解。此外，我们开发了一种流程来进行具有多队列复制的计算高效的相互作用分析。
