Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes
Autoři:
Saebom Jeon aff001; Ji-yeon Shin aff002; Jaeyong Yee aff003; Taesung Park aff004; Mira Park aff005
Působiště autorů:
Department of Marketing Information Consulting, Mokwon University, Daejeon, KOREA
aff001; Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu, KOREA
aff002; Department of Physiology and Biophysics, Eulji University, Daejeon, KOREA
aff003; Department of Statistics, Seoul National University, Seoul, KOREA
aff004; Department of Preventive Medicine, Eulji University, Daejeon, KOREA
aff005
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0217189
Souhrn
Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses. To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases. In this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes—subscapular skin fold thickness, body mass index, and waist circumference—as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of-fit measures (χ2 = 536.52, NFI = 0.997, CFI = 0.998, GFI = 0.995, AGFI = 0.993, RMSEA = 0.012).
Klíčová slova:
Medicine and health sciences – Vascular medicine – Blood pressure – Hypertension – Endocrinology – Endocrine disorders – Metabolic disorders – Body weight – Body Mass Index – Biology and life sciences – Physiology – Physiological parameters – Obesity – Genetics – Molecular genetics – Phenotypes – Genomics – Genome analysis – Human genetics – Genetics of disease – Molecular biology – Computational biology – Genome-wide association studies
Zdroje
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PLOS One
2019 Číslo 9
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