期刊
PLOS ONE
卷 7, 期 2, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0031927
关键词
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资金
- Cancer Research UK
- Medical Research Council
- National Natural Science Foundation of China [30171155]
- China Scholarship Council
- Medical Research Council [MC_U106179471, G0401527, G1000143] Funding Source: researchfish
Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a range of effect and sample sizes. When applied to 12 candidate regions in data (N = 2,417) from the European Prospective Investigation of Cancer (EPIC)-Norfolk study, a region in FTO was found to have stronger association (rs7204609 similar to rs9939881 at the first intron P = 4.29 x 10(-7)) than single SNP analysis (all with P>10(-4)) and a latent quantitative phenotype was obtained using a subset sample of EPIC-Norfolk(N = 12,559). We believe our method is appropriate for assessment of regional association and polygenic effect on a single or multiple traits.
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