Peterson Roseann E, Maes Hermine H, Lin Peng, Kramer John R, Hesselbrock Victor M, Bauer Lance O, Nurnberger John I, Edenberg Howard J, Dick Danielle M, Webb Bradley T
Virginia Institute for Psychiatric and Behavioral Genetics, Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Biotech I, 800 E, Leigh Street, Richmond, VA 23298-0126, USA.
BMC Genomics. 2014 May 14;15(1):368. doi: 10.1186/1471-2164-15-368.
As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation.
The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p=4.3×10(-16)) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p=0.003, frequency=16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR=3.1, p=0.009, frequency 1.2%) and 5q13.2 deletions (OR=1.5, p=0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p=3.15×10(-18)).
Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses.
由于复杂性状的结构包含了范围不断扩大的遗传变异,因此需要整合常见变异和罕见变异的分析方法。体重指数(BMI)是一个典型性状,因为常见变异显示出很强的关联性,但只占遗传力的一部分。利用来自成瘾:遗传学与环境研究中的1850名欧洲人和498名非裔美国人,对单核苷酸多态性(SNP)和拷贝数变异(CNV)进行了联合分析。使用32个经BMI验证的SNP构建遗传风险总和评分(GRSS),并比较了汇总风险方法:计数法与加权法、代理法与估算法。
根据风险等位基因的估算概率构建的加权SNP-GRSS表现最佳,与BMI高度相关(p = 4.3×10⁻¹⁶),占表型变异的3%。除了经BMI验证的SNP外,还从文献中鉴定出常见和罕见的BMI/肥胖相关CNV。在先前报道的8种4 CNV中,只有16p12.3上的21千碱基缺失显示出与BMI相关的证据(p = 0.003,频率 = 16.9%),有两种CNV与II类肥胖名义上相关,1p36.1重复(OR = 3.1,p = 0.009,频率1.2%)和5q13.2缺失(OR = 1.5,p = 0.048,频率7.7%)。所有其他CNV,单独或总体上,均与BMI或肥胖无关。包括协变量、SNP-GRSS和16p12.3缺失的联合模型占BMI表型变异的11.5%(遗传效应占3.2%)。模型对肥胖分类具有显著的预测能力,对病态肥胖具有最大的判别能力(p = 3.15×10⁻¹⁸)。
结果表明,纳入经过验证的效应大小和等位基因概率可改善预测算法。虽然罕见CNV并未占显著的表型变异,但结果提供了一个整合分析的框架。