Zhou Jin J, Yip Wai-Ki, Cho Michael H, Qiao Dandi, McDonald Merry-Lynn N, Laird Nan M
Biostatistics Department, Harvard School of Public Health, Boston, MA 02115 USA ; Division of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, AZ 85724, USA.
Biostatistics Department, Harvard School of Public Health, Boston, MA 02115 USA.
BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S33. doi: 10.1186/1753-6561-8-S1-S33. eCollection 2014.
The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/.
新一代测序技术的革命使得在全基因组范围内获取常见和罕见的高质量序列变异成为可能。由于研究人员现在面临着处理来自测序研究的大量遗传变异信息的分析挑战，因此已经开发了许多方法来评估常见和罕见变异对疾病性状的影响。在本报告中，利用遗传分析研讨会18的全基因组测序数据，考虑基于家系和基于群体的设计，比较了几种方法检测与MAP4基因区域及3号染色体上的变异与血压之间关联的效能。为了对全基因组的变异进行优先排序以便进行检测，首先使用预测算法和表达数量性状基因座（eQTL）数据对变异进行功能评估。在基于家系的关联检验（FBAT）框架中，使用四种基于集合的检验方法——FBAT-v、FBAT-lmm、FBAT-m和FBAT-l——分析20个家系，并使用两种方差成分检验方法，即序列核关联检验（SKAT）和全基因组复杂性状分析（GCTA），对样本中的142名无亲缘关系的个体进行分析。基于集合的检验和基于方差成分的检验都具有较高的效能和适当的I型错误率。在各种FBAT检验中，FBAT-l表现出卓越的性能，表明其在罕见变异分析中具有应用潜力。更新后的FBAT软件包可从以下网址获取：http://www.hsph.harvard.edu/fbat/ 。
