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A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.

ABSTRACT: BACKGROUND: Current Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects. RESULTS: This work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3). CONCLUSIONS: The six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.

SUBMITTER: Park SH

PROVIDER: S-EPMC3287479 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.

Park Sung Hee SH Lee Ji Young JY Kim Sangsoo S

BMC systems biology 20111214

<h4>Background</h4>Current Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects.<h4>Results</h4>This work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of mult ...[more]

PMID: 22784570

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Project description: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??) 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??). 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.

Project description:BackgroundRecently mixed linear models are used to address the issue of "missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects. To incorporate this feature, we propose an efficient Hierarchical Bayesian Model (HBM) that extends the existing mixed models to enforce automatic selection of significant SNPs. The HBM models the SNP effects using a mixture distribution of a point mass at zero and a normal distribution, where the point mass corresponds to those non-associative SNPs.ResultsWe estimate the HBM using Gibbs sampling. The estimation performance of our method is first demonstrated through two simulation studies. We make the simulation setups realistic by using parameters fitted on the Framingham Heart Study (FHS) data. The simulation studies show that our method can accurately estimate the proportion of SNPs associated with the simulated phenotype and identify these SNPs, as well as adapt to certain model mis-specification than the standard mixed models. In addition, we analyze data from the FHS and the Health and Retirement Study (HRS) to study the association between Body Mass Index (BMI) and SNPs on Chromosome 16, and replicate the identified genetic associations. The analysis of the FHS data identifies 0.3% SNPs on Chromosome 16 that affect BMI, including rs9939609 and rs9939973 on the FTO gene. These two SNPs are in strong linkage disequilibrium with rs1558902 (Rsq =0.901 for rs9939609 and Rsq =0.905 for rs9939973), which has been reported to be linked with obesity in previous GWAS. We then replicate the findings using the HRS data: the analysis finds 0.4% of SNPs associated with BMI on Chromosome 16. Furthermore, around 25% of the genes that are identified to be associated with BMI are common between the two studies.ConclusionsThe results demonstrate that the HBM and the associated estimation algorithm offer a powerful tool for identifying significant genetic associations with phenotypes of interest, among a large number of SNPs that are common in modern genetics studies.

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A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.

Publications

A methodology for multivariate phenotype-based genome-wide association studies to mine pleiotropic genes.

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