Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
PLoS One. 2013 Jul 12;8(7):e68941. doi: 10.1371/journal.pone.0068941. Print 2013.
Obesity has become a leading preventable cause of morbidity and mortality in many parts of the world. It is thought to originate from multiple genetic and environmental determinants. The aim of the current study was to introduce haplotype-based multi-locus stepwise regression (MSR) as a method to investigate combinations of unlinked single nucleotide polymorphisms (SNPs) for obesity phenotypes.
In 2,122 healthy randomly selected men and women of the EPIC-Potsdam cohort, the association between 41 SNPs from 18 obesity-candidate genes and either body mass index (BMI, mean=25.9 kg/m(2), SD=4.1) or waist circumference (WC, mean=85.2 cm, SD=12.6) was assessed. Single SNP analyses were done by using linear regression adjusted for age, sex, and other covariates. Subsequently, MSR was applied to search for the 'best' SNP combinations. Combinations were selected according to specific AICc and p-value criteria. Model uncertainty was accounted for by a permutation test.
The strongest single SNP effects on BMI were found for TBC1D1 rs637797 (β = -0.33, SE=0.13), FTO rs9939609 (β=0.28, SE=0.13), MC4R rs17700144 (β=0.41, SE=0.15), and MC4R rs10871777 (β=0.34, SE=0.14). All these SNPs showed similar effects on waist circumference. The two 'best' six-SNP combinations for BMI (global p-value= 3.45⋅10(-6) and 6.82⋅10(-6)) showed effects ranging from -1.70 (SE=0.34) to 0.74 kg/m(2) (SE=0.21) per allele combination. We selected two six-SNP combinations on waist circumference (global p-value = 7.80⋅10(-6) and 9.76⋅10(-6)) with an allele combination effect of -2.96 cm (SE=0.76) at maximum. Additional adjustment for BMI revealed 15 three-SNP combinations (global p-values ranged from 3.09⋅10(-4) to 1.02⋅10(-2)). However, after carrying out the permutation test all SNP combinations lost significance indicating that the statistical associations might have occurred by chance.
MSR provides a tool to search for risk-related SNP combinations of common traits or diseases. However, the search process does not always find meaningful SNP combinations in a dataset.
肥胖已成为世界许多地区发病率和死亡率的主要可预防原因。据认为，肥胖源于多种遗传和环境决定因素。本研究的目的是介绍基于单体型的多基因逐步回归（MSR）方法，以研究无连锁单核苷酸多态性（SNP）组合与肥胖表型的关系。
在 EPIC-Potsdam 队列中，对 2122 名健康的随机选择的男性和女性，分析了 18 个肥胖候选基因中的 41 个 SNP 与体重指数（BMI，均值=25.9kg/m2，标准差=4.1）或腰围（WC，均值=85.2cm，标准差=12.6）之间的关联。通过线性回归，对年龄、性别和其他协变量进行调整，对单个 SNP 进行分析。然后，应用 MSR 搜索最佳 SNP 组合。根据特定的 AICc 和 p 值标准选择组合。通过置换检验考虑模型不确定性。
在 BMI 方面，TBC1D1 rs637797（β=-0.33，SE=0.13）、FTO rs9939609（β=0.28，SE=0.13）、MC4R rs17700144（β=0.41，SE=0.15）和 MC4R rs10871777（β=-0.34，SE=0.14）对 BMI 的单 SNP 影响最大。这些 SNP 在腰围方面也表现出类似的作用。BMI 的两个“最佳”六 SNP 组合（全局 p 值=3.45×10(-6)和 6.82×10(-6)），每个 SNP 组合的效应范围为-1.70（SE=0.34）至 0.74kg/m2（SE=0.21）。我们选择了两个六 SNP 组合，在腰围方面的全局 p 值分别为 7.80×10(-6)和 9.76×10(-6)，最大的 SNP 组合效应为-2.96cm（SE=0.76）。对 BMI 进行额外调整后，发现了 15 个三 SNP 组合（全局 p 值范围为 3.09×10(-4)至 1.02×10(-2)）。然而，进行置换检验后，所有 SNP 组合都失去了意义，这表明这些统计关联可能是偶然发生的。
MSR 提供了一种用于搜索常见特征或疾病相关 SNP 组合的工具。然而，在数据集搜索过程中，并不总能找到有意义的 SNP 组合。
