A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study.
A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study.
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DOI：
10.1186/1471-2350-8-s1-s17
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发表时间：
2007-09-19
影响因子：
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通讯作者：
Cupples LA
中科院分区：
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作者：
Kathiresan S;Manning AK;Demissie S;D'Agostino RB;Surti A;Guiducci C;Gianniny L;Burtt NP;Melander O;Orho-Melander M;Arnett DK;Peloso GM;Ordovas JM;Cupples LA
血脂水平，包括低密度脂蛋白胆固醇（LDL - C）、高密度脂蛋白胆固醇（HDL - C）和甘油三酯（TG），具有高度遗传性。全基因组关联分析是一种有前景的方法，可用于定位与这些遗传表型相关的基因位点。
在1087名弗雷明汉心脏研究子代队列参与者（平均年龄47岁，52%为女性）中，我们对空腹血脂特征进行了全基因组分析（Affymetrix 100K基因芯片）。总胆固醇、HDL - C和TG通过标准酶法测定，LDL - C使用Friedewald公式计算。在约30年期间多达7次测量的LDL - C、HDL - C和TG的长期平均值是主要表型。我们使用广义估计方程（GEE）、基于家系的关联检验（FBAT）和方差成分连锁分析来研究单核苷酸多态性（位于常染色体上， minor allele frequency≥10%，基因型检出率≥80%，哈迪 - 温伯格平衡p≥0.001）与多变量调整后的残差之间的关系。我们对GEE关联结果采用了三阶段复制策略，在第一阶段对287个单核苷酸多态性（P < 0.001）在第二阶段（n约为1450人）进行测试，在第一阶段和第二阶段联合分析中P < 0.001的40个单核苷酸多态性在第三阶段（n约为6650人）进行测试。
LDL - C、HDL - C和TG的长期平均值具有高度遗传性（h²分别为0.66、0.69、0.58；每个P < 0.0001）。在针对每种表型进行的70987次检验中，对于LDL - C，有两个单核苷酸多态性在GEE结果中p < 10⁻⁵，对于HDL - C有四个，对于TG有一个。对于每种多变量调整后的表型，关联p < 10⁻⁴的单核苷酸多态性数量从13到18不等，p < 10⁻³的从94到149不等。一些结果证实了先前报道的与候选基因的关联，包括脂蛋白脂肪酶基因（LPL）的变异与HDL - C和TG的关联（rs7007797；对于HDL - C，P = 0.0005，对于TG，P = 0.002）。GEE、FBAT和连锁分析的全部结果发布在基因型和表型数据库（dbGaP）中。经过三个阶段的复制，在任何测试的单核苷酸多态性与血脂表型之间没有令人信服的统计学关联证据（即，在所有三个阶段联合P < 10⁻⁵）。
通过100K全基因组扫描，我们得出了一组常见序列变异与血脂表型之间的假定关联。在其他样本中对选定假设的验证没有识别出血脂变异性背后的任何新位点。缺乏复制可能是由于检测适度数量性状位点效应（即，<1%的性状方差解释）的统计能力不足，或者100K芯片的基因组覆盖度降低。在弗雷明汉心脏研究中使用更密集的全基因组基因分型平台和更有力的复制策略进行全基因组关联分析可能会识别出血脂背后的新位点。
Blood lipid levels including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are highly heritable. Genome-wide association is a promising approach to map genetic loci related to these heritable phenotypes. In 1087 Framingham Heart Study Offspring cohort participants (mean age 47 years, 52% women), we conducted genome-wide analyses (Affymetrix 100K GeneChip) for fasting blood lipid traits. Total cholesterol, HDL-C, and TG were measured by standard enzymatic methods and LDL-C was calculated using the Friedewald formula. The long-term averages of up to seven measurements of LDL-C, HDL-C, and TG over a ~30 year span were the primary phenotypes. We used generalized estimating equations (GEE), family-based association tests (FBAT) and variance components linkage to investigate the relationships between SNPs (on autosomes, with minor allele frequency ≥10%, genotypic call rate ≥80%, and Hardy-Weinberg equilibrium p ≥ 0.001) and multivariable-adjusted residuals. We pursued a three-stage replication strategy of the GEE association results with 287 SNPs (P < 0.001 in Stage I) tested in Stage II (n ~1450 individuals) and 40 SNPs (P < 0.001 in joint analysis of Stages I and II) tested in Stage III (n~6650 individuals). Long-term averages of LDL-C, HDL-C, and TG were highly heritable (h2 = 0.66, 0.69, 0.58, respectively; each P < 0.0001). Of 70,987 tests for each of the phenotypes, two SNPs had p < 10-5 in GEE results for LDL-C, four for HDL-C, and one for TG. For each multivariable-adjusted phenotype, the number of SNPs with association p < 10-4 ranged from 13 to 18 and with p < 10-3, from 94 to 149. Some results confirmed previously reported associations with candidate genes including variation in the lipoprotein lipase gene (LPL) and HDL-C and TG (rs7007797; P = 0.0005 for HDL-C and 0.002 for TG). The full set of GEE, FBAT and linkage results are posted at the database of Genotype and Phenotype (dbGaP). After three stages of replication, there was no convincing statistical evidence for association (i.e., combined P < 10-5 across all three stages) between any of the tested SNPs and lipid phenotypes. Using a 100K genome-wide scan, we have generated a set of putative associations for common sequence variants and lipid phenotypes. Validation of selected hypotheses in additional samples did not identify any new loci underlying variability in blood lipids. Lack of replication may be due to inadequate statistical power to detect modest quantitative trait locus effects (i.e., <1% of trait variance explained) or reduced genomic coverage of the 100K array. GWAS in FHS using a denser genome-wide genotyping platform and a better-powered replication strategy may identify novel loci underlying blood lipids.