Division of Biostatistics, School of Public Health, University of Minnesota, United States.
Division of Biostatistics, School of Public Health, University of Minnesota, United States.
Comput Biol Chem. 2018 Jun;74:76-79. doi: 10.1016/j.compbiolchem.2018.02.016. Epub 2018 Mar 1.
We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study.
我们提出了一种统计方法，仅使用全基因组关联研究（GWAS）汇总数据而无需访问原始基因型和表型数据来检测新的遗传变异。在后 GWAS 时代，越来越多的汇总数据被公开提供，因此所提出的方法在识别其他有趣的遗传变异和揭示潜在疾病机制方面具有实际的非常有用。我们通过应用于国际 MAGIC 联盟的空腹血糖 GWAS 荟萃分析结果来说明我们提出的方法的实用性。我们发现了几个具有进一步研究价值的全新全基因组显著位点。
