Hebrew SeniorLife Institute for Aging Research and Harvard Medical School, Boston, MA 02131, USA.
J Bone Miner Res. 2012 Feb;27(2):319-30. doi: 10.1002/jbmr.563.
Multiple musculoskeletal traits assessed by various methods at different skeletal sites serve as surrogates for osteoporosis risk. However, it is a challenge to select the most relevant phenotypes for genetic study of fractures. Principal component analyses (PCA) were conducted in participants of the Framingham Osteoporosis Study on 17 measures including bond mineral density (BMD) (hip and spine), heel ultrasound, leg lean mass (LLM), and hip geometric indices, adjusting for covariates (age, height, body mass index [BMI]), in a combined sample of 1180 men and 1758 women, as well as in each sex. Four principal components (PCs) jointly explained 69% of the total variability of musculoskeletal traits. PC1, explaining ~33% of the total variance, was referred to as the component of "Bone strength," because it included the hip and spine BMD as well as several hip cross-sectional properties. PC2 (20.5% variance) was labeled as "Femoral cross-sectional geometry;" PC3 (8% variance) captured only ultrasound measures; PC4, explaining ~7% variance, was correlated with LLM and hip geometry. We then evaluated ~2.5 mil SNPs for association with PCs 1, 2, and 4. There were genome-wide significant associations (p < 5 × 10⁻⁸) between PC2 and HTR1E (that codes for one of the serotonin receptors) and PC4 with COL4A2 in women. In the sexes-combined sample, AKAP6 was associated with PC2 (p = 1.40 × 10⁻⁷). A single nucleotide polymorphism (SNP) in HTR1E was also associated with the risk of nonvertebral fractures in women (p = 0.005). Functions of top associated genes were enriched for the skeletal and muscular system development (p < 0.05). In conclusion, multivariate combination provides genetic associations not identified in the analysis of primary phenotypes. Genome-wide screening for the linear combinations of multiple osteoporosis-related phenotypes suggests that there are variants with potentially pleiotropic effects in established and novel pathways to be followed up to provide further evidence of their functions.
多种骨骼肌肉特征,通过不同的方法在不同的骨骼部位进行评估,可作为骨质疏松风险的替代指标。然而,选择与骨折遗传研究最相关的表型是一个挑战。在 Framingham 骨质疏松研究的参与者中,对 17 项指标进行了主成分分析(PCA),这些指标包括骨密度(髋部和脊柱)、足跟超声、腿部瘦体重(LLM)和髋部几何指数,调整了协变量(年龄、身高、体重指数 [BMI]),在 1180 名男性和 1758 名女性的合并样本中,以及在每个性别中。四个主成分(PC)共同解释了骨骼肌肉特征总变异的69%。解释总方差的33%的 PC1 被称为“骨骼强度”成分,因为它包括髋部和脊柱骨密度以及几个髋部横截面特性。PC2(20.5%的方差)被标记为“股骨横截面几何形状”;PC3(8%的方差)仅捕获超声测量值;解释7%方差的 PC4 与 LLM 和髋部几何形状相关。然后,我们评估了与 PC1、2 和 4 相关的约 250 万个 SNP。在女性中,PC2 与 HTR1E(编码其中一种 5-羟色胺受体)之间存在全基因组显著关联(p<5×10⁻⁸),PC4 与 COL4A2 之间存在全基因组显著关联。在男女混合样本中,AKAP6 与 PC2 相关(p=1.40×10⁻⁷)。HTR1E 中的单核苷酸多态性(SNP)也与女性非椎体骨折风险相关(p=0.005)。顶级关联基因的功能富集了骨骼和肌肉系统的发育(p<0.05)。总之,多元组合提供了在分析主要表型时未发现的遗传关联。对多个与骨质疏松相关表型的线性组合进行全基因组筛查表明,存在具有潜在多效性的变体,需要进一步研究建立和新的途径,以提供其功能的进一步证据。